diff --git a/case_studies/Earnings Announcements/notebook.ipynb b/case_studies/Earnings Announcements/notebook.ipynb new file mode 100644 index 00000000..5c459fd7 --- /dev/null +++ b/case_studies/Earnings Announcements/notebook.ipynb @@ -0,0 +1,947 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Researching & Developing a Market Neutral Strategy - Case Study - USD-EUR Exchange Rate\n", + "\n", + "The following notebook aims to demonstrate best practices when developing a market-neutral signal based on Quantopian's data feeds. Following the steps detailed in [this post](https://www.quantopian.com/posts/using-alternative-data-researching-and-implementing-a-market-neutral-strategy) and demonstrated in this notebook will ensure a well-founded alternative data signal that stands a better chance of holding up during out-of-sample validation and live trading.\n", + "\n", + "### Intro - Why use Alternative Data?\n", + "Fundamental asset data such as price, volume, or company financials has many benefits including its accessibility and simplicity. However, these advantages are a double-edged sword as any \"alpha\" left in these datasets can be especially difficult to extract exactly because of the amount of people using the data. \n", + "\n", + "Because alternative data streams are not as widely available or as easy to use as fundamental ones, finding novel information that has yet to be \"priced in\" by the market is easier. Further benefits include the tendency for alternative data signals to be uncorrelated to ones based on traditional data.\n", + "\n", + "Some of the major drawbacks of alternative data include its lack of structure, cost, exclusivity, and high dimensionality. Luckily, Quantopian takes down some of these barriers through its [wide variety of alternative data feeds](https://www.quantopian.com/data/), many of which are free to use and all of which have been cleaned and standardized to work both in pipeline and as interactive datasets in the research environment.\n", + "\n", + "### Abstract\n", + "\n", + "Through some preliminary research (reading papers, exploring data) we arrive at a hypothesis we would like to test. [This paper](http://www.nber.org/papers/w13090.pdf) by Lamont et al. shows that stocks trade at a premium around their earnings announcement, however the testing sample only goes up to 2004. Let's find out if the premium has survived past then.\n", + "\n", + "** Hypothesis: ** *This anomaly of inflated prices around earnings announcements, observed before 2004 in Lamont et al., is still present today.*\n", + "\n", + "To test it we: \n", + "\n", + "1) Examine the data using Blaze and look at the effects of earnings announcements on a single asset.\n", + "\n", + "2) Use pipeline to filter a universe of assets and look for earnings announcement premia across all assets.\n", + "\n", + "3) Use Alphalens to examine the strength of our signal within the in-sample period." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib as mpl\n", + "import pandas as pd\n", + "import blaze as bz\n", + "import math\n", + "import numpy as np\n", + "import seaborn\n", + "import scipy.stats as stats\n", + "import statsmodels.api as sm\n", + "import statsmodels.tsa as tsa\n", + "\n", + "from statsmodels import regression\n", + "from odo import odo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Researching Alternative Data: Earnings Calendar\n", + "The earnings calendar data used in this notebook, as well as the Morningstar fundamental data, are all available as free datafeeds. \n", + "\n", + "** Hypothesis: ** *This anomaly of inflated prices around earnings announcements, observed before 2004 in Lamont et al., is still in more recent years.*" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "126453 rows of data\n" + ] + } + ], + "source": [ + "# Importing exchange rate data set\n", + "# When importing for blaze/non-pipeline research use quantopian.interactive._\n", + "# When importing for pipeline use quantopian.pipeline._\n", + "from quantopian.interactive.data.eventvestor import earnings_calendar\n", + "\n", + "print len(earnings_calendar), 'rows of data'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This size of this dataset exceeds the 10,000 row limit allowed by Quantopian's data agreements. What this means is that we cannot pull it all into a Pandas dataframe directly, and instead must use Blaze to perform computations remotely, before using `bz.compute` to pull results (which should be smaller than 10,000 rows).\n", + "\n", + "This earnings calendar datset begins in 2007 but has more complete data from 2008 on, so taking this into account we will set our research period as 2008-2011. This range is large enough to observe most trends but small enough to leave room for thorough out-of-sample testing.\n", + "\n", + "Because we cannot pull the whole interactive dataset, let's instead pull the earnings calendar for just General Electric." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Earnings announcements for GE :\n", + "\n", + "DatetimeIndex(['2008-01-18', '2008-04-11', '2008-07-11', '2008-10-10',\n", + " '2009-01-23', '2009-04-17', '2009-07-10', '2009-10-16',\n", + " '2010-01-22', '2010-04-16', '2010-07-16', '2010-10-15',\n", + " '2011-01-21', '2011-04-21', '2011-07-22', '2011-10-07'],\n", + " dtype='datetime64[ns, UTC]', freq=None)\n" + ] + } + ], + "source": [ + "# Defining our asset and test range\n", + "asset = 'GE'\n", + "start = '2008-01-01'\n", + "end = '2012-01-01'\n", + "\n", + "# Pulling pricing data for GE\n", + "pricing = get_pricing(asset, start_date=start, end_date=end)\n", + "\n", + "# Selecting earnings dates for GE and computing the Blaze expression into a Pandas DataFrame\n", + "calendar_expression = earnings_calendar['asof_date'][earnings_calendar['symbol']==asset]\n", + "calendar = bz.compute(calendar_expression)\n", + "\n", + "# Slicing earnings dates to within our test range\n", + "calendar = pd.to_datetime(calendar[(calendar>start)&(calendar" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def highlight_events(ts, event_times, window_length, ax=None):\n", + "\n", + " # Validating axis input\n", + " if ax is None:\n", + " ax = plt.gca()\n", + "\n", + " # Plotting inputted time series\n", + " ts.plot(ax=ax, alpha = 0.5)\n", + " timedelta = pd.Timedelta((window_length-1)/2, unit='d')\n", + "\n", + " # Plotting using red within windowed regions\n", + " for event in event_times:\n", + " window = pd.date_range(start = event - timedelta, periods=window_length)\n", + " ax.plot(window, ts[window].interpolate(method='linear'),\n", + " c='r', linewidth=3)\n", + "\n", + "fig, ax = plt.subplots(ncols=1, nrows=2, sharex=True)\n", + "\n", + "plt.tight_layout()\n", + " \n", + "highlight_events(pricing['price'], calendar, 15, ax[0])\n", + "highlight_events(pricing['volume'], calendar, 15, ax[1])\n", + "ax[0].set_title('%s Price'%asset);\n", + "ax[1].set_title('%s Volume'%asset);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There definitely seems to be spikes in volume around earnings announcements, however the effect of these events on price is not observably significant within this specific sample. We can quantify this by comparing mean returns and mean volume inside the earnings widows with mean returns and mean volume from timeregions outside earnings windows." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Avg returns in window: -0.00248359004906\n", + "Avg returns out of window: 7.22042088041e-05\n", + "\n", + "Avg volume in window: 90682011.4076\n", + "Avg volume out of window: 74918192.5619\n" + ] + } + ], + "source": [ + "def compute_event_function(ts, event_times, window_length, function):\n", + " \n", + " # Defining variables used to find window indices\n", + " indices_in_window = pd.DatetimeIndex(event_times)\n", + " timedelta = pd.Timedelta((window_length-1)/2, unit='d')\n", + " \n", + " # Appending indices in windows to list\n", + " for event in event_times:\n", + " indices_in_window = indices_in_window.append(pd.date_range(start = event - timedelta, \n", + " periods=window_length))\n", + " \n", + " # Returning function output with input of ts within windows\n", + " return function(ts[indices_in_window])\n", + "\n", + "not_earnings_window = pricing.index[~pricing.index.isin(calendar)]\n", + "\n", + "print 'Avg returns in window:', compute_event_function(pricing['price'].pct_change(), calendar, 15, np.mean)\n", + "print 'Avg returns out of window:', np.mean(pricing['price'].pct_change()[not_earnings_window])\n", + "\n", + "print '\\nAvg volume in window:', compute_event_function(pricing['volume'], calendar, 15, np.mean)\n", + "print 'Avg volume out of window:', np.mean(pricing['volume'][not_earnings_window])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These results back up what we saw in the graph, as volume is measurably higher and returns are about even, if not lower than in the periods outside of the earnings windows. However, this is a small sample and we will need to conduct further testing across the whole universe of equities." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Designing a Pipeline\n", + "\n", + "Building a [pipeline](https://www.quantopian.com/tutorials/pipeline) will make pulling in earnings calendar data much easier. There exists built-in factors for this dataset, `BusinessDaysUntilNextEarnings` and `BusinessDaysSincePreviousEarnings`, that will let us skip some steps later on. When we are finished with this stage, the pipeline output can go straight into Alphalens and the pipeline itself can be copied and pasted into the IDE to be used in an algorithm." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Pipeline API imports\n", + "from quantopian.pipeline import Pipeline\n", + "from quantopian.research import run_pipeline\n", + "\n", + "# Importing built in factors, universe, and data\n", + "from quantopian.pipeline.factors import SimpleMovingAverage, CustomFactor, Returns\n", + "from quantopian.pipeline.filters.morningstar import Q1500US, Q500US\n", + "from quantopian.pipeline.data.builtin import USEquityPricing\n", + "from quantopian.pipeline.classifiers.morningstar import Sector\n", + "\n", + "# Import builtin earnings factor and other data\n", + "from quantopian.pipeline.factors.eventvestor import BusinessDaysSincePreviousEarnings\n", + "from quantopian.pipeline.data.eventvestor import EarningsCalendar\n", + "from quantopian.pipeline.data import morningstar" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class NumberOfEAinPastYear(CustomFactor):\n", + " \"\"\" Returns the number of earnings days in the past year \"\"\"\n", + " \n", + " # The specific inputs we need to calculate correlation\n", + " inputs =[EarningsCalendar.previous_announcement]\n", + " window_length = 252\n", + " def compute(self, today, asset_ids, out, earnings):\n", + "\n", + " EAs = []\n", + " \n", + " for row in earnings.T:\n", + " EAs.append(len(np.unique(row))-1)\n", + " \n", + " out[:] = EAs" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [], + "source": [ + "# Assigning the Q1500US as our universe\n", + "universe = Q500US()\n", + "\n", + "# Creating a filter to ensure all assets in universe have had 4 earnings announcements in the past year\n", + "NumEAs = NumberOfEAinPastYear(mask=universe)\n", + "ea_filter = NumEAs.eq(4)\n", + "\n", + "# Buildling our pipeline\n", + "pipe = Pipeline(\n", + " columns={\n", + " 'BDaysSinceEarnings' : BusinessDaysSincePreviousEarnings(mask=universe),\n", + " },\n", + " screen=(universe&ea_filter)\n", + ")\n", + "\n", + "result = pd.DataFrame()\n", + "\n", + "start = '2008-01-01'\n", + "end = '2012-01-01'\n", + "\n", + "# Stores pipeline in result\n", + "result = run_pipeline(pipe, start, end)\n", + "assets = result.index.levels[1].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Finds assets and pricing data\n", + "pricing = get_pricing(assets, start_date = start, end_date = end, fields = 'price')\n", + "volume = get_pricing(assets, start_date = start, end_date = end, fields = 'volume')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The distribution of maximum days between earnings announcements in the Q1500 US:" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "result['BDaysSinceEarnings'].unstack().max(axis=0).hist(bins=40);\n", + "plt.xlim(0,150);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This distribution is significant because it shows that many assets have earnings announcements that behave differently than expected. If every equity had earnings announcements exactly once per quarter, the entire distribution would exist between 60 and 90. Instead there are values in the single digits and in the hundreds. This means we cannot make any assumptions about earnings intervals being uniform or regular despite the fact we filtered for assets with 4 earnings announcements within the past year. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Daily Approach\n", + "\n", + "To observe the effect of earnings announcements, let's sort all asset-date pricing pairs into buckets based off of how close they are to an earnings announcment. Then, we can assign a window length and see average return across all assets and earnings announcments vs. days before earnings announcment." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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LdMcdd8hsNuuRRx7RPffcIy8vL02fPl09evToUK0AAHRV58spNdL5dSwAILUj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\npIqKitShQwedO3dOfn616+9iY2MVExOjlJQU2e1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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Length of earnings window, i.e. 11 is 5 business days before to 5 business days after\n", + "window_length=11\n", + "\n", + "# Creating a DataFrame to indicate the start of an earnings window\n", + "earnings_window_start = result['BDaysSinceEarnings'].unstack().shift(-(window_length-1)/2)==0.0\n", + "earnings_window = pd.DataFrame(index = result.unstack().index, columns = assets)\n", + "earnings_window[earnings_window_start] = -(window_length-1)/2\n", + "\n", + "counted_window = earnings_window\n", + "window_rets_mean = []\n", + "window_rets_std = []\n", + "\n", + "# Assigining numbers designating timedelta to earnings for asset-time pairs within earnings window\n", + "# Finding averages for each earnings day timedelta from -5 through 5\n", + "for i in range(1,window_length + 1):\n", + " counted_window = counted_window.mask(counted_window.isnull(), other=(earnings_window+i).shift(i))\n", + " day_return = pricing.pct_change()[counted_window == -(window_length-1)/2 + i]\n", + " window_rets_mean.append(day_return.stack().mean())\n", + " window_rets_std.append(day_return.stack().std())\n", + " \n", + "# Plotting\n", + "fig, ax = plt.subplots(nrows=2, ncols=1)\n", + "\n", + "ax[0].bar(range(-(window_length-1)/2, (window_length-1)/2 + 1), window_rets_mean, align='center');\n", + "ax[0].axvline(0, c='r', alpha=0.5);\n", + "ax[0].set_ylabel('Average returns across assets');\n", + "\n", + "ax[1].bar(range(-(window_length-1)/2, (window_length-1)/2 + 1), window_rets_std, align='center');\n", + "ax[1].axvline(0, c='r', alpha=0.5);\n", + "ax[1].set_ylabel('Std of returns across assets');\n", + "\n", + "plt.xlabel('Days from earnings announcement');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Across the Q500, filtered by earnings announcment consistency, there seems to be two significant patterns. The first is that returns are overall positive on average with most of the premium existing before the announcment day. The second is that the largest effect on volume is the day before the earnings announcement, with a standard deviation of returns twice as high as what was observed on other days.\n", + "\n", + "Because we are focusing on the Lamont paper we will continue attempting to prove the same hypothesis from before instead of diving into what we found here. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Monthly Approach\n", + "\n", + "Lamont et al. used a different approach to detect earnings announcement premia. For every month in their research period, they compared the returns of a bucket of assets with earnings announcments within that month with the rest of the assets in the universe with no earnings announcements scheduled. The main reasons for this more blunt approach is to accomodate anomalies like early, delayed, or misrepresented earnings announcements." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [], + "source": [ + "def get_calendar_window_data(dataframe, freq, events):\n", + " \n", + " dataframe = pd.DataFrame(dataframe)\n", + " events = pd.DataFrame(events)\n", + " \n", + " # Grouping data by calendar months\n", + " calendar_windows = events.groupby(pd.TimeGrouper(freq=freq)).aggregate(np.sum)\n", + " data_by_calendar_group = dataframe.groupby(pd.TimeGrouper(freq=freq)).aggregate(np.nansum)\n", + " \n", + " # Finding statistics for inside vs outside earnings calendar months\n", + " data_in_calendar_window = data_by_calendar_group[calendar_windows.notnull()]\n", + " data_not_in_calendar_window = data_by_calendar_group[calendar_windows.isnull()]\n", + " \n", + " return data_in_calendar_window, data_not_in_calendar_window\n", + " \n", + "\n", + "returns_monthly_window = get_calendar_window_data(pricing.pct_change(), 'MS',\n", + " result.unstack()[result.unstack()==0]['BDaysSinceEarnings']\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Monthly Returns:\n", + "\n", + "Within earnings months: 0.0142593379822\n", + "Within non-earnings months: 0.0109416079917\n", + "Difference: 0.00331772999051\n" + ] + } + ], + "source": [ + "print 'Average Monthly Returns:'\n", + "print '\\nWithin earnings months:', returns_monthly_window[0].stack().mean()\n", + "print 'Within non-earnings months:', returns_monthly_window[1].stack().mean()\n", + "print 'Difference:', returns_monthly_window[0].stack().mean() - returns_monthly_window[1].stack().mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Across the universe it seems like the earnings announcement premium is present, but small. When assets are within their earnings month, the average monthy returns were 1.4% vs 1.1% when outside earnings months.\n", + "\n", + "Let's evaluate the magnitude of this earnings announcment premium on an asset by asset basis:" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean return in earnings months - mean return in non-earnings months:\n" + ] + }, + { + "data": { + "text/plain": [ + "Equity(2 [ARNC]) -0.045486\n", + "Equity(24 [AAPL]) 0.028194\n", + "Equity(62 [ABT]) -0.004178\n", + "Equity(67 [ADSK]) -0.075353\n", + "Equity(76 [TAP]) -0.007036\n", + "dtype: float64" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "asset_earnings_prem = returns_monthly_window[0].mean() - returns_monthly_window[1].mean()\n", + "\n", + "print 'Mean return in earnings months - mean return in non-earnings months:'\n", + "asset_earnings_prem.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Relation to Market Cap\n", + "\n", + "Let's find the 2010 market cap for all assets in our universe and evaluate if it is related to the presence of earnings announcment premium. A finding of the Lamont paper was that large cap equities tended to have the stronges earnings announcement premia." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Creating pipeline to pull market cap data from Morningstar\n", + "# Using 2010 data to represent the whole sample\n", + "pipe2 = Pipeline(\n", + " columns={\n", + " 'mkt_cap' : morningstar.valuation.market_cap.latest,\n", + " },\n", + " screen=(universe&ea_filter)\n", + ")\n", + "\n", + "result2 = run_pipeline(pipe2, '2010-01-01', '2010-01-01')" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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1116bm2++OTU1Ndl2223z3e9+N9tvv31OO+20NDQ0ZP78+fnoRz+a\nK664Ig0NDbntttsyZ86ctGnTJu3bt8/o0aM3ahahAwAAFMepawAAQHGEDgAAUByhAwAAFEfoAAAA\nxRE6AABAcYQOAABQHKEDAAAU5/8HLcsWZcdgjPIAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mkt_caps = result2.unstack()['mkt_cap'].mean()\n", + "ax = mkt_caps.hist(bins=20, log=True)\n", + "ax.set_title('Distribution of market caps within Q500US');" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Correlation between mkt_cap and earnings premium: 0.0466067309196\n" + ] + } + ], + "source": [ + "print 'Correlation between mkt_cap and earnings premium:', asset_earnings_prem[mkt_caps.index].corr(mkt_caps)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This experiment does not give evidence of correlation between earnings announcement premium and market cap. Let's see if historical volume predictability is a predictor of earnings anouncement premiums.\n", + "\n", + "### Relation to Volume Predictability\n", + "\n", + "To measure historical volume predictability with respect to earnings announcements, we will find the difference in volume between assets close to their earnings announcements and assets outside of their earnings weeks." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.071641792598458473" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "volume_weekly_window = get_calendar_window_data(volume, 'W',\n", + " result.unstack()[result.unstack()==0]['BDaysSinceEarnings']\n", + " )\n", + "\n", + "earnings_vol_effect = volume_weekly_window[0].mean() - volume_weekly_window[1].mean()\n", + "asset_earnings_prem.corr(earnings_vol_effect)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It seems as if historical volume predictability is not correlated with earnings announcement premia." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating Custom Factor\n", + "\n", + "Let's create a custom factor which ranks assets based on volume predictability and long the assets which are within two weeks of an earnings announcements and short those that are not. The magnitude of volume predictability determines long and short weights." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [], + "source": [ + "class VolPredictabilityEAP(CustomFactor):\n", + " \"\"\" \n", + " Assigns volume predictability through the ratio of volume within earnings windows to volume outside of\n", + " earnings windows. Then assigns a sign to weights depending on whether asset is in earnings window or\n", + " not.\n", + " \"\"\"\n", + " inputs =[EarningsCalendar.previous_announcement,\n", + " USEquityPricing.volume]\n", + " window_length = 252\n", + " def compute(self, today, asset_ids, out, earnings_dates, volume):\n", + " \n", + " vp = np.array([])\n", + " \n", + " for i in range(len(asset_ids)):\n", + " earnings_indices = np.where(earnings_dates[:-1, i] != earnings_dates[1:, i])[0]\n", + " window_indices = np.array([],dtype=int)\n", + " \n", + " for j in range(-5,5):\n", + " window_indices = np.append(window_indices, earnings_indices+j)\n", + " \n", + " window_indices = window_indices[(0 <= window_indices) & (window_indices < 252)]\n", + " in_window = volume[window_indices,i].mean()\n", + " out_window = volume[~np.in1d(range(252), window_indices),i].mean()\n", + " \n", + " asset_vp = in_window/out_window\n", + " \n", + " try:\n", + " if (earnings_indices[0] > 5 & earnings_indices[0] < 60):\n", + " asset_vp = -asset_vp\n", + " except:\n", + " pass\n", + " \n", + " vp = np.append(vp, asset_vp)\n", + "\n", + " out[:] = vp" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:15: FutureWarning: In the future, NAT != NAT will be True rather than False.\n", + " from ipykernel import kernelapp as app\n" + ] + } + ], + "source": [ + "universe = Q500US()\n", + "\n", + "NumEAs = NumberOfEAinPastYear(mask=universe)\n", + "\n", + "ea_filter = NumEAs.eq(5)\n", + "\n", + "pipe3 = Pipeline(\n", + " columns={\n", + " 'VolPredictability' : VolPredictabilityEAP(mask=universe),\n", + " },\n", + " screen=(universe)\n", + ")\n", + "\n", + "result3 = run_pipeline(pipe3, '2008-01-01', '2011-01-01')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Analyzing our Factor with Alphalens" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Alphalens](https://www.quantopian.com/posts/alphalens-a-new-tool-for-analyzing-alpha-factors) will help us evaluate the strength of our factor within the sample. We will use 1, 10, and 30-day return periods as our factor is based on a weekly to monthly time windows between assets and the exchange rate and should therefore be evaluated on a long-term basis." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [], + "source": [ + "import alphalens as al\n", + "\n", + "# Formats the factor data, pricing data, and group mappings into a DataFrame \n", + "# necessary for most Alphalens tearsheets.\n", + "# We invert the sign of our factor as we want the lowest correlations to have highest weights\n", + "# and the highest correlations to have the lowest weights.\n", + "factor_data = al.utils.get_clean_factor_and_forward_returns(factor=result3['VolPredictability'],\n", + " prices=pricing,\n", + " quantiles=5,\n", + " periods=(1,10,30))" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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wrxhuSXi0ySkdLncNGeWH2V6wmqzyo+gVM7aMVKJ844nx7YKPwZ/DxwIXPYIG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\nPFYUPI7H72ZnR42LOYmLmZFwCjGmRGYnLlL3CRbJbHTWdjuex+dma+1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JAAAg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Ky2qoesj1pgB+gpQBEmMyW2TVvWKkCDCrFua8IEQmJcbqu1bgOzzSa+ly9ICCF6jwQvhBBC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\nAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Use Alphalens to get mean returns by quantile over 1, 10, and 30 day windows\n", + "mean_return_by_q, std_err_by_q = al.performance.mean_return_by_quantile(factor_data, by_group=False)\n", + "mean_return_by_q_daily, std_err_by_q_daily = al.performance.mean_return_by_quantile(factor_data, by_date=True)\n", + "\n", + "al.plotting.plot_quantile_returns_bar(mean_return_by_q.apply(al.utils.rate_of_return, axis=0));\n", + "al.plotting.plot_cumulative_returns_by_quantile(mean_return_by_q_daily, period=1);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Possible Next Steps\n", + "\n", + "* Explore the results from the daily approach, such as the tendency for the bias to be focused around the days before the announcement and not after.\n", + "* Test the factor on universes larger than the Q500US and add filters." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*This presentation is for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation for any security; nor does it constitute an offer to provide investment advisory or other services by Quantopian, Inc. (\"Quantopian\"). Nothing contained herein constitutes investment advice or offers any opinion with respect to the suitability of any security, and any views expressed herein should not be taken as advice to buy, sell, or hold any security or as an endorsement of any security or company. In preparing the information contained herein, Quantopian, Inc. has not taken into account the investment needs, objectives, and financial circumstances of any particular investor. Any views expressed and data illustrated herein were prepared based upon information, believed to be reliable, available to Quantopian, Inc. at the time of publication. Quantopian makes no guarantees as to their accuracy or completeness. All information is subject to change and may quickly become unreliable for various reasons, including changes in market conditions or economic circumstances.*" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/case_studies/Earnings Announcements/preview.html b/case_studies/Earnings Announcements/preview.html new file mode 100644 index 00000000..8a53ebd4 --- /dev/null +++ b/case_studies/Earnings Announcements/preview.html @@ -0,0 +1,17253 @@ + + + Case Study - Earnings Announcements + + + + + + + + + + + + + + + + +
+
+ +
+
+
+
+

Researching & Developing a Market Neutral Strategy - Case Study - USD-EUR Exchange Rate¶

The following notebook aims to demonstrate best practices when developing a market-neutral signal based on Quantopian's data feeds. Following the steps detailed in this post and demonstrated in this notebook will ensure a well-founded alternative data signal that stands a better chance of holding up during out-of-sample validation and live trading.

+

Intro - Why use Alternative Data?¶

Fundamental asset data such as price, volume, or company financials has many benefits including its accessibility and simplicity. However, these advantages are a double-edged sword as any "alpha" left in these datasets can be especially difficult to extract exactly because of the amount of people using the data.

+

Because alternative data streams are not as widely available or as easy to use as fundamental ones, finding novel information that has yet to be "priced in" by the market is easier. Further benefits include the tendency for alternative data signals to be uncorrelated to ones based on traditional data.

+

Some of the major drawbacks of alternative data include its lack of structure, cost, exclusivity, and high dimensionality. Luckily, Quantopian takes down some of these barriers through its wide variety of alternative data feeds, many of which are free to use and all of which have been cleaned and standardized to work both in pipeline and as interactive datasets in the research environment.

+

Abstract¶

Through some preliminary research (reading papers, exploring data) we arrive at a hypothesis we would like to test. This paper by Lamont et al. shows that stocks trade at a premium around their earnings announcement, however the testing sample only goes up to 2004. Let's find out if the premium has survived past then.

+

Hypothesis: This anomaly of inflated prices around earnings announcements, observed before 2004 in Lamont et al., is still present today.

+

To test it we:

+

1) Examine the data using Blaze and look at the effects of earnings announcements on a single asset.

+

2) Use pipeline to filter a universe of assets and look for earnings announcement premia across all assets.

+

3) Use Alphalens to examine the strength of our signal within the in-sample period.

+ +
+
+
+
+
+
In [31]:
+
+
+
import matplotlib.pyplot as plt
+import matplotlib as mpl
+import pandas as pd
+import blaze as bz
+import math
+import numpy as np
+import seaborn
+import scipy.stats as stats
+import statsmodels.api as sm
+import statsmodels.tsa as tsa
+
+from statsmodels import regression
+from odo import odo
+
+ +
+
+
+ +
+
+
+
+
+

Researching Alternative Data: Earnings Calendar¶

The earnings calendar data used in this notebook, as well as the Morningstar fundamental data, are all available as free datafeeds.

+

Hypothesis: This anomaly of inflated prices around earnings announcements, observed before 2004 in Lamont et al., is still in more recent years.

+ +
+
+
+
+
+
In [32]:
+
+
+
# Importing exchange rate data set
+# When importing for blaze/non-pipeline research use quantopian.interactive._
+# When importing for pipeline use quantopian.pipeline._
+from quantopian.interactive.data.eventvestor import earnings_calendar
+
+print len(earnings_calendar), 'rows of data'
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
126453 rows of data
+
+
+
+ +
+
+ +
+
+
+
+
+

This size of this dataset exceeds the 10,000 row limit allowed by Quantopian's data agreements. What this means is that we cannot pull it all into a Pandas dataframe directly, and instead must use Blaze to perform computations remotely, before using bz.compute to pull results (which should be smaller than 10,000 rows).

+

This earnings calendar datset begins in 2007 but has more complete data from 2008 on, so taking this into account we will set our research period as 2008-2011. This range is large enough to observe most trends but small enough to leave room for thorough out-of-sample testing.

+

Because we cannot pull the whole interactive dataset, let's instead pull the earnings calendar for just General Electric.

+ +
+
+
+
+
+
In [33]:
+
+
+
# Defining our asset and test range
+asset = 'GE'
+start = '2008-01-01'
+end = '2012-01-01'
+
+# Pulling pricing data for GE
+pricing = get_pricing(asset, start_date=start, end_date=end)
+
+# Selecting earnings dates for GE and computing the Blaze expression into a Pandas DataFrame
+calendar_expression = earnings_calendar['asof_date'][earnings_calendar['symbol']==asset]
+calendar = bz.compute(calendar_expression)
+
+# Slicing earnings dates to within our test range
+calendar = pd.to_datetime(calendar[(calendar>start)&(calendar<end)].values, utc=True)
+print 'Earnings announcements for',asset,':\n\n',calendar
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Earnings announcements for GE :
+
+DatetimeIndex(['2008-01-18', '2008-04-11', '2008-07-11', '2008-10-10',
+               '2009-01-23', '2009-04-17', '2009-07-10', '2009-10-16',
+               '2010-01-22', '2010-04-16', '2010-07-16', '2010-10-15',
+               '2011-01-21', '2011-04-21', '2011-07-22', '2011-10-07'],
+              dtype='datetime64[ns, UTC]', freq=None)
+
+
+
+ +
+
+ +
+
+
+
+
+

As one would hope, there seems to be four earnings announcments per year. Let's plot these announcements to see if GE price and volume have observable reactions to these earnings announcemnts.

+ +
+
+
+
+
+
In [34]:
+
+
+
def highlight_events(ts, event_times, window_length, ax=None):
+
+    # Validating axis input
+    if ax is None:
+        ax = plt.gca()
+
+    # Plotting inputted time series
+    ts.plot(ax=ax, alpha = 0.5)
+    timedelta = pd.Timedelta((window_length-1)/2, unit='d')
+
+    # Plotting using red within windowed regions
+    for event in event_times:
+        window = pd.date_range(start = event - timedelta, periods=window_length)
+        ax.plot(window, ts[window].interpolate(method='linear'),
+                 c='r', linewidth=3)
+
+fig, ax = plt.subplots(ncols=1, nrows=2, sharex=True)
+
+plt.tight_layout()
+        
+highlight_events(pricing['price'], calendar, 15, ax[0])
+highlight_events(pricing['volume'], calendar, 15, ax[1])
+ax[0].set_title('%s Price'%asset);
+ax[1].set_title('%s Volume'%asset);
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+

There definitely seems to be spikes in volume around earnings announcements, however the effect of these events on price is not observably significant within this specific sample. We can quantify this by comparing mean returns and mean volume inside the earnings widows with mean returns and mean volume from timeregions outside earnings windows.

+ +
+
+
+
+
+
In [35]:
+
+
+
def compute_event_function(ts, event_times, window_length, function):
+    
+    # Defining variables used to find window indices
+    indices_in_window = pd.DatetimeIndex(event_times)
+    timedelta = pd.Timedelta((window_length-1)/2, unit='d')
+    
+    # Appending indices in windows to list
+    for event in event_times:
+        indices_in_window = indices_in_window.append(pd.date_range(start = event - timedelta, 
+                                                                   periods=window_length))
+    
+    # Returning function output with input of ts within windows
+    return function(ts[indices_in_window])
+
+not_earnings_window = pricing.index[~pricing.index.isin(calendar)]
+
+print 'Avg returns in window:', compute_event_function(pricing['price'].pct_change(), calendar, 15, np.mean)
+print 'Avg returns out of window:', np.mean(pricing['price'].pct_change()[not_earnings_window])
+
+print '\nAvg volume in window:', compute_event_function(pricing['volume'], calendar, 15, np.mean)
+print 'Avg volume out of window:', np.mean(pricing['volume'][not_earnings_window])
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Avg returns in window: -0.00248359004906
+Avg returns out of window: 7.22042088041e-05
+
+Avg volume in window: 90682011.4076
+Avg volume out of window: 74918192.5619
+
+
+
+ +
+
+ +
+
+
+
+
+

These results back up what we saw in the graph, as volume is measurably higher and returns are about even, if not lower than in the periods outside of the earnings windows. However, this is a small sample and we will need to conduct further testing across the whole universe of equities.

+ +
+
+
+
+
+
+
+

Designing a Pipeline¶

Building a pipeline will make pulling in earnings calendar data much easier. There exists built-in factors for this dataset, BusinessDaysUntilNextEarnings and BusinessDaysSincePreviousEarnings, that will let us skip some steps later on. When we are finished with this stage, the pipeline output can go straight into Alphalens and the pipeline itself can be copied and pasted into the IDE to be used in an algorithm.

+ +
+
+
+
+
+
In [36]:
+
+
+
# Pipeline API imports
+from quantopian.pipeline import Pipeline
+from quantopian.research import run_pipeline
+
+# Importing built in factors, universe, and data
+from quantopian.pipeline.factors import SimpleMovingAverage, CustomFactor, Returns
+from quantopian.pipeline.filters.morningstar import Q1500US, Q500US
+from quantopian.pipeline.data.builtin import USEquityPricing
+from quantopian.pipeline.classifiers.morningstar import Sector
+
+# Import builtin earnings factor and other data
+from quantopian.pipeline.factors.eventvestor import BusinessDaysSincePreviousEarnings
+from quantopian.pipeline.data.eventvestor import EarningsCalendar
+from quantopian.pipeline.data import morningstar
+
+ +
+
+
+ +
+
+
+
In [37]:
+
+
+
class NumberOfEAinPastYear(CustomFactor):
+    """ Returns the number of earnings days in the past year """
+    
+    # The specific inputs we need to calculate correlation
+    inputs =[EarningsCalendar.previous_announcement]
+    window_length = 252
+    def compute(self, today, asset_ids, out, earnings):
+
+        EAs = []
+        
+        for row in earnings.T:
+            EAs.append(len(np.unique(row))-1)
+        
+        out[:] = EAs
+
+ +
+
+
+ +
+
+
+
In [38]:
+
+
+
# Assigning the Q1500US as our universe
+universe = Q500US()
+
+# Creating a filter to ensure all assets in universe have had 4 earnings announcements in the past year
+NumEAs = NumberOfEAinPastYear(mask=universe)
+ea_filter = NumEAs.eq(4)
+
+# Buildling our pipeline
+pipe = Pipeline(
+    columns={
+        'BDaysSinceEarnings' : BusinessDaysSincePreviousEarnings(mask=universe),
+    },
+    screen=(universe&ea_filter)
+)
+
+result = pd.DataFrame()
+
+start = '2008-01-01'
+end = '2012-01-01'
+
+# Stores pipeline in result
+result = run_pipeline(pipe, start, end)
+assets = result.index.levels[1].unique()
+
+ +
+
+
+ +
+
+
+
In [39]:
+
+
+
# Finds assets and pricing data
+pricing = get_pricing(assets, start_date = start, end_date = end, fields = 'price')
+volume = get_pricing(assets, start_date = start, end_date = end, fields = 'volume')
+
+ +
+
+
+ +
+
+
+
+
+

The distribution of maximum days between earnings announcements in the Q1500 US:

+ +
+
+
+
+
+
In [40]:
+
+
+
result['BDaysSinceEarnings'].unstack().max(axis=0).hist(bins=40);
+plt.xlim(0,150);
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+

This distribution is significant because it shows that many assets have earnings announcements that behave differently than expected. If every equity had earnings announcements exactly once per quarter, the entire distribution would exist between 60 and 90. Instead there are values in the single digits and in the hundreds. This means we cannot make any assumptions about earnings intervals being uniform or regular despite the fact we filtered for assets with 4 earnings announcements within the past year.

+ +
+
+
+
+
+
+
+

Daily Approach¶

To observe the effect of earnings announcements, let's sort all asset-date pricing pairs into buckets based off of how close they are to an earnings announcment. Then, we can assign a window length and see average return across all assets and earnings announcments vs. days before earnings announcment.

+ +
+
+
+
+
+
In [41]:
+
+
+
# Length of earnings window, i.e. 11 is 5 business days before to 5 business days after
+window_length=11
+
+# Creating a DataFrame to indicate the start of an earnings window
+earnings_window_start = result['BDaysSinceEarnings'].unstack().shift(-(window_length-1)/2)==0.0
+earnings_window = pd.DataFrame(index = result.unstack().index, columns = assets)
+earnings_window[earnings_window_start] = -(window_length-1)/2
+
+counted_window = earnings_window
+window_rets_mean = []
+window_rets_std = []
+
+# Assigining numbers designating timedelta to earnings for asset-time pairs within earnings window
+# Finding averages for each earnings day timedelta from -5 through 5
+for i in range(1,window_length + 1):
+    counted_window = counted_window.mask(counted_window.isnull(), other=(earnings_window+i).shift(i))
+    day_return = pricing.pct_change()[counted_window == -(window_length-1)/2 + i]
+    window_rets_mean.append(day_return.stack().mean())
+    window_rets_std.append(day_return.stack().std())
+    
+# Plotting
+fig, ax = plt.subplots(nrows=2, ncols=1)
+
+ax[0].bar(range(-(window_length-1)/2, (window_length-1)/2 + 1), window_rets_mean, align='center');
+ax[0].axvline(0, c='r', alpha=0.5);
+ax[0].set_ylabel('Average returns across assets');
+
+ax[1].bar(range(-(window_length-1)/2, (window_length-1)/2 + 1), window_rets_std, align='center');
+ax[1].axvline(0, c='r', alpha=0.5);
+ax[1].set_ylabel('Std of returns across assets');
+
+plt.xlabel('Days from earnings announcement');
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+

Across the Q500, filtered by earnings announcment consistency, there seems to be two significant patterns. The first is that returns are overall positive on average with most of the premium existing before the announcment day. The second is that the largest effect on volume is the day before the earnings announcement, with a standard deviation of returns twice as high as what was observed on other days.

+

Because we are focusing on the Lamont paper we will continue attempting to prove the same hypothesis from before instead of diving into what we found here.

+ +
+
+
+
+
+
+
+

Monthly Approach¶

Lamont et al. used a different approach to detect earnings announcement premia. For every month in their research period, they compared the returns of a bucket of assets with earnings announcments within that month with the rest of the assets in the universe with no earnings announcements scheduled. The main reasons for this more blunt approach is to accomodate anomalies like early, delayed, or misrepresented earnings announcements.

+ +
+
+
+
+
+
In [42]:
+
+
+
def get_calendar_window_data(dataframe, freq, events):
+    
+    dataframe = pd.DataFrame(dataframe)
+    events = pd.DataFrame(events)
+    
+    # Grouping data by calendar months
+    calendar_windows = events.groupby(pd.TimeGrouper(freq=freq)).aggregate(np.sum)
+    data_by_calendar_group = dataframe.groupby(pd.TimeGrouper(freq=freq)).aggregate(np.nansum)
+    
+    # Finding statistics for inside vs outside earnings calendar months
+    data_in_calendar_window = data_by_calendar_group[calendar_windows.notnull()]
+    data_not_in_calendar_window = data_by_calendar_group[calendar_windows.isnull()]
+    
+    return data_in_calendar_window, data_not_in_calendar_window
+    
+
+returns_monthly_window = get_calendar_window_data(pricing.pct_change(), 'MS',
+                                                result.unstack()[result.unstack()==0]['BDaysSinceEarnings']
+                                                )
+
+ +
+
+
+ +
+
+
+
In [43]:
+
+
+
print 'Average Monthly Returns:'
+print '\nWithin earnings months:', returns_monthly_window[0].stack().mean()
+print 'Within non-earnings months:', returns_monthly_window[1].stack().mean()
+print 'Difference:', returns_monthly_window[0].stack().mean() - returns_monthly_window[1].stack().mean()
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Average Monthly Returns:
+
+Within earnings months: 0.0142593379822
+Within non-earnings months: 0.0109416079917
+Difference: 0.00331772999051
+
+
+
+ +
+
+ +
+
+
+
+
+

Across the universe it seems like the earnings announcement premium is present, but small. When assets are within their earnings month, the average monthy returns were 1.4% vs 1.1% when outside earnings months.

+

Let's evaluate the magnitude of this earnings announcment premium on an asset by asset basis:

+ +
+
+
+
+
+
In [44]:
+
+
+
asset_earnings_prem = returns_monthly_window[0].mean() - returns_monthly_window[1].mean()
+
+print 'Mean return in earnings months - mean return in non-earnings months:'
+asset_earnings_prem.head()
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Mean return in earnings months - mean return in non-earnings months:
+
+
+
+ +
+ +
Out[44]:
+ + + + +
+
Equity(2 [ARNC])    -0.045486
+Equity(24 [AAPL])    0.028194
+Equity(62 [ABT])    -0.004178
+Equity(67 [ADSK])   -0.075353
+Equity(76 [TAP])    -0.007036
+dtype: float64
+
+ +
+ +
+
+ +
+
+
+
+
+

Relation to Market Cap¶

Let's find the 2010 market cap for all assets in our universe and evaluate if it is related to the presence of earnings announcment premium. A finding of the Lamont paper was that large cap equities tended to have the stronges earnings announcement premia.

+ +
+
+
+
+
+
In [45]:
+
+
+
# Creating pipeline to pull market cap data from Morningstar
+# Using 2010 data to represent the whole sample
+pipe2 = Pipeline(
+    columns={
+        'mkt_cap' : morningstar.valuation.market_cap.latest,
+    },
+    screen=(universe&ea_filter)
+)
+
+result2 = run_pipeline(pipe2, '2010-01-01', '2010-01-01')
+
+ +
+
+
+ +
+
+
+
In [46]:
+
+
+
mkt_caps = result2.unstack()['mkt_cap'].mean()
+ax = mkt_caps.hist(bins=20, log=True)
+ax.set_title('Distribution of market caps within Q500US');
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
In [47]:
+
+
+
print 'Correlation between mkt_cap and earnings premium:', asset_earnings_prem[mkt_caps.index].corr(mkt_caps)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Correlation between mkt_cap and earnings premium: 0.0466067309196
+
+
+
+ +
+
+ +
+
+
+
+
+

This experiment does not give evidence of correlation between earnings announcement premium and market cap. Let's see if historical volume predictability is a predictor of earnings anouncement premiums.

+

Relation to Volume Predictability¶

To measure historical volume predictability with respect to earnings announcements, we will find the difference in volume between assets close to their earnings announcements and assets outside of their earnings weeks.

+ +
+
+
+
+
+
In [48]:
+
+
+
volume_weekly_window = get_calendar_window_data(volume, 'W',
+                                                result.unstack()[result.unstack()==0]['BDaysSinceEarnings']
+                                                )
+
+earnings_vol_effect = volume_weekly_window[0].mean() - volume_weekly_window[1].mean()
+asset_earnings_prem.corr(earnings_vol_effect)
+
+ +
+
+
+ +
+
+ + +
+ +
Out[48]:
+ + + + +
+
0.071641792598458473
+
+ +
+ +
+
+ +
+
+
+
+
+

It seems as if historical volume predictability is not correlated with earnings announcement premia.

+ +
+
+
+
+
+
+
+

Creating Custom Factor¶

Let's create a custom factor which ranks assets based on volume predictability and long the assets which are within two weeks of an earnings announcements and short those that are not. The magnitude of volume predictability determines long and short weights.

+ +
+
+
+
+
+
In [49]:
+
+
+
class VolPredictabilityEAP(CustomFactor):
+    """ 
+    Assigns volume predictability through the ratio of volume within earnings windows to volume outside of
+    earnings windows. Then assigns a sign to weights depending on whether asset is in earnings window or
+    not.
+    """
+    inputs =[EarningsCalendar.previous_announcement,
+             USEquityPricing.volume]
+    window_length = 252
+    def compute(self, today, asset_ids, out, earnings_dates, volume):
+        
+        vp = np.array([])
+        
+        for i in range(len(asset_ids)):
+            earnings_indices = np.where(earnings_dates[:-1, i] != earnings_dates[1:, i])[0]
+            window_indices = np.array([],dtype=int)
+            
+            for j in range(-5,5):
+                window_indices = np.append(window_indices, earnings_indices+j)
+            
+            window_indices = window_indices[(0 <= window_indices) & (window_indices < 252)]
+            in_window = volume[window_indices,i].mean()
+            out_window = volume[~np.in1d(range(252), window_indices),i].mean()
+            
+            asset_vp = in_window/out_window
+            
+            try:
+                if (earnings_indices[0] > 5 & earnings_indices[0] < 60):
+                    asset_vp = -asset_vp
+            except:
+                pass
+                
+            vp = np.append(vp, asset_vp)
+
+        out[:] = vp
+
+ +
+
+
+ +
+
+
+
In [56]:
+
+
+
universe = Q500US()
+
+NumEAs = NumberOfEAinPastYear(mask=universe)
+
+ea_filter = NumEAs.eq(5)
+
+pipe3 = Pipeline(
+    columns={
+        'VolPredictability' : VolPredictabilityEAP(mask=universe),
+    },
+    screen=(universe)
+)
+
+result3 = run_pipeline(pipe3, '2008-01-01', '2011-01-01')
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:15: FutureWarning: In the future, NAT != NAT will be True rather than False.
+  from ipykernel import kernelapp as app
+
+
+
+ +
+
+ +
+
+
+
+
+

Analyzing our Factor with Alphalens¶

+
+
+
+
+
+
+
+

Alphalens will help us evaluate the strength of our factor within the sample. We will use 1, 10, and 30-day return periods as our factor is based on a weekly to monthly time windows between assets and the exchange rate and should therefore be evaluated on a long-term basis.

+ +
+
+
+
+
+
In [57]:
+
+
+
import alphalens as al
+
+# Formats the factor data, pricing data, and group mappings into a DataFrame 
+# necessary for most Alphalens tearsheets.
+# We invert the sign of our factor as we want the lowest correlations to have highest weights
+# and the highest correlations to have the lowest weights.
+factor_data = al.utils.get_clean_factor_and_forward_returns(factor=result3['VolPredictability'],
+                                                            prices=pricing,
+                                                            quantiles=5,
+                                                            periods=(1,10,30))
+
+ +
+
+
+ +
+
+
+
In [58]:
+
+
+
al.performance.factor_alpha_beta(factor_data)
+
+ +
+
+
+ +
+
+ + +
+ +
Out[58]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + +
11030
Ann. alpha0.0258100.011323-0.004321
beta0.0265560.0147380.006449
+
+
+ +
+ +
+
+ +
+
+
+
In [59]:
+
+
+
# Use Alphalens to get mean returns by quantile over 1, 10, and 30 day windows
+mean_return_by_q, std_err_by_q = al.performance.mean_return_by_quantile(factor_data, by_group=False)
+mean_return_by_q_daily, std_err_by_q_daily = al.performance.mean_return_by_quantile(factor_data, by_date=True)
+
+al.plotting.plot_quantile_returns_bar(mean_return_by_q.apply(al.utils.rate_of_return, axis=0));
+al.plotting.plot_cumulative_returns_by_quantile(mean_return_by_q_daily, period=1);
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+

Possible Next Steps¶

    +
  • Explore the results from the daily approach, such as the tendency for the bias to be focused around the days before the announcement and not after.
    • +
  • Test the factor on universes larger than the Q500US and add filters.
    • +
+ +
+
+
+
+
+
+
+

This presentation is for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation for any security; nor does it constitute an offer to provide investment advisory or other services by Quantopian, Inc. ("Quantopian"). Nothing contained herein constitutes investment advice or offers any opinion with respect to the suitability of any security, and any views expressed herein should not be taken as advice to buy, sell, or hold any security or as an endorsement of any security or company. In preparing the information contained herein, Quantopian, Inc. has not taken into account the investment needs, objectives, and financial circumstances of any particular investor. Any views expressed and data illustrated herein were prepared based upon information, believed to be reliable, available to Quantopian, Inc. at the time of publication. Quantopian makes no guarantees as to their accuracy or completeness. All information is subject to change and may quickly become unreliable for various reasons, including changes in market conditions or economic circumstances.

+ +
+
+
+
+
+ diff --git a/case_studies/USD_EUR_exchange_rate/notebook.ipynb b/case_studies/USD_EUR_exchange_rate/notebook.ipynb new file mode 100644 index 00000000..b6b6749c --- /dev/null +++ b/case_studies/USD_EUR_exchange_rate/notebook.ipynb @@ -0,0 +1,1279 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Researching & Developing a Market Neutral Strategy - Case Study - USD-EUR Exchange Rate\n", + "\n", + "The following notebook aims to demonstrate best practices when developing a market-neutral signal based on Quantopian's data feeds. Following the steps detailed in [this post](https://www.quantopian.com/posts/using-alternative-data-researching-and-implementing-a-market-neutral-strategy) and demonstrated in this notebook will ensure a well-founded alternative data signal that stands a better chance of holding up during out-of-sample validation and live trading.\n", + "\n", + "### Intro - Why use Alternative Data?\n", + "Fundamental asset data such as price, volume, or company financials has many benefits including its accessibility and simplicity. However, these advantages are a double-edged sword as any \"alpha\" left in these datasets can be especially difficult to extract exactly because of the amount of people using the data. \n", + "\n", + "Because alternative data streams are not as widely available or as easy to use as fundamental ones, finding novel information that has yet to be \"priced in\" by the market is easier. Further benefits include the tendency for alternative data signals to be uncorrelated to ones based on traditional data.\n", + "\n", + "Some of the major drawbacks of alternative data include its lack of structure, cost, exclusivity, and high dimensionality. Luckily, Quantopian takes down some of these barriers through its [wide variety of alternative data feeds](https://www.quantopian.com/data/), many of which are free to use and all of which have been cleaned and standardized to work both in pipeline and as interactive datasets in the research environment.\n", + "\n", + "### Abstract\n", + "\n", + "Through some preliminary research (reading papers, exploring data) we arrive at a new hypothesis we would like to test:\n", + "\n", + "** Hypothesis: ** *Equity home bias results in international-domiciled equities being undervalued. US equities with strong inverse correlations to the USD-EUR exchange rate show similarities to these international assets, and because they are US equities and subject to US market biases equity home bias will cause them to be undervalued.*\n", + "\n", + "To test it we:\n", + "\n", + "1) Examine the data and look for the presence of US equity home bias within the research period 2004-2011.\n", + "\n", + "2) Use pipeline to identify US equities strong positive or inverse correlations to the USD-EUR exchange rate. We can then sort the equities into groups based off of their correlations and use those classifications to conduct further tests to try to back up the hypothesis.\n", + "\n", + "3) Use Alphalens to examine the strength of our signal within the in-sample period.\n", + "\n", + "\n", + "We finish by implementing an algorithm based on the [long short equity template](https://www.quantopian.com/lectures/example-long-short-equity-algorithm), running the backtest over the research period and walking forward one year out-of-sample, and analyzing the results using Pyfolio." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib as mpl\n", + "import pandas as pd\n", + "import blaze as bz\n", + "import math\n", + "import numpy as np\n", + "import seaborn\n", + "import scipy.stats as stats\n", + "import statsmodels.api as sm\n", + "import statsmodels.tsa as tsa\n", + "\n", + "from statsmodels import regression\n", + "from odo import odo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Researching Alternative Data: USD-EUR Exchange Rate\n", + "This exchange rate data used in this notebook, as well as the Morningstar fundamental data, are all available as free datafeeds. \n", + "\n", + "** Preliminary Hypothesis: ** *The assets in the Q1500US and Q500US universes are all US-based equities and will therefore be affected by the strength of the US dollar. The USD-EUR exchange rate is a good indicator of the strength of the USD and therefore it is worth investigating relationships between the returns of US companies and their correlation with the exchange rate.*\n", + "\n", + "To protect against overfitting, we will conduct our research strictly within the interval 2004-2010, leaving the data for 2011 and after for out-of-sample validation." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Importing exchange rate data set\n", + "# When importing for blaze/non-pipeline research use quantopian.interactive._\n", + "# When importing for pipeline use quantopian.pipeline._\n", + "from quantopian.interactive.data.quandl import currfx_usdeur" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Exchange rate data is small enough to compute directly into a Pandas DataFrame\n", + "data = bz.compute(currfx_usdeur)\n", + "\n", + "# We'll set 'asof_date' as our index, and add a timedelta of 1 day to prevent look ahead bias\n", + "# This is because we will not have a good idea about FX data for a specific day until the day after\n", + "data = data.set_index(data['asof_date']+pd.Timedelta('1 days')).sort_index().drop('timestamp', 1)\n", + "del data['asof_date']\n", + "\n", + "# Renaming columns\n", + "data.columns = ['rate', 'high_est', 'low_est']\n", + "\n", + "# Dropping '0' values in the high_est and low_est columns as well as an outlier high_est value of 14\n", + "data['high_est'][(data['high_est'] == 0) | (data['high_est'] > 10)] = None\n", + "data['low_est'][data['low_est'] == 0] = None" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------- US/Euro Exchange Rate Data -----------\n", + "Start: 1999-09-07 End: 2017-08-08\n", + "Min Value: 0.627189 Max Value: 1.2064\n", + "Avg Value: 0.834329005551 Median Value: 0.791854\n", + "\n", + "Fields: rate high_est low_est\n", + "Frequency: daily\n", + "\n" + ] + }, + { + "data": { + "image/png": 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9VuIyRWGF7mwRQgghx0JF7PqSgqJEFBQdh/gTSjDGXQhM1e/YGzfBefM6VRPx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KsrIySktLeeedd/jDH/7ABx98wAUXXEBlZeVwv40xq7/Xb/369fh8PsrKyvjd737H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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Getting an understanding of the size and structure of the data by finding \n", + "print \"----------- US/Euro Exchange Rate Data -----------\"\n", + "def summary(data):\n", + " print \"%-12s %-15s %-13s %s\" % ('Start:', data.index[0].date(), \n", + " 'End:', data.index[-1].date())\n", + " print \"%-12s %-15s %-13s %s\" % ('Min Value:', data.min(), \n", + " 'Max Value:', data.max())\n", + " print \"%-12s %-15s %-13s %s\" % ('Avg Value:', data.mean(), \n", + " 'Median Value:', data.median())\n", + "\n", + "summary(data['rate'])\n", + "\n", + "print \"\\nFields:\", data.columns[0], data.columns[1], data.columns[2]\n", + "print \"Frequency: daily\\n\"\n", + "\n", + "# Conduct research within this time frame, leaving ample room for out-of-sample-testing\n", + "start = '2004-01-01'\n", + "end = '2011-01-01'\n", + "\n", + "# Plot rate, high_est, low_est for our window\n", + "# Used ffill to fill empty high_est and low_est days with most recent value\n", + "data[start:end].ffill().plot();\n", + "plt.ylabel('Cost of USD in EUR');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Macro vs. Asset-Level Data\n", + "\n", + "One classifier for datasets is whether, for a given point in time, they provide individual values for every asset (such as sentiment, earnings surprises, dividends) or a single macro value (like FX, inflation, or gold prices). \n", + "\n", + "An important concept when dealing with macro data like FX is how to apply it to get a unique value for every asset in your universe. The logic you use to decompose a single macro indicator into many asset-level ranking values requires some thought. Some approaches include:\n", + "\n", + "* Correlation\n", + "* Regression beta coefficient\n", + "* Spearman rank correlation\n", + "* Cointegration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After some experimentation with the data along the above guidelines, it became apparent that assets with a low correlation of returns to the USD-EUR exchange rate consistently outperformed those with a high one, despite the exchange rate remaining mostly flat over the time period. \n", + "\n", + "While it may seem tempting to end the process here and put this signal into an algorithm, such a decision would leave you susceptible to overfitting. Without understanding *why* the signal exists means it might as well have come from random chance, and a signal found on random chance alone will probably not hold up during live trading or out-of-sample validation. To learn more about overfitting, refer to the Quantopian [Dangers of Overfitting](https://www.quantopian.com/lectures/the-dangers-of-overfitting) lecture. Researching and understanding an underlying economic hypothesis, a \"story\" as to why the signal works, will help reduce this risk. \n", + "\n", + "Having a story behind an alpha signal has further benefits beyond reducing overfitting. Should a signal begin to perform poorly, having an economic hypothesis to dissassemble lets you isolate what changed and how to fix it. \n", + "\n", + "## Equity Home Bias Puzzle\n", + "One possible 'story', or explanation, as to why negatively correlated stocks outperform positively correlated ones is the [Equity Home Bias Puzzle](https://en.wikipedia.org/wiki/Equity_home_bias_puzzle). Equity home bias is the tendency for individuals and institutions to hold small amounts of foreign equity investments, despite empirical evidence suggesting \"substantial benefits from international diversification.\" The few possible explanations there are have to do with information immobility and fear of exposure to foreign exchange risk.\n", + "\n", + "It is possible (and we will try to see if this is true later) that US equities with strong inverse correlations to the USD-EUR exchange can serve as proxies for these international assets because of their inverse relation to the strength of the dollar. If this is the case, because they are US equities and subject to US market biases it is possible that they will be undervalued." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** Refined Hypothesis: ** *Equity home bias results in international-domiciled equities being undervalued. US equities with strong inverse correlations to the USD-EUR exchange rate might show similarities to these international assets, and because they are US equities and subject to US market biases equity home bias could cause them to be undervalued.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Detecting Equity Home Bias\n", + "\n", + "Because our story is based on the presence of this bias it is important to make sure it exists within our test period 2004-2010. We will use some of the methods in [this paper](https://www.aeaweb.org/conference/2015/retrieve.php?pdfid=437) to estimate home bias.$^1$ \n", + "\n", + "Data on cross-border US portfolio holdings is from the [U.S. Department of the Treasury](https://www.treasury.gov/resource-center/data-chart-center/tic/Pages/fpis.aspx)(part B) and US/EU market cap data is from the [World Bank](http://data.worldbank.org/indicator/CM.MKT.LCAP.CD?end=2016&start=1975&view=chart). " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ratios of Europe-Based Equities to US-Based Equities:\n", + "\n", + "US Investor Average: 0.0787304762136\n", + "CAPM Optimal Ratio: 0.408553054291\n", + "\n", + "Difference: 0.329822578077\n" + ] + } + ], + "source": [ + "# List of eurozone countries\n", + "euro_countries = ['Austria', 'Belgium','Finland','France','Germany',\n", + " 'Greece','Ireland','Italy','Netherlands','Portugal',\n", + " 'Slovakia','Slovenia','Spain','Cyprus','Estonia','Latvia',\n", + " 'Luthuania','Luxembourg','Malta']\n", + "\n", + "# Pull cross-border holdings from U.S. Department of the Treasury\n", + "foreign_holdings = local_csv('shchistdat.csv')\n", + "\n", + "# Selecting only investments in eurozone nations and fixing date order\n", + "euro_investments = foreign_holdings.loc[foreign_holdings['Unnamed: 1'].isin(euro_countries)][range(2,49,4)]\n", + "euro_investments.columns = pd.date_range(end='2015-01-01',periods=12,freq='AS')[::-1]\n", + "\n", + "# Removing thousands separator commas and converting strings of numbers to ints\n", + "for column in euro_investments.columns:\n", + " euro_investments[column] = euro_investments[column].str.replace(',','').astype(int)\n", + "\n", + "# Multiply by 1 million because CSV data unit was millions \n", + "euro_investments = euro_investments.sum()*1000000\n", + "\n", + "# Pull country market caps from World Bank\n", + "mkt_caps = local_csv('API_CM.MKT.LCAP.CD_DS2_en_csv_v2.csv')\n", + "\n", + "# Select only eurozone and US market caps using country code\n", + "mkt_caps = mkt_caps[mkt_caps['Country Code'].isin(['EMU','USA'])]\n", + "\n", + "# Isolating market cap data by country and to within our research range \n", + "USA = mkt_caps.iloc[1][start[:4]:end[:4]]\n", + "EMU = mkt_caps.iloc[0][start[:4]:end[:4]]\n", + "\n", + "# Finding Euro-USA market cap ratio, Euro-Domestic US investments ratio\n", + "# and the difference between the two\n", + "mkt_ratio = EMU/USA\n", + "holdings_ratio = (euro_investments/(USA-euro_investments))[start[:4]:end[:4]]\n", + "holdings_ratio.index = mkt_ratio.index\n", + "diff = mkt_ratio - holdings_ratio\n", + "\n", + "print 'Ratios of Europe-Based Equities to US-Based Equities:\\n'\n", + "\n", + "print 'US Investor Average:', holdings_ratio.mean()\n", + "print 'CAPM Optimal Ratio:', mkt_ratio.mean()\n", + "\n", + "print '\\nDifference:', diff.mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[CAPM](https://en.wikipedia.org/wiki/Capital_asset_pricing_model) dictates that the optimal portfolio is one with weights based on the market capitalization of equities within the universe. As such, an optimal international portfolio should have a ratio of US to EU equities equal to the ratio of the size of the total US and EU equity markets.\n", + "\n", + "Across 2004-2010, the European equity market cap was 40.9% of the size of the US equity market cap; according to CAPM, any optimal investment portfolio should have similar proportions of US to European equities. \n", + "\n", + "However, the US Treasury data shows that during our time period US investor portfolios had a Euro-US equity ratio around 7.9%, around one-fifth of the optimal amount. This discrepancy could be a result of home bias, and this test suggests its presence during the research period." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Designing a Pipeline\n", + "\n", + "Let's build a [pipeline](https://www.quantopian.com/tutorials/pipeline) to pull in rolling USD-EUR rate correlations for every asset in the Q500US universe. Using pipeline and a custom factor makes finding correlations for hundreds of equities across 7 years of data easier. When we are finished with this stage, the pipeline output can go straight into Alphalens and the pipeline itself can be copied and pasted into the IDE to be used in an algorithm." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Pipeline API imports\n", + "from quantopian.pipeline import Pipeline\n", + "from quantopian.research import run_pipeline\n", + "\n", + "# Importing built in factors, universe, and data\n", + "from quantopian.pipeline.factors import SimpleMovingAverage, CustomFactor, Returns\n", + "from quantopian.pipeline.filters.morningstar import Q1500US, Q500US\n", + "from quantopian.pipeline.data.builtin import USEquityPricing\n", + "from quantopian.pipeline.classifiers.morningstar import Sector\n", + "\n", + "# Import FX rate and other data\n", + "from quantopian.pipeline.data.quandl import currfx_usdeur\n", + "from quantopian.pipeline.data import morningstar\n", + "from quantopian.pipeline.data.psychsignal import stocktwits" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Defining our USD-EUR exchange rate correlation custom factor\n", + "class FXCorr(CustomFactor):\n", + " \"\"\" Custom factor to find correlation of asset returns and FX rate \"\"\"\n", + " \n", + " inputs = [USEquityPricing.close, currfx_usdeur.rate]\n", + " window_length = 150\n", + " def compute(self, today, asset_ids, out, close, exch_rate):\n", + " # Converting data to returns DataFrame to make correlation calculation faster\n", + " exch_df = pd.DataFrame(np.repeat(exch_rate, len(close[0]), axis = 1)).pct_change(1)\n", + " close_df = pd.DataFrame(close).pct_change(1)\n", + " \n", + " out[:] = exch_df.corrwith(close_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [], + "source": [ + "# Assigning the Q500US as our universe\n", + "universe = Q500US()\n", + "\n", + "# Buildling our pipeline\n", + "pipe = Pipeline(\n", + " columns={\n", + " 'fx_corr' : FXCorr(mask=universe),\n", + " },\n", + " screen=(universe)\n", + ")\n", + "\n", + "# Stores pipeline in result\n", + "result = run_pipeline(pipe, start, end)\n", + "\n", + "# Finds assets and pricing data\n", + "assets = result.index.levels[1].unique()\n", + "pricing = get_pricing(assets, start_date = start, end_date = end, fields = 'price')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The distribution of mean FX correlations across the Q500US:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "result.unstack()['fx_corr'].mean().hist(bins=20);\n", + "plt.title('Q500US distribution of USD-EUR correlations');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Further Testing our Hypothesis using Pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The difference between CAPM optimal diversification and observed US investor diversification suggest that equity home bias exists within our time period, but we have not explored whether or not US equities with strong inverse correlations to the dollar share similarities with international markets. If that is the case, there is the possiblity that they will be subject to the same home bias as international assets. \n", + "\n", + "To see if the assuption that low FX correlation US equities can represent international assets holds, let's compare the returns of the following:\n", + "\n", + "* An ETF that tracks the FTSE Developed Europe All-Cap Index (`VGK`)\n", + "* A bucket of 25 stocks with strong negative correlations to FX rate\n", + "* A bucket of 25 stocks with strong positive correlations to FX rate" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Correlations of returns:\n", + " low_bucket high_bucket vgk\n", + "low_bucket 1.000000 0.788119 0.838185\n", + "high_bucket 0.788119 1.000000 0.735325\n", + "vgk 0.838185 0.735325 1.000000\n" + ] + } + ], + "source": [ + "low_bucket = result.unstack()['fx_corr'].mean().sort_values(ascending=True)[:25].index\n", + "high_bucket = result.unstack()['fx_corr'].mean().sort_values(ascending=False)[:25].index\n", + "\n", + "returns = pd.DataFrame()\n", + "\n", + "# Creating equally weighted portfolios of both buckets by first finding pricing using get_pricing\n", + "# then using the pct_change() attribute to find returns, and finally averaging across all assets in the bucket\n", + "returns['low_bucket'] = get_pricing(low_bucket, start_date=start, end_date=end, \n", + " fields = 'price').pct_change()[1:].dropna(axis=1).mean(axis=1,skipna=True)\n", + "returns['high_bucket'] = get_pricing(high_bucket, start_date=start, end_date=end, \n", + " fields = 'price').pct_change()[1:].dropna(axis=1).mean(axis=1,skipna=True)\n", + "returns['vgk'] = get_pricing('vgk', start_date=start, end_date=end, fields = 'price').pct_change()[1:]\n", + "\n", + "print 'Correlations of returns:'\n", + "print returns.corr()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Within this time period, it seems like the `low_bucket` portfolio is more closely correlated with the Euro index than the `high_bucket` portfolio, indicating assets with a strong inverse correlation to the exchange rate share similarities to international assets. **This is far from any sort of proof of our theory**, and we did not account for events like delistings or M&As which could have affected these results. We also still need more evidence to show that assets with low correlation to the exchange rate can be subject to foreign equity bias, and that assets subject to foreign equity bias are undervalued in the first place. Despite this gap, we will proceed to the next step, keeping in mind that our hypothesis could use some more reinforcement." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Analyzing our Factor with Alphalens" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Alphalens](https://www.quantopian.com/posts/alphalens-a-new-tool-for-analyzing-alpha-factors) will help us evaluate the strength of our `fx_corr` factor within the sample. We will use 1, 10, and 30-day return periods as our factor is based on a long-term relationship between assets and the exchange rate and should therefore be evaluated on a long-term basis." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [], + "source": [ + "import alphalens as al\n", + "\n", + "# Formats the factor data, pricing data, and group mappings into a DataFrame \n", + "# necessary for most Alphalens tearsheets.\n", + "# We invert the sign of our factor as we want the lowest correlations to have highest weights\n", + "# and the highest correlations to have the lowest weights.\n", + "factor_data = al.utils.get_clean_factor_and_forward_returns(factor=-result['fx_corr'],\n", + " prices=pricing,\n", + " quantiles=5,\n", + " periods=(1,10,30))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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IiAohhBBCCPHMO3cjhjd+n0t+UQH9mnRi4ZAp2g6Jug5uzPAbxSdBP/Hu+oUc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\nrKnDWV2Lo7oGZ3Utrrp6nLV1uGrtX9/W1eOqrcNZa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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Use Alphalens to get mean returns by quantile over 1, 10, and 30 day windows\n", + "mean_return_by_q, std_err_by_q = al.performance.mean_return_by_quantile(factor_data, by_group=False)\n", + "mean_return_by_q_daily, std_err_by_q_daily = al.performance.mean_return_by_quantile(factor_data, by_date=True)\n", + "\n", + "al.plotting.plot_quantile_returns_bar(mean_return_by_q.apply(al.utils.rate_of_return, axis=0));\n", + "al.plotting.plot_cumulative_returns_by_quantile(mean_return_by_q_daily, period=30);" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 1 10 30\n", + "factor_quantile \n", + "1 -0.000333 -0.003301 -0.009091\n", + "2 -0.000023 -0.000614 -0.001685\n", + "3 0.000018 0.000496 0.000637\n", + "4 0.000178 0.001620 0.003299\n", + "5 0.000162 0.001811 0.006867" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean_return_by_q" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For a full Alphalens tearsheet, run the following cell:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "al.tears.create_full_tear_sheet(factor_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Implement and Backtest the Strategy in the IDE\n", + "We can use the [long-short equity algorithm template](https://www.quantopian.com/lectures/example-long-short-equity-algorithm) to make this step easier. \n", + "\n", + "The long-short equity template works by ranking equities within a universe along some ranking factor. It then longs the top of the ranking and shorts the bottom, rebalancing every month. It also uses the Optimize API, with sector neutrality, beta neutrality, and position concentration constraints, to handle ordering logic and assign appropriate weights.\n", + "\n", + "To implement a long-short equity strategy with our `fx_corr` factor, we find the custom factor in the algorithm and copy and paste our `fx_corr` in its place. We can also make any other changes we deem suitable. For this algorithm, lets:\n", + "\n", + "* Comment out the other factors ('value' and 'quality') as we want to isolate our FX factor instead of aggregating it with two others to create a combined rank\n", + "* Comment out lines these lines turn off slippage and commissions but we want their effects included\n", + "* Switch from the Q1500US to the Q500US as it was the Q500US we use throughout our research\n", + "* Reduce both `NUM_LONG_POSITIONS` and `NUM_SHORT_POSITIONS` to 100 from 300. We are switching to a universe one third the size so we should proportionally scale this size of our our long and short positions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Analyze Our Backtest Using Pyfolio\n", + "Our in-sample research up until this point was almost entirely contained within the time period 2004-2010. We will run the backtest from 2004-2011, adding in a single year of out-of-sample testing. As a result, the most important part of the below Pyfolio tearsheets will be performance within 2011.\n", + "\n", + "$$\n", + " \\\n", + " \\overbrace{\n", + " \\underbrace{\\textit{2004, 2005, ... 2010}}_\\text{In-Sample}\\:\\:+\n", + " \\underbrace{\\textit{2011}}_\\text{Out-of-Sample}\n", + " }^\\text{Backtest}\n", + " \\\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100% Time: 0:00:03|###########################################################|\n" + ] + } + ], + "source": [ + "import pyfolio as pf\n", + "from pyfolio import tears\n", + "from pyfolio import timeseries\n", + "import itertools\n", + "import functools\n", + "\n", + "# Get backtest object\n", + "bt = get_backtest('5984b88018063e557a3a9cd4')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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RBIMiIiIiIqJuCIKAr459hZzKHKgUKijkCuhNeod6lyRf4jQgshfpH+mpblI/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9vms9KUl5eHzEqWoc2I6jmGcQNq+2oF+36484ecwLp8+TIX57N582YkJSWBYRjOd7qiogIZGRkB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\nQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "algo_performance = bt.daily_performance\n", + "benchmark = get_pricing('SPY', start_date=start, end_date='2012-01-01', fields = 'price').pct_change()[1:]\n", + "pf.plotting.plot_rolling_returns(algo_performance['returns'], factor_returns=benchmark);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's try to find the main drivers of the algorithm's performance. First we can use the Fama-French factor tearsheet from Pyfolio with a 60 day rolling window to measure exposures to the three fundamental factors (market cap, book to price, and momentum). \n", + "\n", + "CSVs with the returns for these factors can be found [here](http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html).$^2$" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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j+fLl4Hke4eHheOmll+z6mZycjDfffBMDBgxw6v/mzZtx/PhxMMbA87y8r16v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vavuQZ5pYj+qfN1+x43BHGxo5SCZ07KFZ7HIUGqI4JOFFjktRYXMZUl2+tqGmTWCxwhGN+9ConP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pGRJSIqRIIkQXK7IHd2QPZ4ItYr9I+OJZw9AphByhuO2lo4GxoiHjfOD0r2Tp92wg/PGImi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\nY3f7gZTeU4air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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pf.plotting.plot_rolling_fama_french(algo_performance['returns'], rolling_window=30);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's try to decompose returns into segments explained by the above factors. This will help us see exactly how much of the algo's returns over the time period are atrributable to exposure to these factors.\n", + "\n", + "\n", + "### Returns Decomposition into Fama-French Factors\n", + "\n", + "Below analysis was inspired by Exhibit 3 in [a report by AQR on Measuring Factor Exposure](https://www.aqr.com/-/media/files/papers/measuring-factor-exposures-uses-and-abuses.pdf).$^3$ It yields a breakdown of how much exposure to each risk factor contributes to the algorithm's returns in excess of the risk-free rate. " + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [], + "source": [ + "def find_vifs(data):\n", + " data = pd.DataFrame(data)\n", + " cols = data.columns\n", + " VIFs = pd.Series(index=cols)\n", + " for x, column in enumerate(cols):\n", + " # Calculates VIF using steps described here: \n", + " # https://en.wikipedia.org/wiki/Variance_inflation_factor#Calculation_and_Analysis\n", + " VIFs[column] = 1/(1-regression.linear_model.OLS(data.iloc[:,x], \n", + " sm.add_constant(np.column_stack((\n", + " [data.iloc[:,(x+i+1)%len(cols)] for i in range(len(cols)-1)] \n", + " )))).fit().rsquared) \n", + " return VIFs\n", + "\n", + "def decompose_returns_custom(algo_returns, risk_factors, plot):\n", + " \n", + " # Get excess returns for algo and risk-free rate from Dartmouth using Pyfolio\n", + " risk_free = pf.utils.load_portfolio_risk_factors().loc[algo_returns.index]['RF']\n", + " algo_rets_over_rf = algo_returns - risk_free\n", + " algo_returns_ann = algo_rets_over_rf.mean()*252\n", + " \n", + " # Write index for betas dataframe\n", + " betas_index = ['Alpha','Alpha t-stat']\n", + " for factor in risk_factors.columns.values:\n", + " betas_index = betas_index+[factor]+['{} t-stat'.format(factor)]\n", + "\n", + " # Create dataframes to store betas and return contributions\n", + " betas = pd.DataFrame(columns = [risk_factors.columns.values],\n", + " index = betas_index)\n", + " returns_decomposition = pd.DataFrame(index = itertools.chain(['Alpha'],risk_factors.columns.values),\n", + " columns = risk_factors.columns.values)\n", + "\n", + " # Nested iteration through models and factors in each model\n", + " for factor in risk_factors.columns.values:\n", + " model_factors = sm.add_constant(risk_factors.loc[:,:factor]).loc[algo_rets_over_rf.index]\n", + " model = sm.OLS(algo_rets_over_rf, model_factors).fit()\n", + " for i in range(len(model_factors.columns)):\n", + " beta = model.params[i]\n", + " betas[factor].iloc[2*i] = beta\n", + " betas[factor].iloc[2*i+1] = model.params[i]/model.HC0_se[i]\n", + " if i>0:\n", + " returns_decomposition[factor].iloc[i] = beta*(risk_factors.loc[algo_rets_over_rf.index].mean()*252)[i-1]\n", + "\n", + " # Annualize alphas\n", + " betas.loc['Alpha'] = betas.loc['Alpha']*252\n", + " returns_decomposition.loc['Alpha'] = betas.loc['Alpha']\n", + " \n", + " # Write column names\n", + " rets_decomp_columns = []\n", + " for i in range(len(risk_factors.columns.values)):\n", + " rets_decomp_columns = rets_decomp_columns + ['Model {}: Add {}'.format(i, risk_factors.columns.values[i])]\n", + " returns_decomposition.columns = rets_decomp_columns\n", + " \n", + " # Finds variance inflation factors using function defined above\n", + " VIFs = find_vifs(risk_factors)\n", + " \n", + " # Plotting conditional on input\n", + " if plot:\n", + " \n", + " # Make more colors to prevent default colors repeating themselves\n", + " colors = mpl.cm.jet(np.linspace(0, 1, len(risk_factors.columns)+1))\n", + "\n", + " # Create bar graph, with horizontal lines at 0 and annualized algo returns\n", + " ax = returns_decomposition.T.plot(kind='bar', stacked=True, rot=-30, color=colors)\n", + " ax.plot(ax.get_xlim(),[algo_returns_ann]*len(ax.get_xlim()), linestyle = '--',\n", + " color='black', label = 'Algo Returns');\n", + " ax.plot(ax.get_xlim(),[0]*len(ax.get_xlim()), color='black', linewidth=4);\n", + " ax.legend(loc='best', bbox_to_anchor=(1.0, 0.5));\n", + " \n", + " # Fill in green and red zones to represent positive and negative return contributions\n", + " ylim = ax.get_ylim()\n", + " ax.fill_between(ax.get_xlim(), 0, ylim[0], facecolor='red', alpha = 0.1)\n", + " ax.fill_between(ax.get_xlim(), ylim[1], 0, color='green', alpha = 0.1)\n", + " plt.ylim(ylim)\n", + " \n", + " plt.ylabel('Excess Returns');\n", + " plt.title('Excess Returns Decomposition')\n", + "\n", + " return betas, returns_decomposition, risk_factors.mean()*252, algo_returns_ann, VIFs\n", + "\n", + "def decompose_returns(algo_returns, plot):\n", + " \n", + " # Loads Fama-French risk factors from Dartmouth using Pyfolio\n", + " risk_factors = pf.utils.load_portfolio_risk_factors().loc[algo_returns.index]\n", + " del risk_factors['RF']\n", + " return decompose_returns_custom(algo_returns, risk_factors, plot)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Variance Inflation Factors:\n", + "Mkt-RF 1.399355\n", + "SMB 1.091903\n", + "HML 1.659207\n", + "Mom 1.601415\n", + "dtype: float64\n", + "\n", + "Betas: Mkt-RF SMB HML Mom \n", + "Alpha 0.0548948 0.0552833 0.0554222 0.0549158\n", + "Alpha t-stat 1.96972 1.98902 2.06561 2.0494\n", + "Mkt-RF 0.0558085 0.0575993 0.109451 0.116019\n", + "Mkt-RF t-stat 4.33964 4.1668 7.45709 7.93011\n", + "SMB NaN -0.0212644 -0.059929 -0.0683201\n", + "SMB t-stat NaN -0.762268 -2.20101 -2.38552\n", + "HML NaN NaN -0.20553 -0.176732\n", + "HML t-stat NaN NaN -6.97187 -5.536\n", + "Mom NaN NaN NaN 0.0401703\n", + "Mom t-stat NaN NaN NaN 1.77766\n", + "\n", + "Factor Excess Returns:\n", + "Mkt-RF 0.046373\n", + "SMB 0.022173\n", + "HML 0.008204\n", + "Mom 0.003777\n", + "dtype: float64\n" + ] + }, + { + "data": { + "image/png": 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0CK0AAAAAANMitAIAAAAATMthaM3Ozta3334rSdq5c6fefvttZWZmVnthAAAA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\nnBwtW7ZMnTt3Vu/eva/6gAZaCvoDuIJ+QEvFtg8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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "algo_returns = algo_performance['returns']\n", + "ff_decomposition = decompose_returns(algo_returns, plot=True)\n", + "\n", + "print 'Variance Inflation Factors:\\n', ff_decomposition[4]\n", + "print '\\nBetas:', ff_decomposition[0]\n", + "print '\\nFactor Excess Returns:\\n', ff_decomposition[2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The VIFs are all under 10 meaning multicollinearity is too small to warrant exclusion of any risk factor based on correlation with each other. Despite this, the breakdown of algo returns will be somewhat volatile so it is best to look at it across the entire sample like above as opposed to on a rolling basis. \n", + "\n", + "### Adding More Risk Factors\n", + "\n", + "One of these factors, none seem to explain much of the algorithm's performance. HML had a small negative impact on returns as the algo has a significant negative exposure (t-stat: -5.4) but the factor performed well across the sample. Let's look at a couple others factors to see if they help explain some more of the returns. The ones we will investigate are:\n", + "\n", + "* Volatility\n", + "* Short-term mean reversion\n", + "\n", + "We can generate returns for these factors on our own:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [], + "source": [ + "class Vol_3M(CustomFactor):\n", + " ''' Volatility Factor'''\n", + " inputs = [Returns(window_length=2)]\n", + " window_length = 60\n", + " def compute(self, today, assets, out, rets):\n", + " out[:] = np.nanstd(rets, axis=0)\n", + " \n", + "class ST_MR(CustomFactor):\n", + " '''Short-term Mean Reversion Factor'''\n", + " inputs = [USEquityPricing.close]\n", + " window_length = 5\n", + "\n", + " def compute(self, today, assets, out, price):\n", + " out[:] = np.mean(price[-5:-1])/price[0] \n", + "\n", + "universe = Q500US()\n", + "\n", + "pipe = Pipeline(\n", + " columns={\n", + " 'VOL' : Vol_3M(mask=universe),\n", + " 'STMR' : ST_MR(mask=universe)\n", + " },\n", + " screen=(universe)\n", + ")\n", + "\n", + "start = start\n", + "end = '2012-01-01'\n", + "\n", + "alt_result = run_pipeline(pipe, start, end)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "assets = alt_result.index.levels[1].unique()\n", + "pricing = get_pricing(assets, start_date = start, end_date = end, fields = 'price')\n", + "\n", + "# Using Alphalens to get DataFrame with factor data\n", + "VOL_factor_data = al.utils.get_clean_factor_and_forward_returns(factor=alt_result['VOL'],\n", + " prices=pricing,\n", + " quantiles=5,\n", + " periods=(1, 5))\n", + "STMR_factor_data = al.utils.get_clean_factor_and_forward_returns(factor=alt_result['STMR'],\n", + " prices=pricing,\n", + " quantiles=5,\n", + " periods=(1,5))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Variance Inflation Factors:\n", + "[ 1.4053305 1.09301338 1.66741612 1.61632882 2.11854957 2.15006662]\n", + "\n", + "Betas: Mkt-RF SMB HML Mom VOL \\\n", + "Alpha 0.0548948 0.0552833 0.0554222 0.0549158 0.0556588 \n", + "Alpha t-stat 1.96972 1.98902 2.06561 2.0494 2.08226 \n", + "Mkt-RF 0.0558085 0.0575993 0.109451 0.116019 0.117195 \n", + "Mkt-RF t-stat 4.33964 4.1668 7.45709 7.93011 8.04882 \n", + "SMB NaN -0.0212644 -0.059929 -0.0683201 -0.0670823 \n", + "SMB t-stat NaN -0.762268 -2.20101 -2.38552 -2.36404 \n", + "HML NaN NaN -0.20553 -0.176732 -0.178689 \n", + "HML t-stat NaN NaN -6.97187 -5.536 -5.63988 \n", + "Mom NaN NaN NaN 0.0401703 0.039923 \n", + "Mom t-stat NaN NaN NaN 1.77766 1.77078 \n", + "VOL NaN NaN NaN NaN -0.0269783 \n", + "VOL t-stat NaN NaN NaN NaN -1.57769 \n", + "STMR NaN NaN NaN NaN NaN \n", + "STMR t-stat NaN NaN NaN NaN NaN \n", + "\n", + " STMR \n", + "Alpha 0.0547811 \n", + "Alpha t-stat 2.05229 \n", + "Mkt-RF 0.116249 \n", + "Mkt-RF t-stat 8.16945 \n", + "SMB -0.066438 \n", + "SMB t-stat -2.38641 \n", + "HML -0.181765 \n", + "HML t-stat -5.76387 \n", + "Mom 0.0435178 \n", + "Mom t-stat 1.95458 \n", + "VOL -0.0639397 \n", + "VOL t-stat -2.71606 \n", + "STMR 0.0628349 \n", + "STMR t-stat 2.15087 \n", + "\n", + "Factor Excess Returns:\n", + "Mkt-RF 0.046373\n", + "SMB 0.022173\n", + "HML 0.008204\n", + "Mom 0.003777\n", + "VOL 0.029951\n", + "STMR 0.032243\n", + "dtype: float64\n" + ] + }, + { + "data": { + "image/png": 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AAAAAwE3OYWi98847FRcXp6NHj6pGjRpq2bKlPD09nVEbAAAAAOAm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\n/aR27dpp6tSpys3NVWxsrAIDA2n3GuzXcsNmsyk1NVWLFy9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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Using Alphalens to get factor returns\n", + "VOL_rets = al.performance.factor_returns(VOL_factor_data)[1]\n", + "STMR_rets = al.performance.factor_returns(STMR_factor_data)[1]\n", + "alt_factors = pd.DataFrame([VOL_rets, STMR_rets], index=['VOL','STMR']).T\n", + "\n", + "risk_factors = pf.utils.load_portfolio_risk_factors()\n", + "del risk_factors['RF']\n", + "\n", + "new_risk_factors = pd.concat([risk_factors, alt_factors], axis=1, join_axes=[algo_returns.index]).ffill()\n", + "\n", + "expanded_decomposition = decompose_returns_custom(algo_returns, new_risk_factors, plot=True)\n", + "\n", + "print 'Variance Inflation Factors:\\n', expanded_decomposition[4]\n", + "print '\\nBetas:', expanded_decomposition[0]\n", + "print '\\nFactor Excess Returns:\\n', expanded_decomposition[2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For a full Pyfolio tearsheet, run the following cell:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create full tear sheet\n", + "bt.create_full_tear_sheet()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Decay of US-Europe Equity Home Bias\n", + "Although our factor performed well in the above out-of-sample testing, further out-of-sample testing shows that it begins to falter after 2013. A possible reason for this is the decline of equity home bias as our factor is dependent on US investor aversion to international diversification. Let's use the home bias calculations from our research stage earlier on in the notebook, and expand them to encompass 2004-2015:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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SkoqcdlgeBfOgp6eHb7/9Fp9++ikyMzPRsGFDfPvtt88c5unXbm5uuHfvHoYM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\n7VFsAAAAAFR7FBsAAAAA1d7/AzX24bsu2TWgAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Isolating market cap data by country and to within our research range \n", + "USA = mkt_caps.iloc[1]['2004':'2015']\n", + "EMU = mkt_caps.iloc[0]['2004':'2015']\n", + "\n", + "# Finding Euro-USA market cap ratio, Euro-Domestic US investments ratio\n", + "# and the difference between the two\n", + "mkt_ratio = EMU/USA\n", + "holdings_ratio = euro_investments/(USA-euro_investments)\n", + "holdings_ratio.index = mkt_ratio.index\n", + "diff = mkt_ratio - holdings_ratio\n", + "\n", + "# Plotting\n", + "diff.plot();\n", + "plt.title('US Portfolio Diversification minus Optimal Diversification (Approximate Effect of Equity Home Bias)');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the exception of peaks right before and after the recession, equity home bias between the US and Europe has been in constant decline and with developments in globalization, trade, and communications it is expected to continue on this downwards trend. Based on this, if our economic story about home bias is correct, we coiuld maybe expect our factor to perform worse in the future as equity home bias declines." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Possible Next Steps\n", + "\n", + "* Explore biases for other foreign markets given that US-Europe home bias is declining. Although the only exchange rate offered as a data feed is the USD-EUR exchange rate, Quantopian offers currency futures data which could be used to a similar end as the FX rate was in this notebook\n", + "* Find a daily or monthly measure of US-Europe equity home bias and put it into a factor model to see if it composes much of our algos returns\n", + "* Aggregate this factor with uncorrelated alpha factors\n", + "* Attempt to fortify the hypothesis by finding better evidence for our theories that assets with strong inverse correlations to the USD-EUR exchange rate would be subject to equity home bias and that such bias can cause assets to be undervalued \n", + "* Further out-of-sample validation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### References\n", + "Rob Reider, Jamie McCorriston, and Max Margenot\n", + "\n", + "$^1$Wynter, Matthew M. \"Why Has the U.S. Foreign Portfolio Share Increased?\" SSRN Electronic Journal, March 2014, 6-7. Accessed July 19, 2017. doi:10.2139/ssrn.2679196.\n", + "\n", + "$^2$French, Kenneth R. Factor Returns. May 31, 2017. Raw data. Dartmouth College, Hanover, NH.\n", + "\n", + "$^3$Israel, Ronen, and Ross Adrienne. \"Measuring Factor Exposures: Uses and Abuses.\" SSRN Electronic Journal, October 2015, 4-5. doi:10.2139/ssrn.2841037.\n", + "\n", + "\n", + "*This presentation is for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation for any security; nor does it constitute an offer to provide investment advisory or other services by Quantopian, Inc. (\"Quantopian\"). Nothing contained herein constitutes investment advice or offers any opinion with respect to the suitability of any security, and any views expressed herein should not be taken as advice to buy, sell, or hold any security or as an endorsement of any security or company. In preparing the information contained herein, Quantopian, Inc. has not taken into account the investment needs, objectives, and financial circumstances of any particular investor. Any views expressed and data illustrated herein were prepared based upon information, believed to be reliable, available to Quantopian, Inc. at the time of publication. Quantopian makes no guarantees as to their accuracy or completeness. All information is subject to change and may quickly become unreliable for various reasons, including changes in market conditions or economic circumstances.*" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/case_studies/USD_EUR_exchange_rate/preview.html b/case_studies/USD_EUR_exchange_rate/preview.html new file mode 100644 index 00000000..92264b2c --- /dev/null +++ b/case_studies/USD_EUR_exchange_rate/preview.html @@ -0,0 +1,20579 @@ + + + Case Study - USD-EUR Exchange Rate Draft 3 + + + + + + + + + + + + + + + + +
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Researching & Developing a Market Neutral Strategy - Case Study - USD-EUR Exchange Rate¶

The following notebook aims to demonstrate best practices when developing a market-neutral signal based on Quantopian's data feeds. Following the steps detailed in this post and demonstrated in this notebook will ensure a well-founded alternative data signal that stands a better chance of holding up during out-of-sample validation and live trading.

+

Intro - Why use Alternative Data?¶

Fundamental asset data such as price, volume, or company financials has many benefits including its accessibility and simplicity. However, these advantages are a double-edged sword as any "alpha" left in these datasets can be especially difficult to extract exactly because of the amount of people using the data.

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Because alternative data streams are not as widely available or as easy to use as fundamental ones, finding novel information that has yet to be "priced in" by the market is easier. Further benefits include the tendency for alternative data signals to be uncorrelated to ones based on traditional data.

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Some of the major drawbacks of alternative data include its lack of structure, cost, exclusivity, and high dimensionality. Luckily, Quantopian takes down some of these barriers through its wide variety of alternative data feeds, many of which are free to use and all of which have been cleaned and standardized to work both in pipeline and as interactive datasets in the research environment.

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Abstract¶

Through some preliminary research (reading papers, exploring data) we arrive at a new hypothesis we would like to test:

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Hypothesis: Equity home bias results in international-domiciled equities being undervalued. US equities with strong inverse correlations to the USD-EUR exchange rate show similarities to these international assets, and because they are US equities and subject to US market biases equity home bias will cause them to be undervalued.

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To test it we:

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1) Examine the data and look for the presence of US equity home bias within the research period 2004-2011.

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2) Use pipeline to identify US equities strong positive or inverse correlations to the USD-EUR exchange rate. We can then sort the equities into groups based off of their correlations and use those classifications to conduct further tests to try to back up the hypothesis.

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3) Use Alphalens to examine the strength of our signal within the in-sample period.

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We finish by implementing an algorithm based on the long short equity template, running the backtest over the research period and walking forward one year out-of-sample, and analyzing the results using Pyfolio.

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In [1]:
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import matplotlib.pyplot as plt
+import matplotlib as mpl
+import pandas as pd
+import blaze as bz
+import math
+import numpy as np
+import seaborn
+import scipy.stats as stats
+import statsmodels.api as sm
+import statsmodels.tsa as tsa
+
+from statsmodels import regression
+from odo import odo
+
+ +
+
+
+ +
+
+
+
+
+

Researching Alternative Data: USD-EUR Exchange Rate¶

This exchange rate data used in this notebook, as well as the Morningstar fundamental data, are all available as free datafeeds.

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Preliminary Hypothesis: The assets in the Q1500US and Q500US universes are all US-based equities and will therefore be affected by the strength of the US dollar. The USD-EUR exchange rate is a good indicator of the strength of the USD and therefore it is worth investigating relationships between the returns of US companies and their correlation with the exchange rate.

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To protect against overfitting, we will conduct our research strictly within the interval 2004-2010, leaving the data for 2011 and after for out-of-sample validation.

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In [2]:
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# Importing exchange rate data set
+# When importing for blaze/non-pipeline research use quantopian.interactive._
+# When importing for pipeline use quantopian.pipeline._
+from quantopian.interactive.data.quandl import currfx_usdeur
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+ +
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In [3]:
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# Exchange rate data is small enough to compute directly into a Pandas DataFrame
+data = bz.compute(currfx_usdeur)
+
+# We'll set 'asof_date' as our index, and add a timedelta of 1 day to prevent look ahead bias
+# This is because we will not have a good idea about FX data for a specific day until the day after
+data = data.set_index(data['asof_date']+pd.Timedelta('1 days')).sort_index().drop('timestamp', 1)
+del data['asof_date']
+
+# Renaming columns
+data.columns = ['rate', 'high_est', 'low_est']
+
+# Dropping '0' values in the high_est and low_est columns as well as an outlier high_est value of 14
+data['high_est'][(data['high_est'] == 0) | (data['high_est'] > 10)] = None
+data['low_est'][data['low_est'] == 0] = None
+
+ +
+
+
+ +
+
+
+
In [4]:
+
+
+
# Getting an understanding of the size and structure of the data by finding 
+print "----------- US/Euro Exchange Rate Data -----------"
+def summary(data):
+    print "%-12s %-15s %-13s %s" % ('Start:', data.index[0].date(), 
+                                    'End:', data.index[-1].date())
+    print "%-12s %-15s %-13s %s" % ('Min Value:', data.min(), 
+                                    'Max Value:', data.max())
+    print "%-12s %-15s %-13s %s" % ('Avg Value:', data.mean(), 
+                                    'Median Value:', data.median())
+
+summary(data['rate'])
+
+print "\nFields:", data.columns[0], data.columns[1], data.columns[2]
+print "Frequency: daily\n"
+
+# Conduct research within this time frame, leaving ample room for out-of-sample-testing
+start = '2004-01-01'
+end = '2011-01-01'
+
+# Plot rate, high_est, low_est for our window
+# Used ffill to fill empty high_est and low_est days with most recent value
+data[start:end].ffill().plot();
+plt.ylabel('Cost of USD in EUR');
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
----------- US/Euro Exchange Rate Data -----------
+Start:       1999-09-07      End:          2017-08-08
+Min Value:   0.627189        Max Value:    1.2064
+Avg Value:   0.834329005551  Median Value: 0.791854
+
+Fields: rate high_est low_est
+Frequency: daily
+
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+

Macro vs. Asset-Level Data¶

One classifier for datasets is whether, for a given point in time, they provide individual values for every asset (such as sentiment, earnings surprises, dividends) or a single macro value (like FX, inflation, or gold prices).

+

An important concept when dealing with macro data like FX is how to apply it to get a unique value for every asset in your universe. The logic you use to decompose a single macro indicator into many asset-level ranking values requires some thought. Some approaches include:

+
    +
  • Correlation
    • +
  • Regression beta coefficient
    • +
  • Spearman rank correlation
    • +
  • Cointegration
    • +
+ +
+
+
+
+
+
+
+

After some experimentation with the data along the above guidelines, it became apparent that assets with a low correlation of returns to the USD-EUR exchange rate consistently outperformed those with a high one, despite the exchange rate remaining mostly flat over the time period.

+

While it may seem tempting to end the process here and put this signal into an algorithm, such a decision would leave you susceptible to overfitting. Without understanding why the signal exists means it might as well have come from random chance, and a signal found on random chance alone will probably not hold up during live trading or out-of-sample validation. To learn more about overfitting, refer to the Quantopian Dangers of Overfitting lecture. Researching and understanding an underlying economic hypothesis, a "story" as to why the signal works, will help reduce this risk.

+

Having a story behind an alpha signal has further benefits beyond reducing overfitting. Should a signal begin to perform poorly, having an economic hypothesis to dissassemble lets you isolate what changed and how to fix it.

+

Equity Home Bias Puzzle¶

One possible 'story', or explanation, as to why negatively correlated stocks outperform positively correlated ones is the Equity Home Bias Puzzle. Equity home bias is the tendency for individuals and institutions to hold small amounts of foreign equity investments, despite empirical evidence suggesting "substantial benefits from international diversification." The few possible explanations there are have to do with information immobility and fear of exposure to foreign exchange risk.

+

It is possible (and we will try to see if this is true later) that US equities with strong inverse correlations to the USD-EUR exchange can serve as proxies for these international assets because of their inverse relation to the strength of the dollar. If this is the case, because they are US equities and subject to US market biases it is possible that they will be undervalued.

+ +
+
+
+
+
+
+
+

Refined Hypothesis: Equity home bias results in international-domiciled equities being undervalued. US equities with strong inverse correlations to the USD-EUR exchange rate might show similarities to these international assets, and because they are US equities and subject to US market biases equity home bias could cause them to be undervalued.

+ +
+
+
+
+
+
+
+

Detecting Equity Home Bias¶

Because our story is based on the presence of this bias it is important to make sure it exists within our test period 2004-2010. We will use some of the methods in this paper to estimate home bias.$^1$

+

Data on cross-border US portfolio holdings is from the U.S. Department of the Treasury(part B) and US/EU market cap data is from the World Bank.

+ +
+
+
+
+
+
In [6]:
+
+
+
# List of eurozone countries
+euro_countries = ['Austria', 'Belgium','Finland','France','Germany',
+                 'Greece','Ireland','Italy','Netherlands','Portugal',
+                 'Slovakia','Slovenia','Spain','Cyprus','Estonia','Latvia',
+                 'Luthuania','Luxembourg','Malta']
+
+# Pull cross-border holdings from U.S. Department of the Treasury
+foreign_holdings = local_csv('shchistdat.csv')
+
+# Selecting only investments in eurozone nations and fixing date order
+euro_investments = foreign_holdings.loc[foreign_holdings['Unnamed: 1'].isin(euro_countries)][range(2,49,4)]
+euro_investments.columns = pd.date_range(end='2015-01-01',periods=12,freq='AS')[::-1]
+
+# Removing thousands separator commas and converting strings of numbers to ints
+for column in euro_investments.columns:
+    euro_investments[column] = euro_investments[column].str.replace(',','').astype(int)
+
+# Multiply by 1 million because CSV data unit was millions    
+euro_investments = euro_investments.sum()*1000000
+
+# Pull country market caps from World Bank
+mkt_caps = local_csv('API_CM.MKT.LCAP.CD_DS2_en_csv_v2.csv')
+
+# Select only eurozone and US market caps using country code
+mkt_caps = mkt_caps[mkt_caps['Country Code'].isin(['EMU','USA'])]
+
+# Isolating market cap data by country and to within our research range 
+USA  = mkt_caps.iloc[1][start[:4]:end[:4]]
+EMU = mkt_caps.iloc[0][start[:4]:end[:4]]
+
+# Finding Euro-USA market cap ratio, Euro-Domestic US investments ratio
+# and the difference between the two
+mkt_ratio = EMU/USA
+holdings_ratio = (euro_investments/(USA-euro_investments))[start[:4]:end[:4]]
+holdings_ratio.index = mkt_ratio.index
+diff = mkt_ratio - holdings_ratio
+
+print 'Ratios of Europe-Based Equities to US-Based Equities:\n'
+
+print 'US Investor Average:', holdings_ratio.mean()
+print 'CAPM Optimal Ratio:', mkt_ratio.mean()
+
+print '\nDifference:', diff.mean()
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Ratios of Europe-Based Equities to US-Based Equities:
+
+US Investor Average: 0.0787304762136
+CAPM Optimal Ratio: 0.408553054291
+
+Difference: 0.329822578077
+
+
+
+ +
+
+ +
+
+
+
+
+

CAPM dictates that the optimal portfolio is one with weights based on the market capitalization of equities within the universe. As such, an optimal international portfolio should have a ratio of US to EU equities equal to the ratio of the size of the total US and EU equity markets.

+

Across 2004-2010, the European equity market cap was 40.9% of the size of the US equity market cap; according to CAPM, any optimal investment portfolio should have similar proportions of US to European equities.

+

However, the US Treasury data shows that during our time period US investor portfolios had a Euro-US equity ratio around 7.9%, around one-fifth of the optimal amount. This discrepancy could be a result of home bias, and this test suggests its presence during the research period.

+ +
+
+
+
+
+
+
+

Designing a Pipeline¶

Let's build a pipeline to pull in rolling USD-EUR rate correlations for every asset in the Q500US universe. Using pipeline and a custom factor makes finding correlations for hundreds of equities across 7 years of data easier. When we are finished with this stage, the pipeline output can go straight into Alphalens and the pipeline itself can be copied and pasted into the IDE to be used in an algorithm.

+ +
+
+
+
+
+
In [5]:
+
+
+
# Pipeline API imports
+from quantopian.pipeline import Pipeline
+from quantopian.research import run_pipeline
+
+# Importing built in factors, universe, and data
+from quantopian.pipeline.factors import SimpleMovingAverage, CustomFactor, Returns
+from quantopian.pipeline.filters.morningstar import Q1500US, Q500US
+from quantopian.pipeline.data.builtin import USEquityPricing
+from quantopian.pipeline.classifiers.morningstar import Sector
+
+# Import FX rate and other data
+from quantopian.pipeline.data.quandl import currfx_usdeur
+from quantopian.pipeline.data import morningstar
+from quantopian.pipeline.data.psychsignal import stocktwits
+
+ +
+
+
+ +
+
+
+
In [8]:
+
+
+
# Defining our USD-EUR exchange rate correlation custom factor
+class FXCorr(CustomFactor):
+    """ Custom factor to find correlation of asset returns and FX rate """
+    
+    inputs = [USEquityPricing.close, currfx_usdeur.rate]
+    window_length = 150
+    def compute(self, today, asset_ids, out, close, exch_rate):
+        # Converting data to returns DataFrame to make correlation calculation faster
+        exch_df = pd.DataFrame(np.repeat(exch_rate, len(close[0]), axis = 1)).pct_change(1)
+        close_df = pd.DataFrame(close).pct_change(1)
+        
+        out[:] = exch_df.corrwith(close_df)
+
+ +
+
+
+ +
+
+
+
In [9]:
+
+
+
# Assigning the Q500US as our universe
+universe = Q500US()
+
+# Buildling our pipeline
+pipe = Pipeline(
+    columns={
+        'fx_corr' : FXCorr(mask=universe),
+    },
+    screen=(universe)
+)
+
+# Stores pipeline in result
+result = run_pipeline(pipe, start, end)
+
+# Finds assets and pricing data
+assets = result.index.levels[1].unique()
+pricing = get_pricing(assets, start_date = start, end_date = end, fields = 'price')
+
+ +
+
+
+ +
+
+
+
+
+

The distribution of mean FX correlations across the Q500US:

+ +
+
+
+
+
+
In [10]:
+
+
+
result.unstack()['fx_corr'].mean().hist(bins=20);
+plt.title('Q500US distribution of USD-EUR correlations');
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+

Further Testing our Hypothesis using Pipeline¶

+
+
+
+
+
+
+
+

The difference between CAPM optimal diversification and observed US investor diversification suggest that equity home bias exists within our time period, but we have not explored whether or not US equities with strong inverse correlations to the dollar share similarities with international markets. If that is the case, there is the possiblity that they will be subject to the same home bias as international assets.

+

To see if the assuption that low FX correlation US equities can represent international assets holds, let's compare the returns of the following:

+
    +
  • An ETF that tracks the FTSE Developed Europe All-Cap Index (VGK)
    • +
  • A bucket of 25 stocks with strong negative correlations to FX rate
    • +
  • A bucket of 25 stocks with strong positive correlations to FX rate
    • +
+ +
+
+
+
+
+
In [11]:
+
+
+
low_bucket = result.unstack()['fx_corr'].mean().sort_values(ascending=True)[:25].index
+high_bucket = result.unstack()['fx_corr'].mean().sort_values(ascending=False)[:25].index
+
+returns = pd.DataFrame()
+
+# Creating equally weighted portfolios of both buckets by first finding pricing using get_pricing
+# then using the pct_change() attribute to find returns, and finally averaging across all assets in the bucket
+returns['low_bucket'] = get_pricing(low_bucket, start_date=start, end_date=end, 
+                                    fields = 'price').pct_change()[1:].dropna(axis=1).mean(axis=1,skipna=True)
+returns['high_bucket'] = get_pricing(high_bucket, start_date=start, end_date=end, 
+                                     fields = 'price').pct_change()[1:].dropna(axis=1).mean(axis=1,skipna=True)
+returns['vgk'] = get_pricing('vgk', start_date=start, end_date=end, fields = 'price').pct_change()[1:]
+
+print 'Correlations of returns:'
+print returns.corr()
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Correlations of returns:
+             low_bucket  high_bucket       vgk
+low_bucket     1.000000     0.788119  0.838185
+high_bucket    0.788119     1.000000  0.735325
+vgk            0.838185     0.735325  1.000000
+
+
+
+ +
+
+ +
+
+
+
+
+

Within this time period, it seems like the low_bucket portfolio is more closely correlated with the Euro index than the high_bucket portfolio, indicating assets with a strong inverse correlation to the exchange rate share similarities to international assets. This is far from any sort of proof of our theory, and we did not account for events like delistings or M&As which could have affected these results. We also still need more evidence to show that assets with low correlation to the exchange rate can be subject to foreign equity bias, and that assets subject to foreign equity bias are undervalued in the first place. Despite this gap, we will proceed to the next step, keeping in mind that our hypothesis could use some more reinforcement.

+ +
+
+
+
+
+
+
+

Analyzing our Factor with Alphalens¶

+
+
+
+
+
+
+
+

Alphalens will help us evaluate the strength of our fx_corr factor within the sample. We will use 1, 10, and 30-day return periods as our factor is based on a long-term relationship between assets and the exchange rate and should therefore be evaluated on a long-term basis.

+ +
+
+
+
+
+
In [12]:
+
+
+
import alphalens as al
+
+# Formats the factor data, pricing data, and group mappings into a DataFrame 
+# necessary for most Alphalens tearsheets.
+# We invert the sign of our factor as we want the lowest correlations to have highest weights
+# and the highest correlations to have the lowest weights.
+factor_data = al.utils.get_clean_factor_and_forward_returns(factor=-result['fx_corr'],
+                                                            prices=pricing,
+                                                            quantiles=5,
+                                                            periods=(1,10,30))
+
+ +
+
+
+ +
+
+
+
In [13]:
+
+
+
al.performance.factor_alpha_beta(factor_data)
+
+ +
+
+
+ +
+
+ + +
+ +
Out[13]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + +
11030
Ann. alpha0.0430560.0490520.050816
beta0.0931420.0852540.083962
+
+
+ +
+ +
+
+ +
+
+
+
In [14]:
+
+
+
# Use Alphalens to get mean returns by quantile over 1, 10, and 30 day windows
+mean_return_by_q, std_err_by_q = al.performance.mean_return_by_quantile(factor_data, by_group=False)
+mean_return_by_q_daily, std_err_by_q_daily = al.performance.mean_return_by_quantile(factor_data, by_date=True)
+
+al.plotting.plot_quantile_returns_bar(mean_return_by_q.apply(al.utils.rate_of_return, axis=0));
+al.plotting.plot_cumulative_returns_by_quantile(mean_return_by_q_daily, period=30);
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
/usr/local/lib/python2.7/dist-packages/alphalens/plotting.py:767: FutureWarning: pd.rolling_apply is deprecated for DataFrame and will be removed in a future version, replace with 
+	DataFrame.rolling(center=False,min_periods=1,window=30).apply(args=<tuple>,func=<function>,kwargs=<dict>)
+  min_periods=1, args=(period,))
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
In [15]:
+
+
+
mean_return_by_q
+
+ +
+
+
+ +
+
+ + +
+ +
Out[15]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
11030
factor_quantile
1-0.000333-0.003301-0.009091
2-0.000023-0.000614-0.001685
30.0000180.0004960.000637
40.0001780.0016200.003299
50.0001620.0018110.006867
+
+
+ +
+ +
+
+ +
+
+
+
+
+

For a full Alphalens tearsheet, run the following cell:

+ +
+
+
+
+
+
In [ ]:
+
+
+
al.tears.create_full_tear_sheet(factor_data)
+
+ +
+
+
+ +
+
+
+
+
+

Implement and Backtest the Strategy in the IDE¶

We can use the long-short equity algorithm template to make this step easier.

+

The long-short equity template works by ranking equities within a universe along some ranking factor. It then longs the top of the ranking and shorts the bottom, rebalancing every month. It also uses the Optimize API, with sector neutrality, beta neutrality, and position concentration constraints, to handle ordering logic and assign appropriate weights.

+

To implement a long-short equity strategy with our fx_corr factor, we find the custom factor in the algorithm and copy and paste our fx_corr in its place. We can also make any other changes we deem suitable. For this algorithm, lets:

+
    +
  • Comment out the other factors ('value' and 'quality') as we want to isolate our FX factor instead of aggregating it with two others to create a combined rank
    • +
  • Comment out lines these lines turn off slippage and commissions but we want their effects included
    • +
  • Switch from the Q1500US to the Q500US as it was the Q500US we use throughout our research
    • +
  • Reduce both NUM_LONG_POSITIONS and NUM_SHORT_POSITIONS to 100 from 300. We are switching to a universe one third the size so we should proportionally scale this size of our our long and short positions
    • +
+ +
+
+
+
+
+
+
+

Analyze Our Backtest Using Pyfolio¶

Our in-sample research up until this point was almost entirely contained within the time period 2004-2010. We will run the backtest from 2004-2011, adding in a single year of out-of-sample testing. As a result, the most important part of the below Pyfolio tearsheets will be performance within 2011.

+$$ + \ + \overbrace{ + \underbrace{\textit{2004, 2005, ... 2010}}_\text{In-Sample}\:\:+ + \underbrace{\textit{2011}}_\text{Out-of-Sample} + }^\text{Backtest} + \ +$$ +
+
+
+
+
+
In [6]:
+
+
+
import pyfolio as pf
+from pyfolio import tears
+from pyfolio import timeseries
+import itertools
+import functools
+
+# Get backtest object
+bt = get_backtest('5984b88018063e557a3a9cd4')
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
100% Time: 0:00:03|###########################################################|
+
+
+
+ +
+
+ +
+
+
+
In [7]:
+
+
+
algo_performance = bt.daily_performance
+benchmark = get_pricing('SPY', start_date=start, end_date='2012-01-01', fields = 'price').pct_change()[1:]
+pf.plotting.plot_rolling_returns(algo_performance['returns'], factor_returns=benchmark);
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+

Let's try to find the main drivers of the algorithm's performance. First we can use the Fama-French factor tearsheet from Pyfolio with a 60 day rolling window to measure exposures to the three fundamental factors (market cap, book to price, and momentum).

+

CSVs with the returns for these factors can be found here.$^2$

+ +
+
+
+
+
+
In [18]:
+
+
+
pf.plotting.plot_rolling_fama_french(algo_performance['returns'], rolling_window=30);
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+

Let's try to decompose returns into segments explained by the above factors. This will help us see exactly how much of the algo's returns over the time period are atrributable to exposure to these factors.

+

Returns Decomposition into Fama-French Factors¶

Below analysis was inspired by Exhibit 3 in a report by AQR on Measuring Factor Exposure.$^3$ It yields a breakdown of how much exposure to each risk factor contributes to the algorithm's returns in excess of the risk-free rate.

+ +
+
+
+
+
+
In [55]:
+
+
+
def find_vifs(data):
+    data = pd.DataFrame(data)
+    cols = data.columns
+    VIFs = pd.Series(index=cols)
+    for x, column in enumerate(cols):
+        # Calculates VIF using steps described here: 
+        # https://en.wikipedia.org/wiki/Variance_inflation_factor#Calculation_and_Analysis
+        VIFs[column] = 1/(1-regression.linear_model.OLS(data.iloc[:,x], 
+        sm.add_constant(np.column_stack((
+            [data.iloc[:,(x+i+1)%len(cols)] for i in range(len(cols)-1)] 
+        )))).fit().rsquared)      
+    return VIFs
+
+def decompose_returns_custom(algo_returns, risk_factors, plot):
+    
+    # Get excess returns for algo and risk-free rate from Dartmouth using Pyfolio
+    risk_free = pf.utils.load_portfolio_risk_factors().loc[algo_returns.index]['RF']
+    algo_rets_over_rf = algo_returns - risk_free
+    algo_returns_ann = algo_rets_over_rf.mean()*252
+    
+    # Write index for betas dataframe
+    betas_index = ['Alpha','Alpha t-stat']
+    for factor in risk_factors.columns.values:
+        betas_index = betas_index+[factor]+['{} t-stat'.format(factor)]
+
+    # Create dataframes to store betas and return contributions
+    betas = pd.DataFrame(columns = [risk_factors.columns.values],
+                                        index = betas_index)
+    returns_decomposition = pd.DataFrame(index = itertools.chain(['Alpha'],risk_factors.columns.values),
+                                        columns = risk_factors.columns.values)
+
+    # Nested iteration through models and factors in each model
+    for factor in risk_factors.columns.values:
+        model_factors = sm.add_constant(risk_factors.loc[:,:factor]).loc[algo_rets_over_rf.index]
+        model = sm.OLS(algo_rets_over_rf, model_factors).fit()
+        for i in range(len(model_factors.columns)):
+            beta = model.params[i]
+            betas[factor].iloc[2*i] = beta
+            betas[factor].iloc[2*i+1] = model.params[i]/model.HC0_se[i]
+            if i>0:
+                returns_decomposition[factor].iloc[i] = beta*(risk_factors.loc[algo_rets_over_rf.index].mean()*252)[i-1]
+
+    # Annualize alphas
+    betas.loc['Alpha'] = betas.loc['Alpha']*252
+    returns_decomposition.loc['Alpha'] = betas.loc['Alpha']
+    
+    # Write column names
+    rets_decomp_columns = []
+    for i in range(len(risk_factors.columns.values)):
+        rets_decomp_columns = rets_decomp_columns + ['Model {}: Add {}'.format(i, risk_factors.columns.values[i])]
+    returns_decomposition.columns = rets_decomp_columns
+    
+    # Finds variance inflation factors using function defined above
+    VIFs = find_vifs(risk_factors)
+    
+    # Plotting conditional on input
+    if plot:
+        
+        # Make more colors to prevent default colors repeating themselves
+        colors = mpl.cm.jet(np.linspace(0, 1, len(risk_factors.columns)+1))
+
+        # Create bar graph, with horizontal lines at 0 and annualized algo returns
+        ax = returns_decomposition.T.plot(kind='bar', stacked=True, rot=-30, color=colors)
+        ax.plot(ax.get_xlim(),[algo_returns_ann]*len(ax.get_xlim()), linestyle = '--',
+                color='black', label = 'Algo Returns');
+        ax.plot(ax.get_xlim(),[0]*len(ax.get_xlim()), color='black', linewidth=4);
+        ax.legend(loc='best', bbox_to_anchor=(1.0, 0.5));
+        
+        # Fill in green and red zones to represent positive and negative return contributions
+        ylim = ax.get_ylim()
+        ax.fill_between(ax.get_xlim(), 0, ylim[0], facecolor='red', alpha = 0.1)
+        ax.fill_between(ax.get_xlim(), ylim[1], 0, color='green', alpha = 0.1)
+        plt.ylim(ylim)
+        
+        plt.ylabel('Excess Returns');
+        plt.title('Excess Returns Decomposition')
+
+    return betas, returns_decomposition, risk_factors.mean()*252, algo_returns_ann, VIFs
+
+def decompose_returns(algo_returns, plot):
+    
+    # Loads Fama-French risk factors from Dartmouth using Pyfolio
+    risk_factors = pf.utils.load_portfolio_risk_factors().loc[algo_returns.index]
+    del risk_factors['RF']
+    return decompose_returns_custom(algo_returns, risk_factors, plot)
+
+ +
+
+
+ +
+
+
+
In [56]:
+
+
+
algo_returns = algo_performance['returns']
+ff_decomposition = decompose_returns(algo_returns, plot=True)
+
+print 'Variance Inflation Factors:\n', ff_decomposition[4]
+print '\nBetas:', ff_decomposition[0]
+print '\nFactor Excess Returns:\n', ff_decomposition[2]
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Variance Inflation Factors:
+Mkt-RF    1.399355
+SMB       1.091903
+HML       1.659207
+Mom       1.601415
+dtype: float64
+
+Betas:                   Mkt-RF        SMB        HML     Mom   
+Alpha          0.0548948  0.0552833  0.0554222  0.0549158
+Alpha t-stat     1.96972    1.98902    2.06561     2.0494
+Mkt-RF         0.0558085  0.0575993   0.109451   0.116019
+Mkt-RF t-stat    4.33964     4.1668    7.45709    7.93011
+SMB                  NaN -0.0212644  -0.059929 -0.0683201
+SMB t-stat           NaN  -0.762268   -2.20101   -2.38552
+HML                  NaN        NaN   -0.20553  -0.176732
+HML t-stat           NaN        NaN   -6.97187     -5.536
+Mom                  NaN        NaN        NaN  0.0401703
+Mom    t-stat        NaN        NaN        NaN    1.77766
+
+Factor Excess Returns:
+Mkt-RF    0.046373
+SMB       0.022173
+HML       0.008204
+Mom       0.003777
+dtype: float64
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+

The VIFs are all under 10 meaning multicollinearity is too small to warrant exclusion of any risk factor based on correlation with each other. Despite this, the breakdown of algo returns will be somewhat volatile so it is best to look at it across the entire sample like above as opposed to on a rolling basis.

+

Adding More Risk Factors¶

One of these factors, none seem to explain much of the algorithm's performance. HML had a small negative impact on returns as the algo has a significant negative exposure (t-stat: -5.4) but the factor performed well across the sample. Let's look at a couple others factors to see if they help explain some more of the returns. The ones we will investigate are:

+
    +
  • Volatility
    • +
  • Short-term mean reversion
    • +
+

We can generate returns for these factors on our own:

+ +
+
+
+
+
+
In [21]:
+
+
+
class Vol_3M(CustomFactor):
+        ''' Volatility Factor'''
+        inputs = [Returns(window_length=2)]
+        window_length = 60
+        def compute(self, today, assets, out, rets):
+            out[:] = np.nanstd(rets, axis=0)
+            
+class ST_MR(CustomFactor):
+        '''Short-term Mean Reversion Factor'''
+        inputs = [USEquityPricing.close]
+        window_length = 5
+
+        def compute(self, today, assets, out, price):
+            out[:] = np.mean(price[-5:-1])/price[0]     
+
+universe = Q500US()
+
+pipe = Pipeline(
+    columns={
+        'VOL' : Vol_3M(mask=universe),
+        'STMR' : ST_MR(mask=universe)
+    },
+    screen=(universe)
+)
+
+start = start
+end = '2012-01-01'
+
+alt_result = run_pipeline(pipe, start, end)
+
+ +
+
+
+ +
+
+
+
In [22]:
+
+
+
assets = alt_result.index.levels[1].unique()
+pricing = get_pricing(assets, start_date = start, end_date = end, fields = 'price')
+
+# Using Alphalens to get DataFrame with factor data
+VOL_factor_data = al.utils.get_clean_factor_and_forward_returns(factor=alt_result['VOL'],
+                                                            prices=pricing,
+                                                            quantiles=5,
+                                                            periods=(1, 5))
+STMR_factor_data = al.utils.get_clean_factor_and_forward_returns(factor=alt_result['STMR'],
+                                                            prices=pricing,
+                                                            quantiles=5,
+                                                            periods=(1,5))
+
+ +
+
+
+ +
+
+
+
In [23]:
+
+
+
# Using Alphalens to get factor returns
+VOL_rets = al.performance.factor_returns(VOL_factor_data)[1]
+STMR_rets = al.performance.factor_returns(STMR_factor_data)[1]
+alt_factors = pd.DataFrame([VOL_rets, STMR_rets], index=['VOL','STMR']).T
+
+risk_factors = pf.utils.load_portfolio_risk_factors()
+del risk_factors['RF']
+
+new_risk_factors = pd.concat([risk_factors, alt_factors], axis=1, join_axes=[algo_returns.index]).ffill()
+
+expanded_decomposition = decompose_returns_custom(algo_returns, new_risk_factors, plot=True)
+
+print 'Variance Inflation Factors:\n', expanded_decomposition[4]
+print '\nBetas:', expanded_decomposition[0]
+print '\nFactor Excess Returns:\n', expanded_decomposition[2]
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Variance Inflation Factors:
+[ 1.4053305   1.09301338  1.66741612  1.61632882  2.11854957  2.15006662]
+
+Betas:                   Mkt-RF        SMB        HML     Mom           VOL  \
+Alpha          0.0548948  0.0552833  0.0554222  0.0549158  0.0556588   
+Alpha t-stat     1.96972    1.98902    2.06561     2.0494    2.08226   
+Mkt-RF         0.0558085  0.0575993   0.109451   0.116019   0.117195   
+Mkt-RF t-stat    4.33964     4.1668    7.45709    7.93011    8.04882   
+SMB                  NaN -0.0212644  -0.059929 -0.0683201 -0.0670823   
+SMB t-stat           NaN  -0.762268   -2.20101   -2.38552   -2.36404   
+HML                  NaN        NaN   -0.20553  -0.176732  -0.178689   
+HML t-stat           NaN        NaN   -6.97187     -5.536   -5.63988   
+Mom                  NaN        NaN        NaN  0.0401703   0.039923   
+Mom    t-stat        NaN        NaN        NaN    1.77766    1.77078   
+VOL                  NaN        NaN        NaN        NaN -0.0269783   
+VOL t-stat           NaN        NaN        NaN        NaN   -1.57769   
+STMR                 NaN        NaN        NaN        NaN        NaN   
+STMR t-stat          NaN        NaN        NaN        NaN        NaN   
+
+                    STMR  
+Alpha          0.0547811  
+Alpha t-stat     2.05229  
+Mkt-RF          0.116249  
+Mkt-RF t-stat    8.16945  
+SMB            -0.066438  
+SMB t-stat      -2.38641  
+HML            -0.181765  
+HML t-stat      -5.76387  
+Mom            0.0435178  
+Mom    t-stat    1.95458  
+VOL           -0.0639397  
+VOL t-stat      -2.71606  
+STMR           0.0628349  
+STMR t-stat      2.15087  
+
+Factor Excess Returns:
+Mkt-RF    0.046373
+SMB       0.022173
+HML       0.008204
+Mom       0.003777
+VOL       0.029951
+STMR      0.032243
+dtype: float64
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+

For a full Pyfolio tearsheet, run the following cell:

+ +
+
+
+
+
+
In [ ]:
+
+
+
# Create full tear sheet
+bt.create_full_tear_sheet()
+
+ +
+
+
+ +
+
+
+
+
+

Decay of US-Europe Equity Home Bias¶

Although our factor performed well in the above out-of-sample testing, further out-of-sample testing shows that it begins to falter after 2013. A possible reason for this is the decline of equity home bias as our factor is dependent on US investor aversion to international diversification. Let's use the home bias calculations from our research stage earlier on in the notebook, and expand them to encompass 2004-2015:

+ +
+
+
+
+
+
In [24]:
+
+
+
# Isolating market cap data by country and to within our research range 
+USA  = mkt_caps.iloc[1]['2004':'2015']
+EMU = mkt_caps.iloc[0]['2004':'2015']
+
+# Finding Euro-USA market cap ratio, Euro-Domestic US investments ratio
+# and the difference between the two
+mkt_ratio = EMU/USA
+holdings_ratio = euro_investments/(USA-euro_investments)
+holdings_ratio.index = mkt_ratio.index
+diff = mkt_ratio - holdings_ratio
+
+# Plotting
+diff.plot();
+plt.title('US Portfolio Diversification minus Optimal Diversification (Approximate Effect of Equity Home Bias)');
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+

With the exception of peaks right before and after the recession, equity home bias between the US and Europe has been in constant decline and with developments in globalization, trade, and communications it is expected to continue on this downwards trend. Based on this, if our economic story about home bias is correct, we coiuld maybe expect our factor to perform worse in the future as equity home bias declines.

+ +
+
+
+
+
+
+
+

Possible Next Steps¶

    +
  • Explore biases for other foreign markets given that US-Europe home bias is declining. Although the only exchange rate offered as a data feed is the USD-EUR exchange rate, Quantopian offers currency futures data which could be used to a similar end as the FX rate was in this notebook
    • +
  • Find a daily or monthly measure of US-Europe equity home bias and put it into a factor model to see if it composes much of our algos returns
    • +
  • Aggregate this factor with uncorrelated alpha factors
    • +
  • Attempt to fortify the hypothesis by finding better evidence for our theories that assets with strong inverse correlations to the USD-EUR exchange rate would be subject to equity home bias and that such bias can cause assets to be undervalued
    • +
  • Further out-of-sample validation
    • +
+ +
+
+
+
+
+
+
+

References¶

Rob Reider, Jamie McCorriston, and Max Margenot

+

$^1$Wynter, Matthew M. "Why Has the U.S. Foreign Portfolio Share Increased?" SSRN Electronic Journal, March 2014, 6-7. Accessed July 19, 2017. doi:10.2139/ssrn.2679196.

+

$^2$French, Kenneth R. Factor Returns. May 31, 2017. Raw data. Dartmouth College, Hanover, NH.

+

$^3$Israel, Ronen, and Ross Adrienne. "Measuring Factor Exposures: Uses and Abuses." SSRN Electronic Journal, October 2015, 4-5. doi:10.2139/ssrn.2841037.

+

This presentation is for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation for any security; nor does it constitute an offer to provide investment advisory or other services by Quantopian, Inc. ("Quantopian"). Nothing contained herein constitutes investment advice or offers any opinion with respect to the suitability of any security, and any views expressed herein should not be taken as advice to buy, sell, or hold any security or as an endorsement of any security or company. In preparing the information contained herein, Quantopian, Inc. has not taken into account the investment needs, objectives, and financial circumstances of any particular investor. Any views expressed and data illustrated herein were prepared based upon information, believed to be reliable, available to Quantopian, Inc. at the time of publication. Quantopian makes no guarantees as to their accuracy or completeness. All information is subject to change and may quickly become unreliable for various reasons, including changes in market conditions or economic circumstances.

+ +
+
+
+
+
+ diff --git a/case_studies/google_trends/notebook.ipynb b/case_studies/google_trends/notebook.ipynb new file mode 100644 index 00000000..a2827539 --- /dev/null +++ b/case_studies/google_trends/notebook.ipynb @@ -0,0 +1,131 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "from odo import odo\n", + "import pandas as pd\n", + "import numpy as np\n", + "import scipy.stats as stats\n", + "from statsmodels import regression\n", + "import statsmodels.api as sm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 10+ years, monthly data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chipotle - strong positive correlation - Googling is proxy for demand\n", + "\n", + "Because people Google chipotle when they want to find the address of the nearest one?" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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BA3Wv4XBpte52j5Ej1eKyR4/6by8txZhgVZ8ZKiwNHQo33aS64j33nDd06YWl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\nDqgL5brVPQ3B0r/h8SaDpVfn7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BxDhgyxdFtVQQONBhYsUM9XrTJvv6NH4eJFGDwYMoM7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\n8UEL3+4BqHFIqelaxny4jXupGfTtUKvAY9va2rBgY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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "'''Chipotle'''\n", + "asset = 'CMG'\n", + "\n", + "trends = local_csv(asset + '_gtrends.csv')[1:].set_index([pd.date_range(start='2004-01-01', end = '2017-06-01', freq = 'MS')]).astype(float)\n", + "trends.columns = ['Google Trend:' + asset]\n", + "\n", + "pricing = get_pricing(asset, start_date = '2004-01-01',\n", + " end_date = '2017-06-01', fields = 'price')\n", + "ax = trends.plot(c='r');\n", + "pricing.plot(ax=ax.twinx());" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exxonn mobile - negative correlation\n", + "\n", + "Googling of Exxon probably increases with bad news like spills or scandals which hurt stock price? Googling is definitely not a proxy for demand.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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TVgMIntRSUXkdvvj+KE6U1rTKcVPT2exOvPhRnvT8UEFli7zPXc99iz888Q2s9uDzdrUH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8bKvrOwRJYA6UBZbu7AGDuWHsWpeK2t5K6YmQlccYXf5dmgcFC+rPGqH8YNkYoIvV6HBNFA2el0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\n+O+//K9144ZUN4yLA0aOlO+JgwPw+uuybtjzz8saZ6dOSTVFj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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "'''Exxon Mobile'''\n", + "asset = 'XOM'\n", + "\n", + "trends = local_csv(asset + '_gtrends.csv')[1:].set_index([pd.date_range(start='2004-01-01', end = '2017-06-01', freq = 'MS')]).astype(float)\n", + "trends.columns = ['Google Trend:' + asset]\n", + "\n", + "pricing = get_pricing(asset, start_date = '2004-01-01',\n", + " end_date = '2017-06-01', fields = 'price')\n", + "ax = trends.plot(c='r');\n", + "pricing.plot(ax=ax.twinx());" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/case_studies/google_trends/preview.html b/case_studies/google_trends/preview.html new file mode 100644 index 00000000..905540e6 --- /dev/null +++ b/case_studies/google_trends/preview.html @@ -0,0 +1,14030 @@ + + + Google Trends CMG + + + + + + + + + + + + + + + + +
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+
import matplotlib.pyplot as plt
+from odo import odo
+import pandas as pd
+import numpy as np
+import scipy.stats as stats
+from statsmodels import regression
+import statsmodels.api as sm
+
+ +
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+

10+ years, monthly data¶

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+

Chipotle - strong positive correlation - Googling is proxy for demand¶

Because people Google chipotle when they want to find the address of the nearest one?

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In [61]:
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+
'''Chipotle'''
+asset = 'CMG'
+
+trends = local_csv(asset + '_gtrends.csv')[1:].set_index([pd.date_range(start='2004-01-01', end = '2017-06-01', freq = 'MS')]).astype(float)
+trends.columns = ['Google Trend:' + asset]
+
+pricing = get_pricing(asset, start_date = '2004-01-01',
+                     end_date = '2017-06-01', fields = 'price')
+ax = trends.plot(c='r');
+pricing.plot(ax=ax.twinx());
+
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Exxonn mobile - negative correlation¶

Googling of Exxon probably increases with bad news like spills or scandals which hurt stock price? Googling is definitely not a proxy for demand.

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In [63]:
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+
'''Exxon Mobile'''
+asset = 'XOM'
+
+trends = local_csv(asset + '_gtrends.csv')[1:].set_index([pd.date_range(start='2004-01-01', end = '2017-06-01', freq = 'MS')]).astype(float)
+trends.columns = ['Google Trend:' + asset]
+
+pricing = get_pricing(asset, start_date = '2004-01-01',
+                     end_date = '2017-06-01', fields = 'price')
+ax = trends.plot(c='r');
+pricing.plot(ax=ax.twinx());
+
+ +
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+ + +
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+ diff --git a/case_studies/sentiment/notebook.ipynb b/case_studies/sentiment/notebook.ipynb new file mode 100644 index 00000000..077e7d07 --- /dev/null +++ b/case_studies/sentiment/notebook.ipynb @@ -0,0 +1,327 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#Researching & Developing a Market Neutral Strategy\n", + "## Stocktwits & Twitter Trader Sentiment\n", + "\n", + "The process involves the following steps:\n", + "\n", + "* Researching partner data.\n", + "* Designing a pipeline.\n", + "* Analyzing an alpha factor with Alphalens.\n", + "* Implementing our factor in the IDE (see backtest in next comment).\n", + "* Evaluating the backtest using Pyfolio." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 1 - Investigate the Data with Blaze" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "from odo import odo\n", + "import pandas as pd\n", + "import blaze as bz\n", + "import numpy as np\n", + "import scipy.stats as stats\n", + "from datetime import timedelta\n", + "from statsmodels import regression\n", + "import statsmodels.api as sm\n", + "from quantopian.interactive.data.psychsignal import aggregated_twitter_withretweets_stocktwits as sentiment\n", + "from quantopian.interactive.data.sentdex import sentiment_free" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
sourcesymbolbullish_intensitybearish_intensitybull_minus_bearbull_scored_messagesbear_scored_messagesbull_bear_msg_ratiototal_scanned_messagessidasof_datetimestamp
0stocktwits+twitter_withretweetsADAP0.000.000.000.00.00.07.0490152016-01-01 05:00:002016-01-02 05:00:00
1stocktwits+twitter_withretweetsLIFE0.002.56-2.560.02.00.02.0490232016-01-01 05:00:002016-01-02 05:00:00
2stocktwits+twitter_withretweetsEQGP2.250.002.251.00.00.01.0490252016-01-01 05:00:002016-01-02 05:00:00
" + ], + "text/plain": [ + " source symbol bullish_intensity \\\n", + "0 stocktwits+twitter_withretweets ADAP 0.00 \n", + "1 stocktwits+twitter_withretweets LIFE 0.00 \n", + "2 stocktwits+twitter_withretweets EQGP 2.25 \n", + "\n", + " bearish_intensity bull_minus_bear bull_scored_messages \\\n", + "0 0.00 0.00 0.0 \n", + "1 2.56 -2.56 0.0 \n", + "2 0.00 2.25 1.0 \n", + "\n", + " bear_scored_messages bull_bear_msg_ratio total_scanned_messages sid \\\n", + "0 0.0 0.0 7.0 49015 \n", + "1 2.0 0.0 2.0 49023 \n", + "2 0.0 0.0 1.0 49025 \n", + "\n", + " asof_date timestamp \n", + "0 2016-01-01 05:00:00 2016-01-02 05:00:00 \n", + "1 2016-01-01 05:00:00 2016-01-02 05:00:00 \n", + "2 2016-01-01 05:00:00 2016-01-02 05:00:00 " + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sentiment[:3]" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Start Date: 2016-01-02 End Date: 2017-06-15\n", + "Columns: source\n", + " symbol\n", + " bullish_intensity\n", + " bearish_intensity\n", + " bull_minus_bear\n", + " bull_scored_messages\n", + " bear_scored_messages\n", + " bull_bear_msg_ratio\n", + " total_scanned_messages\n" + ] + } + ], + "source": [ + "sid = symbols('XOM').sid\n", + "sentiment_df = bz.compute(sentiment[(sentiment.sid == sid) & (sentiment.asof_date >= '2016-01-01')]).set_index(['timestamp']).sort_index()\n", + "print \"%s %s %-8s %s\" % ('Start Date:', sentiment_df.index[0].date(), 'End Date:', sentiment_df.index[-1].date())\n", + "print \"Columns: %21s\" % sentiment_df.columns[0]\n", + "for i in range(1,9):\n", + " print \"{:>30}\".format(sentiment_df.columns[i])" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pricing = get_pricing('XOM',\n", + " fields = 'price',\n", + " start_date = '2016-01-01',\n", + " end_date = '2017-06-01')" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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T12zh96/uxRpaX9RidqBUKrh65TR+e/v5fPPasn4DJQiW4QE4XX3L8GwODwZd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\nQYNSqUCvVeNwerE7Pdz/7HYcoSzVSDR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XMmWF2fQMjOByBS+y9zGFy92AZZRv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\nxo0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NGsWYMWPo2bMnGo2G+Ph4HB0d8fLyYtOmTfTp06feVtWor4qLi4mLi+Pxxx9ny5Yt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\nRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = sentiment_df['total_scanned_messages'].plot(c = 'r', alpha = 0.3);\n", + "pricing.plot(ax=ax.twinx());\n", + "ax.hlines(xmin='2016-01-01',xmax='2017-06-01',y=0);" + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "std: 0.0109092872406\n", + "std after spike:\n" + ] + }, + { + "data": { + "image/png": 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QsvEYyIBHDTTsOt2lnoDCE3Zps5L34Qk/hAoPIYSkC3VjyqYtNSGEbAgGMuAJpiw0f7xq\nVFALu7R5r9P8hcJBEQMeQghJF6qIT4WHEEI2BoMZ8Fit5WjXAjU8cX14mr8vXdoIISTdsA8PIYRs\nPAY+4EkyoQVc2kJFP/In9uEhhJD+x6ZpASGEbDgGM+BR5rAk85lqVBB2aZOctiS53nU1PJxMCSEk\nVbRqakMIIaT/GcyAp8XGcqppQWwfnjZsqcPpcYQQQnpLqynPhBBC+p+BD3iSTGhqSltdH55WAh42\nHiWEkFTDGh5CCNl4DGbAo+ZoJ3h8rYHCIy+VyKUtpCaxhocQQtKFFXBp691xEEIIWT8GM+AJKDzN\nH6/aUsc2Hm0j15subYQQki4smhYQQsiGYwMEPElqeJSUtg5c2tiHhxBC0k2rNZ6EEEL6n4EMeEy7\ntQnNsOJT2iQpLtFGYF0fHloAEUJImrBZw0MIIRuOgQx4Wk5pa2BL3ZnC0/y908pqqYbv7TuNas3s\n9aEQQkjXaHV+IIQQ0v8MfMCTJFBRU9rqTAssUXiSuLSFjqOPZ9OnXzmPB75xEK8ene31oRBCSNdo\nNQOAEEJI/zOYAU+LgYbRyLRA/k3wkuHH1DUx7SPyxSoAoGpQ4SGEDA7sw0MIIRuPwQx4WlZ4mru0\nbbQ+PKWyAYA7oISQwUIdljm8EULIxmDgA54kdtKG2ng03EtHangSzIzhR/SzLXWp4gY8/fsRCCGk\nDrq0EULIxmMwA54WXXgaKTyyHZgspW1wbKnLVSeVrZ8/AyGEhGEfHkII2XgMZsCjxCxJJjTVPto0\nYxSeDdaHRxQeLggIIYNEcEOshwdCCCFk3RjMgCewg9f88aozm6rw2K3uBLoP0TXn34FIaevjz0AI\nIWGY0kYIIRuPwQx41BqeusqaetQgJxjwKK+ZYF6UQEt3I55+nkxZw0MIGUTo0kYIIRuPwQ94kpgW\nmNEpba0qPPKQjAQ8fTyZlpnSRggZQKzARhbHN0II2QgMZMBjdsm0QH1msoAnqPAMREobFwSEkAFC\nHcv7WYUnhBCSnIEMeIIpC80fb8bYUgcnxuav4ys8et1x9BuliuPSZvfxZyCEkDCtzg+EEEL6n4EP\neDpReNS1fiKFx/3XjXcGQuExuSIghAwQZovzAyGEkP5nMAOeFmtvAo1HY2p4kkyMfkpbfys8NcPy\nAj+uB0g/cG46j70HL/b6MEgfEKzN7OGBEEIIWTcGM+AJuLQ1x0zo0nZproCX3pyOfZ0604I+DXjK\nVcP7uV8/A9lYfO3hI/jdv9iPfLHa60MhKUdNT6bC09/87lf2474/f7HXh0EI6QMGP+BJsGCvxQY8\nQbXnz//+Dfz2l18IBASB9x0Q04JS2f98dGkj/UC5asK2gaV8pdeHQlJOIAOgT8foVrEsG5/+i5fw\n+Evnen0oXeWVo7N45a3ZXh8GIaQPGMyAp8VO2moaWzClzX+MbdkoVQ1Ylo2yW9AfRoKDfrelLqkK\nT39+BLLBkE2OlQIVHtIYq8VU5UFgabWCZ169gGdevdDrQ+ka1ZqJQqmGUsVAtRo9JxNCiDBQAc/e\ngxdx4vxSy43lAo1HrWiFx7L9RVXViAt4nH8l4FGDp35CDAsAprSR/sB071sGPKQZQVObHh7IOlIo\n1QAEDXr6HVXNXS3XengkhJB+INvrA+gWpmnhM199Ce9/91TLtqNxjUfDLm3yurWYSWNQFJ5yhSlt\npL8wTCo8JBmtbogNAkU3IIjbrOtHFvNl7+d8oYptm0d6eDSEkLTTdsBz33334eDBg9A0Db/xG7+B\n973vfd7fnnvuOfz+7/8+MpkMfvzHfxy/+qu/CgD49Kc/jZdffhmmaeKXf/mX8YEPfKDzT+BiWDZM\ny0apYrScsiC7XpoWX8Nj2f5rVWsxCo/7b7+7tFHhIf2Gn9LGGh7SmMD8sEHGt0LJGdOrA6TwLCoK\nT6HMjY5B5IkD5zC/XMa/+qfv7vWhkAGgrYBn//79OHPmDPbs2YMTJ07gN3/zN7Fnzx7v7/feey++\n/OUvY+fOnfjoRz+KD37wg5ibm8Px48exZ88eLC0t4Wd/9me7GvD46osZcOFJsoMn5gLDuUysS5tt\n297/xyo87q8zfd6Hp6TUKPXpRyAbDKa0kaQE3Tc3xgBXEIUnZrOuH1EDnnyRKW2DyENPnsCFuVUG\nPKQrtBXw7Nu3D3fccQcA4LrrrsPKygoKhQLGx8dx7tw5bN26Fbt27QIA3H777Xj++efx4Q9/GDfd\ndBMAYPPmzSiVSrBtG5qmdeWD+PU1ViDQSDKf1QwLugbkshkvNcZ5bvBneY9KzKThu7QNjsKzUVI+\nSH9j0rSAJKTVlOdBQFLaarXBUXiWVvyUtlUGPANJzbRiN5jTyPmZPKYXirjlB3b1+lBIBG2ZFszN\nzWHbtm3e/09OTmJubi7yb9u2bcPMzAx0Xcfo6CgA4K//+q9x++23dy3YAfwFT82wWm48aloWshkd\n2YwW6MljBx6j1PA0mTQ80wKrf25UFbWGZ6PsgJL+xmQND0lI0LSgs/HNtm0vmEgzfkrbACk8q4pp\nQSn93wFpHdO0YLnlCv3Al//udXzqyy8EMoVIeuiKaUGjoCL8t0cffRTf/OY38aUvfSnx6x84cKDp\nY1bLzkBeKJYDAcnJU6cwgZmGz13JFwDYME0DxZLhvd9K0Z8czl+4gPxqCQDw5ltHYRfq+xkcOfIW\nAKBYLAAApqdnYo994uhRAEB+crLpZ1tvTp5e9n6+eOkyDhwo9fBooklyTaSZNH//caT5nBeKRQDA\npZmFVB9nI6KuiX79LGlmeWXF+3lubr7uHLdyzp94bRnPvbmKu3/2SowOpdf09PgpZ0wvlqupu6aa\nHU/cWHnq7Jz38+XZBRw96s/X/TSu9pKk10KS+UoeI3TjOyiWHBVv//4DyGWTb5AnPZZuz8PTs4sw\nTBsvvfSyd7xpu982Mm0FPDt37vQUHQCYmZnB1NSU97fZWb8R2PT0NHbu3AkAeOaZZ/CFL3wBX/rS\nl7Bp06bE73fLLbc0fczCShn45iVoegbQAMAZ/K699u245ZZrGj536PHHMVypYGw0i5phee83u1gC\nHroEALjqyqtweu4SgGVcc807cMvNuwOvceDAAVx//fXAY7PYsnkCmJ3H9h1TuOWW90e/6eKifLim\nn229efn8IQB5AMDOnbtwyy039vaAQhw4cCDRNZFqUvz9R5H2cz70D48CMGAil+rjbEjomkj7Oe9X\nvvHCs8C0ow5MTm4LnONWz/k/HN6PmpnHO9/1Hlw1lXxOW29eOvMagDxsW0vVNZXofMeMlXv2Po2M\nXoGmAdCGnflXSNFnTCstXetJ5it5jNCF7yD79w8DMPGDN70fm0ZzyZ+Y9Fi6PA8/+OzTAKp4/w/9\nEEaHsxzDe0CjALOtLanbbrsNDz/8MADg9ddfx65duzA2NgYA2L17NwqFAi5evAjDMPDkk0/ix37s\nx7C6uorPfOYz+OM//mNMTEy087YNkZSWas1q2XbUNC1kMxoyuh5sPAo19aF5Hx6vhicjfXj6U9Ys\nlVnDQ/oL1vCQpHQzpU3MANKecuOZFvRRPUQzFvMVbJ0YxtZNw8gzpW0gMRQzqn5A6o36tX570GlL\n4bn55ptx44034q677kImk8E999yDb33rW5iYmMAdd9yBj3/847j77rsBAB/60Idw7bXX4utf/zqW\nlpbw67/+655Zwac//WlcccUVXfkgMnGFa3iSXHc100bGreGpNXBpa2pLLX14ND1wTP0GbalJvyFm\nI8WygZphIZdNb3oR6S2BgKfD8a3YLwGPW8NjWTZM00Im09/3h23bWMxXcM0uR1UrFKtdNUEi6UA2\njfvFuEDWj/269ht02q7hkYBGuOGGG7yfb7311oBNNQDceeeduPPOO9t9u6bIxBUuFkty3ZmmhaFs\nBtmsHjQtCPXzkf+P2yWTh+t9bktdrqq21P35GcjGwlIMQvJFNiEk8YQ3sjqh6KrhaS9SLijGClXD\nwmifBzylioFqzcTWCec+r5k2ylUTo8MD00udwN/ISvv9JRhUeFJNf496CnGOaEkmNMO0kMloyOp6\nyJYagZ+9lLZYhcf5NzNQttQ9PBBCEqJuLjCtjTTCtKPH+HYQhSftY73qJDcIvXiW3B48kxPDmJwY\nBgAUmNY2cPStwpPy8WCjMjABT9wFlizgsZHN6MhktEDgVN+Hx/k5tvEogjU8rVz0f/3YUTz01InE\nj19LmNJG+g219m6lUGnwSLLRUce0TlX4gqvwqNdfGikodZlJF4+/+5X9+MO/emWtDqkjpOno1olh\nTLpqbr7IjY5Bw6/h6Y+Ax1N4uFOcSgZG/42buJLMZ2JakM3osG3ntTK6FujDY1m2tzPYVOHRpA9P\n8ov+oadOQNc1/Ivbr0v8nE4plmvIZTN19Q4l9uEhfQYVHpIU23bGd9OyO0ppM0wLFTf9N+0911T1\nI2kvnteOz2G8FWesdWQx79gVT074qatsPjpYqM3e+yWlzVd4enwgJBIqPJCUNt1vGBohSwZS2mJ2\nGyQ4yLSh8NQME0v5yrq5kdQMC7/66cfx+W+9Vve3csWAeyqo8JC+gAEPSYpl2ci6mzyd7OeoG0Np\nVnhs20ZJSWlr1jhbME0rtXWoiytuStvmYUyMDwEAShUGPIOEWl7QNwoPTQtSzeAEPDEXWLMLz7Zt\nJaXNOR1y0YZNC5rV8IgklHNfp9bCrkTVnYTmlsqJn9MJM4tFzC+XcWmuUPe3UsXA6Iizs8f7lvQD\nqmkBAx7SCMu2kdVb35QKo6omaQ0MAGc8Vw8vqcJTM21YKd1ZVxUe2ahM8VdA2kA1kOqXgIe21Olm\nYAKe2AmnyXUnz8tmNC+1S3YWAiltiktb3M0nwZXsHibeSbNs7zhml4qJntMpEuiEP4tlOW434yNO\ntiN3Kkg/YFq259C0nGcND4nHsuBtbnUyvhWVupg0p7SJJbVQbUHhSev4r5oW6F7Ak85jJe1hKGu6\nfkhpMy1/U5zXYjoZmIAnHFHrCXd95EbKRqS01bm0Jazh0TUNGV1L7IajprHNLpYSPadTJOAJT9Tl\nqjM5jrkKD29cknacWgxg99Q4AOD8zGqPj4ikGcut4QE6U7BVq+c0KzyqQxuQrImjbduBjbhuM7tY\n6ih9WzUt0N2aWTv9a2LSAv2m8KhBGRWedDKwAU/Wm9AaX3ii5mQzOrKZkMKjurQp0XusS5s8XgNy\nWT156oDyerNL6xTwzDsBj2EEz4/04JFiVU4iveXgsVmmaDVB0tk2jQ1h5+QoTl9e6fERkTRjuaY0\nmtahwtMnKW0SmEmQF1eDqiKfZy1qk4rlGn75vkfxZ995o+3XWMqXMZTLYHQ469ebcnNuoFDvqb4I\neAwGPGlnYAKe8IQjKQvNAh5TVXgy4q5Wr/CoNTyVBApPNqsnTh0IBDzrrPAYIYVHCnHHmNLWc2YW\ni/itP34Of/3Y0V4fSlNM06rbSV6/93bNQnQNb79yC5byFS/lhZAwlm1D1zVomtbRwqQYMC1I74JM\nao22uv1qkqRay+JtLQK5fLEGw7Qwv9x+vepivoLJiWFomuZlc3TaRJakC1UxMdbJzKkT1HUc103p\nZGACnjqFJ9NaSlvGtaVWf6detGpKW7M+PPL+SSV7NfVtdnF9a3iM0GcpuXnp40xp6zl5V9npB7vV\nv3niOH7xkw/3RI2SRVlG13HtlRMAgDOXqPKQaGzLdhbKWmeL5P5ReJwxXQKeJJkHUj+xFp/L6LCZ\npGXZWHIDHiB5+jrpL1R1sZZiF0RBDdDSPB5sZAYn4LHbU3jUlDaR/KNS2izbhlzPsTU87t81zXm9\nthSedUhpMy0b0wui8ATPT6kaUnh44/YMcfkLq3Bp5Ni5RZQqJhZW1sdlUMULeDIa3n7lZgBgWhuJ\nRRQeXdM6rOHpD1tqUV63bnIDngTzkt+aoftjjx/wtLdrny9WYVq213DUq+Hh5txAYQRqePpL4eG1\nmE4GJuAJR9TZhM4takpbNhu2pfYfp/bhaabwaO7rtVvDs9Y3y/xSyQvq6hQeN03Dq+Hhjdsz5LoI\nf0dpRAKdpEYd3URSUHXdD3io8JA4LMtZJGu65jWTbodin5gWSEqbNOlMsng0InrRdQsJDltp26Cy\npBgWAIo/qjxzAAAgAElEQVTCk+LvgLSOek/1wxxI04L0MzABT51Lm6vwNLOlrqkpbbrzHDOiD4+t\n2FLHLerkEDRNQ64FhUcNjCpVE/k1TmFSe+/UubRVgi5tjHd6h+Ta94Ml54LbCLAXxaVy72d1HVdN\nbUI2o+MUAx4Sg6/wdJjS1je21MEaniTzkmyIWXb3F28ynrW7iFV78ABgDc+AElB4+mAODNTwpP9w\nNyQDG/BkE+b1ym5TTjEt8FPalMdZdgJbanmCY1pgmFaiySJcRDq3xmlt4tAGNFB43JS2NO9cDjqy\nE2ukOF0GcK77JXcR0ovUA8+0wK3Du3rXJpy9nOe1SyKxLBu65qg8nbhQBmypU3yPFkM1PEnuUdWE\noVGWxNcePoLPfPWllo6n6wqP1rnFOEkffW1LzYsxlQxMwNOuS5tvWuDbUse5tMn/x9l6eq7Ubg0P\nUD+ov3Z8ti7dRm7mTW4a2VobF0wv+K8fLgYsVZzJ0Fd4Wr9xLcvGoy+e8XbiSHvIdZZ2hWelUPWC\nsiSWt91GapykBu9tOydQrZleEEaISsClrWsKT3oXOBKYTXqmBUkUnmQF2E+/ch7PHrzY0jwh92u7\ni9hFpeko4N/3g77IrNRMnN1AtYnqRl8/pLTVaEudegYm4Il3aUsW8GQzWp3Coz434BgSs0Mmg76u\n+e+vqkGGaeETf/I8vvDQocDzJKVt99QmAGtvXCDvN5TV6+xUwzU87dy4x88v4X/+1av47t7TnR1o\nG6wUqljsQeH8WlDrk4BHNSpIYnnbbeTelNSWkaEMACc9lKSLs5dX8JF7voeXj8z07BgsqzsBT0Fx\naVuL4v5uUZ/SlkDhsdS5L/6zLayUYVl2Syq02aFLWzjg2Sg1PHseeQu/9ntPYH55fVpX9Bo1TbQf\nUtrYhyf9DE7AE+vS1vh5puLSlsuETQuUHQblhovLgfYfrnmvpU4uc0slVI36fiWySLzS7RS/1r14\n5GYcymXcLvX+5yyH+vC0sx6QCbbQg74s//3PXsR/feDZdX/ftUAC6zSnywDA4orf8yapUUc3ketZ\ndnqHcxn3WNI/STYjzf1d2mHvwYtYKVRx4sJSz47Bsp06y4yudVT3Uar0h8JTLBvQdQ0TY0MAkgUa\nSXarSxXDywhoxaxEgqP2A55QDY+ktLX1av3D8moFto2O+hf1E0Zgkzn946AalHVihkLWjoEJeMKL\nwmzCQkbVtECCJM+0IOb1TcuOXIjIe2kakHEd39QbdXq+WPc7YP0VHlMJeIDgwOLX8LTfh0cas/bC\nsWspXx6YppNynaR9dyug8PRgYvJtqZ17LicBTw+uv25y+EwRd/7md3FxbrXXh9I1XjsxB6C3CxjL\nspHRNGhaZzux6oZOmuvsCuUaxoaz/kZAEoUnNN9FoSrpcc24o1+7M9OCpZVol7ZBX2PK91BSUikH\nGfXeZEob6QYDE/C0r/A4F2kuo3tBUlQfnnAvlKgJ23dpA3LZeoXnsls7E05RktfaOTmGjK6teQ2P\n3IzDXsDjH4/04RmVPjxtzCLymXuRUmSYdl1voX5FlMS0D/bBlLbe2VKLwjOUq7/3+pHZ5RqqNRPn\npwcj4ClXDRw5vQigt2mawRqe9l8n2Hg0vfdosVTD+GgOuawz3icJNtX5Lm7xNq/c9y0pPF57h/bu\nz8V8GeOjOW/DbqOktMlcXKykvxF1NzD62LSAjoHpZHACnrBpQUKFRzUtqFN4lHvMCu3gRe9oBfvw\nAMH0t5nFxgrP8FAG27eMrLnC46e0BT8v4O8ejQ1noWnt7Zp5AU8vFr+mlfoUsKTUTDelLcWLKSC4\n09uLNDIzlNI2lJWd7HSft2bIVRxOgU30XNvGwaOzqWrYd+T0gtJ0snffjW3b0DRnodzuwsSybBQr\nRl8stgvlGsZHcv5GQJI+PMr3k0jhaWFzy6vhaXOcXlqteE1UAadmFhj8RaasQQqljaHwBOqmu7hB\nUijV8NKb012/XqjwpJ+BCXjqGo+6AUez607UnKyueUYDnsKjJLW1ovBAU2p4lMlFUtrqFB53YTaU\n1TE1OYaFlfKa7oDKuZLUH3UwkZS2keEsdE1r68atuJ+nFwGPYdlNA4QHHz6C//sPnkp13j2g9OEx\n0n2cC3k14OmlLbVzzw15NTyNjyXtCyQ5PLVWJClvnFrAb33+OXx/35kuH1X7vHZ8zvu5V6ql7bpt\nSh+edhcm5aoB2wYmxpzU37RuspiWjVLFxNho1r8vEmwEBEwLYs7RglK718pY7/fhaX2sMEwLK4Uq\nJjcrAc8GcWmT72EjKjzdHC++8+xJfOKLz+PUxe463tGWOv0MTMBTp/Bkkik8nrOartTwuAtm9SXD\nE1rkYkqt4RFbamVymV5w+t+EgyUJOIayGUxtHV3zwsRwSpv62cpVA7msY9HdrotRtYc1PKbpLGga\nBTOHT87j+Lkl5AvVdTyy1umXGh7VtKDVnfsT55dwbjrf0fuHTQuSpLQ98+oF/LtPPBxIx0sbcusV\nG+Tsm6aFZ169ULdBIr28LsymJx1ODXh6dU3LtaJrnaW0yXciRgDhDbG0UHLVwfGRHIa8utIECk/A\nljr6sy20WcOjNjVtddNJCvfFsADYODU8XkrbBqnhCbi0dTHgkcbuK4Xu1vqy8Wj6GZiAJ17haTwK\nygSoaZr3HNlNiHNpA6Ltd70aHmjIZV1bamVykZS28GvJwiyX0zE1OQpgbXvxeClt2aArHeDsJo8O\nO/U7ut6ewtPLGh4vWG0w4pTdOqV8Md0BT9VrPJru0XO+A1vqT/3pi/j9B1/u6P3rangSpLQdP7eE\nxXwFl+YKsY/pNX7OfvwC5+lXL+DTf/ESnnvtYuD3ogqtdRPjpBTLNRw7t4Rtm6X5ZY8CHmWDq5OU\nNjEskIAnnPKcFlbdOqPx0RwyGR26riVTeFTTgpjPtthmDY8ZCKZaO29hS2rAd2kbpDSimmHhD//q\nFRw+4W8SyPewUQKeQB+eLs6B8lrdXp8EFJ4BuhYHiYEJeOJreJo8z/27rqa0WWJa4D+uLkiJ2CVT\nXdqymaA1bqVmeikAdQqP+/+5rI6prW7As4YLlXqXNjXgMTEiAY/WXtpPL13aZJBs5JpUdge61WK6\nUwPkukizNbFt21hcKfsmHS2mqeSLVW9R1i7yXethhafBsXhpNU3ObaHDY+sEX+GJP4YLM46Cs7Qa\n3K2URdFcSnp2vHFqAZZl4+YbdgLoXUqbN95rnaW0Fd06is3jTsCT1vRYuQ6kzcBQVk+k8NQSpOcs\ntFnDE1zIJn4aAHgOnFsnIlLaBsiY+ujZRfzDi2fxxIHz3u98hSfd81a3UOe9bm6Q1JQ1WTdRj5G2\n1OlkcAKe0AWW9VzaEqa0KWlonmlBQOEJpbRF7JJ5D49oPDqz4Cs2hhnsfSM3ylA2g6nJMQBruzMr\n52o4xpZ6zA14NE1rS5qVc9OLonH57hoFCRLw5EvpVnj6ofFooVRDzbCw071uW5mYbNtGrWZ2PJn5\nKW2hGp4G11/Nu07ix4cX37iMu37ru3j95HxHx9cuSWp4RDUuV4KTtzxnfikdKXsHj80CAG65YReA\nFKS0uS5t7a5LwgpPWgMeOU5pJJ3LZhIZi5gJdqsX2lV4lEmlZYVnJdiDB1A2N1P6HbTDeXcjI5Ba\n6P68ERWebpqvyGtR4dl4DEzAE84zTprX66syGrK6pHjVKzzhFKmoAV4NnsIubTOhFDX1eAMpbaLw\nrGHzUVnkhRUe27ZRrhhep3pdb6+Gp+K5tK3vwGzbtjeBNppIK25K22rKU9q8Gp4UmxZIrdkV252A\np7WFjw3L7jyg81LaMuGUtvhjkXugUe3Febe2qFdpb0lqeGbccULSNAUJeJZWK6lwajt0Yg7ZjI73\nvWsHgN4F8fU1PO3dW+JmOeEpPOnclBDrbOmrNpTTUatZePDhI3VpkCpGiylt7dTwhH9OgpfStjki\npS29w2TLnJ9xxp6ofki9aOi9FhTLtbpxS0W9p7rZ50peq9sKj7EBXdrSMLe0wsAEPOH5JnENj9Td\naJq3YEqi8ETtSvuP0JANFYhOLwQDnqguwrlMxq/hWQeFR1J/VKtY07L9Gp42U9p8W+r1XQSoQU6j\nBZWn8KQ8pU3Oo2lZDb+HN08trGmA3AgJeK7cPg6gNYVHPl+nCk+dLXULKW2NFB5JteuF2yDgu0Q2\najQotX5hFUhNe+l1Z/Z8sYqTF5bxA2+fxPioM7b0qoYn0By6KzU86XZpk+Mck4Anm8H8cglfe+Qt\n3P/XB2Ov7aAKU/9dlasGCsp12U7jUee1WztvkroZsKVO2IKin/AUHrUfkt18POgn/r/7n8WnvvxC\n7N/7TeEJmBYM0LUYx96DF/FzH/sOzl7urtvdWjJAAU+cS1vj50WpMlLDE3Rpa6GGB2rjUed5Ykkt\nxZbqQO+ltOV0jI3kMD6SXVPTAvksQyGXNtWSGuiCwrPOpgUBK9WYBYhl2d5xrYVpwWK+3LXXrXmB\nd/yOUaVm4jf+aC/+5G8PdeU9W0VqRK7c0XrA4ytYHQY8ZjjgaZ7SJrtxjXbmpX6nF+YbgD/+xKW0\nmaaFOTeYiUtpA3pvXHD4xBxsG7jpXVP+GNsjhccMpLR1UMPjBhJpr+GRni0SaOZyupfSli9W8aRS\nI6LSrA+PODNK4NFuDU+rl4GX0ra53qVtkHbVReGxIua0QVF4ZheLDcemtarhWTOFZ4M1Ht372kXY\ndrqcQJsxMAFPeOGStIYn6NLmmhZ4N5eiGLiP82tzIlza3F+pr1UNKTy7d25yjle5OeQxEiRNTY6t\ni8Lj1fC4n1cWSaMd1/D47mLruRAI7xw++Mhb+MxXXwoemxKoroVpwX/93N6Gu1atoDqe1UwrckJf\nLVa93hS9YN69Tq9wA55WUtrkHuo8pc0NeEJ9eGoNjkV2ThulSshisWcKj6S0xfTdmF8pe9dEODWk\nmKKAR+yob3rXDm9sTINLWye21IWQLXVqU9rKoZS2bHDK//YzJyLnSCMilUpF6nf8VNbkn7+TVKXF\nfAWaBmxxA03AT2nrlzXmaqmGz3z1JZy+FL0zXqmZ3nohqrfLoNTwGJbdsPls1KZwN1gXhWeAgu8o\nbNvGG6ec2tZeNpFulYEJeOJc2ppdeKprT8ZTeKzA3wB/MT2ca1Qf4KdLeC5t7uOmF4tOY1G3RifQ\nRbgmLm3Oc3ZsHUWxbHg7zAV3gOxWLUGdS5v7eSXVy09p66wPT/jntSZsY/n84Ut4+pULgVoddZBr\npMRYlt3ysdu2jemFYtdSiFQZv1g28Eu//Qi++cSxwGMk7apRLvRaIpbUktKWpCBakM9nWXZHgbFV\nZ0vt3MeNAhVp5trI3EJ2Uis9OrfNanjUNMZyaPJW017mepzS9trxOQwPZXD9NZMAnI0dWciV22iq\n2glyTjOab0v96Itn6+6rZkgg4dXwpDSlTbWlBvw5BgB2bhvD2ct5nDi/XPc8NSiJmkMX8xLwOPd9\nuzU8Lae05cvYMj7szdVA/zUefevMAp5+5QKeP3wp8u8XZ1e96zSqhmdQXNoMw2ro1hhoPNpFRXit\nXNo2kmnB7GLJW+ek2VQpzOAEPKHrK5tJ9tHUxqNZz6XNDvwN8Afp4SEnGIhKafOPwVd45Oaani9i\nanIs0gq6aphukOQ8J1zH88rRGTz9ygU8eeBcos/UDMuyoWm+ouQpPOWgwtN+DU+9IcN6EHYWkv8/\nN+1LrurCsJHC89BTJ/DRj38fy6vJm5NVaiYM0+pavrEaPMwsFrGwUsaJC8HFidjjhlOa1gtRD7Zv\nGXEWsq3U8CiP7eSc1dfwuApPo8k0kcLT4xqeJju6qhFKOO1N/f/5GIWnZpj4nT/f7zmorQWL+TLO\nXs7jvW/f5o032YyOmmHhwuwqfuG3vovH9p9ds/cPoyr6Ykv9zSeP4y++92ZLixT5Tjan3KWtzpY6\n58+LP+xahEdZrzdVeJaDAU8r43xwIdu6wqNaUgP913jUaJLKe16ZrwKLaCX1vN8X1GIw1GixbEbU\nOXeDterDs5Fsqd84veD9rH7u5dVKz7JNkjAwAU84pSCTcNdH/i5FrIDqWqa8vvs7cTCLarCoFsTm\nPJc2E8VyDfliFbu2j/lBVUiuzWUz0Fxp3ndqcxY0MiF1K83NtGxkdN+kQdL1/Boe5zNqenspH+oC\ncT3rH8IKj3yPZ123LSCohDRSeI6eXUSpYgSe2wx/gdydwVkdSGThEt5JjlIhLMteNxOD+eUyxkay\nGBvJIZfVW+rDoy6SOunLYng1PM69lUuk8DSv4VlNSQ1PuRq9wFEDnqiUtmF3rIobN85cymPvaxex\n92C8W1enHD7upD3c9O4p73cSGF+aK8CybFye70694sxCEQ8+fKTh4si3pYaX0lYzTBimjeUWOq/L\nve4rPOnc5YyypQaceczLNoi4tprtVktK25U7nJS21mp42jMtqNRMFMtGoOko0H+NR2W+jQsSpX4H\nCJ4fWavYdu8U/W4hn6tRwCObUk7vqPQrPIHeVekcDrqGpLMBwXXKPV/Yh3v/tDsp/WtBXwQ8SSYT\nGexksZPxangaP0/+7uSWN1B43NeXgv6o1B3/tYBs1t9lFuvYXdvGfFUlVJCXU3Krw81HZXLtVi6+\nZdnQNc0LyjyFpxpWeLS2JhF1IF/P3fFwfwdZCJ9TgpZKQoVHJvRW0tNkgdyodqQVVNVD1LfwBBHl\nJPYPL57Bv//UIzhxfqkrx9GI+eUStm9xrtehbKalXH51oOykL4t877obwA8nUXgSuLT1XuHx/41a\n4ARS2sIKT9nAzslRDGV1zMc0H5XF8FqmJBw87qhHN7l21ICr8ChKaLfqXx4/cA5fe+QtvH5yLvYx\nag2PrjkpbXLNttKzqOjV8LgubSldbEfZUgPAVTs2KY22689/oB4y4voQe+h2FB4zsDGV+GmRTUcB\nReHpk8ajcj5jFZ6ZaIVHvU96UcdTrZldW4N4jZ8bjNGmklVjWnbXAtq1Ung2ki31m6d8hUdta3L2\n8krXNrDWgr4IeH7ptx9puuiQC0wUGBnMmyk8wcajwQlAvWbDrx+VghN0aXNeq1IzMT3v1N7smhyr\nC6oAZyBRi0ml+agsaGRR262u6aZlI5Pxa5ZkIPVS2ob8gKedlLZKzwKeoKON4aW0ta7wyOQaTgc6\ndXEZv//gy5G1BxJAVY3GNtJJCSo80YvTglfD459nCbDXuli9XDWQL9awY4vjmJTLJeviLqiLpE52\n8PzGo5p7HMn78DR0aSv3VuFRr6EopzZpZrx1YhilCJe2seEctm8dja3hkWtqLRfrh47PYWwki+t2\nb/F+JwqPKKHdWhzI97282qA2zxvvNc+FUq7ZuPH12LnFunGsUK5hZCgTqdiniUK5hmxG99I8pUfV\nlVPjSqPtTkwL2qnhaU/hkbohteko4MzdQP/sqsu5jRufzs3kMTyUwVBWj3Ue7UUdz3f3nsL/ed+j\nuNgFVy45Bw0VHsmqGa4vA+gEX+HpbtAYUHgGOKVttVTDmcsr3nwr53O1VINh2qlWH/si4FnMV7DS\nYBID/IFTamwkvcVuco94zmp6veIRJQ+NuK8fNcDLozXFAKFmWJ7jyq7tvsKjLrRqpuUt0gBf4ZEF\n66qi8HRjIS0KT9Y7xqDT0+iI2FK3txjpnWmBkhtuWd4EcTZG4SmUa7ETrkyu4UXQkwfO4/GXzuG1\nE/W7yGoufDckeFUtEdetuIBHeigB/vfYitrSDpLH7ys8raUeqI/tJKUtbEud0Z0aukbXnkxOcTUE\nNcPyrpXe9eHxidrRnVksYWIsh8mJ4cAk46RoWRgdyWLHllEs5aObj8q1s1YKz+xiCRfnCvjBd+4I\nFJlLDY8ood0KFuS7bGZGAvi21Lbtq/VRtU7npvO4+w+ext8+dSLw+1LZwNhIzhnrdS29KW0lw7Ok\nBvwMiKt2jDc09mnWK2dhpYzx0ZyncLXWh6c90wKxwlabjgJKPVafLDI9hSfimrEsGxdmVrF7ahOy\nWT1Yl2qrAc/6LyqnF4qoGha+8r03O34t+VyWHX8NyHUia64kc8szr1zAJ774fMP7UV6n2/PjoCk8\ntm3jT/72UJ25xpHTC7BteCY0cj7FMr5cNVNry90XAQ8A1Mz2FJ6mttTKjl8mtFsXdc1KMBC162t7\nBbFATvdreLyAZ1u0wlOrWV6wBQDbtoxA1+pT2koVM9DsrV1My4au6957egpPhC11O9dttUc1PIGJ\n1LS8CWVuqeTtiKlKiG1H75SVKob3uHBKmwQe0xGy7aoS8HQj0DOUa77opbSFaniU95Q6nvVaqMu5\n2b7VVXiymZZc2tR6n04CRN+0wL+Hck3S6/yUtujHqNdFr2t4gHqFx7ZtzC6VMDU5hpGhLMoVo87k\nYHQ4ix3udxOVmlmISZPsFodOOOls71PS2QDfpU3ukW4tVGUca1Q06wU8mubVfshxiOOgiii9YbW0\nUK55gURG11Kr8BTLNS+dDfANPXZPbfJdSSO+/2A9Qv1nW1wpY9vmEWQzOjStRdOCgC114qdhyd2E\nUpuOClqbBju9wGv0HTE+zSw6QcXVOyeQ0fVYpa0XAc+KW+O29+BFHD272NFrJXFgk+tE1nRJxqnn\nD1/CS29OY7nBGNDtlDbbdtLt0t54dClfwX/8ncfw3GvJajZXSzV8++mTePj5M4HfS/3OTe92xnU5\nn5LmGj4XaaJ/Ap4mJ1AuMLk5ZDBPmtKmuqSpOYlhNrnFn1EDjv9oDZlshMKzbTyyhqdqmAH3nGxG\nx7bNI3WmBUB30pSssGlBqA+P7KhoWnsTeaVHLm3hVAl1MSt50RIUyO5m1G6wqDtA/fmWhfDlhXqL\n8NWSYn8d87mXVyuJJ+aAwhOX0qYszCVIk4G8FQOBdhD1a4er8OSyekv1S9VQn6F28fvwaN7vhnOZ\nhp/fbKLwBALJHtfwAPWB+fJqFdWaiZ2ToxgdzsJSlAp142JHSC1WkfqOtbJUlv477393MOARhUeO\nt1u7ofI58g0WO3JOpYZH/V3UOZLrUr0ebNtGsVzDmBtIZDLpDXgKpRrGRv2ARxqlXnvFZm8MjDr2\nRipMzTCRL9awbfMwNE3DcC7TosLTXkrbgqfwjNT9rd16017gpbRFjE8yT71tl1NjFXYeFda7+ahp\nWsiXal7fqT/9zusdBZiBOq6YtZ08RjZgkyyi5XpqFMx0O6Xtf339Vfynzz4RmMPsHl2Lh47PxQY0\nr5+cx4XZVbx+cj7y72FkHgl/P2+eXoCmAe+7zhnXPYUn75u+hNskpIWBCXjqU9qkkLExkQqPZ1pQ\n/3jZLYvKqQ87vmUzmtdEbHQ4g4mxXKxL25DSHwFw6njmlsswLTugHCQJeAzTwu999QBefmsm9jPr\nuoasF3zZgc8kFqYZvfUaHiPUIHM9F4vhrtTqYvbsZSetTW7EbW7dSZRxgaROAE5Rfs2wPOccCXSj\nFJ5CsXFK2ytvzeDf/Lfv47lD0f0XVMxQbxr5bsKKxGqpXonwU9rWOOBRLKkBZ/e4lfolNc2qo5S2\nUB8ewO0o3zClrXENT9R5XW/sBiks4tC2c3LMy3GXujL1PvYCngYKTyfBZhy2beO143OYGBvCtVds\nDvwt69YmVLqe0pZA4VHGaE0L/i1KBZPrclVZYFYNC4ZpY0zMXXQ9lYttCSrHR/yUtp/+J+/EJ/7D\nj+BdV2/1A56I798I9OEJ/j0ceAzlMi3aUtuRPzdDNqK2RQQ8mq71iWWBP+ZEKTwyz7xtp6PAGeqc\n1kOFp1CuwbKAm6+fwq3v2YXDJ+Zx4Ej0+iIJiRQezxk3ecDjqTcJHDq7Na4fP7+Es5fzgX5/vbKl\n/tLfHcYffv3VyL+dvew0uk26JpP5RB0LaoaFo2cWce0Vm7HFVVrle1lSNorXu79aUvom4Gm2ILK8\ngKe1lDZvx0/TkK2zpa5/7thIFpoWUzToPlzspZ20Gifg2bVtHJqm+TU8ynvUDMsLPoSpraOwLBuL\nK+XA7mISa+rTF1fw1Cvn8dTL5yP/7qS0acjqQbVJern4Ck/raQLhia9nNTyuLbUEmGJcIAGPqBIn\nLizjpTenA6+jKjyL+Qq+9vAR/F+ffhyX5wveYlJUOxV1URQ1qHzn2VOwbXgmFo0I11wkSWkrr3NK\nm9TwyKI6Sr1UmV4o4k/+9pD32QIKT5P7e7VUw2e/dsAbtFV80wL/Hhpqkl4n91+cupE2hSe8wSKG\nJpLSpj4mkNLmXudR9SkFT+FpP+CxbRuXI67n6YUiZhdLeN+7tnsuWoKk0sr7d1vhWWlQwyOLRl3X\n6o4rUuEx6hUeGftFOclmtK45zXWTYsiSGgA2jQ3hh3/A6b8TTuFWaaTwSK7+djfwGB7KtLR4bFfh\nkYA0KuDppcKzUqjiaw8fSTzXyRgetdEgCs/VOyfqFJ5emhZIP7rtW0fxi//svdA04M++83rbmxVJ\nAh6/hifeKKruOU0UHtu2vfPerXFdNo7mFJfHXl2L5YqBkpLerHLGXQMlvVdlraSuvU9eWELVsPDe\nd2yrm+/VjeK0Ghf0TcDTLD0nXMOT2JZa6m50396yUQ2PrmsYHc5G7rB4u4fu/w/ldCyuVFCqGNi1\nzXFeCys8MqEOhQMeaT66WGo5pU1saONuaElpy2aDO3zhGh5xMWoFeU9ZS/SqhkcWu9dcMQHANy6Q\nG1GMIR74xkF84ovPB3b05cYdyuqwbeDR/Wdh2c4iTiaa6YVC3aCiqkXh3bv55RJeevOy87cEC8xw\ngN/MpQ1QFR4z8hi6jaS0qbbUQHzw8sgLZ/Dtp0/i4LE593FKDU+Tc/LGyXk8eeA8Xnj9ct3fvD48\nSkrbUBOFx7NFjVmoFlJWwxOv8Ix696t87+p9LOpb1Ljh21K3Pzk//tI5/If//iiOnwtaoB9xG9Pd\n+M7tdc+RzR35TF2zm22xhkcLSTxzy+W6e1qu0WDA4xy3qP0ZXevoHK4VcsxqDY9KQ4WnQVAiDm2e\nwqQubAkAACAASURBVNOiHb36eq0qPEO5jJeBoNLLGp6HnjqOBx95KzajIoyc76jx6fzMKnQNuGpq\n3DXD8D9TL00LpCZmx5YRXHvlZvzTW6/Gmct5PPFSe83QwxkuUXg1PO74luRaaXRu5X3lNHZrXC9G\nmL/0KuCpGk6GTdS5kiyXRoGebdt45pULuDi76q2V1M/1hmtH/Z53bPc2rjyFZ5UpbV0jaUqb7HaK\netHswpM/a+4EmM2o3eLrn5vRNYwNZyNT2tQ+PICj8MhFsNMNeMJRsRfw5EIpbV4vniIKpZrnhpMo\n4HEnpLgb2rIs6Lrm7YjLYlP68HiNR7XWG4/KxDc+6uT6RjXhfOHwJdz35y923dlIXbzKZ986MYyt\nE8OewiO/F1VCKKoBj6vwvNO10pXC5dVSzfveSxWzbmHVSBV4bP8571waRvOTGlYnijEF5u0qPDXD\nwvf3ne5ol2tuuYxcVveuTbm24xY/sjMs9ufqZ2ym4HopfVG70dKHR1cDniamBdJ4tE8UnmIluKMb\nSGlzN3nk+5fzOzaSU1LaImp4utCH58gZp3j5UkjlkfMXtRsv10mhy7bY0om+kUubraQw66GAp1oz\nUaqGA556hUd+HhvxU9rSWMMj53csJuCRLIhmjUfD94gEPNsmFIWnhXqIYDCV+GmuUcJwXaAKyFzV\nm+/g8AmnJiLpAtpTeCLGvHPTeezaPo5cNoNMRg8oh5ZleRuJ3VB45lcce+EkiEvudnc8+cgH34Oh\nrI6//P6bbY2PjRSectVAuWJ497OsR1pReOIUBnWekeCgE2zb9oyMVHo1HMgmZ/g7qRmWZyfe6Dp9\n7rVL+PRXX8JfPnzEy/hRgycxLHjvO7Z5G1dhlzaACk/HNDUtCKW0ZTLBgtQ41AkQcCaBqD48gq5r\nGB2JVni8HSZ3UBpWjAg8hUcCHum27N7EdSltbi+eC7MFVA0L17h58MkUnmYBjxO45cI1PGUDuuY3\nbsy0kSYgpgCN7EqfefUinnvtEqYXu9ugSp2Y5ThyGR3X7JrAzGIR5arhnRNxFvMerxynKDzvunpr\n4DGFUi3wvYfT2lSVSB2cLcvGoy+ejfxbHOHr3SsgDKe0RZkW1JrX8Ow9eAGf+8ZBPPvqhabHEsfC\ncgk7toz6KZzu9R6nxkpRo0wQrfThkc8WdT3KxKjW8AxlMzBMK3YhKvefYVoolmt48fXLgceqC9xq\nzezJjl2gD09ovPFT2hSFR1La3OBodDiLzeNDGMrqDWt4Otl4uBAyA/GOtxpMj1URlVs2GZIsVFcK\n1abFtskUHudfJ6Ut4n2Kwc8RZVpQDAUSGV2DlUJb6mLJVaJG4xSe4PivEqcsAErA46qHjmlB8tq9\ndvrwmJaNpXwlMoAGnO+z1Xjn7545if/3fz3TUcBfrho4ds4J+pO+jgQx4fF5ebWClUIVb9u5CYCz\naRtwaTNtbyOxGwrPN/ct4JNffD7RY+WeEsV4anIU//yfvBNzy2V877nTLb+3uukXvv7++5++iN/4\no72KS1vrpgVxc184k6CT2lHAmZci56Qm17Vp2fiDPS8ndkxLisy94fH44uyqn+4Xc26K5Rq+8NAh\nAI7xSynUCsO2bbx5egE7to5i5+RYfUobTQu6R1KF5wo3sJAbs9lkqhaxAk4qnG9aUP9cXdMwNpxr\nqPDoSg2PsCuk8Hhe/LXGKW2nLy0DcJWKTcOJangkpS0uyjY9hac+pW1kOOstYNur4XFea8J1A4oa\nePybsrs3hbobJjd1JqPh6l0TsG1ncSbn5NpdwULqQMDjKjzvDgU8q8VgwBOuXQgukv1jOXxyDpfm\nC3j7lc57hgd407LxV4++5S1ineeHangi+vDYth1SeIIpbY0UDlkAq0FaKximhcV8JRA4Nktpk/Na\n8gIetYan8bUgA3i0whMR8ORk9yn6dVWF55EXzuC3v/wCvvH4Ue/vcl4kmFjPWjRB/aThXcSZxSKG\nhzLYPD7kpXxI81E1pU3TNKf5aAOXtk7SsS64u4bhCU7uM1GfVHLhlLYE7//gI0fwsc896y22o5Bx\nu1I149N5ZYNLr09pA4B8qX5nFHB2g+Ua8FPa3GyClLq0yWbIeEQKGOBvCkbVHzVSeML9cIZzmdg0\nmijaMS0olmqw7GiHNsDZY2zWcy/MgSPTePP0QsNrqhlvnV5sqNhEEVfDo9bvAK77X6gPj2wkhhXf\ndiiULcwulRJteKy4mSpSEwgAP3XbOwCgLYvqgDV56LwdP7+Mc9N5mKZTazzUpDZURT5L3II7/B1V\nm7Q7aUZYaZMhpdm68/J8AY/tP4fH20wJjEPm1PDcL+lsQH0wJPzl949490KxYtSltF2cK2B5tYr3\nvn0bANSntKkBD00LOiOpLfVP/eg78Gf3/GSdM1Ds85QiVsBVeLyItv7xmubU8BimVbeYCj98KErh\nCdXwyOI/LqXt9EVHct40msOOrSOYT9B81FN4GtTw6Jrv0lbzBgnDW+DJZ21Z4XHfU+wroxaK8pio\noLETjIDC4ypnGR1X73ImkHPTeW8gfPtVm/E/fv3H8S9uvy7weMDZqRgZymD31KbA6y+tVmCYflpB\nncJTjLalfuR5R935qR99u3ucwWv58Ik5fPV7R/DgI0eUzyK7W851UYwoMK/UzMjP7Ac88YO5DE7t\nSs8LK2XYNrB9sz8BDoUk7jBeSpsEPKpLW5PJrJHC49tSK6YF7v0UFfTZtu+AZ1gW8m7t1YMPv4Xj\n551alHBKVi/S2gJ9eOpqeErYOemoa7IDWp/S5vzebz4aSofsMKWtVDG8CTK8wKgkUHi8lLaI8Sx8\nviWlptAgQFc/R5w1tbrBpaa07XA3yFaK0QGPerxhhUcP7cSnBT/1rnENT9Q9FVRhgtdHOKXNv9eS\n3SNB04LGj33j1Dz+6G8Oeu5lsQqP1rozlpyfTtSSQyf9BtRJA57wZqfgWVKLwhPh0rZJAp5Sd/rx\n2XZjRVRYLlSha8DkhN8DSSzO20mvU6+BcHpbvlhFuWqiZpjI6mqD9AS1r00UnnBw1Wmda3g8kk3u\nZuumGXft0M01kLPpEJ3SdmbaT12MmsuOn1/Cd549id1T4xh3M5hkA03O2Rsn/XQ2AEpKmwnTtLBc\noMLTNZKmtGUyOrZvGYUmg3kLLm2AI/M3VHjclDagfqD0evqgucITruHJhRSe8dEcRoYyXm78+EgO\nk5tHUDWspgN03CJEMC0bmYxW1wS1VDECCxTHtKDhW3lcni/g3/y372OvK9E2SmmTQabbN0U4GACc\nSeOaXb5xgSzEhocyePfVk94Eoh7nUr6MyYkRr/5Bgjepm9jt7sBdDllTq+llEgyvFqt47tBF7J7a\n5DVgDC8wRZE7eGzWu4bkupAANErhkcFWvsdWGo+K6067Kpvv0OYvQLJeDU/9a5qWjSV30SoTZK0F\nl7ZyQ4XHeW44pc153fpjURenlmn7ao9l43987WVUayYK7oJClOJeGBfE2VIXyzUUSjUv7dWr4fFS\n2oLmI6LChXey5TO224dH1B31vQWZyIcbKjzRLm1L+Qo+cs/38NBTJ7zfybXc6DpRP0dcHY83T2hB\nl7YrdziLzIYBj3u/SSqg2ng0bN3cjJePzOCtMwstPadVwscZJnFKW4RpwdhI1lMW5TtOuinQisLz\nwuHLuDBbwH7XSVNdcKtobbRQkPG6URDdDKnfAZJvHMjjwqm/viW1r/BYlu01trRtYDiXRS6rd0Xh\nkcNVi83jWC5UsWksF9hUGs5loOtaWwGj+r2rSteyciyFkoFMRvfGiyQBj6WovFGEVbVOLfnDn13K\nAZoGPG42R1T9T7uonyWs4ojCM5TV686Nadl44BsHYdnAr/zL92PT2BBK5Zr3GhJEvnnaNywAnHFP\n05zvcqVQhW37axEqPB1iJHRpk0lM5rJmDaDCKW3BGp7ogEd2TsPRefi15OKfGMt5u2y5UJDhBzzB\nhYGmaZiaHPUCsk1jOa/DtGqbHIUoPNXYGh5X4Ql12i5VTC+YA6A05ms+kRw8NoulfAUvui5aEiRE\nTYKyII6TVtslmcJjQFPqlIZz2cDjJVd8cvMwtm8Zxa/83E34T3f+EAB4jWDf4aamTSvNRw3T8nZE\nAN+s4cmXz6NmWPjJf3yt9x2HB275vmYWS14QJedIFq1yfaufUSZqWZSXq85OizehJlB42l3ISxG8\n5PED/k5v1MSUL1S9zxCl8DSbzCpJFB7VltpVV6Ouv8COomV5E8WN79yOc9N5fOW7b3qLoV4qPHG2\n1DJZ7nQDntGYlDYZp6Yimo9Wa6a/6dLmpH9RDXhiFB5VMRZ8hSfapW1msYhK1cTFOf/15fpotKhU\ng2FRhMKo84Sa0XbFdudchgOeqA2GYkg5abXxqGFauPfPXsQD33gt8XOasZgv153HKFtqlYYpbcrv\n6myp3Q0hQe61xAqPm1Id9dphZJPp5AUntXv7ljiFp/VsBHHVbNcAoFIz8dYZP50rscLjqRAxCs8u\nv4YHcMZ8WVtkdA3jIzlvs6ITZC5RU5GisCwb+UIFW8aDwaamxRs4NX/v6JQ21do4X6yGao2TKDzR\nCkfUewFdUHhC147UbTfbaJe1RFi57wS16Xf481+cXcXocBZTk2N11933nzuFY+eW8BM//Da8//op\njI1kUXTtrQFF4Tk1j9HhLK511z+apiGX0VEzTK9+Rzb2qfB0SLNJ2euv4E5imrdYb/y6vrOapLQp\nLm0Rz9XdlDYgQgoPvZbcqHIRABGmBe6FGa7hAYCprf7zxkdzXv7yYoMBqlw1vIk5vobH9hqjAs5A\nYppOjvrYsKrwOP8mmUhkB0EmKKnhiVpQy83YdYUnpoZny6YhTIwN4dx0HpWaieFcxvuOwruTK4WK\nkyvuTug/9aPvwM3XTwHwC8W3Tgxj2+bhgMIj51wm8lrNhG3bePj5M8joGv7prVf75zs06Ko7768e\nm3We7z4mbMEaXIA5368systVI3BOG/WhkV29dhfy0nNAzekeaqDwqEG6DKTqAiFpSlvU4swzLQjY\nUrvBV8SEFu5tId/H//HTN2L31Cb87dMn8NaZRei65jVX643C47pCjmQDioVqSQ34LkbhlDZP4dlS\nH/CoE3W7pgUXZv2APzzWyHfcqIZH3WRQkdeKamDcaFGpXkNxvXg8RV8PurRduWPcfV4jhcc5rkIo\nZTCjtxbwnL2cR7VmRjrntcP8cgm/+ImH8Y3HjwV+nzSlLUrhi+vDUzMsLK9WA6llsnnUikvZqHtd\nGBHnrVIzPYMKqVmVxbkaaKloWuuNR8OKXascPbMIw7Q8N8+kCo8avKubiedn8ti6adjbLPQCUsV8\nRTJMSl1ReNyAp4nCs1KowrSAzZuG6v42NpJtM6VNVfn886YeS6FcQzajx6a02bZTfzmv3EdeHV+c\naUE44Om2wjOUVOHpfkqbOt+rQY1t27g0X8SVO8YxMhx0VFxYKeMr33sT4yNZ/PufvhGAM16UKkYg\nq8QwLVyYLeCdu7cEMimyWR01w/LmdxlH6dLWIUlS2tRCVPm3eUqbBEpuSlvGz8eOumYzuj+BxCk8\ngiy6dioBj6/whFLachEBz6S/mBwfyXlyfqMdmQXFjaniLrrDWLbbeFRReMoROff+OYx9Ow+xt5S3\na1TDU12jgCdK4clldGiahmuumMCluQLyxVogzSY8Wcu5VVMnhocyyGY0L9AcHcli17bxQMGnFLmL\nClepmTh+fgmnL63gf7vxCmydGPYVHjM+4DkYCnhGh4OLFdv2JypZtKppV5UGuzwqMrGUK+19BzLJ\nqPbecZ8PCO7c+aYF8QrPvkMX8YkvPo///NkncPz8kieRR9YbRKS0eYvqqJTKUM64vPf4aA53/+sf\nhq5ryBerGB/JeQv2XtXwaJqGyYmRwDUy6+Z/+yltocajoZS2KIVHTeNpt4ZHHNqA+sWunzpar/Dk\nMsGxLvydyjUZ6KtVC+aTR6EGw3F1Cb4KH0xpmxgbwvhoDit1pgX+//s1H8H+NpkWbalPuHViK4Vq\n03O/mC/jvz7wLE5dXI59zMJKGZYNXJoLmaiEjjNMo8aj6v2ofj8yPqoBj8xzSe8R07S8dLioYOub\njx/Dxz73LI6cWQgYuYTfV0XXWut9Uq2Z3gKxXYXn0Amnfkc2xJLX8AQDSMA5d9MLRU/dAXzF2rTs\ngDI5PpJtO0gTnDpG5+flJgGPjD0yp6uMjeS8Rb9ptufUp/6sWhvbthP0xaW0HTu3hP/19Vfx7adP\ner+TMSA2pa1O4emuaYHcC83WnaLSRwU833n2JF49mqynk4o6n6qff2GljGrNxJU7xh1Hxaq/LvzG\n48dQLBv4d//svd5mwuhwFrbt3+uGaXvnLbyBlcvqjoGRO7+LUt7J2q7qbnhIL7duMlgBj7Jj56W0\nNbn/olLa5KaJreHxFJ7QQOkpPM6/vsIz7j3Ed2kLpbRl6ndCp5TF5KaxnHdBNkppm18ODhjhXX7J\nBc7ouj/hmXZd01H5rEAy21jVBQRoXMMjx9TtlLZohcf5jFfvmoBlO8WC6iLMV3icY5Ebd+tmP+DR\nNC2QFjI2nMOu7WOwLNvbgfSL3J3n1QwLj7zgmBX85D++FoDf9yJ8LS8sl5HRNezYOorXjs3Csmwl\n4KlfMBqhIEt28Cs1M7Cz0qj52oqn8LT3Hch1tn1LfWpLlKrSTOEJn5M/+dvDeOnNaZy6uILXjs0q\nCk/8brQa8Aw3SK9TLVFN0y/0zGUyuP6aSfzCHdcDcIxCvOujRzU8uq5h2+YRrBSq3mfxU9qc7z2q\n8aim+YGQ13xU2QlVdybbLbi/MKemtIVtqQ1klcWKStiCP7xQ9XsuqTuWrsLTIEAwWqjhcRqP+r8f\nyunYsWWkocKz6pkW+H2OgGCtRRLEGANovtg8dHwOh0/MRzbcFeR6DqfXeG5ysbbU8Y1HzZiUNrmP\nJzcHN4SA1mp45NqM+jqlueFbZxbr5rptMSltrdbwqOeq3RqewyfmoWnAD7kBT+IaHvW6dq+vi7Or\nsG2/fgfwFR7VXt9RfHOouOnL7aLeK81S2mSdE1WPNzrspD9VaiZ+7y8PBNovNMIMuLSp11fwWJwa\nnrhUcGc8U+/1ZtbL8h2Jc2GtQ5e2cGph0hoeSWkrV43AdbuUr+Dz3zqEPf9wNO6psajnR53XZSPk\nKjfgsWz/PEgd5k/ccrX3eFGuJfhUNwXD47mT0mZ595OsWdut4Tl1cRm/+MlH8LHPPYuPfe7Zhj3V\n2qFvAp5m+cGmbQdSWryUtiZCtzoBAkGFJ2r81LXkNTwS7QdS2rxdtWDh4lAzhWc0h60JFJ75UJpE\neKHm27IqC3DT8lNQhv1BzavhaXLzrhSqdQPVcC6DoWx0t/tWFJ79b1xuuLupYkbW8Dif4Wpl52wk\nQuERCTjsQCRsUgOekSyucIPYaTetTfLBJe2wWjPx8pFpbB4fws037ARQb1ghzK+UMbl5BDdfP4V8\nsYaTF5e96yKqq7hMdHU1PBUz8H3H3TOrxaqn2rW7EzO3VIKua9iqnCev8WhEvZ16fchCLE7hqdRM\nzC6WvAVZoWxEpjkJarqHdywNangCk63lD+bZrPP8O++4Hv/7D16BH3nflb4C2GZg2Am27WzcyOJS\n7nu16SjgBzYyyZTKBkaGMt75EBVO3QxRF3mNFk62beMvvvcmXnKLxtXfX5xdxVVeCkO9whPl0AbU\nT5r1KW31wa3cnw1reBK4tKnXirpBlstmsH3rKCo1O7CRFW1aILUxfkpb1OeI48R5fzxTlc8o5HPE\nfR7AX0CHF+7hBqlhwo6hgdcMpLT550CuoaiUtlZqeIZyOnStPqXNtm0ccwPCl9+agW3743UuqwfG\nYRUdrfXhCfZVav3ertZMHDmzgLdfudnbiGzVpQ3wFYbz02JJ7c9T6vcjz1FriDspeI9LI4tC7seh\nbH3AMzaShWU5Y0G+WKtzLo2jpgQ5tQbHkg2l3qssuPeO+v2ZzUwLFDUf6LyGR8YK2eD1FJ4GL2ua\nltcWwraDY6fUqq22sdCPU3gk4Lly+3jdBt5KoYqhrB5YE8lGzkJEI9FwrbmktMnrSQp4eD5Yylea\n9pDMF6u4909fRL5Yxa5tY149dTfpm4CnWa5lWOEBpI9M49dVc7oB5waTwSVqx0hrUMMTrgeSIEYN\neMLNPuNMC4BQDU/ClDaZkOSGDu+8qgXeWSW9LkrhSeopfzaiW/NQLoMhVz4N4wU8TQbsFw5fwie/\n9AL+6G+SFfeqA6J8bvmMVys7Z4GAJzQA+DuYwYBnPBTwyHd62R3gV0vOAKUWueeLNezYMuotiHxX\npGC6yOJKGds3j+Cmdzs7hQePzjZUeMLd333TAiNRwKNeP+0qF/MrZWybGA6lkcVbQaupCr5pQXRa\ng/Q3ksavxVIttt4DCDo0Cp7CE5XSprxvQOFxjz+b0fGbv/SP8Uv//MbeKjyAp/AAwMKKM2FIMCjX\n6Ki7SeEpZ6aFrKIYbx4fQi6rB3p4BRWe+LF1pVDF1x89ir975mTg90v5CoplA9deuRm6rtWdH8fx\nsX5MA/x7UgiPL1GOfBJEN3RpU00LYgIEP4XZH6cBZ1yWejQ1MDQiAp6iW1sg14uaetQM07QCGzhR\nav1SvoLf+cp+zCwUseJupDTa6Qyn1XrHW6656bjR03yjQC0wRinBT9T42E4NTyajI5vN1KW0XZor\neOf58HEnZew9bt+PyYnhyN5JgKPwWLajsp29vIJX3pppuMBSA56wMpaEo2cXUTMs/OB1O/y63MQu\nbfUpbWGHNiCo8KimBbIgDa8/zk3n6zY841Dvo2aLSvlew60zAH9xLIFO0poYNYhWj2Ux5CSZyejI\nSUAd2kiTazFQj9jMtMA9vk1uA9dGda5JkPe+ynV5bJTSVqoY+M+ffQJf+e6bgY07dYPlxIVgW4RW\nCG4a+j+L06+T0uaaNNX8gGfz+FDgvpI67oWIObtO4XFT2mTM9gOe4LX5iS/uw8c+92zssZuWjc/8\nxUuYXijiFz5wPW676SoA7aebxtE/AU/CGh6VJH1k6lLasrrrTx+tDem65l0QdQFP6LE3X78TN1w7\niR+4dtL7XTbrBxmAvziMSv1QFZ5NisLTyLRg3r1IZee1TuFRdjhlwjNM27tAAyltCWt4zk47g7Va\nqzSUy2B4KFO3+LVtu6nCUyzX8OIbl/EHe14B4LvXNEOduFXTAgC45go14FFS2kL55zL4bw3Zn8oA\nCTiDvOSqilObDFCy21epmihVjECgpLu7VeoiKl+swjBtbNsygve/27GtfvXYrLdQjwp4vBqecK+Y\najClrRKze6XuoiVZpDx/+BIefdVfpFmWjYXlkpdKJ0iAH+Wo6NU/DWeUlLZohUd2pN71NifgKZRr\nTRSeqBqe+OAr3GPEU3gy9Yup8PWxnkgNjx/w+ArPjq1+ID0c6sNjmlbgs2iahh1bRjEfY1pg2fGL\ndVlEh9VsSYXYPbUJI0OZugmuUjUj63eA+rGuroYn4rv2angaKjy2d7/FmRaojUfV+WIom/F68aiL\nRnUBp/ZtUa2e1eLyZpybWUXVsLxgMGos33foIvYevIi9r13EitvbQnpFRSEL6PAiqVgyYpuOOscd\nv1A3lX5j6rUhi6DtETU8rx6dxff2nW4a+BmmhazrvhWe1o+d89P9ZDF6zRUT2D01jhvfuT32NXXN\nyUQ4fWkFX3joMO75wj7c/QdPxaa5qalI7fS0OeyaKrzvuu11DRiboV4nsogP9+ABfJc20wzW8IxF\npNSXqwb+y/98Gr/yu495rSEaoY69zdIq5X6MMlYStUn6yiS1uI+r4alTeDJ+49GwGiNzdSlC4Ynb\n7KtTeDoMeGQNeOWUs95qlNJ29MwiTl1cwbeeOh74vTq2ivrbTo2WGhCqGQkX55SAJ7SBly9UsDnk\nviffqRqYl+MCnkzGUXhqovAMuY/3j+XyfAHHzy9jeqEYOzZ89Xtv4pWjs7j1Pbvwr3/yBzDmjq+d\n1qqFiR8NU0ajYlXAdx5T0bXmlsrhPjyqFWRcDU+caUHYAOHW9+zCre/ZFXhMvS21W1wfMZhs3zLi\nqVTjoznksjqGhzINa3hkh+TK7eM4dm4p0nMdEA91zWu0GnZ2ks+qfq44zlxyFJ5bf2AnvvvcaQBu\nSlsuU1enY1q2kk7lpuBUDLxxat7LVz92fskbMMZHc8gXqyiUarG56II6cKqmBYATFIy5DbWiTQvc\nGp4I0wIgmNI2Opz10okkpW3Z3VGWvjTyOuF0kmxGDyyi1AXE5MQI3n7lZrxxagHvu84JfkYjFixy\nL8iidcumYei6Vu/SlkDhUXfTw/eP8PfPnsKrx/L4zxWnMe1yoQLDtL3+LoKkPETtmi3my9A04Irt\n494AXI3pw3NxVgIex/2oWPY/V2NbarWGJz69LugQVK/wqPS8hkfTFXdGp/h0MV/xrg/AGTuyGd07\nR4Zp1dXJ7Ng6isMn51AzLOSyet3i2DQtZPT6zy+pFeGARhzadk+NuwFPSOGpGoEeTSphxSHsvBdl\nWiDBe6N5wLAsjI9kYZpW3U6xEGdLncvp2O6ZO/jPjUppK5ZrAeezVlLaxLDg/e+ewguvX44cyy+5\nY8pSvoJ8wVV4GqW0hVJcveMt17wFSBSNG4/aGMo532sg4FmOr+H53r7TAIC9By/g//nIrXWbRgC8\nnjLZrNNfxQzVUEjAs3l8yFPptk4M4z/+y5ugfeCW2M+ia47Co56DxXwF+WLNa5Cp0qnCc8hVn977\nju1+E+MWbakB//o6N5PH8FAmYAKjKjzyc0bXMDZar/AcO7vkrUd+58/34w//y0/gHVdtiT2GVmp4\nyg0UHlkveApPk/YhQpxLWzjFM5PRY00x5LFqTyIZS2RcD481cnzSf6/TgEeuI1/hibelFvVG/jQ8\nlPE2RgVJaStVDGdMjlFno1Dn03BK21Aug22bRwL1djXDRKliYmI8uK6KWnN4Ck/oeLJZLZDSaTly\nMgAAIABJREFUNjqcxVBWD8wXLyr1h8Vyrc784tmDF/CNx4/hyh3j+C8fucU15sh5j+8mA6/wNE9p\ns73HAsHdusg+PGpKW8gaMkkOsafwSA1PTdyMouxbM5icGMZQ1rnpHcem4YZ533LRi4VkeKES7leU\nzehuDxmp4YlQeBpM5LZt4+i5JWga8MNurYp8nv+fvPcMs+y6yoTfE26supVTd3Xuro5Sp2pJVrBl\nSzJywoDHHksGmwFmYAAPGPMQhmFs+D4bGOAxDzwehvANw2cY22CwjW2wBZYsywpWKEmtVufcXTmH\nm+8J34991j777LPPuedWt/xhz/rTXVU3nLDP2mut913vyqSMEMogBuHVuo3HXriGh3/9n/Abf/5t\n/P03LuDC+DL2bOnGe+4fwe/87D1449FNAHyaU5ypZGzJYWiaxufxZJWiBez1i6ssMCe1NTKZ0tbT\nmYVp6Jj2EB7aNIjqtrxWDb0PkGTPIXDiveryoZF+1Bs231DzKtECJ0hhac8zNbFaI9jD07Ac5b0T\nK3q1ho3Lkyt41y9/KdSnQUZJA302HXOfhPDE9vCsVtHRlkZ7Ls0abh0XDcv2q3ciwuPd650CwhNH\nabPtoEIjO5bovoIAwuM1ZOoalAmfTAH4Tprrsl47EsJYXK1ymo6I/gIMOaMqnGW7vHBD1tuVhev6\nCTYFeUSHkyuuH/3zZ/D1567yNSar+fkITwGZtBmgpzqOe2MIjyRaIKLCcZQZChAGevKYWSxHKFSy\nf3Ut2MOTDlDaBIRHWMskWlCqBpETQw8WseLsqifuQn19ywpfPuWJQSwXaxzhiUKsxO8te0jof/7j\nJ/HES+MoVRqRktTsuNW9EewzHeXU+KUYlTaAFdqOn5/Hh/7gcaXKEgXapq57CE/wmp2/vgRdA+45\ntJH/rrM9mspGpulMlpr8AwW61O8mW1EIpFoNqhqWgzNXl7B1qIDO9oyvItbi4FGA+SfHcTExW8Rw\nf3sghqFzcASVNkPX+Z4gJmqnLjPEiQQUmvW9iut6uViPLWpSMVClJCtT2taF8ATodcECgKlrkfPd\nqFig6uEB4mewtXOE50ZV2jyEx2PUkM9T7bsXJ4L3hAaiU+xVrDT43ge03qMlngvFga7rYmq+hI19\nbdA0LUA/pYJCCOHJhH0GzXiTC2kp02CUNu/v2TQbSCzGnaLgSlFCqq9OreIPP/sSsmkD/+XHbuf3\nhdZVq9S+M02GOX9vJTyhHh6taf9JWKVNmJOjeCvNxQDCQ6Pk5Ell4nRaQJBvVVSXAeANRzbhLsH5\nd7VnsFKsRSYhtOijBn9SECEmPLbtoqIYFJikh+f5UzO4cH0Zo3sHMSzA8emUwSUQRatJjXUvn5uD\n7bh45+t34Dd/8k589mNvw+/+p9fjA2/bjwM7ejHUyxyJOPMmysSqFQVIYoWHHEwmpUJ4iNJWRWdb\nJlRZoYoQwB5GQ9cw0J3jjp4SHkJ+KDCQm2xNj/NKxkUSvACCNqxXL7GER6nSJvXwkHxytW6HKu2q\n5EOkDVTrLOFxXOBVT2ZVNkJsyIkRNUoeApiKoB4A7Hp0F7LCkEwL9YbDq5XiNaGBlhv62pDLGChX\nLFSI5qQKYhXoVDoVjTYFZald1vMS8fz9/4vwsMCcaJKLK9WQYAFZNmPyZ5ghPMHr0SfN4qGNmjY7\nMZGcXSzjxTOzeOqVKb5BVaTCCd2jjQqERx6aK1sY4YkXLYgKjmSzbBemoWGwJ49y1Qr1tAB+ICLL\nUqdTBkcs58UeHgrQDQ2lSgOWzaqZaoSnecBLvRq37mT0LBWljXzdcrHGe3eSIDyOyyr9r15cwL88\new2240ZKUgPxstSW7fC1L57X4moV2bShpD4DwCd/6U340bfvx9JqFb/x58+E9x/vWA2DDS2U5/1c\nnFjBlqEOjGz2aeBdbWGkSDY2eNS/vxSAzkUkPDciWnD++hLqDZujrFFzYqJMlqWeXSqjbjmBPlMg\nmJCKYhsqhIeU7d40ygqEc00axOUZaHHUIV+0IJrS1moPj6VAeOoNG6WqFUAAGMKjnu9Gz46YsIoJ\ntMpn32xKW6naQDZtYINHce/00EQlwjO+grasiYO72LohBI7iyMtSQtRqsB9AeLxrtVKso1KzhIRM\nlfAEEReVyElkD4+hw3X94n8mbQT2g2KlwWdqsZ+Dfuyvvnoa1bqNDz10FFuHOvjvqaDU6oDdj/7Z\nM7F//55JeGSVNoBEC1qjtAURnvDrA7LUTVTaVMZoZHqouU6F8ADAT7zzFvzi+3wov7sjC9txI5tY\nyeH4ogXqHh5DGLTasARKm3AczRAey3bwF18+CV3X8GPv2B+A49MpRr8TnTUQfCirdT8o+eG37MXR\nPQMBhAkAdyRJEB6RG03nLfYy+AiPQrSgQaIFNSUVQ1ZpAxg9ixzKcrEGTfMVsSgBkhGelKmjITh7\nOeE5sKMXhq7xDUEFL4s9PIT+ZdImanWLV+NoDap6WJaFZKxWt/k9mI5Q2KF+IrpG81ySWu7hUScZ\n1bqFctVCdyHjJzxVC3XL8eVBJYSntzOLbNpEPptCUUR4VEMSHUeR8EQPQQ0MHnUcWJaDlKJ/B1h/\nD89KsYaf+d1kfPooc1wXmqbxxHJprRaSpCZLmz59lNHTwpQ2wE94yH8Q5Skw7dxbH6ulmoDwhHt4\n2nMpdLSlkfXWHvlaSo6ifFozhKci9fDUIqiPsjFaps5R1hlFkcSvlAf9tGnoygGtDctGytTRlkuh\nVGnwjV8MCviekYDSNj5bRGd7GsMDBehaWLTAdV3u65bXaly0oFRtRH6+mBAS8kZKZ3E04KjBo45H\nOyZaaECW2lOUFIt6t+zsxcFdffitn74b6ZSBd983gjffsRWlqhWaDUQBqWnorPgjfPb4zBpqdRsj\nm7t4z2VHWxrpiHUkGh0O+U3qYZ1ZVAf+N0Jpo/k7t3gJDxciShg8y7LUvH9HUBIFggmpv279Hp6K\nd9y24+LM1UUM97dh5zBDxReWo2nvQBjVi+vjiaO05SVKW1yPneu6+JVPfgt/8vlXgkp13v9pX9rY\n74/xYD084X5M13U5GlSp2bzv2mmW8IQQnhvt4WEo6p6tPfjov38d3n73dgBhn1auNjA5X8TOTV34\nufcewS88fISL8pBPIcob0elbTXhEhIf2AlGhDfBp57XGOhOeEKWN/UyFsWzaYAiP9/oXz8zAdlz+\nmTLCM7NYRj5r4m6hqA/4fqsV9LVhOU2LF99FCU98wKFCeHRNCdKE3gf4qIwpKGmpVdr8IDTcw+O9\npsl3MmULr7nOe+BUzkRlzaSpLYs1LBNtK6qHRzco4WFzh7hogbDY/R4e9bE88swVTMwV8eAdW7Fl\nqAPZtMkfnkzKrwKKEosBSlvNQrHcCCSRshHCM5Ug4ZHVz9j5CQiPt4mK55gWEB4KzFXD7dqkHh7A\np6/NLJaxvMYoW+kUkwOm6yxTShilzb8Gi9I8m1zGxF5PlUj8LtFElTY6rozHt6fAnBy6KuAnhGew\nNw/Ldrjji5IU9ecmEaUtPHQUEBAe6Vnlw1w7ssKz00DD8ivl9J56w8b8coVXpPLZFJaFfgxV5cyy\nYxAepWhBsIeH9bUkQ3hOXV7gAU+cnbq8gOszRT5Idj3GKG3s2cikDSyuRiM8ok+xHDckwEAN+Rcn\nVvAff+dRPD42DgDoame/F68JBeGrpTqvyFXrNn+mbNvB9EIJwwPt0DQN2TSb7dCQ1kkugtLWVKWN\nU9rCDcjNZKkZwkPBbng9V3kyZgYpbSkDbVkTKVMLDG+mnqe2LEt4fKlnEeHxqUdxVm/YmFkoYdNA\nAYauobM9E0J4ltdqPMBcKdaw5lHaXDdaqla8d5TwNJOkBsRELXhN6Wd6hmzhvi8XayH/WMin8fGf\nvhu37vL7yob72wPHwz9bRHhMPTCH5/z1JQBMnZGa9+XEPsp8gQX2gc0QHkrkTUNDucXA8tWLrGJN\nIgrkexIrlIkIT8PmqJ+M8IhyzCLCQz6fUJlr06soVy3s397L+9BaQXiA+D4eX5JYgfDkgv3McQnE\n3FIFpy4v4tTlBSVqS/sS3TuAPVt+IU2gl1YaoaZ6uSCgpDPfbISn4guYHNs3yD9X9gWXJ1fhusCO\n4U4M9uRx37EtfuLKEx6G8NBzVKo2MD6bXHlPLDRSHDC14LMlgKQIT7hIQj45TGnzEp5KHbrGfLuI\n8BCd7Z5Dw97rgs/aSrGGTgWC66/x5M+m3L6hsu+ehKepLLWj7uFpsp7FuTSAqKLmRs7hyXhBbRSl\nrVnGE0B46vEIj2xEb4lyUA2bbdB8QnxUD484d8gSeniEICWO0laqNPDpfz6LXMbE+x7cy39PQXA6\nZWCjt+lREzoQ7uFZK9fRnktF0gAHEyA8DcsOIUlkIjXt8O4BfOBt+/CW123jvzMNHaahodawIxXa\nAF+lLW36ct5DwrEtr9V4348I/bfnggEHQ9QUCI+Alhzy5KmBCJU2UmWq+gmPTGkjJ6ZMeNZqMA2d\nBy7UE6aqiAMCwiP18MiUtkiu9aovBEFOfq3cgOuGFWGmF0pwXb8JtC1rBhy5ekiiC11CNOgeNOvh\ncRxH2eRPJiI8V6dW8V//5Gn85v/z7aaVJ6ralmLUtZoZzeHRNA09hSyWVqt88nx/TzAQJPERgIoe\naoTna8+wIsX+7T34+fce4etE1Ti8WqoHKnK0ocwslWHZLg9qCZUVB58C4ancZKE5PBLCwCltAtWF\nrNngUUPXQwqKotGGW8inQj08mqahI28EBrTyhMdDeFTDPI2IOSGyTc2X4Li+Eld3IRvqWRALO0tr\nNc6dB6KlqcWERVa0jKW0RfQe0bPozxRhPy8Xa3BdKAtCsg1z3x88HrpGpq6HKG0kWDCyuQv5bAof\nfM9h/Ojb9zf9LsAvWNLnU6EsqoeHEsK+rhzKNaspE0Q8/tNXFrF5sMD3CU3zFOcSBs+ifwwgPAMS\nwqNQaTMUg89PeZSh/dt70JY1kcsYTWee0DHQ/Ji4WTxcllo1h0fan+KUCun+1ht2qOgE+IJL5PsB\n9mypfLksSqJCQFUqsHTevCCY8J6tlcN9Tq7rhgRMooa1E3pDfalAuHB+cXwFuYyBEQ/5KZYb+JVP\nPok/+tuXEx2jWNyj/4sKbQACc+WiEh5VzFHhSW9wDRDiUyw3kEmbXgHM5MptY2dm0d+dwy0ehVdM\neBzHxWqprhRWiZJejzMZgFDZuhOe3/7t38ZDDz2Ehx9+GCdOnAj87emnn8Z73vMePPTQQ/jjP/7j\nRO+JM9PQEvTwhJuOk/TwhChtAm82SqWNZvHIQQ+9WmuS8TB1mmD1UuVMVOZLU6sh64bFZnBETb8W\nHSfgIQ6Oq5zDwx9eRSLxuUfPYbVUx3vuHwkkCO964y782wd2wzR07sCpgsXON0g7K1Ya3OmqLJs2\n0dORie3h+eiffRsf+dNnlHQnsdJt6Brec//ugHw2wJxAvWEHAnPZqIojOjeqJF+fWUOpavHrIKJ1\nKkqbGBjNLpWRTRuBJmiSpwYieni8tSkiPNk0GwBHVWDqzVBRsRZXq+gqZHhyK06sVgXyHOEhSpu3\nmcqBDzm/MNfan91BTp422EzKhKFrHOGZkhx0Xrp+6h4eJ0RnVVUFyYIN+h7CE6GGQ8/RaqmO3/3r\nF1D3FGmePB5PVaMgRtVHktRc+M9gd0cGy8UaT/z7u+SER4ftrQvbcUM9aJTwVGoWNA345fcfwwO3\nb1HOEKF7U65aoX4vAJiY9ft3AP8aUUJEAZJMT+XHKlPamszhEddwXFBpe+uAz8hS+AxKGtpzaUml\njZ1DR87AaqnuiyR4a6Mtl0LdcnhRJK/wk80obXJg29WRQaUWVGkSCzuy3yXFNtlU/W9kiShtjpzw\nsM+j4MjmASkhtc17auj5lSlt9FkM4WHIIJ3n+evLMA0d2zYwLv+Dr9uKw7sHkMR0KeHp6cggkzYw\n24TSNtCdh+smC5YA4IKnfEoBHJmsvhln8gya8dkidC1I5WKfKSI8fu9tm9TDQzTXTYMFJkHflWuK\nCtB1oll/UUgYICA8StGC4DMedw0IwatbTiAxomNZ4sI/uUCPcUqB1svIaKVqhZItVbGLU9q8PqEk\nogUvnJ7B+/7rV0Nofa1hh/rk/DaA4GdcmWRKtjs2+n0q4jzHat3CxOwadgx38eLqzGIZq6W6UthE\nZUFKm3o/vdk9PJzSVmnwz6a44sUzsyhVGrh9/xDvyxJRakpSO9sV8Rbv4WkB4XmtEp7nn38eV69e\nxWc/+1l87GMfw8c//vHA3z/+8Y/jk5/8JD7zmc/gqaeewsWLF5u+J85SptG8h0eh0pZEltrvu/Ef\nMCA47Ev8XPp/PmuGKW0OvSb2K3lwAjTv4ZGNgnF5IyFjFUmNV1flKoc8lZ7mwqi46fTwypdwZrGM\nf3jiEvq7c3jnG3YG/nbv0U14/1v3AYCQ8PibcHAasIViuc6dT5QN9rRhbrmirKDWGzZOXprH9Zm1\nADeaLGronmgkDxk1dBTwER6RDkfo07lrzJETPUhMeGR4OOX1TAGigkp7AOHavaWbq2ep4OWGzaon\nlu07W1o/JI9NDkZ2+sVyHQsrVWweaOcBjThgjDbPwPd5jpSLFqxU0dmeDtEw6Wd5zVE1TuzhWfUC\n6VRK9/qa2DWhCjdx8OUKtVKlLU60QEVpkxCjWITHu64vnJ7Btek13HVwAzQN+Ppz15SvJ6MkX27S\nbMWohwdgyaXrMtpDT0dGOfGaSbH6Tfai0fBRgA1ypH4VUxH0itVT0c/QhkKS1Jv6g6qHtaQITzPR\nApKl9n4vimBEBVSMw8+ed5FqKhshVu35lDSHhx1TR54dM6GYRHck2gUpqInPpaimFWfycEnVIOmp\neXbMhFKJFoXwWAJiLPfhxc3h0XUNuhamtJGflYcocjS60BzhGeptg66FKW3ko9ngVl/drGE5uDy5\nim0bOyLppXGm6cFjNwwdA925WITHNHRepFJVks9cXQwVFonOeuuOvsDvU6aemB4VHDxq4/rMGgZ7\n20LnLfbwiNL78hweiiHoOeztzGGt3Iil+NCxHhzpg64BT74cXcCpxiE8kn92nGiUJ4DwCM8K3TMq\nrojiNoY3L9A0tEDxaknqkS1Xw5Q2FcLDKW1Z9vwn6RF54iVG/6V5g2S0ZvKKNgC5iEPHK9LARQGf\nK1OrcFxg53AnP6fr3vc17OZJGSCLFvg9POJQZbEQTgqQ4Tk8CpW2avTgUYCtJ/L3WS92oet2+4Eh\njqiJCQz1jakSnly2dUrba4bwPPPMM3jggQcAADt37sTq6ipKJbYJXr9+HV1dXRgcHISmabj33nvx\nzDPPxL6nmTFn0qyHx4lQaYv/bF9Zjf2sakAVgyn6DobwRFDamiA8hKoAvrNKK6onKtuztRv5rIkv\nfPOCkubVsJjiFJfTjRItoITHZPQ6UVaQLOrh/dQ/noJlO/jAW/cFFM9ko409kPAoeLiykplsG/ra\n4DiucvOamCvCcdkDrkZ4ml/XdIpJOnPJVcWGTiptonMb8gKrM1e9hKcQprSFZKk9hMd1XW9TsgOc\nZTrmI3sGkDZ1Jfpl246v0JYLJjyrxWDVRg74L3szk7Zv7OQOSkx4ZhRrij6jVrfhui4WViro7Qhz\n6zvb0+hsT+PctaVAoWFJ7OHxnDwlZmlvHgdtwER/9Ht4ggFb1BweGdGghG9RMY8lQKdw4hEecZM3\nDQ0/828O4dCufpy+shhALkVzXddHeG6Y0uYnPAC7B6qqN/UeEoVVXvc0fBQAn2IN+BU6lWgBEEQM\naEMRFdoACMUVy/uXijgJEZ5QoCKLFjRHeBxh+Gw+m0Ihn1ZT2njCkw7sFykp4SFaG/OnOkc1xr1z\nDwwejWj+l01GeIieLAbV5NP3CX18RB1djVBqExMW+VrKCKlshkQrA/zzoD2JAti4AEW2lMnkwScj\nER49ECxdnVqFZTucztOqcYTHS/5MXUd/dx7FSiMQ1JarDcwulVGsNNCeS/GCihxYPXl8Ar/0R9/C\nX3zpJAB2X8rVBh84qkJ4mlEaycSEYH65itVSPURnY58pCCiJKm3Z4OBzTov39uL+rrD4hmzkb4d6\n8ji8ZwBnry3xAFu2Wt2GpkX08KhEdRTPgeO4uDBOCU8Q4aFjoUJLl1AYIz+W9lgYZEQFHfZ8ULnW\n8OmS3nVTIjy8Z1rHyOYuTM6XAomhZTv41D+dwteeucKP+8WzswDCqrzyHkyma+HnsFRpQNeCjA2x\nh4cGju7c1Ml9C094ElMl1QjPUG+ex3I+pc3hiLGM8GTTRkh4yxeBCqu0kdFnUwz53KkZ5DIGbt3Z\ny69RMZDwBIVzRGPUTaOlocDy6ASVrSvhmZ+fR0+P75C7u7sxPz+v/FtPTw/m5uZi39PMklRPnBtV\naRMgVMBDeKTkQPx/PsMQHvHzk6U7QUpbHD9WZd2FLH7qhw6iUrPxiU+/GKYjeMEbZdmhOTwSamXq\nrKdENYdH4/Cs/x0XxpfxxMsT2LW5C284sin2WDva0uhoS0dS2ujSyYOoZKPEYlZRsaWhp7W6rdxw\n5Eq3ykg+mztcBWWDHlhRo749n0ZbLhXq/RGRj5AsteFfUwrG5YQHAD74nsP4xIfu5RLYolmKhIec\nDFXJeMIjFQpoPsP2jR0+KlT0Aym5Km4LvVHVOpMNrdbt0NBRgK2XW3b2YWGlGkAGxGGuPOHxjjOd\nMgL8d1lVJhHCoxAt6GhLo68rxwc9iibP4bG8vjeViSjAHbdsQGd7Bg/cvgUA8Ojz15XvWVqr8WDk\nhihtro8W93siBUf3DuBn330o9FpKIqI2JoCJbGgacOetfsLDKbxC0Cz2lawFeng8SttcsBGW1h5t\nOFwAJbNehCc4hyfQwxOxD1hCIA0w9HVmsRKmhZVZc20+YwZQffo/R3i8YNGymUobJSdE51PJUqsQ\nZtHGZ9eQMnV+L316sojwlGAaGnYJXH/qh4lEeKQAU7z3cT08dOzNEB66P7QWVIM8Vbaxrx3La7Wg\nbDDv4dH4mm1YNqc77V5nwiP38BiGhkHvOouo9Z9+4QQ++HuPYXG1grac6ScPQmB1cXwZf/CZlwAw\nf9iwbPz8Jx7Hf/7vT+H05QUM97eHWAByjOIIqIxs4v26Os32L7rHoul8XYkIj86r3zzhkVgihCLE\nKbXx+2AaeOAY+TM1al2tW0iZurLPVimqo3gOJueL/HjrDVsaDRCktAUQHm+/TJtGkNK2Sopu7LqV\nKxZ/1okxIvcvAz5CbJo6Do/0w3F8ulm1ZuFjf/EsPvfoefzvR84AYL03tD/KBW5a1zIioutayO8U\nPfq5eA1FtV/ap3YMd/Fn9rrna5ImPGI/Ur3hYK1cR7HSwIZef21lhN5uQngK0vOsaT6KmJOEFaIo\nbYC/D1ABrN6wcXTPIFKmwQvGYgGwWQEln021hvAkEC2IxrtbsLikIupvSZsEAcCxGyjVgbGxscjX\nNCwblXI58BrLslCpOrHvW1xijvb48ePIpXXMzbGA8OTJ05iarnjH6i+ks2fPorSQRqNWhu24ePa5\nMaS8mRcLi0wL/9LlS8ildax1d0Nl9VoFDcvGuXPnsLS8DaYBvPTSi0kuBQCgAy72b8nh1JVF/NFf\nP443HPB5oZVaHRnTwfmzpwEAE1MzgfOfXGQP7/zcHMbGxlCplGDZDuaXVmAaGl4WjmN+nl2bV189\nidlxtmAfPc6uz+g2I9Exd+WB6/MlPPvcCzANDWcvh6uu5eJy/D2aZwnTiVPnYK+xIJNe/+zL7Hgc\nF5idCw+dunjhAn9PlFmNKiq1Bs5fngAATF67gPry1cBrXNfF0Z1t2NxvB461kAVK3n66vDCFsbFV\nNOr+Bnv+7ClMZHynUC4xJ3b23HlcnqkB2Il6aT7y/OcnWcVITFvOnb+ImQnmVNZWFjA2NoaVZeYw\nF5bZ9V1dZlWp02fOA6Vx/t4XXmHXqLw8gYX5cAXwlTNXMNzmJwmiE714+Sr0KhtO6taLymPuSrOE\n6cuPjuHYCHO0V66zwsbVi2cwMV/zfsfUW5YWF2DbFkplC2NjY7g8uYhCTsfJV48DAJYXVwOfv7YW\n/t5avQ4deuj3ve0uzo7X8PiTz6GQ84PvS5d91IIFHy4qlVLsGgSA7d11jI2NIWO5yKQ0fO2Zi9g3\nUA4lW5emxYShjhdeeKHp4EQAKJw7x97j+Q3XBWrVKsbGxtCfdvCuu3qwb3MKJ14JN7EW19hzMPYS\nu25rqyuh87lrt44Dw724dukUKLSZm/X83akzKM2zjWd2MUhDIjtx8jSqSzlcnlhCZ97AyRPsu+bn\n2PP56qkzqK/kcO48e//kxHWMjS2EPme5FNyY6vUGP1bX9YsvpTI79zPj/jqdmVU/K5U6W6dra+y8\n01oNlu3gm089z5MYAJhfWkUmreOll17EtBdsGrrLP5Nee/zUBRQwy4b5VitYXmCUn0sT7PmZvH4F\nYzZbw7PeNTwlXEOAPTtXZ+u4PFPFpekappcaGOxKcR+7MMue1ZOnzyPbmAIAXJ9ZQUfe4N8HAGmw\n8z938RrGCuGBkuPjwd915HTULKBUdTB+7RKM6kToPWSu62CtGNw351dZkLG6zPz/yuoqxsbGcP4S\n+56J65cwFvOZZKbLfMGj33oBG3tYUDWx4O0/83Mo19g9e/Gl43jmBLsX9bUpjI0FC6Hyc6GykudX\nF5eY77pw4TzqZeZrnn7+FSwMsyTgwtVZXwjCqWN5kfnJ46+eQnkxh2LFxp89Mot6w4ahAzPzq3ji\n6RdQrlq45BWLhjrd0Bq0rDqqdT/W+KvH5uAC+MB9/ZCt3vCDuCvj7Fwra+F1PTnhSYyfv4i2LNtD\npqen8PJLJaRMDXOLbK3PzLFn7PSpE8imdJRW2Lp6/vhpWGvqJObCRfaaifGr2L8lj2xKwyPfvoT9\ng5WQP1teLaFPc3Hu3LnQPVD1VJ4/fxH5TDD+OS7s+7bjYmbW9wtz84sYGxvD+PQCNA2qaoRJAAAg\nAElEQVQ4f+YEHIvdu6VF9jfXtVAsWfwaXfCG2moNtm5On7uA2ipLQk2N3d/p2XmcO8d8MR3L9DR7\n39kzp5Fz2evGXr2C2aeew6e/OY+JhQY0jdFMH3/yObx4wT/uq+NTGBvzffuFSfb/5cXZwO8BF2tF\nf58aGxvD8iorZIj3mPbWmdkFFKsOTAOYHT+H1TI7LipqVqr1pnsTAExOLfH/r5UreOxb7D267R/L\n9BJ7/q5PTGN6roaUoeFVxX5i6B7Ka7ioAJj1YsFrVy9zvwcA83O+76nVmB9ZWvR/15+vYGxsjCf5\nkzML/Fhe8faJxblJjI2FC5M6LKwW4+N30U5fbj6rcV0Jz8DAQACdmZ2dRX9/P//b3Jzf3DUzM4OB\ngQGkUqnI9zSz9rYcVop1jI6ORr/obyZQKLQHXpP5yhwyaTP2ff/48rcBVHH0yGHksymcnjsNnFrD\nrpHdWGzMAKcvIJ1KodZgC2X//r0Y2dyNf3n1eVycnsTe/beiq5DB2NgYuru6gWsV7Ny5g/V8RHxv\n59PfgusCu3fvRuolHdk04s9NYXv21/HB3/sGvnliDd//Jl/T3f3bSXQU2jB69BDwlX9GodAd+OzC\ntSXga7PYsGEIo6MH8PnnnsK1uXlAS6M9pwde+8LVV4Dzl7Fv/37eSPqZJ5+AoWt411vuiJ3iTfb0\nxZdxbe4qNmzZjS1DHZirXwGwFHjNjq3DGB3dq3w/AMw3rgIvvoxNm7didHQzxsbG+HF+9fizAFjA\nZabzAIINfgf27w3IPKvs7599ChML89BSbQBKuOd1o8pm32PHwu/d8epzmF5iwcrhW/ZgdN8g/u7Z\nJzGxwBz6XXeMBuhWX3vlWZyfnMa27TswsToJVIA7R/fzmQ4qS39+Gg3bQs5DFbds3eahYnPYuW0T\nRkd34+z8GTx79iwqdQe6BuzetR1ff/k4Nm3ZhtGjPhL31088DtPQ8X1vvB3VJy7h8ROMstHVzpri\nYQafodVSHfhbFnz1D2zA4HA3gBns3bUZo6N7Qsc6uHkN//j8Y1hptPHP+esnHkc61cBdrzvGBuQ9\n/iT0VBuACjZtHMLE0hRKVQsHDx3G6mfGsX97L3/vVOUSHnvFFzjJ5vKhZ0X/4gza8tnQ788vnsXZ\n8TPI92zB6P4h/vvrxYuA5GB7ujojn8G2z0+jVLXw7rffxYOB+8aP46tPX4Hevhmj+wYDr59+6jIA\n5utcF9h/y6FEzwq84gv5DedzE8jn/fO9O+atXz/5PDA+iZ0jewHMoK+vJ5FPOb94Fjh5Brt2juDQ\n7n44jovy33xZ+dpNW7Zj/95BrH16HIdH+vnnT1cu4esvn8CmLdsxengY19YuAFjG/j27MHrLhvBp\nrlaBf/A3TV03+Gc1LBvOZ1gwnUqlMTo6irIxAYA9Tx2dXcrzWl6rAX83ib4edt4npk7i1LUL6N+4\nI/BsWV/+GroKGYyOjuLC4lngxCpymbS/3pbY4Lpsey+OHr0VzqfH0dVZwOjBPfjCM09jpcSCkUO3\n7uN+5czcGeDkWYyM7OaSsk8dn8Tv/82YQLPRcXBXH37ojbv4erFz0/jCM8+it38jRkdHUK42UP70\nOPZu78ORgyP43JNPse/avx0vXHgVbR09GB09HDr3E1MngZM+ij7Y1wFNY8Mojxw6wAccqizzD7NI\nZzKBa3p1ahX4ygw2DA1Av3wF+TzzCc9ePg5gDceO3MpnmsXZZPkSnjt3Ap19mzHqsQHariwCj8xi\neOMQ8y1XrmHf/gP44nPPI5M28OAbbw/RU+XnQmUX//bbwOwC2toKAKo4sG8v+gbLePT4GDp7NmJ0\ndAcA4C+/8Q0AbC8f6O3CyM4NePT4K9i4aRsO3roBv/bHT2G1bOMDb9uHJ16awOxSGZu2jgDw1+ub\n7tgb8KkAUPjGN1BtVPh1/KOvfA2Vmq1cq+7npkAlrFKd+ZNb9u7E6OjmwOvmG1eB51/Glq3bGBr4\n2Dw2bxrG6OhuFL4yD817br7w/FMAqrjjtlGGVrbN4kvPPoP2rkGljwaA6eplAEsY2bkTrzsyjDdd\nj/Zn2j8+glwmjd27dyvvQf4LMwH0Y9OWrYw+K7x27PoJAEso5NNYK9eRzbUDXiJfKDDf+6ePfB1d\n7Rpuu+0YvjT2NCYW5jA02I/R0UNo/5dHUao0+PX84gtPAyjjtkO78Y1XXkD/4DD2HxgCvjSN/h4W\nq2TyBezePcIOwHvf42fHAJRx5NBBdBXSePWvHsPUiosnnljF5EID9x3bjN7OLD736Hl09G3D1Itn\n+Dm0FYK+Z7x0EcA8Rm8dCawH8++mkPP8NsUqtc9NYbA3uLe6rgv9c1+CZuYwv7qCHcNduP22YyiW\n6/jDL33Vfx30RL78iXMvAighnzXhukBn/2YAszh8YAdGR9l8oMn5IvDVR9HZ1YPLc3Po6lDHx12P\nPYbV8hp6utqwUl6Fmc4BqGPvnhGM7vXXx7n5M8CpswCAvh52fS4tn8O3Tp6GrgHvfusdHMHJfGEa\nmunv0+cXzwJYxuFb9uDInjBNu+/pb2FhbQlHjx5NVDCcrV8BEC56i7YuStvdd9+NRx55BABw8uRJ\nDA4OIp9n8PHw8DBKpRImJydhWRYef/xx3HPPPbHvaWYpo7logWrauqZpkTNkyGSVNpHSRiiUSJWj\n13EovCbI7EkCCFGWMnU4LqPT1Bp2YsEC0Qr5NH7h4SOwHRe//7/HUPUG/5EsNfHnw5OuPUqbd4gE\nSa6V65wGR6bxOTzsPcVKA+evL3l9RAkCOISFC4ieIlK94lTaAFEFKszRJEoA4N+LAAUxCaXN+/yZ\nxTLSKSN2doVspNQGiD087PNyGSO0edP6sm0X8zGUtsB7vHtExyX28NB13LuVBV+uy3onMorBm7bt\n4Or0GrYMFZhevnC/h3rzyGWMEKVNHmZGvQ3y0FGy4f529HRkceLCPF83S2s1dBcyDCr3zoH6EZho\ngQHLcjC9UPYkqf3rEW6KDT/QJEcs285hFuhdlCrgSupjBKUNAP7Hr96PT/3Gg4F19cBtjAaiEi8g\nCifRVNZLa6M5PEmMjr8WQ2lTmUzHor46VbN7tWaF+ncAv1enJvXwZCN6eEKy1MI9FWWYW5nDQ7Qs\net5pAOMrF/wim+u6AVVI8tOi+lRHzhctoO9KmXqI5ir6CN5rIVB5Xj4/B8t28La7tuH/+sk78ZmP\nvRUf/+m7cUwIJtslXrtI5+wSaB7Up5CU0tZVyGDP1h5k0kbkc0pm6FqoyVykhYmUN65w18Rf+8dN\ns3j8KrkoKkBU5vGZIq7NrGHncGc42UlotFc1REobiVcIlDaRXteWS/lqUFUL//3vjuPM1SW84cgw\n3n3fCLraMyhXLa70du+RTXjwdVtxxwG/eEJGvZlk9QYTAooafEzPHflBFa0nqocHYOuP6D61ug3T\n0Pgz3+fRjeN6eCyB2gUI/kxBa6vU7dge45A0tSJWO39tCYauYc9WhrSIDea+aEGVU0dJHIjOKeP1\n2ZItr9XQljX5nluqNnhss6GvDW05EycuzIf6+CiONE2mErhtqAPLazVMzpfwnvtH8KGHjnA66SsX\n5nDu2hJ2eAUDmdJGNLSdm4IFBZnS1rBs1Bs2Fz4iI7Xfy5OrsGyX+6yctOclprQJsVWtYYcU2oDg\nmIXVUj2Snkr3lJ7RaiJKW7CHZ++2nsC6LuRSwR6emDEgAFvjjuMqB8iq7DVTaTty5AgOHDiAhx56\nCL/1W7+Fj3zkI/jCF76Ar3/96wCAj370o/jwhz+MH/mRH8E73vEObN26VfmepNash8d12WRo1Rye\nZrLUPEnRZVlq1+93ERIY+g5RUlA8DqB5Dw+f9eO4qDfsxENHZTu8ewDvfP0OTMwV8ZdfOcWVisQ5\nPFE9PLSxULNzsdII8XHpvCnwOHFhHo4LHB5JhswBioTHu4/ig9ZMpS1qplC1bgUC9JLHwxadcyKV\ntpSf8FBgntRENaVu3sPDvlPFn+dTuR0Hi8tVpFNG07kWdA4UZDVsl292hETt39HDPzubNgSVMt9Z\nTMwV0bAcbPekMUXBifZ8GoM9bZhZLAfopuJzV6vbXL2qT9HDA7Bn7uCuPiwXa0xQwnGx7CU8AMI9\nPKYB01NpUzloOfmMUmnTFYktbVxyH48tBLJkUT08AOOUd0tCFiObu7B1qIBnT06FppST8AJt7q1O\nzCajOTxJjPpiKJBImvDIU+KpgX7LkE+TpcehXLP4uQ0LTdZ+vyDNa7ICv5ctbvCo6K8cRQ+PHNyT\n2Vydjn326L4BpEwdT7/iU8NqDRsNy+GBB52XKDKSz7A5W/PLFb72U6YeekZFBFgX9gwyWhPve3Av\njuwZUCZ/csJDMtpDvW0BaXzqU4iSpZYTlq72DH7kLXvxp796f9N+G8PQQ88U/WzqOgwheOOCD7mE\nPTxeojY571Mkxft00JPf/7vHzsNxXM5SWI/xvYoSHl1Hvze0VBS7EffrtlyKizp8+VsX8ejz17Fr\ncxd+7r1HoGkaD8QuT7GCybF9A/jgew4r5dZF9U3AT7zE/kgAXDZe3mtVQR/t0ZbjhvqJ27Ipfi7V\nuh3w5X0tiBbQ8z+yuQtbhgp49tXpUGJdq1uxPcYUoNPzJCspWraDSxMr2DrUwZPlUjWY8FRqFio1\nm18Hv4eHHV8qFRSuWlqroquQ9Rv/BZW2XMbED7xhJyzbxd9/44JyyCkp4t2yoxe6DvzkD96KD7xt\nPzRN44jo1565AscF7jq4AbqGkKLbxQk2N0ecGwSwtSgmPMUIcQM6Vjq+HV6BTpy1RNdH1QLiOC6O\nn5vz1SxpxlA+Dct2eeFNLCBScWq1VEetbqMjIvaiQiMdM+/hMaQ5PILvpMIxFZTkwkB7Po2SsLZW\nImSxyaIERaIsScKz7h6eD3/4w4Gf9+zxodNjx47hs5/9bNP3JDVR2UoVjMrDNMmSyFK7/L3ed/EK\nvMNVCAIIDyU80tAowEeLmiI8hg4bbGOp1e2QLGAr9oG378cLp2fw1acv4+HvY/cgZRowDbZZRam0\ncUTL9I9VfiDpNOgavnyO8Z0P7W4l4SGlNvbwUfDS0ZbmCj7NKoa8KiGdy/hMMYDglasN6DpVupJX\nuulBbViOcgZPnA0KM32omkHJhsrBiQjP4moFG0famq4XcvokmGDbvoy4KFqwf3sPjp+fRyYi4SFZ\nUHLoYhDWlk1B79ZwZWoVa+WGcnBptW6j0QThAXy57qW1Ggr5NGzH5U2+IZW2lM6DBVoP4gYiJ42O\noiHWcZwQugswVbiejkwo4aENOZv2keOkCQKZpml44PYt+J9fOolvvjSOd77el2dfXK0inzW5WtK6\nER6EpfajjNYIPSNJkE3AH25IqpEkwLFlqIDTHk++u5DF4moV1ZqNiSoLXsUmay5aEJKlTojwSBPT\n+e+5Spuo6KSu9lmCShvANuwjuwfw3KlpTMwVMdzfHpCkFl8rygGzOSZZLKwEE572XCqgxBUULQjL\nUq8Ua9C1+GJOuzSbgiTZN/Tm0SZ8X1d7BvmsGYnwyAFmZ3sG6VRzdIeugZww+VVwHbqu+aIFlTpy\nGSO2OCBaf1cOpqFhSkR4uCw1EzgxDfB1NrI5ukenmfmiBT4ro7uQhWnofMaM67oo1ywM97chn01h\ndO+A3yA+U0R3IYNf/7Hb+X5D/vyKJ4wjFz1ES5k6FyrQNX9Y80qpxhMvwF/T2YwZ8AvNEB5bWt+5\nLAuUG5YdYokwlcJUSCFPNL62Pb+haRruP7YF/+srJ/HNF8fxjnt2eNfTgWW7yhk8/vex57w9l8Ja\nuREqSlybXkPdcrBrcxePKSpCENsQ5lvJCQ9dg0zKgGW7XlGXDazcPFgIylIL6N7+7b0Y3TuAsTOz\n+MbYdbz5Qe+7JCW30X2DuHVnH9Kv38GPZ7Anj1zG5EnZsX2D+MI3LwaS5WrdwvjMGvZu6wmPQ9GD\nhXbOxlDEObmsCXgEBBEpasulQiiYLFv+9IlJ/LdPvYBffv8xvP7wMN+rKeG4OrUKQ9cCM9tobVMy\nHBV7iveUzhcIxotAUICG/P2dBzfiZ+o27j8WpGi25VK4MmXxMRIrXGAp4hgEKeskvqySAAla9+DR\n76TxKmQEnYGjFkqEJ/6z5SSFT812XOXnckqbUFkIf1b8d4rD/moNJ1bauZllUga2b+yE4wo0IaHS\nH57DE6R+mAIVaJtQ1QXEIVrsxF65MI9cxsDuLck3poGePExDD1HaxEVeaFIxlAMqsmszbCNq4w+l\nDVPXAvSmRJQ24fqrZvDEGU30LuRTvoSmGT1Dh+79SrGGWsNpSmcDgJQRRBVVKm0AcMSTK86mTWRM\nX34SYJv9l564BF0Dp9UENsmcyRMVkQYgihbUxOGsMddJHDImKrQBviOlNZUyDR4s8IpUfzTCoxLC\nUs3hIdu5qQvzK9XArBOq8on3PWkQJ9obj26GoWshWhshWiplmlbMcZsXT8hoA69GyFI3e5+P8LDr\ntFXwBbRhsuF4qoRHLUsdNXiUDW/2fw4iPCIFU0Vpi0d4RErUXQdZ/xChPEWJBsopbdK97+3MYWmt\nFqhqaprGpal1LThjyFBQ2laKdRTa0pHrUjwOH+Fhz91QHyuCdLWnPZltE4V8OlqWWkFpS2qmoSkQ\nHl9JzRATnnKjKRovmmHoGOptC8zi4fdJ15FJGdg64B/rehXaAL9g6Qe0LFnr785xSlqtYcNxXAz1\ntuETH7oXd966kfvPlKnjv/zY7YHAiq4jJTxx11Xc023H5XGHjP76CXPw2VBVuWkfE+fwUHDNq98V\nC7W6zcdQkO0c7sLUfCmy2CJT2gDgTaOboOtaQK0tbgYPGcVCdA6WNDeGFPhGNnfxZ40GIKe9Yvay\ntE/4c3g8hIcU/Ro2Voo1uG5Qza1cawSU7ADgrXex3qdvvTSB05dZUi0jPACQlloKdF3jPcvdhQx2\nDHcinzVRFhIQPjdnU3jNypQ2jvAo4gFx3pDoc2V1VxXDidYl+Q3aqwmBHZ8tYrAnH/CJpsFmbxEz\nJmqIMMUutJcT1Tg8/83/mfbTTMrAW+/cFmIutfPklF2PlWIN7blU5N7bJsmvN7PXjNL2nTZ62KJo\nbbwvJZTwIDw1U7KowaNsajl7jYgc+RUW7+aJCA+aZFeeUVZsWUwSdz09PKIRhYqgP/r8TNoI9fDQ\nnuwjPP4SUHFRAXYJV0t1jM8WsWdrT0vVcEPXMNzfhvHZIlzX5ccTpLTFIzxREttXp1iALM5uMAw9\nkOS0gvAAaBnhGejOQdOCmyGntCkQHro3c16FheSX44zOgVBFy3aVTvSwh7xlUgavyFGw+NzJaVya\nXMHrD2/iwap43u25lHJgozj0sVa3mayvrsUONCQnuVpq+ENHvQQpZRoBR5g2db4Gae7AkHBN5Gso\nS+g6wsBJlREv+uKEj/LQxiief6sID8Du+W37B3F5cpWjSLbtYKVUQ1chKwxbW9/wUXEOTzOj44+T\npY57H11XkqTu68rxe9znVagrNQsTc0WYhi+tDIQHj9JzGjV4VNM0/hxQcEAosviM8x4eAdWR5/DQ\n4EZR7pjsjgNDMHSNJzyEkND6JJ8vb8x9nTm4rk+Fog2ZB2PZoLysanjrSrHWdF5NOmUgbeqhHh5a\n/4d3D+DQSD80TUMhn4oOXr17R8FTV4I5OWS6Hqa0iRLfhu6PUCiW602LU7Jt7GtHsdLgyZo8K2Vk\nA/MLbVkz8Ny3akRJp/4R2qcHunNYLtZQa9jCoEjfp2weaMd9xzbjl99/DHu2BsVturz5IHNeD1Bc\nwkPruWE5gQQ9nPD4tCsyQg9lMxQ9PD6C6Qf6qj5gogeqZPnpOIFgst/dkcWxvYO4ML7Cg2mip6Zj\nCkJ0Pf2EJ7ieiFkwsrmLB8XlmgVDZ7OYLNvhVNquQpAJwGWphd4TsYhG312uWAJdkr0nmzbx7vtG\n4AL4xGfGUKlZaFgOdC+RjzOilx3dO8BlmkVUis/NGQ4LgrA5PP7PsQiPd55bhgoBPyTve6rYl/Zp\nok02LNvr3/bl5OWCqqZpyAjMhihBk1t39WHTQDu2bgiKk4QHj/rXMcrfk8kFwJViXTmDhyzfIqUt\niSz1d0XCIw4oU5k8W4asFYRHprRZtr8JKyltHOHxbwb/rCZdPHQ+vHpyAwgP4PMyaSFRAJlJm6G+\nFxkaFx98uVpBe7rtuDh3jVVp9m6NVzxT2aaBAio1C4urVX4PCy0kPFGUNpp8HEh4dE2qaDRf4ukb\nQHhSpoH3PrAHP/CGXaHPUw1UpXu/5gUAURUW0TilLRtGeMTv2L6xE4d392N070CA0ua6Lj77L2eh\nacC/fWCEv15EONqyQsKz4Cc8YrBZrVsoVupol+YJyFbgjq3ONzIxkfw+b44NAKRSBg8Wrk2vBWb1\nsHOOFy2QK5+y7fKS+AvCxs8RHoFytR6EBwDu95p9v/UyUxZbKdW96mMGbV5w+J0QLaDj9wePtkaF\noyCFI3iFDDq8wJkQnoonWrChry3gN2iDJUTEHzwa7dfIR9G9pttaVYoWiJS24B7wxW9exM/+3mMc\nQRaf/fZ8Ggd39eHC+ApmFsuBoaOAP+NIvvfUn0Y9NX7C4wfnoukCTRVgz2ex0kBnAqpyez6Fkndc\nUwsl9HZm+XP58w8dwW/+5J3eMRiwIuh89L0UPLSM8EjMCVtISightWwH5aqVWLCAjPfxeCiPjMTt\n2siu6cjm7sRrXWXkjiwBQQKAAS8xn1sqC3NThKGxho5fePgoXqdQExQTVkPXYufFiQN8xTUq9/DQ\ntRV9XFRiTOwLonIBYUp9mRCeiITnwvWohIcq9sG1f/9tjIZEKA89y6mYGKWDr7us99nB9XT+2jLS\npo6tGzr4vsSKVBpvV6D5cWGEx0t4TNrPgvS3lKkjbeps8KgX24jraNuGDtxzaCOmF8r4n196FQ3b\nSRQTkNriPYeGAbB9qFz15y76ggUKhEfTYAuFdvI7UT08gF+YI5MRQGXCs0AJD7se9YaDtKkH9nUV\ng0REA+UiN9kbj27C//iV+0N0M3mGmtjT06xw3873wzocx8VqKb4o1Cas8ST2PYPwNE14FANCgWQ9\nPGGEh7jAaoSHXidPOxY/q5lqAT1wlJHeCKUNEBAeL7ASKW2RPTw0eFRweLLUqI/wuDjj8aypEbsV\nE4ULlAhPk6qhPyxLSnimV9HTkeFNmgB475L4czMLUNpaRHgA4IffshcPvm4r/zlJDw9VLVSD22RL\n8YSHfV4UpU3XNfzfP3UXHn5wb0CNZezMLC6Mr+DugxsDzehiRaZNRHiEJt+GRGkrlhvKRE40CgzW\nynUlBe5H377fP2bNX4PFSoM3aJOJjj+bNiLpN3GUNiCo1Ea0F/G+rwfhARiqljJ1jJ1h/W0ionUj\nlDbyW681wsPRZq6U5AcT9IxSwjO3VEGpagWaYAEFwuPRVeL8muijAN8vNRMtkPtVLo6vMDTGq3bK\nid5dB9mQ1WdOTPJeGVq/dG1lug7RmogqQuuT1rCchBsc4WHH5itvNUdD2nJpFCt1NCwb88uVSJTD\nNJiyp1qlkH0vJVhJvlc89jDC49PCDIOptMVVqeOMnmcSLhB7eACgryOFX/qRUfz7H7ylpc+Vje4l\n0ano8wf40OoK36uT+FwgmDh2FTKxCVkQ4RETHjWlTTyGqARVRHh8KmCQ0rZWrjOWiPSsjXh+73wE\nwmNJIh9kt+0fQiGfxuNj43BdlweRcUXZd79pBL/4vqM8ubSkPePK9Cq2D3fCNPRAgmUYTCDEsly+\nT4R7eDyaeIqurz8gnAoQlIw4Eed0/22bsX1jBx759lVMeMN/m9ldt27A//qv38fp37msyUSmvHO7\nOLGCtKlj84B6YKz4nNJer9o36Tx3SEhRMoSH+Sfy2Q3LRiplBBIPVcJDFL50ysAmxcBb0WR/GqfS\nlono2SQT98O1ch2OG53sA/41YAp8Dj72F8/iq09fjny9WCyLsu+qhEeeGk8WVeVNIkvtOMHGYM6b\ntR2hh8e/TKEeHjGrTNjDwxEe7kxu7DaQs+OUNkJ4UqoeHik59I5ZlLUko3N1XeDsVUJ4bizhqUsJ\nTzbdvAlW1cNTrjYwu1TBlsGOgLM3DC3wkCapdAcpba0hPCoj+F8lb00Oolljt+o9tOYsy0Gp2mDV\nrYiNSER4PvvPTCf/vW8OzmQIiBZEUNrEYJNR2hqxlU5AcGyVRqiHB2C9HX/woXtx560bcGikP3D/\nZYqfafgQfT5rhlQX/WKHeg31dmbR1R4ULqDgQUz41ovwZNMmbtnRiytTq1hYqfDz7WrPhHo0WjE6\nr6SCgbyI4q2rxKIFQoEH8BM2MeGhgsK1aYaoikIdQJhyWq3byKaNWBSQjpfWICULTWWppY2fGv39\n8w7ex9fdwhSWnn5lCmte4hknS83Ol/kAophRMNsj9aGR8YTHW1fNJoiL1p5LoVRpcEn2KIqrqk+I\njO7dwZE+DPe388AziRm6HqIgBSltLHiT6YBJjZJjUvcTVdTI3nBkU6B/YT3my1IHEaQBQamN+m1V\nhSiVifevWSFM7DMWhTWWpYSH1oi450QlqJxtIvQT63pwbyF/I1fX+7tz6GhLt4zwpEwd+7f3YLlY\nQ7HSEHp4ov3jQE8ebxzdzOlNYq/15YkVOI7LWRiBvkkv4WkIlDa6zj2d7Bmk9aaktHUQxdREudoI\nCZeQmYaBN9/OCpKVmp3I1zPxEr+QSntvudJAw7JxbXoV2zZ2KGXUQz08MQgPISgjUv9aezYorCKL\ntdQaNha9JJEjPBZDeMSYQFaQA/x7sH2D+vhFk2NC1XqRPzfKCsJ+mMRHiqIFk/MlPHtyGn/y+Vdw\n8lJ4mDXAAIRmhb7vioTHTIjwyNXQJLLUrusGpF9FShtZkNLG/vWb5cIIT7NGY7kae6OUNnq/j/D4\neui2R0eQj5GSPGoo3bohvOH4cqsOzl5bwqaB9paaVslEpTaqfnV6wVSSz+MIT2V3vrEAACAASURB\nVMO/1tTvsWWoEHD2xDsnaxnhSUAxa2axlDbveHi1McHMH5m3bTsuSpVG7MZNSfTL5+Zw9toS7rx1\nA2/EJMtICA+p+wQpbcFqpWU7aGtS5Q0iPMFqHNmuzV34tX93OzrbMwGYXFWRImg7l0mFGrRFVSaV\naZqGHZs6MbtUCfURiE3160V4AOCoN4jtxTOzvAeGUdrWj/DQfpl4Do93/nwOT0QCGH4fUXE8Stta\nDbmMiWza5IhBT2eWTR/3NqkBOeGR5mRVa1bTRJ73pWWiER7XQzTIZ1Bw5P/d5SgM+WE52OkqZLB/\nRy/OXF3kPoNT2ryXyps4rVXqs6O/d0UhPLwPah0JTz4FxwUuTzIEcqhPnayo9iUyeiYeevMe/Mmv\n3t/SfmIYWkj5MEBp0xgC5EtSt4bw+LN42D7TkPosbpbRHfQTKvb51Gs2u1TmBUF5bkyUib1QXU0K\nYX6MYks+M0hpo6A8Zeg8iYhaJwGEJzSHh90H8jdysKlpGnZt7sLMYlkpdqHq4SHr9ZKNhZUq9ydx\nKm1kqjV6jgsWdHufIxcnJdEC7xk7PNKP3/nZe3CvN9CT1nTDEpMjn2JaFmSpVWi/uPetx9fnhZ7t\nq9Nrgbk5sumaFmAWqejnZD/whp34+fceCTFnaO+g4oUc+84KRUnyN40GU3JrSmnz/PWOCDqbaPJ8\nulACJKq0RYwhIGvL+xTvZe4jo+M/UZaa+ugcF/j9v35BqVhZrVl8P4my74qEJ2kPj1zlTSRLLSkh\niZU0mf4l/p8eAFGljSM8sd8YRnhuVLSAFjhtShzhUQzslKFxGtypqrDRZbk6vYpKzVoXnQ3wZ3aM\nz/gID/XwNBs6CrCHzDS0wHlQtXnLUBDhMXUtEPwmGWR30xGeOEobITwt0CvkfgfLclCqWErVFzK6\nJlQNe0hCdwAp4fGSisGePGaXynztNxrhamWzxmVy7GslH+GJbfgVnKqo0EaWz6ZgGhoyKSNUwLAV\nvG3Z/AGkrNppKSht60V4AGB0L1PHGzs7K1QfswLC07pogWoGWJz5RRSSD20t4fFFC/yZSXfeugG3\n7OzFtqGOQAIjIwikiFWr20y0oVhrOryXU9rkHh7v+MX+QUL223JmoHizVm7wwgH1Z6gS3ztv3QDX\nBZ48zvqsCIGkNSNT2qgngWZO0bESwiM/dz6ljRKe5JQ2Kg5QY3ckwqP7wa9s4jDPVs3Qw1Q5sQ/G\n8FTc1ovw9HRkkU4ZXCLZvoFjjTOxOMc+n/082B2mtCUdLC0OoW6K8AjBfpxogdjDxBPpJj08tmIO\nDx0XVflVMQTR2i4oaG0ibVE2onTOL1f48xin0saPl6Nc4VEIPsITpLSlTB2WJ0ttGppPN9U1HNjR\ny68RFfBk0QKA7Q/Vus0FdlRra8uQT9dfj6/3WxgavmBBRMIgy1LHzeHp787hgdu3hIrk9x7dhDff\nvgWj+9jeIlN5qdADMH/jeH4ynfIZEboW9tWAv+9FJWyiyYWzWEpbk0ILFbmXVqu+QEtPtFCJ2DYy\nt8wSvB0bOzG/UsUffvalUGxfqduRyqBk3x0Jj6BqpjJfpS34+0SUNmm2T1C0gP1OJUstyiGKn+V9\ncex3mnLCc7MQHpnSphjYKSM8P/1vDkHXNbz7Pr+ZnYzOlZxWK3LUouUyJvo6sx7CY3tSq+zhTzrE\nLpMK9iNd9RKerSGER+MPqa5F93bIn02WpCrbzHZu6kRPR0bZ0Bjq4UlAaaP1T8qADa8pOq7aKlZ5\n7zgwFOIIA8FNjJzxYE9boIomVitpeTfj8RuGzueGLK1V0dGWjq2qiU5TFfAd2zeI0b2D0PXw4NGo\n/j3R+ADSCbZR8YTnBlXayDYNtGOgO4eXz81hUaCE5TImdF1bF6XNVRRb4oz7FI7wtEppY43Rq6Ua\nr7LefmAIv/0z9yCbMQOVM5nSpmlMtW9+pYITF+dRqlo4uCt+VpeP8HiopXdPiIdNVXjbcXjfXz6b\nChS9xE2fglkVsnXXrayPh65NSKVN2sSJZkLoJE94vMq3vP7lZKRVShvgB6VxPTyAGuGxbAdaQl8n\nm1/gExMeX7LY8FTc1iTBh6Sm6xo29rVhco6pdFqvFcIjzeGh69XbmYWua5hdKvO9Wp5kH2d0D5sJ\nQaQEhKchoeKiiWqChHZ0RgxeFOmmcq8i7Z+0RlUxBAkXkCy0aHEID1E6F1aqvmhBgiQhpVij568t\nI581OdKXloqTpqFxSltXe/TQb67U23CwtFqFroGLqpAPocKS6jnobM/wBOmGEJ6qxRU/IxEeuYeH\nIzzJn53NgwX83HuP8PeIaqmATzvXNY/xUW14ogU+wtPfnVfeN9r3ohI20cS9WdfCyaT4+c1Q/WGh\ntWFcMd5ANioELK5WOcLz499/AAd39eHZk9P4ypPBfp4kzILvjoSniSx1tEpbEoRHprT5mxfJTAfm\nukgVFuUcnthv9B9e2qRvNOGhqgkFVuR4qBIpBlwywnP3wY34h997Z0iwAPA3ETrOViVJRds0UMD8\nShUrpTrSKYPTYJI2wTLFORHhYcjU5sFCsIdH92Wpk1YR/QnB6Ruq9JPt3dqD//ejb+GBtmhyr0Ui\nSpsRXnOW7cRS2qjqDgDvffNu5Wt0XePn7ic8wT4e1aDHJPesPZ/mc3iSVkcBNQT/E++8Bb/+43ew\narQsWmA3T3h84QJCeDwe/U1CeDSNVSNLlQZevcj4xd0Ftnm351LrpLStr4fHHzzaIqXNdrBarMFx\n1cFdAOHpCVcNbz8whLmlCv7siycAAPcc3hj7vSlTZ3M4vHtA50sVZVqPjNJGCU8Q4RETHvLDqvPu\n68phj1Cs8UULvGOR/G9b1gw089Omv2tTF37sHfvx/cKQQkCB8JBoQUQgKxodC63NqLlccT08tu2u\nO2FXIUdhlTaHCz4kQeRl29jfhmqdVeZfK4SHI4ISpc0wdPR2ZjEn9vAkRHgAP+hqhvyLCanoM1dK\nskqbj/BwSluEfxSHVMtsE47wEKVNEeiNxCi1xVLaOhjCs7BS8WWpE8QoplSYLlUamJgrYtemLiWa\nappeD4+H8MQllXIPT0d7ht9j8uNRfXxkRNu/MYTHwqXxFTY3Z0M4ZgJY3BScw8PWQNLeMdGi2E2k\nIEnntLRaZcNJU34PT5QvuXVnH0Y2dyXqmwv0QytQvkAPTxOmUn9XDtm0gesza5ziOqwQfSCjouHE\nXJHTiwd68vjw+46isz2Nv/jyyUBv7v85lLaIxuUkstTycD96WBqiSpvwsfTgpkzW0Kmaw9MsSKFm\n4JUSq/7c/B6eIDdYrDJFJYcqo+tCAYc8ZbcVI+GC6YUSMikD/d15vGl0U2gab5SxIar+tb42s8Zm\nheRSAWdvGv7g0aRBADnMnpvQv9PM/ObWcON8lNF5UNWb1k0zB3pkdz/efPuW2AnmdO6UHNPwUeII\n1xvhZy5JpaqQT2G5WEOp0mgq9U0FjS5hroLKxKnvZDwojbnXA905FPIpTkVQIjw3mOhSQH1lahWa\n5j977blU4jkCovEenhYpbbTptzp41LYdnyqiQCaIKtCWNZXI4vd7k9mvzxTR1Z7BgR19sd9718GN\nuPfIJh608B6eGtHX2HfYXsLD5GeNAMo/JSY8/LzV14uGkKZT/hwoPwgL7xuiiiStT03T8K43jYSq\nkjfSw0P9cJWajbZcKpIyRvdTtQdajrNuxMSUjh3wVebasimW+NmuIPjQetGLGqcn54q+SlvCHrOk\nRnuV47L9WtzTB7rzWFit8vOK8zGyURDerLdTRHgCw5rrdkAuV1Spo3UV2cMj0PRkJLuNIzxsran2\nkZ6OLLoLGWXCE0tpWyfCYwqxE+CjlmJDvijQZOo6Uqbh0bGc2D4pUaVtea2KHuG11F/Eix4Rsc22\nG0l4CEUq13F5cgVbhzpCQzjJZEpbqdJAJoE4k8rEdSUaKbQR62Z+mSW+adNXaYtKeN593wg+8aF7\nW7qn4rFE/b1ZwqNpGjYNtGNirojxmSIK+ZRy4K78+sm5Ei/A9nVl0duZw4ceOgrLdvC7f/UCyp6K\nW91yvjcQHnGxqyxKpS2RLLUjITxcpU2YwyM4Z3KkmsYmYJclhEfTmosWELd7eY054JvVwyMnPOSs\nxSnz/rVqfuvpelLCE/WAJzFKeFyX3U9D1/Dh943iDsX8A5WJQ1QrdQcLK1XOyw2qtPkIT9IggK7/\nzejfaWayjn2SHp4juwewb1sPr6xTj0CzhOc3/sOd+Ln3Hol9TTZtBJRdZISH+ieyKf9aJqnyFnJp\nntQlVThqNoRVHlIJJOvh0TSNTR5fYJPHVQmPfF9atd1Cf5tI4WvPM4SnmR+STdU/GGch0YKEa18M\nUvjwP0VwR+tUhe4ADEU7sKMXAOuZaUavevd9I/jFHx7l50d+iaT6KSi1bSZakE4ZMA090M8gimsQ\nXSnqe0meWkzWuEqbYiMPJDxN1oYsS90apc3/ng290epqMookmmU5kSqFzUy+/gAwv+IPnzW84I1L\neq8D4Rn2+vIm5koCwnFzKW2BoqW0/w50s0Gy1PeZVJYa8BGeZsNcAwiPVCQSldpa6eERE+mwaAGp\ntEVT2jRNw8jmbsyvVDn1jYwN4FTHKtTDs7BSEYSVEgTHkkqb37/j+8Z0YK8OKqrG7ROEDK2W6qjU\n7ICP4oyZJkUPQjRuhNJ29toS6pYTSwdjNDP/52KlEdtvG2c84Qn18JSRTRvY4jFzqMclZepcGXHf\nttZnJsrWLOFpRaUNYIychuVgaqEUS2cjGx5oh2U7OH9tCd2FDI9Bj+0bxA+9cRcm50v4k8+/wtdp\ns2f7uyLhSRnqm07mq7QFf59s8KgkS62aw6MQLQAYF1iUpXZdtymdDRCRFz8rvxEjJ0LUNVqkqoSH\nV4oSVI7pVH2VpBtBeMSmwdbPN5s2Ua2zIZpzK+w86WGXezHo/BNT2rzrpwr0brbJKFkSWeoHbt+C\n3/1Pr+cPMwVUrVAzoqy3MxcIYqnJkRIeqqjnMv61TKLUJA6WTUoHUQkWiMbRAOGZjlPmEY02qEsT\ny16AqAUC2RtFeLZt6AwNqARYQGvZfh9KUlv/HJ51ihbYrq+UpLhfVEGW+3dEe/j79qC3M4u33Lkt\n0XcDCCE8lLC1c4SHTa7PpHQBHWVrckrRwxP1zA/1tuG+Y5tx9yGfaucnPGF/JCYrzSqhtGfQHJCV\nYh26riV6TsQEYoNCQpZMpB7KZt1kStv8CqOP9HXlYBjBHp71IDx0XlPzxVhk4UYsyMIIfjb5tCse\nDTqpaAHAfO8Dt21pKtgTRHg8lNL7HlElTezhoSQicvBooIcn6Od4D3E1XviIDyCVhAsalhPZ55fL\nmMhnTU+lrQXRAm8IJSk+nucKbQLCY0p7tfBsxVPa2OtoXxKTI0J4KOiNKuYSBW09SAtRz1+9OA/A\nF8JRmaqHZz2FAkDNbnJdFzOLZQz1tvFrRj0u6ZSB7Rs78amPPog3egp3N2KtJDxJYhmxdUKeuacy\nKpTXLQf93bnA397/1n0Y2dyFb4yN45+8+TxN1UGbfuO/AjMT9vDIm50sD6h+b7DKIUL8vvpbWLQA\nYDDnnDCkUVZ8izKqHpZrzSeSJ7EoShtVjgIVJqqIJ0he6FxqNwPhGfQX93p6ljIpg0/8nvUSHqrY\nBHt4NH6/km6qwwMFvOPu7Xj9keGWj6tVE69hOqUnruAD/jokXvh6OMGy/fqP3xHoCwgjPF7Ck9ax\nBC8YTRD0iA6+OaXNQ3giIHgynQfHDgw9KGfcLLkVB5Bajssasps481YsZerYMdyJs1eXAhs3Bb2l\nSiPRhkDW8hweSbQgacVfLPDEKeo1Q3gA4NBIP/7yIw8mO2DPyJ/S+Va8oaVczMCjtBHCQ8eaThmc\nx07vA+KLMr/w8NHAz/TajKJ6LSbszZJHutaWQGnraEsnerbFpGgoDuHh1EOFLPVNprTNL1fQlksx\n0Q1P+GfVo9GuJ3DbyBGeIqe33WzRAk0oNcpLn6SpKaFvpdq+e0t3IrEerlBm+TLq/d15lKZWA/uv\nqKjX353H7GIlMjE2BLaJTNvPZUyvR5m9NmpPFft4bts/FDiOOJSttzMXQHiS7P3+HB72nvPXl9HZ\nng4EqgFKm1CcBOILYxTj+AmP/1pKoqpNfMCWoQ50FzKhwclJjJLkCW+elEqQiEwXxqE4LhshIRZ8\nWzFVwsNQLguDPXmeLMsS+s323KQm+j5VPNUKpQ0IFr43xfTv8Nf0+6/v7wr6x5Sp45fffww//d8e\nxd8/dh4ASWNHFxe/KxKeZj08XKVNjg4SixaoER40Q3gyJio1i38HU3xrfj5yRefGRQvUc3jUCA/7\nN0nlmFParNZoMirr6cgilzFQqdnrGrTqK87ZmF1mjo1T2tJSwtMipc3QNfzUuw62fEzrMfGYWr3v\n9BzQ5tfqTAyVyRzadMpAT0cG07yHh6BiAeFJQmnLiwhPPHJGfUN7mgQWIq2HjsCSmpSjbJcg0Wp5\n1U2xwnmjlDaAHf/Zq0uB820TpksTVSSJtdJrB4iiBc0Df9X7LMXwP9EoARlsYahlEpMpVdW6hYyQ\n3DiOi1rDQS6b4uhow2LDHRdWKugqZLC8VvMbllugdh0e6ccP3rsTr7s1TKsN9vAkpLQJKm39Ca+T\n+AzHUTpjER7LWbcIgCznDAALyxU+dJEXWYp1pEx9XXtVV3sG+ayJyfkST5hvviy1/395DQz2+M+d\nrt14gVFlPgvF5tT7vq4crkytYmUtTGkzDR0ffM9hVGtW5DPO++uc8BweTdOQz5goNUF4CNk+r0B4\n4m5Bb2cW12fWODqVpM/Y4GuUUSDnlho4tm8wUASWKW2i341FeLyYZnYpjPDQZ1YiZnGRZVJGy3Oq\nyMQkWdcQmmknmq77hfa65cJx11coAICUES72U9I32JvnRe35ZR/huZkW2CNvAqVNlAdvBeEBEEJ4\nAIbcb9vYyfvU2D4VnfB8d1DaEs7hCffwNJeldqUkJSVQPOhzxSxW/Ipc1oTj+jQMJER4smkDKUN0\nAjd2G+j95BTjEB4aMpeEQ+1T2m4c4dE0DcNedr+eh5Kq49W6zSltBI+ahh5Q5TFbFC34Tpp4TK1e\nB3kjvxkIj8oGe9owv1yBbTv8mcun/e9OQmspBBCe+ITntn2D+PNfewCHdw/Evs5HePyHupxwntFQ\nbx5tWZMhPLYD09Sh30RKGwCMeAlbV4DSFlZKTGKtFCYA329VeA/POihtqzGUtgQIz3pMJVqQzZgB\nueSG5VHaiDJjO5hZLMN1fVorHyHQQlGmPZ/GT7zzFmUiSkNXgQQ9PNKxlqpWbDOufAxkQzGVZ/E+\nyWY57g0jPHT9y9UGSlULvV7CQ0yA5WINhXwq0f4mm6Yxaeqp+RL3Jzed0iYcl5zzirNIctn1nUMz\n8wePCgiPdw1XVJQ2b+YMJZYqExMIlfx+XvD/UcFmdyGLvq4cLlxfDhR/4yhtANDnPROkppUkRhHH\nh9D7RDob+5xoSltsDw9R2hZUCI/n+5qotAE00209PTz+/jI8UIid98ISHhZbVuvsfq93rzYVsS9P\neESEhyhtN2EfU30/EJ/wpM1kbJWhnjy//psSJDwb+tp4fN4f8ayMCGjb94RoQZRSBVmUNG0SWeoo\nlbZAD4/3d10LvpaUO+hBk2f6RJmmaZKzujGgTZakpEWaz5owDT2g0sYrRa0gPI3kSi1xRtn6ehIe\nf4iqhdmVBgZ78oEgl/5uGhrfpP9VJjzCNWz1Oui6FnAqr13Ck4fjuJhfqaoRniS9CbnkPTyapkXO\nHxFNDo4BP5FodkyapmHnpi5MzhdRLDdg6PpNR3juODCEB27bgvsE5UG6DkXFZOg4c9eJ8NC1aTXh\nadiOMP06HHjs3dqN7kJm3bO4oownsYIsdTZtBJAHTmkTEB5ZlpVsvc37srWG8PjXnmTJtyhk/lWW\nFOHhzAOlLLVzwz08FIjTsNV+CeFZLdVansEj2sb+djQshwes65kZFGdaIOEJXgsxqWilf6cV4/1l\nVhDhAYIqqaJoQTMTFRRVwkx5xf6nspHNXVhaq/EZYUASShvz2TzhSVDs5LGT42BitnnCY+hakNIW\nQ8OS+5TFflvq4Sk3QXhuxMRYI65/BwjSdCnhWS8bQxX7khz/UG8bCm1paFqY0nazzGiyR9L9U8mi\nKz/P0LFpoB26Fl/gIUunDE6zVyE8gN+nBuD/EFnqyDk8CUULhLeJjYJ8Do/3O/nz/eGj7EFzkZxz\nLwarNwvhIaPrpWkap3yQJRnUSObLUt+cqpyf8LT+OdQ0PbdUQanqBKBRwK9wBRGem+/4btREp7Ee\neoj5HUp4ACZ92RB6eICgrG+cFVro4UlqKkWpIh+I2Pxa7BjuhOuyavXN7uEBmD/4+YeOBOgOdFwy\nwuM4LsoxctWtzuGRg5ekSEdQlrqKQj6lvBZvOLIJn/qNt6DnJt1LfpzCNHmAIbjZtMn9U71hw3HZ\nuhPnsdGmLycWN+uZJyVNIDmlzbIdfOvlCQAIiCPEWTrFVBJTph57bX2ERy1asF6KmDx4lKgxhHr5\n5+auS7CAjHp3rkwx4YCbL1ogFC2lvY1oukBr/TutmF84cHnfIwVogR4eQZa6menCs6FSoxTlteMq\n20TnPS/IUzc84ZYoI4SvVrdxYEdvIhog659lnz3OEZ5ggUREINgcHv8Y4pTw5D0nQGmTenhei4Qn\nmza5L242sFMs4lR4wrO+Z8dPpNUIj6EzCX0qWNxsSpum+Up6KoYP/b0Vmuh/+MFb8KGHjyaOf0jN\nTe7hIRPnHcYhb8D3SsITgVrQz3Eoj+O40ALO0qcOcJqEwJsVzZ++2+Dfk/RZEx3vzZKlJhM3aEp4\neJ9RC3K3dP1kqtx6bdMNUNroGpHTlgMd+rvYw3OzeeI3w4IIT+vHJ77/tdq8ibY0s1AOITxJK1VU\nDU6Z+k1RkwOClXSykjfULcmGIjrGlCSJ+lqhgVGUtj/74gn86G8+ggVPEUs2XsRJSmmTns2k5yPS\nZpZWa7GzMF4LE2mKrut6w+NMfq8JPU+bRqAYRQptcuHjZj3zqjk8zc6h1rDx9Ikp9HRksX97b+Lv\n2jxUwO4t3bE+WUw8ZLPs9YsWiPsdAL4e+7wKv4iY3UjPIAkXLK5W0dmeRk/nzV1n4mOiAvmop6oV\nSepWTKzEU5xCCM9q0Ud3/cGozdepoWvQNPUcHiCIVsXFELsUA0gZpS36uwnhyWfNkNhHnJmGgYbl\nYHKuhP7uXKgvhwYOA2wEiClQouLQN5mqFaC0pYK+4rXY93Vd42tn53C0YAG9FmBxU7XO7tt6i5Mq\nWWpCSakwKQ4QvdkID+DvJVGfnTL1RPMEyQ7u6sebRpPNXwSA+49tweHd/SFfT7ZlqMCP7XuD0qZo\n3BLNjuhLoQcrDuVx3GBQIW6qcnIgb0j0gNKD5joAEglTA205/8bcrMGjZIFGwPYM6pbDjzGpjC8Q\n3jhuNCjc7lW+RX58UvMTHiZ1uUWaEkxJn9jP86+R0najCM/NCkDiTFRqo2pl1ttUkk5ap9d1d2Rv\nGmdeTsABP5FIsqGIlTk2r+nmIjwqo+MiJAoALk+u4J+evoxq3cYLp2eU72t9Ds/6Eh5RzrpYaTQV\nmLjZRqdHCoy24yKTNvgzTMME0ylf0UmkRg33tysR+hu1jrbkstR0XGNnZlGqNHDP4Y0tqS9+/D/e\njY/8xB2JvkOF8Nj2+ufwiI3xQHAGDxDcJ24E4aEqbcrU8Wv/7vYbFuqRLSA8pIWvBfXxvFaUtuAc\nHlYkKuTTyKYNSaWNKKfJ1oeh67Dt8BweQCqa/n/s3WmcFOW5N/5fVXX3TM/07CvDMgzDKggyAyIQ\nBAmiEuMGRMWjJ+YxLln+iUePOWjyxJicuKLx0agf16jRaEAxfFCJiKhBQD0DAnMUUZRlWAZmgNmX\n3v4vuu/qqt67p6fX3/eNON3TU11dXXVfdV33dQfZn2qGR9O4IFRJ22kjizGptgS3XFUftBW9N4NB\nQntnHzp7rBhV5ZsJkSTPgqvapgWFeVlBrxPaMY53cCReL1TTgoESJYQ1CSlp02d4CvOy1MH9z39w\nhvpYhMu9hUUc24GuKZNHl2HKmLLY/2G3OVOH4vc3zAo4TjYosnqshSppS4subYEm+OozPP6/BK6s\njCbNqmnTqXgFOt6vr+2FbwTgRJQZngGe/MUg318mRtu4ICfbGFH3J+8T0EAHhVVlFvzxJ7PDrm/X\nEl/u/e7F47zr3bUZHs8XNPlK2rQZmmg+d6NmHZ/BL2nrhtVmdy1M6v674dbxi8FRLAfQ6nonUZa0\nVZVa1E6BBkU/hycWTQv88WR4PHd5/7L2c/XC1LD7GM47a6TP73nPHwzFN+CJrKSt1b1Sd7BOSYNB\nu7iidvE48Vn3qAsfelYqFxmenGwD8nNNMBmViNtxhxJNSZsoHZ47NbL1L8L5Hnvm8OhHNK7lE6I/\nN3uXiYqSNhHwaK8T0XaaAlzzHhbOqMZZkyojyn6FSzeHx8+xX+4uL8sZpKy4fh0ed3mRQUaBJctr\nDo+nLXU4DIoEm8N/hsccZoYnP9eEiuIctXGBw+k6hwYLDCw5Jtz9k++EtY367ZXR5j7XBVpmwGSQ\n0W+169pSh5rnqRvTeN1EE9dRT1vqwTmXT6otRXtXf8jgRQ14nECvdWBNC4xeS7LYHU4cO9mtm7dS\nWZKLe376HTy5ehemjot94CGujYHOMb/+UfCbNfFQO6wAXx44iWyTAY4g02VTI+AxBm9a4G+9HABq\njBMs6vXu0qZtWiC5AyHvwEfQZniMCH8dHtfvxi7gAVwDAnGHQ1uCoW1NXVVq8bTwjqCkTYjFoPD0\n2tKofk/so2Z3KUt5sdnrcddnYdDcuU/GkjZthscYRSmjeE9Ggxzzel2hrNAMWZZcGR6rA0bNYDPc\nO1X5uSaMGlqAM2J45ydYhiec7ZJlCaOGFuJ/v2mFQZH0GZ7BKmlzB35iawLCyQAAIABJREFUO1tO\n9WDbl8cwqbYErad68dme4+6SJP3fj3gdnigzPJLkWrdKTHoNNfCINe3dUHH+ytI0LRCDGJNR8bT+\ndTctGFZmgSRJ+oAnZhkezTo8Ifal9rz4w++dFvPGDtpt8M7weEqkYlPS5pnD4zoOYhXwKIqsuxMd\na/oMj5+Apzh+GR6xjIPRoKDAYsI3h9rd4wxJDVjDzvAost91eIDIxhCjhxfiox2HcfxkD/Ldwfxg\n3OPRDooDBjxGBeixQlEk9bsT6kaL9v1530RTl2vws4xILN1yVX1YzxMfkcOhmcMTo4VHW0/1wO5w\norJYv28njirBw7fMi+pvhCJuDA5WFUQsXHx2LQDXfti182DA5yXvO9AIvQ6P74Q+IMw5PD4Lj2qa\nFnh9gYJleMTfia5pwcAHrtoTgvccHsBz9zFgcOiHT8CTwABC1Ig6nK4TivfATM3wKJ6FRwdrEDsQ\n+gxPFHN43O9psLI7gOsiW1poVjM8RoOsZpbCLWtRFBkP/8c8/NsFE2K4XZ7JoILofhZu5kmUtWlL\nH4HBz/CINbLEf6sr81E/vhw9fTZ8se+Ez+9FvA6PQf+8SIJ9RZHVSa/xLmnTZu1EcGM2eebweBY+\n9NT7Hz/Zg36rHZWlrkGsrtVtjDI8RoOint9DXeiL8rJwxbnj8Jv/MwOL54+Jyd/3pi5u6hXw2CLM\nGHjzLmlrbetFTrZBHUzHqqRtsOnn8PjL8IiAZ/AzPFarmEDuyvDY7A51jBBNhsffOjwAdHMjQ80D\nHqMpa7MF6GobC9rvn/egXBBzboyaDE+ogMcYJODxHj8lupRdOy9RzOGJuqRN8RxXAHD0hOuGb0WQ\nRYpjLVSGJxlUlVlw0+IpIcfSyfsONLy7oXkL3KVN/7g/TqdTN1dFklytf11NC/R3VbxPEOLk6WlL\nHX6GR5ysjGH2Lw9FOwFee2CKFrMirR5NW2rx78GqjQ2Htu1hQY7is8/ULm2yrGlakIQlbQPt0uZ+\nT4PVsECoKMrBifZedPXaYDLIsGS7tjvYuhGDTc3waAZ9nT3WiBZEFBNOvVf5HqyTuWu1ek/pXZ+7\nvj/LqKBuvGvdoQY/83gCNWIJJNqSNu/nhlozKda0WTsR3GRnebq0iYVUjYqsfkZNx1xlrWJApb1x\nEMvvvMjyhDo2JEnCVeePx5malexjzXMjzrekDRh4SZt43eOaRUcB/cA8L8pOU/EQaHFwYVJtCeZP\nG465U4cOyt/3n+GR1ZJycf1V5/CEGZgrshxwHR5R0iZLoQf5on17U3OHOngejOuj9sZLZYBBuRiU\nKhGUtLlK1SW/z/U+9hM5TgH0N9oHug6P981+MXexMsbroQWjThFI4oAnXCnxDsTgTkxg9Sbm8Piu\nwyMOvMCv7XD4rp1jkCVdhsdT0qb/XU+Gx9OlLdIMT6zKkrR3eLQngCKLV4ZHnDjDuAuiW5A1wQe7\n9v0V5PqWJWjX4Qk1yS6RtPsxms9evKfBalggiHk8pzr6YDQoKM034oH/bw4WnzN6UP9uMOo6PJrv\nc2ePNaJ9MVqb4YlDlzZZlpBrNqolbb39nrKtCSOLAQAHmjt8fs87uxxKtCVt3s8ttCSoS5vTqe6b\nbE3TAl2Gx72dB5tdLW/FOg7eixnGSrgBTzyoc50CZXiiHORp12/q6bOhq8eqLjrp/boDKWkbbPqF\nR333RbbJ1W2sdljwDlvR8pfhMRoU9Rhqc3dq82Tkwi1pkwKuwyPGRVkmQ8gbrWJJiKZjnZ7FXwcj\nw+NuMCVLns543sT31aBI6gLs4cwdFKX63ssceF9HE32jU39Oi23TgqOiJXU8MzwhurSlktSYw+Pu\n194VYN0Kfz3qgfBL2rzvoqp1s17lX75tqd1zeNSStgjm8Li/ALHqVmMKUdJ20ifDE/o1te8l0eVh\n2raHhbm++0y7Dk8yd2nTDiCi6tIWh5I2QH9CFdnDcdXFg/o3Q1EnWGszPN39EbVSHlqeh9lTqjB1\nbHlcMjyAq2W2aJ/dJ7IYJgW5ZiNkWUJHl+8sS886PNEFPJGUOCUyw6NoPtNe93vONhnUm0siCDIa\nPBmeg2qGx7ekLZZ3d2dMqoTFHN3K7LFm1GQQtGy2yBaa9aZdQ0i0pC7RtIxOxZK2RNzh1w5M+212\nSJLre6WWlKsZnsjWtDPIMvqsdk8nWj9tqcNZ1qKsKAcmg4ymYx2aIDmsTYiIOJfkW0wBz6mixbQi\nyxhXXYzi/GxMqg3dyCLLPU/Zp6TNJ8OTHCVtdocTPVYHZCn6dugiqyLaUnsyPKEX7YwVdR2eJDgP\nDlRKBDySJCEn2xhwob5A5R/htKV2dWnznaticzgAnwxPgDk8fZ45PJF2aYtZwGPwf5fTu6QtkgxP\nPOY4hEvbX70wWIZHltQTXjIMVLy5FupybVc0GR5jvAIeTco8nFW248G7o5TD4URXj1Vd3ykciizh\nv66ZDgDYq2nTOpiDpNwcI1qPuAaTImORZVQgSRLyc0zo6A4c8IS7Wd4lbJGVtPneIIkX7d1Qa5/o\n0qaoF/g+Pxke0bhETIrWnkNj2ajk8gXjgAUxe7kBUbzm2giBlmQI/3XdmSOHU21YUKYpaYtV04LB\nph3jJiLg8ZS0OdBvc8BocH2/fUrKI21LrUiw9wVahyf8MYQiS6gqs7gyPNaBHTPBiCAn2CK6aobH\nIGH08EI8/9vzwnttoyh/CzWHJzlK2kRb6lyzMeqlGURWy6ZmeLqgyJK6MGw8sKQtAXKyDerEP2+B\nJuKrx1iIOTzebfsN7jSy99ygQE0L1HV4XH81xDtxyTYpEa9QG4x2HRrtlysv1wRZ8pS0qdmwML6A\nyVvSFjzDY0jiOTyAZ19Gl+Fxz+GJY8BjjKK5wmBQNINjwPW9czijH4hpO97Faq0gfyxmI/ptDvRb\n7eoAXsxJy8s1od1fhifCdXi0K2IDkQX7Yj/Ikn79mXjwDA6Anj7PvvFuWmDQZHhcjUskda6JvqQt\nOb/zA2UIlOGJMGPgTZtha3G3JtcOplImw6O57g5Wl65gtBkem82hZh3EmnNtXe4MjyPSpgWB1+GJ\nJMMDuMraevvtaHaXRQ1GSZt4X8VBsu7qWCXCTIwpQEmb99gkEZ+/lvY61dvvCGtR7GCvJcuSZw7P\niW6UF+XENahPhaYF4UqZdxAswxOo1bIYxIRaeNR7sKMoMqx2p2+XtkBtqXsjz/BIkoQLZo3EFeeO\nC+8XQhAnPX8T+PItWb5zeMLp0iZHN4AaDCFL2jTr8CRzlzbAsy+jCXbVLm2D1F5VSOoMj/v7HklL\nan/iVfroWYvH6mla4P7s83NN6Oyx6lptA5GvwwPoB1GRXBA9ZShZcb87rs1ciAYF5izNwqOaVvva\nz6m8yOy3tjzR5SyDRRuYaHkyBgPt0uZUS9q0c3i0177BaukcC9prlb+21INNXSPKfWNDlAGL9ZzE\nHJ5IPy/F3aXN3w2QSKtERCb828NtrtcehK+KuOZ6ByW654iStgg3QOxT7yx0snVpk3QZHqdukflo\nGA0yrDY7evtsONXRF9FCsLEgAlOjkhzjgIFImatDbrYRPX12n4EBELhLWzhzeJwO3yBFZHic0AcH\n3oMPcRHu7rO6twPhL5wBYMbEIZg9pSrs5wcjvvT+ovBCzeJn6iKtYQxsdHN4Ep3hMWozPH5K2rRz\neJJ4HR7As3hoNPs0Hm2pAVcnHGOS3dkRg1nxfY+0JbW3eE3GVNfi6e5XB/XagMfp9LwXwbMOT+Sl\naQZFiur34t2SGvBah0fdN9oube6SNkXWlVRoa9izMjjDY41wErw3WazD43CqazGVFGrn8HiapAxm\nFnSgQi08OtjE52PVlLQBnsF5W4d+Dk+4NxYM7i5tdj83Ks1RZHgA4BsR8AxKl7YIStoi/Pviex56\nDk9yNC3otzpgtTsHlOEBXOc+q82B5pPxb1gAMMOTENpFPr0FylqE05bab4ZHlt1tqV3/HyjDI7ZL\nLbWLIMMTa2pv+wABT1evDf0BJj8Gor1Tlui7JqIESJKAfLO/DI9YeFTS3LlPzgu0muExRn7nR7yn\nwe7SJsuSujr5YC1wGil1vkesMjxKAjI8mqYFgKdMyHseT6Tr8ACeu6uRBvqKGvDEt0MboMlcOJzo\nFXN4TAb1s9E1LdC8r0rNooba4zPR5SyDJeAcHvcAOtpstjZz1NrmKmkr07Wldj2el8TzdwD9XDfZ\nu0Y9DkRJqc3mgNXmyfCoXdq6vOfwRJDhsWszPJ7fs5hNyMk26DJywYiAZ9/hdtc2DMJ3RVQeiGuH\nPyZN+X0kLjtnNK698DTdwuqA6/wVqi15PIk/L5rRDPTmpCvD41BLEeOe4RFNC9Ig4EneHLUXEfB0\n+2lD66++FdBmeAK/rsNv0wJXW2qHO/sjAiJ/QUJOtkG3Dk+ivmzBTiLaTjGRDKS0141EH+xGg6v7\nWmFelt9ARlyQXcdJfAay0VLn8ERR0havLm0AUFGci0PHu9zbG+RLFCfq4MwZm4AnbhkeTcCjbVoA\neAZE3vN4PI1Ywv87higDODFYjnfDAsB7kT53W+osRR3Y+Vt4FNCvQ6FdgyxZv/MDFXgOT/hNaIK9\nrmhaYM4y6BbnFN+5aLOo8aLL8CToEDAaZFeGx+rJ8BgNCnKzDZ621BE2mTAoMhxOT2ZIez4wGmQ8\neuv8sIPRoWWugOdwi6vpx2BkQmZPGYpRwwpRURK4i5j4vkZ6Q3Lm6YGrYUwG2TPfL0kWHm1XKxBi\nEPDYHTjqbtZSGWTfDgYlTtfJeEiZdyBOwv4WHw3cpS2Mkjan02fwbzDI6p00SZLUTJG/jL45Kzky\nPFlBStq0nWLsEayyLCVRhgcATh9dimkTKvw+Nv20CvzqmmmYNbkq6ZsWqF3aojiBxKtLG+C5k5R0\nGR6HKGlzBzzRNi2I0xwe8Vl1dnvm8Iiug2qGxyvgiXQdHsBTehDpQEJ8TxJS0qad4Nvn2Tc+C49q\nmhYA+gzPYHVpSyaB5vCoTQuivPBoFx5tOdWD0kJ9lk/83WRuWAAAknYOT4IiHoO79Mhqs/ss/n3K\np0tbeNvoKY+yQ5Z9S1XLiszIDrPlcXaWQQ16AGAwpmSYswwYVVUQ9Dna8vNY0WZ9kqWkTWTtB7pI\neKIzPGLMkQxjwIFKmXcgMjxdPb6NC0J1afOqAlA5nU732jn6n4u6WYd7IVFR2uW/pM2I3n4bHE6n\n3/K4eAkW8KgZno7IMjxyEs3hAYDf3zALP1t6ht/HjAYF35ky1D1YEpPsEr/N/mRnGZBllKM64cer\nSxugCXiS4LMHNN1v3AGPWNsm2hrpeN25EgFZl6akTTuHB/CT4YlwHR7Ac0GKdMAnJqVGsp5RrOhK\n2jQZnmALjwLeGR7XvpSkxA92Bou27bGWfcAZHtf+6u61otNr0VHAc51I5pbUgNfCowm60WUxm3Cq\now82u1PX6KXAkoX2rn44HM7I5/C4P9d+myMmx/Z/LKtT/x1JQ5RYEsFJpF3agslyZ438BYXxJvar\nuIk18AyP4gp4xBo8cc7wcOHRBBAZHn9zeAJ3aXP91xmgHCdQJyTFXdIGdwAj7h75O0GYswxwOgGr\nzelqcR3+W4qpUE0LAFfAE6j8z59kC3jCVV7sumhXxHFxrkhcd9EkOM1HovpdEejEY76FmByZLJ+9\nZx0e16BBlLRFG/yJu+KDvb6AvqTNPTHfq6Qt4ByeaJoWRPh+xPMTmuFxONXgxqzJ8HgCHkU/h6fE\ndw5PunZoAzw3OnxK2hwDbUvt+j1x97i00H/Ak+wZHu3lLBFd2gBXtuXI166yI20r/wKLCQ6H09WN\nMdI5PO43ZrXGJuAZO6II/++WeVi54SuMqbIP+PWiIYKTWFZgGNVzQOJveIhtaI/RHB6DJsNjzjLE\nfT6dwjk88ZcbRoYnUFvqQBVtTvUuqv7nBkWG0+m6mLhK2oJkeNzp5D6rK6xyPTf+8x08AY9vnlo3\nhyeittSef6dSOnPM8CL87fcXJG3d+fiRxUBN6JWl/Vk6fyymji2PS1p7vHsV7HHVRUB/dAFaLHky\nPK7/H3BJm7hzNehNC9xd2nr6/balBtwXR00M65mkHP7fUefwRHjRF/s1EXN4tBkecTPLZFTUz6bf\n6snwiAtuXo5JN4gwDcIAKtkEzvCIgCe69y72mQh4SrwyPCIgyotDRnkgdG2pEzTo1QaLJq+SNsBV\nUm6LsKueJ8Njj9n84JqqAtx29TQ0NDTE5PUidcbYckwZc8x1XYkRU5TlvIPBZw7PQJsWKDJsNjuO\ntnZhSGlu3DNYRmZ44k+dw+NnLR5/LRuB0G2pRYdr3y5t7rtpNgckyXP3yG+GJ1sEPA5Xhidhc3gC\nD97UDI8m4JHDGEklU1vqSCVrsDNQhXlZAecxxVppoVldBbuhIfEBT6AMT7R3nw2KqwlG2SCvWi0C\nss5uq9+FRwHfgMeZKRkeTVvqvn4bsk0KZPdie1rakrZKr7asnoUMEz/YGSwi8LB7Z3hsAytpE9e6\nQBmelGlakOCFRwF9dzvvkjZAH/BE0qUNcAX+yZC9iIVRQwvwhxtnx/Q1RYYnnHHNYBPjplhleIwG\nV+OK3n573OfvANq21Mkxl3cgUijgcXdp6w3StCDCttSBBhXi4mG1OXRd2vydSM3aDI8zcRkeccfY\n32BHO4fHHkH3p2RaeJTIew5PxwDvoEmShMdum69b1HYwiO0Tc3hEx0HAK8OjSfqJLFZEc3jEXc4I\nL/qnjSxGU3NH0M5Kg0Vb0tbTZ1ebOXgP7gyKDEuOEVkmBaOHFeoeMw3CJOhkY3Cv3WXzmpA60JI2\n8XsnxKKjXk0LxLXDO8hMNsnQlrhM04rZu6QNcC0+GujmbCAimO/utSX9PKpESqoMj/ccnhgEPEIi\nyvSrSnNhUCTd8Z2qUijgcQ8a/GR4AnVpEye+QCVtnsX99D8XB5jV7ippC5bhEdvVb3NleBK3Dk94\nXdocDmfYE/tSdQ4PpSdxPIpBQ2ePFSaDPKAucvGYm5CTbYQkeebwaLuK5WYbIUuxWYdHbVoQ4UX/\norNrcdHZtRH9TqwomoUve/ttyM7yX4tvNMjIyTbi0VvPUYNEQdxNT4bBzmBR20fHuKTN0yXP9f/e\nTQtmTKzE47+ar+vulYy0l7NETcYvK9Q00tDcDddWWNjsjogWBhbHut3hTJsMz2AQ+zsZ9pFa0hbD\nDI+QiBsPF8yqwbz64erN/VSWMu8gWIbHc9fE/6BcBDY+Pw8wqFAnCtockKDN8Pi+hjbDk8gubcEC\nHqNBRq7ZiFMdfcg2GcK+IGiflqwdzyhziIG82qWt25oSdz1lWUJOthGd3a45PNr1l2RZgiXHFKN1\neFKvfag4pzqcrqYFIgD1PpeL85q/DkWeOTyp874jpZZZe5W0WQfYpc17gFjiVdImSRKGledF9drx\npL1WJWouV8AMT64r4Gnv7IPV6ojo+6kN7pOhXCtZJVOWV3xMngqEgd1U05aSJaKkDUBaBDtACrWl\nzg0yh0dMbBVpfyHUwD5QlzaDpqRN17TAb4bHM4cHiZzDYwgc8ACuu0ynOvtgdzjCviDoStqY4aEE\nkzXZAMDVBCB3gBeTeLGYjeh0l7R5l9Dl5Zh8MjzOqDI87qYFSXDRD5d2bkpvn03dN97nqGD146Yk\n6tA0WAYrw6M9VsxZitocKNXo2lInQdMC7Q3CAk3ToOaT3RGVBmkz0Il6X6lABJjJcA4Qx6K4OR/b\nDE9ydp5NFSlzZcwxi4DHN8NzsLkDADCkVJ92D3cOj3eQIi62Vu+mBSHm8Dic+smT8STuGgcaGBTm\nudYCsNmdYWd4WNJGyUTRlLQ5HE509VgHXB8dL5Yco7ukzY4so35QmZ9rQod7nQ5hQOvwpFBpl3Zh\nRbvDqS6i6H2uDbYWVFZGtKXWB/uCLcI2x960+7mkwJzwNUyipV14NFElbeYsg3o+0pbZFrizNIeO\nd6KrxxrRoFWb4UmGwXyyEueHZNhH2m0wKtKAx07a3y9PUIYnXaTMFUIEFv4Cnv1H21FaaPYZ/Hi6\ntPl/zUBd2rQtQCXJ0zHI/8Kjru3qtzkAOCElaI/muydGBurRXmjJgtPpalwQ7klB1n1xU+ZQoTQl\na0raevpscDiTf0FEwWI2oq/f7prD45Xhyc81weEE+vo95zYxNz2iLm3qxN3U+a6K9yfmZorzvL85\nPIGIwWU6z+FRZAmSFKwt9cCaFgC+83dSifZ7kshBr8jeaI9XEbTsOXAKADAkgoAnT1vSlqLBaDwk\nV0mb53PKNg38MxNjr+L8LN38T4pc4o+OMCmyBHOW4tO0oL2rHyfa+zBySL7P74i7PoHaUgfs0qbp\nHCSHKGlLljk8VaUW/PEns7H4nDF+H9euxRNualz7VljSRommbVogWlKnTIbHXXrndMJvwAMAXZqb\nOZ75heH/DaM6hyd1BkYiGyVuZIl9o+ju2AcfyGTCHB7AlcHyaUst1nWJcpCv/T3vltSpRDeHJ4GZ\nPrEPtRkeRZGRl2NS15mKJMOjLWlLpcxtvBmTKMOjHSdmmwZ+LIqxV7IupJ5KUuoKYc4y+szh2X+k\nHQBQXek7sVItaQvRtMBn4VHt4F7yHMD+MzyuAVef1QGnw5mggjaX02tLA9aLahcVDDfgYUkbJRNt\n04JOMSE0ydcHEbSZKO+7dGJQ093nObcNaB2eFBr4+2R4RFtqzXswhFj/IRMyPIDr/fm0pRYlbVGe\nn7WD6BKvltSpRBcgJ/DwF2vxeJdgitbUADCklCVtsaZ2aUuCc58U44BHjL0qkrw1fCpI/NERgVyz\nwaekbZ874PGX4QlZ0hagnbV2HQvXOjzw+zwAyNGuwxPgOclAtKYGwj9x6gKeJDiRUGbzZHgcKZjh\n8WynWGtGfcwdDPX02tWfeVrmh38+UVKxaYF7m7vcn6e/DE+omy2ZMIcHcA3mvDM8YhHeSNdeUl9T\n83upXNIGza3GRB4HZUWuQan3XFrt9TeS1sKWHJNn/MGAJ6Bkalyi/ZzMxlgEPK73lqgObekkpa4Q\nOe4Mj7ZEbf9Rd4bHT8AjOAMsBKp2afNuS62566Xr0uavaYHo0uZehyehKZ4gCi2RZ3i0gy2WtFGi\nqaWmTk1JW4rM4dFmXgOVtPVoMjyOAOemYFKyaYFXRyOzn6YFoQIeT/1+6rzvaBgUCVabPsMj/j/a\n9679vVQuadN+TRIZ904/rQI1Vfk4bVSx7ufi+itJkQ1cFVlSb5Ykw2A+WYmy1mS42aOfwxO7DE8l\nS9oGLPFHRwRysg2w2Z26k/6+I+1QZP9rBQTK8Ow5cBI3P/Q+Dhx1dXfzKWnTfGkkaLq0BZnD0291\nwulM3gxPUV4UGR7N0cGSNko0dZFEuxOd3SLDkyIlbboMjz7gEWV52ux1oOxzMMZUbFogi4DH9XmK\n7FekGZ7CvCy1nChdKbKsZnQE0bUt2vNz2szh0byPRGZ4qivz8f9uOQdVXh1jRVOh0kJz0Bbr/gRa\nm4o8jMm08GiMS9rqx5fj9NpS1I0vH/BrZbqUarovWlN39VphMipwOJw4cLQdQ8stfk/4/tpS2x1O\nPPL3z7DvSDu+2HfC/bwgGR5ZUk+m/s43BkWGySC75vAkcB2eUHRzeKJoS51KgyhKT+JiZndq5/Ck\nRoZHG5h5Z3hEZ0UxqRnQrsMT/t9IxTk8infAk+VbnhaqnFaWJTzxq+/67Nd0Y1Akn4VHB9y0QNel\nLXXn8GivVcnYWltkeCLp0Cbk55pwuKWLJW1BmJJoHR7t6SoWXdpqqgrwx5/MHvDrUIpleMRdUnF3\n99jJbvT02TGy0n85mzhBODU3xd75eL8678dqc9XMB5vDI0lS0KYFgKtxQZ8tsV3aQtHN4Yli4VFm\neCjR1AxPCnZpyw2naYGfLm2Zsg5PT5/rXKxmeJTIzj25ZmNKBXrRMCiyn4VHB7YOj8G9/7NMyoAX\nSEwkXZe2JIx7xfU3moUjRWvqZBjMJyt1HZ4kOAfo5vDEIMNDsZNSn4Y4abR19gHQdGgLMH9HDBbE\n4KGzux8vvvWF+ri4WxasS5uE4E0LAFdZW1+/w+9rJQtzlkG9AxpuhkdihoeSiJLCAY9FN4dHn1gX\nAY9uDo9YhyeKOTyp1GDE+/1lR9G0IFMoihw4wzPAhUdLU3jRUUB/TZOTsPSrxJ09G1pmCfFMX+L8\nkKzl8skgaUvaYtC0gGInpT4NkRY+5Q549h0N3KEN8AQfojzkb+u/REd3P8aNKALguVh4X3QNmv+X\nw8jwmLMN6LO6g6dk7VoAz/4LtxZY+3Y56KBEE98/e6q3pfYuaVObFmi6tEXVltr13GS4yxku7wFK\ntt+mBUl4yz4BDIrkM4fHpi48Gt11R5IkTBxVgvoUnx+gvaQpSRgYTJ9QgZsWT8b5M6sj/l3R1ERO\nocxtvCVT45JYNy2g2EmpT0MNeDpEhsfVdCBghscdfDgBHGzuwJubvsWQklxcMq8WgKfDje8cHm1J\nm2YdngAn0pxsA6wBskXJROy/cG+Aab+4zPBQoolAPTUzPJ7AzLtpQZZRgckg69YYi2odnlRsWuD1\n/tR1eLRzeHizBYD/DM9AS9oA4J6ffgc/vuT0AW1bomlvNCbDoNeboshYNKtGXbcvEvksaQtJ7dKW\nBNk9fcDDzyyZJP7oiICYeK9meI60w5xlQHmR/+4y4rhzOJx4+h+NsDuc+NFFE9U6cc8cHv3vGXRt\nqQHJvZcCZniyPCUqyZx2Fvsv3AyPNhDkoIMSTdFmeHqsMBlk3YrmyUzXltrPNls0K7ED2nV4wv8b\nnqYFyXsO8uZT0pblW5rClvguBlnymcMz0JK2dCF7VWWkEzXDk2bvK5bEwqPJkAXTjps4hye5pNSn\nIVYrPtXRB6vNjkPHO1FdmRew9lh0V/v082Zs+/IYzhhThhkTK9WLqcjweJ9I9Bme4OvwAK71gTy/\nEMUbixMR8IQ7L4BlJZRMtCVtXd3WlOnQBrgG8DnuNbv8dRPLzzV3/mZbAAAgAElEQVR5NS1w/Teq\ndXiS4C5nuHxK2kyekjZxWk6lOUmDSVFk2B1O3Tp07V39kGXJbxCdSVzXade/062bmdqWOgkG88nK\nmERd2mLdlppiJ6U+jcI818S/Ux19aDrWCYfDGXTBUXHcrd30DWRZwnUXT4IkSerAQNwdk7y+JAG7\ntAUIrIryI2/5nAgFlggDHs3TUumuMaUnfdOCfuSmyBo8gii/yzb5rgZgyTGit9+u3sGPah2eNMrw\nAJ7Pm9llF/G5irV3HA4n9h1pw7AAyzJkmnQNeJjhCU1keJKhnFeRGfAkq5T6NCxmIxRZQltnn9pa\nOlDDAsBzgrA7nFg0c6QaHIkTYuAubV4lbSFOpLqVk5P4nORpWhB5lzZmeCjRPN9bB7p6rOr6NalC\nzOPxdzde7dTW78ryeNbhCf+EMn5kMaaOLcOUsWUD3dS48e3SpikPdt944mDeRfG6Udd8wrUsQ82Q\ngkRuVtIQ1/tUynCGw9OWOr3eVyyVFpqRbVJ8FnxNBHFOkyXAZEjiAWEGSqmFR2VZQoHFhFOdfSFb\nUgOeAXtejhHLzh+v/lykhgOtw6MtaZMlSZ0nEKhsQNtbP5nvwkRc0sa21JRExHHb1WOFw6lvBJAK\nRAmev5K2vBwTugH09NpggXYdnvBfv6zIjLtumBWDLY0f75sv2nOsJ8PDmy2Ap/JANCr49nAbAKCm\nKvA1MJNIsgTYnREt1psKKktyMbzCggkjixK9KUkrP9eEv951QVKUdopxU67ZmNTjwUyUUgEPABRa\nsnG4pTOsDI8oIbnqvPHqHVTAc+HwZHgCl7QBwKiqAvzi8qmon+C/dac2w5PMx3dhhCVt2vfCu6yU\naGIA3N4lWlKnVoZHNC7wH/AY0QyojQvUkrY0K8/xpl0zJcuk6N4vS9r0xI06keH59rDrGlhTxQwP\nAMgQGZ70+s5kGRU8dtt3E70ZSS8Zgh3A0wU3lRfyTVdRBTw2mw3/9V//hcOHD0NRFNx9990YNmyY\n7jlr1qzBCy+8AEVRsHTpUixZsgR2ux133HEHDhw4AIfDgdtuuw11dXUR/e3CvCx8c7gNew6cQnF+\nti6Q8TZ/2nAMr8jDaTXFup/7Znjg93HXYxJkWcKCM0cE/Dvl2oAniWvaPF3awi9pkyXXBGp2SqJE\nE4NhsfBwqrSkFhbOqIbFbERpgW9XSXEe6xYBj2hakMx3UGJAeyoye81tEudhBjwu3nNPmeHRC9VN\nlSgexPGXatenTBBVwLN27VoUFBTggQcewEcffYQVK1bgoYceUh/v6enBY489htdeew0GgwFLlizB\nwoUL8e677yI7Oxsvv/wyvv76ayxfvhwrV66M6G+LTm0d3f2oGxd8sTSTUcHEUSU+P/fp0ubdtEDb\npS2Ma22WUYFBkWCzO5M6w1Ocn+3qFpUV/hdRkiTA6eSggxJODP7bRIYnxS4o0yZUYNqECr+PiQVU\ne3q95vAk8wklBiT3DSWHw6lrWAAww+NNBIBqSduRdhRaslCUn53IzUoangwPjxdKHG1JGyWXqM4M\nW7ZswYIFCwAAs2bNwrZt23SP79ixA5MnT0Zubi6ysrJQV1eHbdu24aKLLsLy5csBAMXFxWhra4v4\nb4tObUDw+TvBiDk6gRYe1a3DE+525bou1g6nM8QzEyfXbMQfbpyFa79/Wti/I4JBzuGhRBPf2y73\noqO5KVbSFkx+ruu9eJe0pXm8A8AzQPDuXqc2LeC5B4BnP9gcrqYdx050YySzOyrPZPEM+NJQ0hLj\nyVSbY5oJorqStLS0oLjYVSbmukMnw2az+X0ccAU3x48fh8FgQFaWq6zq+eefx4UXXhjx3xbzUIDg\n83eCUTTdnlzvwetxr3V4wtquXNfFuvVUb1TbFC+TaktR4qekJhBxBzbd6qIp9Xgfgul0QREZnu5e\nVzDniKJLW6oS7zHb5D/Dw3JaF3Fdstud6hxWzt/xEJdqJTmmclCGEuctZniST8iStpUrV2LVqlXq\nwN/pdGLnzp265zgcDn+/qnJ6ZT1eeuklfP7553jiiSfC2siGhgb13ydbutR/d59qQkPDsbBeQ6ut\nyxWcdfe45gIcPXIEDQ3d6uPNp6yev9Hdrfv7gRRaXGfZw8fbsWdPEwCgoyhwV5W8PXtCPicZOJ0O\nKBLC2gfxlGzbE6lU+fy1Er3PrTb9eeTo4f1Rff+TkTjnHGluRUNDA5qbTwIAvvjiC5w4kuYXTqfr\n+tHfpz/XWq2u0sXmo4fR0NARt81J9HEeyIlW1zGxc1cj9h9z7Rv0tSbt9oYr1PaHc67M27MHdrtr\nTu7er79GZ2lp7DYwjYV77IT7GWgl8toW7rYMxnW4td11Lrf2nASQn/Lfz3QSMuBZunQpli5dqvvZ\n8uXL0dLSgnHjxqmZHYPB81Ll5eU4fvy4+v/Nzc2YOnUqAFcA9f777+Oxxx6DEuatmPr6evXfUu4x\nvLF1C2RZwnnzzoyqZemJ9l7gH0chyQoAO4YOHYr6+nHq4webO4C3mgEAFkuu7u8Hsunz9wEAvVYn\nxo4dKzY88C+cPBn6OUnA+PpRSJIU1j6Il4aGhqTanqikyOcvJMM+t9ocwN8Pqf9/xukTcFqN7xy9\nVNTa1oPNeAembNf55uNvdwBfdeH0SRMxvCIv0Zs3qIyrm9Fvs6K8tEh3jOVu2IATHZ0YVVON+vqa\nuGxLMhzngWxvagT27MW4cRPwzYl9AE5h/uwzoq50SAZh7e9wzpUnT8L06f+gt78f48aNS5nzaiJF\ndKyH+RnoJPIzCHdbBuk6PGZcG6rKLGjc+VnSnk/SVbAAM6pagdmzZ2PdunUAgPfeew8zZszQPT5l\nyhQ0Njais7MTXV1d2L59O+rr63Hw4EG8+uqrePTRR2E0RnfXUjQtGFqWG/X6DGpJm2ha4L3wqNc6\nPOEQc3jSjSRJnDRMScG7rDLVmhYE49ulLXPm8IjPNTvLq0sb5/DoiLmlNocD3x5ug0GRMaw88Qst\nJgtZksIuQScaTDVVBUnTJps8ourStmjRInz00UdYtmwZsrKycM899wAAnnzyScyYMQNTpkzBLbfc\ngh/96EeQZRk///nPYbFY8NRTT6GtrQ0//vGP4XQ6IUkSnn32WV12KJSyohwYFBljR0SfggzVtEDb\nljrc8+eQYteAxXUBSt7GBZGSJYkNCygpyLIEd9NAAJ55L+nAZFRgNMrozbB1eADtHB7vpgXs0qal\nXresDuw/2oERFXk8N2tIkqS7dhMRaUUV8MiyjLvvvtvn59dff73674ULF2LhwoW6x2+++WbcfPPN\n0fxJVX6uCQ/dPBelBdG34lTbUtv9t6U2RtG0oCTPgId+ORdlRWZg67+i3rZkI8sccFDykCUJdnfE\nk04ZHgDIMSno6tVneDKh45SnS1ugttS8UwoABvf+OHisA/1WOzu0eZGlzLhBQETRiSrgSbSB1iyL\n0gCnurif/nF9l7bwX3f08MIBbVcymjNlKMzZKXmYUBpSZAl2hxMmowJTmpUMmLOMOOVeVNWZIQuP\nAp6MutmnpI0ZHi1xXfr64CkA7NDmrawoB+as/kRvBhElqYwcycpeC5MFW4cnEwYcwdxw2eREbwKR\nKp1Xsc4xG3D0RDdsdodmHZ70P/8EzPC4B/hsS+0irktfqQEPMzxaV5w7zqcjLBGRkJEBj+I1F8B3\nDg8vsETJSNz1t6TRoqOCyHB0dls16/AkcoviI3DTAmZ4tMR16UCzq0U3Mzx6rpsh6X+DgIiik7FX\nEm3HJ58ubTIzPETJKJ0zPCLg6ejuz6iStkALj7JpgZ64LjkcTpQUZCM/N32adhARDbaMvZIoQRoT\naCc+ZsB4gyhliFbFFnP6DfZysl1BXEd3P7u0QZPhYcYdgL60j9kdIqLIZOyVRJfh8RpUSJpWzJlQ\nQ0+UKkSJV3qWtLkyHNqStkw4/4Rch4cZHgCe/QFw/g4RUaQy9koSrKQN8EwQzYDxBlHKkNUMT/oF\nPDlZrvfU3pWpGR7vpgVsS62lbaZTM4QZHiKiSGRuwBNirR2FGR6ipJPWc3jc7d87e/oDtsxPR+Lm\nk3dbas7h0dNes7gGDxFRZDKySxugz/D4C2qY4SFKPuJ7m5uGJW2eOTzWDF14VH85mjq2HKc6+lCY\nl5WIzUo64ppkMiqoKrMkeGuIiFJL5gY8mrtl/u6iinrpTBhwEKUK8X3My0m/pgViDk9HV79nDk8G\npHgClbSdd1Y1zjurOhGblJTENau6Mk93w46IiELL2FqBcDM8RJQ8xLyOtCxpy/LTpS0DbriIm0ve\nTQtIz+DeTyOHsJyNiChSGXuF0Xdp831cdGnLhEnDRKlCXXg0LdtSuzM83f3qzzLh9DOyKh8n2nuQ\nZWRzgmBGDS1AdWUe5tYNS/SmEBGlnIwNeAy6krZgTQvitklEFILatCAN5/AYFAUmo4zWbity3fN5\nMuGGy48vngTHRZMy4r0ORGFeFh79z/mJ3gwiopSUsSVtcrhNC8CLMFGyUNK4SxsA5GQb0Nndn1Hr\n8EiSxDkpREQ0qDI24NHO0WGGhyg1iO9qOmZ4AMBsMqpzeHjuISIiio2MLWnTrlot+ZvD477jmAmT\nholSxeQxZcg2GdJ2MUpztoKePjusNjsDHiIiohjJ2IAnVEmb2raagw6ipHH1BRMSvQmDypxtBGxA\ne1d/RjQsICIiigeWtMF/JySjwnV4iCi+ctytmdu6+jl/kIiIKEYyNuDRlbT5zfC4mxZwzEFEcSIC\nnr5+lrQRERHFSuYGPNoMj58Uj0FtWsBRBxHFhznbU2XMUw8REVFsZG7AI4fq0sYMDxHFl8jwACyn\nJSIiipXMDXgUbUmb7+MGmXN4iCi+zFnM8BAREcVa5gY8Ibu0cbRBRPFlzvasL8SAh4iIKDYY8MB/\nlzYxh8ff/B4iosGQwzk8REREMZexAY9BCd6lTfs4EVE8mDmHh4iIKOYydlQvh9m0gIMOIooX3Rye\nBG4HERFROsnYgEebwQneljpum0REGc6gyGrQw3MPERFRbGRswKNvWhD4ca7DQ0TxlJfjalzAUw8R\nEVFsZGzAE6qkzWBghoeI4i8v1wSA5bRERESxkrEBjyHUOjwK1+EhovjLM7sCHp56iIiIYiNjA56Q\n6/CIxznoIKI4srCkjYiIKKYyN+DRNi0I0paaGR4iiidR0sb5g0RERLGRuQFPqKYFCpsWEFH85eWI\nOTwJ3hAiIqI0wYAHIdpSx22LiIg8AQ/PPURERLGRuQFPmCVtTPAQUTyxLTUREVFsZW7AE6KkzeAu\nafOX/SEiGixqhocRDxERUUxkbMAjAhogUJe2jN01RJRAnMNDREQUWxk7qpc1AU2wOTzs0kZE8cS2\n1ERERLGVsQGPPsPj+zi7tBFRIuTncuFRIiKiWMrYgEfXpY1NC4goSVjMRmSZFGQZM/b0TEREFFOG\nRG9Aomjn6PgLeIwG2f08RjxEFD+KIuOPN81G0749id4UIiKitJCxtxCVEE0Lxo8sxqXzRuPsqcPi\nuVlERBg7oggFuRl7P4qIiCimMvaKql941PfxLKOCH31/Yhy3iIiIiIiIYi2DMzyet87GBERERERE\n6SlzA54QC48SEREREVHqy9yARwnepY2IiIiIiFJf5gY8IRYeJSIiIiKi1Je5AU+ILm1ERERERJT6\nMjbgMejW4UnghhARERER0aDJ2ICHGR4iIiIiovSXsQGPzC5tRERERERpL2MDHoOiLWljxENERERE\nlI4yNuDRr8PDgIeIiIiIKB1lcMDDttREREREROkucwMe3cKjCdwQIiIiIiIaNJkb8LCkjYiIiIgo\n7WVuwONuWsDsDhERERFR+srcgMcd6TC7Q0RERESUvqIKeGw2G2699VYsW7YMV199NZqamnyes2bN\nGixZsgSXX345Vq1apXuspaUFZ555Jj799NPotjoGDAoDHiIiIiKidBdVwLN27VoUFBTg5Zdfxo03\n3ogVK1boHu/p6cFjjz2G559/Hi+88AKef/55tLe3q4/ff//9GD58+MC2fIBkmSVtRERERETpLqqA\nZ8uWLViwYAEAYNasWdi2bZvu8R07dmDy5MnIzc1FVlYW6urq1Ods3boVeXl5GDt27AA3fWAUWYIk\nsSU1EREREVE6iyrgaWlpQXFxMQBXSZgsy7DZbH4fB4Di4mIcP34cVqsVjz/+OH75y18OcLNjwxX0\nMOAhIiIiIkpXhlBPWLlyJVatWqUGBk6nEzt37tQ9x+FwBH0Np9MJAHjyySdx5ZVXwmKx6H4eSkND\nQ1jPi4bDYY/Z64vXyduzBwDQUVQU8LnhPIcCG8xjIh5S8fNP9X2e7PwdE9zn8cd9Hl+h9nck11Mh\nlc6riRTusZ5qn0G42xKP6zDPJ8kjZMCzdOlSLF26VPez5cuXo6WlBePGjVMzOwaD56XKy8tx/Phx\n9f+bm5sxdepUrF69Gv/617/w3HPP4cCBA9i1axcefvhh1NbWBt2G+vr6iN5UuEyvH4UsSTF5/YaG\nBs/rnDzp+m+w1w3nOeSXbl+nqhT7/NNinyc7r2OC+zz+uM/jK6z9Hcn1VOBnGFJEx3qqfQbhbssg\nX4d5Pom/YAFmVCVts2fPxrp16wAA7733HmbMmKF7fMqUKWhsbERnZye6urqwfft21NfX4+WXX8Yr\nr7yCV199FfPmzcNvf/vbkMHOYGJJGxERERFReguZ4fFn0aJF+Oijj7Bs2TJkZWXhnnvuAeAqWZsx\nYwamTJmCW265BT/60Y8gyzJ+/vOfq2VsyURR5LDL6oiIiIiIKPVEFfDIsoy7777b5+fXX3+9+u+F\nCxdi4cKFAV/D3+/HmyJLsAeffkRERERERCksqoAnXSiyBCZ4iIiIiIjSV0YHPMX52ei3MsVDRERE\nRJSuMjrg+e2PZ3IODxERERFRGsvogMdiNiZ6E4iIiIiIaBBF1ZaaiIiIiIgoFTDgISIiIiKitMWA\nh4iIiIiI0hYDHiIiIiIiSlsMeIiIiIiIKG0x4CEiIiIiorTFgIeIiIiIiNIWAx4iIiIiIkpbDHiI\niIiIiChtMeAhIiIiIqK0xYCHiIiIiIjSFgMeIiIiIiJKWwx4iIiIiIgobTHgISIiIiKitMWAh4iI\niIiI0hYDHiIiIiIiSlsMeIiIiIiIKG0x4CEiIiIiorTFgIeIiIiIiNIWAx4iIiIiIkpbDHiIiIiI\niChtMeAhIiIiIqK0xYCHiIiIiIjSFgMeIiIiIiJKWwx4iIiIiIgobTHgISIiIiKitMWAh4iIiIiI\n0hYDHiIiIiIiSlsMeIiIiIiIKG0x4CEiIiIiorTFgIeIiIiIiNIWAx4iIiIiIkpbDHiIiIiIiCht\nMeAhIiIiIqK0xYCHiIiIiIjSFgMeIiIiIiJKWwx4iIiIiIgobTHgISIiIiKitMWAh4iIiIiI0hYD\nHiIiIiIiSlsMeIiIiIiIKG0x4CEiIiIiorTFgIeIiIiIiNIWAx4iIiIiIkpbDHiIiIiIiChtMeAh\nIiIiIqK0xYCHiIiIiIjSFgMeIiIiIiJKWwx4iIiIiIgobTHgISIiIiKitMWAh4iIiIiI0hYDHiIi\nIiIiSlsMeIiIiIiIKG0x4CEiIiIiorQVVcBjs9lw6623YtmyZbj66qvR1NTk85w1a9ZgyZIluPzy\ny7Fq1Sr158888wwuueQSLF26FI2NjdFvORERERERUQiGaH5p7dq1KCgowAMPPICPPvoIK1aswEMP\nPaQ+3tPTg8ceewyvvfYaDAYDlixZgoULF+LYsWN4++23sXr1auzevRsbNmzApEmTYvZmiIiIiIiI\ntKIKeLZs2YJLLrkEADBr1izcfvvtusd37NiByZMnIzc3FwBQV1eHhoYGfP3117jgggsgSRImTJiA\nCRMmDHDziYiIiIiIAouqpK2lpQXFxcUAAEmSIMsybDab38cBoLi4GMePH8ehQ4dw+PBhXHfddbj2\n2muxe/fuAW4+ERERERFRYCEzPCtXrsSqVasgSRIAwOl0YufOnbrnOByOoK/hdDohSRKcTiccDgee\nfvppNDQ04Ne//rVufg8REREREVEsSU6n0xnpLy1fvhwXXnghZs+eDZvNhu9+97v44IMP1Mc/+eQT\nvPrqq1ixYoX6/PPPPx+7du3CqFGjsGjRIgCucrjNmzcH/VsNDQ2Rbh4REREREWWY+vp6vz+Pag7P\n7NmzsW7dOsyePRvvvfceZsyYoXt8ypQp+M1vfoPOzk5IkoTt27fjjjvuQGFhIV555RUsWrQIe/fu\nRWVlZdQbTkREREREFEpUGR6Hw4E77rgD+/fvR1ZWFu655x5UVFTgySefxIwZMzBlyhS88847ePrp\npyHLMq6++mp873vfAwA88sgj+OijjwC4Mj9TpkyJ7TsiIiIiIiJyiyrgISIiIiIiSgVRdWkjIiIi\nIiJKBQx4iIiIiIgobTHgISIiIiKKAmeGpAYGPJSU+vr6eBKhtHf8+PFEbwJRXPB8Tunoyy+/xJEj\nRwDwGI+X/v7+qH6PAU+Yent70d7enujNSHu9vb24/fbb8bvf/Q4vv/xyojcnY7z++uv45JNP0Nvb\nm+hNyQjffvstbrjhBvzud7/DypUrE705GWPbtm04evQoAA5O4qG7uxsrV67EiRMn1MXLKT56enrw\npz/9ieOWQdLd3Y0nnngCV199Ne69914A4DEeB2+88QZ+9rOfobGxMeLfjWodnkyzevVqPPbYY5g3\nbx7q6upwwQUXJHqT0taqVauQlZWFG2+8Ebt27YLD4YAkSTyRDAKn04mTJ0/izjvvRH9/P2prayHL\nMqZNm5boTUt769atQ11dHZYtW4Yvv/wy0ZuT9vbt24ebb74ZQ4cOhdFoxH333Qej0ZjozUprW7du\nxQMPPICqqiq0trZi8eLFKCsrS/RmZYSVK1di/fr1OHr0KCZNmoQFCxYkepPSyqZNm/DYY49h5syZ\neOKJJ/Dtt98CcC3ZIsvMIwyGLVu24KWXXgIAZGdno6qqKuLXYMATwrFjx/Duu+/i6aefxpAhQ9DZ\n2ZnoTUprn376Kc477zxUVFSgra0NbW1tKCoqSvRmpSVJkmC32wEATzzxRIK3JjM4nU44nU58/PHH\n+PWvf428vDzY7XZ8/vnnOO200xK9eWnF6XSqN0q+/vprLFy4EDfddBNOnDihBjva51BstbS0YMGC\nBbjxxhsTvSkZ48SJE3j44YfR29uLW265BZs3b8aYMWMSvVlpx2Aw4N5778Xw4cOxadMmfPTRR1i8\neDGDnUHS2tqKVatW4fLLL8ecOXNw//33o6GhAeeee25Er6Pceeeddw7OJqauzs5OmEwmAK4L4muv\nvYbFixejs7MTH3/8MSRJQklJSYK3MvWdPHkSv//972Gz2TB69GgArjTxnj178OGHH+K1117De++9\nh3379mHGjBkJ3tr0YLPZsG3bNpSUlMBgMODLL7/E/v37cfrpp+OZZ57B3//+d/T29qKoqAgWiyXR\nm5sWDh48iGXLluGMM85AeXk5JEnCkSNHsGbNGmzfvh0NDQ34xz/+AYfDgaFDh8JsNid6k1OezWaD\nzWaDweC6p/fGG2/g1KlTmDt3Ll588UXs2rULZWVlyM/PT/CWpo8jR46go6MDeXl5AIC1a9eivLwc\nI0eOxB/+8Ad89tlnMJvNqKysTPCWph8xZjEYDKiursbll1+O0tJSvPvuuzh+/DjOOOOMRG9iShNj\nlf7+fowZMwbDhg1DQUEBAMBisaCxsRETJkxAbm5ugrc0fYixSlFREfLz83HeeeehuroaAPDVV1+h\ntrYWlZWVEd20YsDj5dVXX8WKFSswYcIElJWV4cSJEzh69Ci+/fZbPP/883A6nXj55ZchyzImTJig\nllxR5P73f/8X77//PrZv346LL74YsizjyJEj2LNnD2RZxp/+9CdMmjQJjz76KM4++2wOTmLgzjvv\nxD//+U9UVlaiuroahYWFeO6553DkyBHk5eVh9uzZaGhowCeffIJ58+YlenPTwueff47169fjyJEj\nOO+88wAAZWVleP/99zFs2DD89re/xfDhw7F582bU1NSw7GeATp06hcWLF2P37t1qKc/w4cPx0ksv\n4X/+539gsVhw/PhxbNiwAUOGDEFFRUWCtzj1tbe3Y/HixXA4HBg1ahRyc3Nhs9lw//33w2azYdSo\nUbDZbNi6dav6HIoNMWYZP348KisrUVxcrGaSW1tbUVRUhNraWmYzB0CMVT777DN1rGK32yHLMg4d\nOoQdO3Zg4cKFzPDEkBirVFVVYcSIEQCg7vM333wTHR0dqKuri+i45qfjZd++fRg7dixee+01AEBl\nZSVycnKwc+dOXHPNNbjtttvw85//HI888ggA8ACP0M6dO9V/b9myBddeey2qqqrw+OOPAwCmTp2K\n0tJS9PT04MSJExg1ahSmTp2Kd999N1GbnPJER5OOjg4cOHAAU6ZMwe7du3H06FGYzWZccskl+OCD\nD3D++efj7LPPxrJly9De3o6DBw8meMtTk9VqxdatW9Ha2goAaGpqwooVK/DVV19h3bp1AIDy8nJM\nnz4dO3bsAADMnDkTbW1tarcfil5LSwvq6uqwbds2dX5UYWEhZs6ciQMHDuCnP/0p7rjjDhQXF6uP\ns4FBdMR++/rrr9WS771798LhcGDOnDmora3Fjh07sGTJElx55ZWYMGECvv32W7WUlgZOjFlWr16t\n/szpdEKWZXR2duJf//pXArcudQUaq3iXf48ePRpffvkl3n//fQDgsT0A/sYqX3zxhdrNVJxvLr74\nYuzatQvd3d0RjcEzPsOza9cufPbZZ6ipqYHNZsPmzZtxwQUXoKGhAQBQW1uL4uJi7NixA0OGDMHY\nsWMxYsQI7Nq1C6eddpqa1qTgdu/ejd/+9rfYuHEjvv76a9jtdvzgBz/AiBEjMHz4cDzzzDOYPXs2\nKioqoCiKWj5YWlqKdevW4ZJLLmEpRISam5vxyCOP4OOPP3ZDHIUAABw5SURBVMaQIUNQWVmJ008/\nHUOHDsXOnTvhdDoxZswYTJw4Ee+99x4KCgowbtw4HDlyBI2Njbj00kt5RzBM4i7Tx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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dates = sentiment_df.index[sentiment_df['total_scanned_messages']>np.std(sentiment_df['total_scanned_messages'])*4]\n", + "\n", + "ax = pricing.pct_change(1).plot();\n", + "ax.plot();\n", + "for i in range(len(dates)):\n", + " ax.axvline(dates[i], c='r', alpha = 0.3);\n", + " \n", + "\n", + "\n", + "print 'std:', np.std(pricing.pct_change(1));\n", + "print 'std after spike:'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sentiment volume seems like a lagging indicator of price shocks. " + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0192386307822\n", + "0.0143365216364\n", + "0.0247164886291\n", + "0.0114021376902\n", + "0.00693723389722\n", + "nan\n", + "nan\n", + "0.015326202527\n" + ] + } + ], + "source": [ + "stds = np.zeros(len(dates))\n", + "for i in range(len(dates)):\n", + " stds[i] = np.std(pricing.pct_change(1).ix[dates[i].date():dates[i].date()+timedelta(days=5)])\n", + " print stds[i]\n", + " \n", + "print np.mean(stds[:-2])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/case_studies/sentiment/preview.html b/case_studies/sentiment/preview.html new file mode 100644 index 00000000..e5ea3846 --- /dev/null +++ b/case_studies/sentiment/preview.html @@ -0,0 +1,14800 @@ + + + sentiment + + + + + + + + + + + + + + + + +
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Researching & Developing a Market Neutral Strategy¶

Stocktwits & Twitter Trader Sentiment¶

The process involves the following steps:

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  • Researching partner data.
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  • Designing a pipeline.
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  • Analyzing an alpha factor with Alphalens.
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  • Implementing our factor in the IDE (see backtest in next comment).
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  • Evaluating the backtest using Pyfolio.
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Part 1 - Investigate the Data with Blaze¶

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In [1]:
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import matplotlib.pyplot as plt
+from odo import odo
+import pandas as pd
+import blaze as bz
+import numpy as np
+import scipy.stats as stats
+from datetime import timedelta
+from statsmodels import regression
+import statsmodels.api as sm
+from quantopian.interactive.data.psychsignal import aggregated_twitter_withretweets_stocktwits as sentiment
+from quantopian.interactive.data.sentdex import sentiment_free
+
+ +
+
+
+ +
+
+
+
In [103]:
+
+
+
sentiment[:3]
+
+ +
+
+
+ +
+
+ + +
+ +
Out[103]:
+ + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
sourcesymbolbullish_intensitybearish_intensitybull_minus_bearbull_scored_messagesbear_scored_messagesbull_bear_msg_ratiototal_scanned_messagessidasof_datetimestamp
0stocktwits+twitter_withretweetsADAP0.000.000.000.00.00.07.0490152016-01-01 05:00:002016-01-02 05:00:00
1stocktwits+twitter_withretweetsLIFE0.002.56-2.560.02.00.02.0490232016-01-01 05:00:002016-01-02 05:00:00
2stocktwits+twitter_withretweetsEQGP2.250.002.251.00.00.01.0490252016-01-01 05:00:002016-01-02 05:00:00
+
+ +
+ +
+
+ +
+
+
+
In [166]:
+
+
+
sid = symbols('XOM').sid
+sentiment_df = bz.compute(sentiment[(sentiment.sid == sid) & (sentiment.asof_date >= '2016-01-01')]).set_index(['timestamp']).sort_index()
+print "%s %s %-8s %s" % ('Start Date:', sentiment_df.index[0].date(), 'End Date:', sentiment_df.index[-1].date())
+print "Columns: %21s" % sentiment_df.columns[0]
+for i in range(1,9):
+    print "{:>30}".format(sentiment_df.columns[i])
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Start Date: 2016-01-02 End Date: 2017-06-15
+Columns:                source
+                        symbol
+             bullish_intensity
+             bearish_intensity
+               bull_minus_bear
+          bull_scored_messages
+          bear_scored_messages
+           bull_bear_msg_ratio
+        total_scanned_messages
+
+
+
+ +
+
+ +
+
+
+
In [167]:
+
+
+
pricing = get_pricing('XOM',
+                        fields = 'price',
+                        start_date = '2016-01-01',
+                        end_date = '2017-06-01')
+
+ +
+
+
+ +
+
+
+
In [168]:
+
+
+
ax = sentiment_df['total_scanned_messages'].plot(c = 'r', alpha = 0.3);
+pricing.plot(ax=ax.twinx());
+ax.hlines(xmin='2016-01-01',xmax='2017-06-01',y=0);
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
In [177]:
+
+
+
dates = sentiment_df.index[sentiment_df['total_scanned_messages']>np.std(sentiment_df['total_scanned_messages'])*4]
+
+ax = pricing.pct_change(1).plot();
+ax.plot();
+for i in range(len(dates)):
+    ax.axvline(dates[i], c='r', alpha = 0.3);
+    
+
+
+print 'std:', np.std(pricing.pct_change(1));
+print 'std after spike:'
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
std: 0.0109092872406
+std after spike:
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+

Sentiment volume seems like a lagging indicator of price shocks.

+ +
+
+
+
+
+
In [156]:
+
+
+
stds = np.zeros(len(dates))
+for i in range(len(dates)):
+    stds[i] = np.std(pricing.pct_change(1).ix[dates[i].date():dates[i].date()+timedelta(days=5)])
+    print stds[i]
+    
+print np.mean(stds[:-2])
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
0.0192386307822
+0.0143365216364
+0.0247164886291
+0.0114021376902
+0.00693723389722
+nan
+nan
+0.015326202527
+
+
+
+ +
+
+ +
+
+
+ diff --git a/case_studies/unemployment/notebook.ipynb b/case_studies/unemployment/notebook.ipynb new file mode 100644 index 00000000..82cae02d --- /dev/null +++ b/case_studies/unemployment/notebook.ipynb @@ -0,0 +1,2131 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Boyd, John H., et al. “The Stock Market's Reaction to Unemployment News: Why Bad News Is Usually Good for Stocks.” The Journal of Finance, vol. 60, no. 2, 2005, pp. 649–672. JSTOR, www.jstor.org/stable/3694763\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "from odo import odo\n", + "import pandas as pd\n", + "import blaze as bz\n", + "import numpy as np\n", + "import scipy.stats as stats\n", + "import datetime\n", + "import statsmodels.tsa as tsa\n", + "from statsmodels import regression\n", + "import statsmodels.api as sm\n", + "from quantopian.interactive.data.quandl import fred_unrate, fred_gdp" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
valueasof_datetimestamp
03.41948-01-011948-01-02
13.81948-02-011948-02-02
24.01948-03-011948-03-02
33.91948-04-011948-04-02
43.51948-05-011948-05-02
53.61948-06-011948-06-02
63.61948-07-011948-07-02
73.91948-08-011948-08-02
83.81948-09-011948-09-02
93.71948-10-011948-10-02
103.81948-11-011948-11-02
" + ], + "text/plain": [ + " value asof_date timestamp\n", + "0 3.4 1948-01-01 1948-01-02\n", + "1 3.8 1948-02-01 1948-02-02\n", + "2 4.0 1948-03-01 1948-03-02\n", + "3 3.9 1948-04-01 1948-04-02\n", + "4 3.5 1948-05-01 1948-05-02\n", + "5 3.6 1948-06-01 1948-06-02\n", + "6 3.6 1948-07-01 1948-07-02\n", + "7 3.9 1948-08-01 1948-08-02\n", + "8 3.8 1948-09-01 1948-09-02\n", + "9 3.7 1948-10-01 1948-10-02\n", + "..." + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fred_unrate.sort('asof_date',ascending=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "unemployment = bz.compute(fred_unrate).set_index(['asof_date']).sort_index()#.drop('timestamp', 1)\n", + "# data.columns = ['unrate']\n", + "# data['GDP'] = bz.compute(fred_gdp).set_index(['asof_date']).sort_index().drop('timestamp', 1)\n", + "# data['unrate_change'] = data['unrate'].pct_change(1)\n", + "# data['GDP_change'] = data['GDP'].pct_change(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "dates = pd.to_datetime(data.index, yearfirst = True)\n", + "\n", + "new_index = []\n", + "\n", + "for date in dates:\n", + " next_month = datetime.datetime(date.year + (date.month / 12),\n", + " (date.month % 12) + 1, 1)\n", + " for i in range(7):\n", + " test_date = datetime.datetime(next_month.year, next_month.month, i+1)\n", + " if (test_date.weekday() == 5):\n", + " annc_date = test_date\n", + " \n", + " new_index.append(annc_date)\n", + " \n", + "unemployment['new_dates'] = new_index\n", + "\n", + "# data = data.set_index(['fixed_dates'])" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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valuetimestampnew_dates
asof_date
2015-05-015.52015-05-02 00:00:00.0000002015-06-06
2015-06-015.32015-06-02 00:00:00.0000002015-07-04
2015-07-015.32015-07-02 00:00:00.0000002015-08-01
2015-08-015.12015-08-02 00:00:00.0000002015-09-05
2015-09-015.12015-09-02 00:00:00.0000002015-10-03
2015-10-015.02015-10-02 00:00:00.0000002015-11-07
2015-11-015.02015-11-02 00:00:00.0000002015-12-05
2015-12-015.02016-01-09 04:08:01.4321942016-01-02
2016-01-014.92016-02-06 04:02:32.4608302016-02-06
2016-02-014.92016-03-05 05:01:14.2790022016-03-05
2016-03-015.02016-04-02 05:00:44.6653632016-04-02
\n", + "
" + ], + "text/plain": [ + " value timestamp new_dates\n", + "asof_date \n", + "2015-05-01 5.5 2015-05-02 00:00:00.000000 2015-06-06\n", + "2015-06-01 5.3 2015-06-02 00:00:00.000000 2015-07-04\n", + "2015-07-01 5.3 2015-07-02 00:00:00.000000 2015-08-01\n", + "2015-08-01 5.1 2015-08-02 00:00:00.000000 2015-09-05\n", + "2015-09-01 5.1 2015-09-02 00:00:00.000000 2015-10-03\n", + "2015-10-01 5.0 2015-10-02 00:00:00.000000 2015-11-07\n", + "2015-11-01 5.0 2015-11-02 00:00:00.000000 2015-12-05\n", + "2015-12-01 5.0 2016-01-09 04:08:01.432194 2016-01-02\n", + "2016-01-01 4.9 2016-02-06 04:02:32.460830 2016-02-06\n", + "2016-02-01 4.9 2016-03-05 05:01:14.279002 2016-03-05\n", + "2016-03-01 5.0 2016-04-02 05:00:44.665363 2016-04-02" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "unemployment.loc['2015-05-01':'2016-03-01']" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-------- FRED Unemployment Rate Data --------\n", + "Start Date: 1948-02-06 End Date: 2017-06-02\n", + "Min Value: 2.5 Max Value: 10.8\n", + "Avg Value: 5.80300120048 Median Value: 5.6\n", + "Frequency: monthly\n", + "\n", + "-------- FRED GDP Data --------\n", + "Start Date: 1948-02-06 End Date: 2017-06-02\n", + "Min Value: 266.2 Max Value: 19007.3\n", + "Avg Value: 5706.81732852 Median Value: 3367.1\n", + "Frequency: quarterly\n" + ] + }, + { + "data": { + "text/html": [ + "
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unrateGDPunrate_changeGDP_changefixed datescontraction
fixed_dates
1948-02-063.4266.2NaNNaN1948-02-06NaN
1948-03-053.8266.20.117647NaN1948-03-05NaN
1948-04-024.0266.20.052632NaN1948-04-02NaN
1948-05-073.9272.9-0.0250000.0251691948-05-07False
1948-06-043.5272.9-0.1025640.0251691948-06-04False
1948-07-023.6272.90.0285710.0251691948-07-02False
1948-08-063.6279.50.0000000.0241851948-08-06False
1948-09-033.9279.50.0833330.0241851948-09-03False
1948-10-013.8279.5-0.0256410.0241851948-10-01False
1948-11-053.7280.7-0.0263160.0042931948-11-05False
1948-12-033.8280.70.0270270.0042931948-12-03False
1949-01-074.0280.70.0526320.0042931949-01-07False
1949-02-044.3275.40.075000-0.0188811949-02-04False
1949-03-044.7275.40.093023-0.0188811949-03-04False
1949-04-015.0275.40.063830-0.0188811949-04-01False
1949-05-065.3271.70.060000-0.0134351949-05-06True
1949-06-036.1271.70.150943-0.0134351949-06-03True
1949-07-016.2271.70.016393-0.0134351949-07-01True
1949-08-056.7273.30.0806450.0058891949-08-05True
1949-09-026.8273.30.0149250.0058891949-09-02True
1949-10-076.6273.3-0.0294120.0058891949-10-07True
1949-11-047.9271.00.196970-0.0084161949-11-04False
1949-12-026.4271.0-0.189873-0.0084161949-12-02False
1950-01-066.6271.00.031250-0.0084161950-01-06False
1950-02-036.5281.2-0.0151520.0376381950-02-03True
1950-03-036.4281.2-0.0153850.0376381950-03-03True
1950-04-076.3281.2-0.0156250.0376381950-04-07True
1950-05-055.8290.7-0.0793650.0337841950-05-05False
1950-06-025.5290.7-0.0517240.0337841950-06-02False
1950-07-075.4290.7-0.0181820.0337841950-07-07False
.....................
2015-01-025.617615.9-0.0344830.0053532015-01-02False
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2015-03-065.517649.3-0.0350880.0018962015-03-06False
2015-04-035.517649.30.0000000.0018962015-04-03False
2015-05-015.417913.7-0.0181820.0149812015-05-01False
2015-06-055.517913.70.0185190.0149812015-06-05False
2015-07-035.317913.7-0.0363640.0149812015-07-03False
2015-08-075.318064.70.0000000.0084292015-08-07False
2015-09-045.118064.7-0.0377360.0084292015-09-04False
2015-10-025.118064.70.0000000.0084292015-10-02False
2015-11-065.018128.2-0.0196080.0035152015-11-06False
2015-12-045.018128.20.0000000.0035152015-12-04False
2016-01-015.018128.20.0000000.0035152016-01-01False
2016-02-054.918221.1-0.0200000.0051252016-02-05False
2016-03-044.918221.10.0000000.0051252016-03-04False
2016-04-015.018221.10.0204080.0051252016-04-01False
2016-05-065.018437.60.0000000.0118822016-05-06False
2016-06-034.718437.6-0.0600000.0118822016-06-03False
2016-07-014.918437.60.0425530.0118822016-07-01False
2016-08-054.918651.20.0000000.0115852016-08-05False
2016-09-024.918651.20.0000000.0115852016-09-02False
2016-10-075.018651.20.0204080.0115852016-10-07False
2016-11-044.918860.8-0.0200000.0112382016-11-04False
2016-12-024.618860.8-0.0612240.0112382016-12-02False
2017-01-064.718860.80.0217390.0112382017-01-06False
2017-02-034.819007.30.0212770.0077672017-02-03False
2017-03-034.719007.3-0.0208330.0077672017-03-03False
2017-04-074.519007.3-0.0425530.0077672017-04-07False
2017-05-054.419007.3-0.0222220.0077672017-05-05False
2017-06-024.319007.3-0.0227270.0077672017-06-02False
\n", + "

833 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " unrate GDP unrate_change GDP_change fixed dates \\\n", + "fixed_dates \n", + "1948-02-06 3.4 266.2 NaN NaN 1948-02-06 \n", + "1948-03-05 3.8 266.2 0.117647 NaN 1948-03-05 \n", + "1948-04-02 4.0 266.2 0.052632 NaN 1948-04-02 \n", + "1948-05-07 3.9 272.9 -0.025000 0.025169 1948-05-07 \n", + "1948-06-04 3.5 272.9 -0.102564 0.025169 1948-06-04 \n", + "1948-07-02 3.6 272.9 0.028571 0.025169 1948-07-02 \n", + "1948-08-06 3.6 279.5 0.000000 0.024185 1948-08-06 \n", + "1948-09-03 3.9 279.5 0.083333 0.024185 1948-09-03 \n", + "1948-10-01 3.8 279.5 -0.025641 0.024185 1948-10-01 \n", + "1948-11-05 3.7 280.7 -0.026316 0.004293 1948-11-05 \n", + "1948-12-03 3.8 280.7 0.027027 0.004293 1948-12-03 \n", + "1949-01-07 4.0 280.7 0.052632 0.004293 1949-01-07 \n", + "1949-02-04 4.3 275.4 0.075000 -0.018881 1949-02-04 \n", + "1949-03-04 4.7 275.4 0.093023 -0.018881 1949-03-04 \n", + "1949-04-01 5.0 275.4 0.063830 -0.018881 1949-04-01 \n", + "1949-05-06 5.3 271.7 0.060000 -0.013435 1949-05-06 \n", + "1949-06-03 6.1 271.7 0.150943 -0.013435 1949-06-03 \n", + "1949-07-01 6.2 271.7 0.016393 -0.013435 1949-07-01 \n", + "1949-08-05 6.7 273.3 0.080645 0.005889 1949-08-05 \n", + "1949-09-02 6.8 273.3 0.014925 0.005889 1949-09-02 \n", + "1949-10-07 6.6 273.3 -0.029412 0.005889 1949-10-07 \n", + "1949-11-04 7.9 271.0 0.196970 -0.008416 1949-11-04 \n", + "1949-12-02 6.4 271.0 -0.189873 -0.008416 1949-12-02 \n", + "1950-01-06 6.6 271.0 0.031250 -0.008416 1950-01-06 \n", + "1950-02-03 6.5 281.2 -0.015152 0.037638 1950-02-03 \n", + "1950-03-03 6.4 281.2 -0.015385 0.037638 1950-03-03 \n", + "1950-04-07 6.3 281.2 -0.015625 0.037638 1950-04-07 \n", + "1950-05-05 5.8 290.7 -0.079365 0.033784 1950-05-05 \n", + "1950-06-02 5.5 290.7 -0.051724 0.033784 1950-06-02 \n", + "1950-07-07 5.4 290.7 -0.018182 0.033784 1950-07-07 \n", + "... ... ... ... ... ... \n", + "2015-01-02 5.6 17615.9 -0.034483 0.005353 2015-01-02 \n", + "2015-02-06 5.7 17649.3 0.017857 0.001896 2015-02-06 \n", + "2015-03-06 5.5 17649.3 -0.035088 0.001896 2015-03-06 \n", + "2015-04-03 5.5 17649.3 0.000000 0.001896 2015-04-03 \n", + "2015-05-01 5.4 17913.7 -0.018182 0.014981 2015-05-01 \n", + "2015-06-05 5.5 17913.7 0.018519 0.014981 2015-06-05 \n", + "2015-07-03 5.3 17913.7 -0.036364 0.014981 2015-07-03 \n", + "2015-08-07 5.3 18064.7 0.000000 0.008429 2015-08-07 \n", + "2015-09-04 5.1 18064.7 -0.037736 0.008429 2015-09-04 \n", + "2015-10-02 5.1 18064.7 0.000000 0.008429 2015-10-02 \n", + "2015-11-06 5.0 18128.2 -0.019608 0.003515 2015-11-06 \n", + "2015-12-04 5.0 18128.2 0.000000 0.003515 2015-12-04 \n", + "2016-01-01 5.0 18128.2 0.000000 0.003515 2016-01-01 \n", + "2016-02-05 4.9 18221.1 -0.020000 0.005125 2016-02-05 \n", + "2016-03-04 4.9 18221.1 0.000000 0.005125 2016-03-04 \n", + "2016-04-01 5.0 18221.1 0.020408 0.005125 2016-04-01 \n", + "2016-05-06 5.0 18437.6 0.000000 0.011882 2016-05-06 \n", + "2016-06-03 4.7 18437.6 -0.060000 0.011882 2016-06-03 \n", + "2016-07-01 4.9 18437.6 0.042553 0.011882 2016-07-01 \n", + "2016-08-05 4.9 18651.2 0.000000 0.011585 2016-08-05 \n", + "2016-09-02 4.9 18651.2 0.000000 0.011585 2016-09-02 \n", + "2016-10-07 5.0 18651.2 0.020408 0.011585 2016-10-07 \n", + "2016-11-04 4.9 18860.8 -0.020000 0.011238 2016-11-04 \n", + "2016-12-02 4.6 18860.8 -0.061224 0.011238 2016-12-02 \n", + "2017-01-06 4.7 18860.8 0.021739 0.011238 2017-01-06 \n", + "2017-02-03 4.8 19007.3 0.021277 0.007767 2017-02-03 \n", + "2017-03-03 4.7 19007.3 -0.020833 0.007767 2017-03-03 \n", + "2017-04-07 4.5 19007.3 -0.042553 0.007767 2017-04-07 \n", + "2017-05-05 4.4 19007.3 -0.022222 0.007767 2017-05-05 \n", + "2017-06-02 4.3 19007.3 -0.022727 0.007767 2017-06-02 \n", + "\n", + " contraction \n", + "fixed_dates \n", + "1948-02-06 NaN \n", + "1948-03-05 NaN \n", + "1948-04-02 NaN \n", + "1948-05-07 False \n", + "1948-06-04 False \n", + "1948-07-02 False \n", + "1948-08-06 False \n", + "1948-09-03 False \n", + "1948-10-01 False \n", + "1948-11-05 False \n", + "1948-12-03 False \n", + "1949-01-07 False \n", + "1949-02-04 False \n", + "1949-03-04 False \n", + "1949-04-01 False \n", + "1949-05-06 True \n", + "1949-06-03 True \n", + "1949-07-01 True \n", + "1949-08-05 True \n", + "1949-09-02 True \n", + "1949-10-07 True \n", + "1949-11-04 False \n", + "1949-12-02 False \n", + "1950-01-06 False \n", + "1950-02-03 True \n", + "1950-03-03 True \n", + "1950-04-07 True \n", + "1950-05-05 False \n", + "1950-06-02 False \n", + "1950-07-07 False \n", + "... ... \n", + "2015-01-02 False \n", + "2015-02-06 False \n", + "2015-03-06 False \n", + "2015-04-03 False \n", + "2015-05-01 False \n", + "2015-06-05 False \n", + "2015-07-03 False \n", + "2015-08-07 False \n", + "2015-09-04 False \n", + "2015-10-02 False \n", + "2015-11-06 False \n", + "2015-12-04 False \n", + "2016-01-01 False \n", + "2016-02-05 False \n", + "2016-03-04 False \n", + "2016-04-01 False \n", + "2016-05-06 False \n", + "2016-06-03 False \n", + "2016-07-01 False \n", + "2016-08-05 False \n", + "2016-09-02 False \n", + "2016-10-07 False \n", + "2016-11-04 False \n", + "2016-12-02 False \n", + "2017-01-06 False \n", + "2017-02-03 False \n", + "2017-03-03 False \n", + "2017-04-07 False \n", + "2017-05-05 False \n", + "2017-06-02 False \n", + "\n", + "[833 rows x 6 columns]" + ] + }, + "execution_count": 166, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Getting an understanding of the size and structure of the data\n", + "print \"-------- FRED Unemployment Rate Data --------\"\n", + "print \"%-12s %-15s %-13s %s\" % ('Start Date:', data['unrate'].index[0].date(), 'End Date:', data['unrate'].index[-1].date())\n", + "print \"%-12s %-15s %-13s %s\" % ('Min Value:', data['unrate'].min(), 'Max Value:', data['unrate'].max())\n", + "print \"%-12s %-15s %-13s %s\" % ('Avg Value:', data['unrate'].mean(), 'Median Value:', data['unrate'].median())\n", + "print \"Frequency: monthly\\n\"\n", + "\n", + "print \"-------- FRED GDP Data --------\"\n", + "print \"%-12s %-15s %-13s %s\" % ('Start Date:', data['GDP'].index[0].date(), 'End Date:', data['GDP'].index[-1].date())\n", + "print \"%-12s %-15s %-13s %s\" % ('Min Value:', data['GDP'].min(), 'Max Value:', data['GDP'].max())\n", + "print \"%-12s %-15s %-13s %s\" % ('Avg Value:', data['GDP'].mean(), 'Median Value:', data['GDP'].median())\n", + "print \"Frequency: quarterly\"\n", + "\n", + "# Fill monthly data down through days\n", + "data = data.ffill()\n", + "\n", + "# Add boolean column denoting a contraction(true)/expansion(false)\n", + "data['contraction'] = data['GDP_change'].map(lambda x: x<0).shift(3)\n", + "\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Fill contraction column with NBER data (www.nber.org/cycles.html)\n", + "\n", + "peaks = [\n", + " '01-01-1980',\n", + " '07-01-1981',\n", + " '07-01-1990',\n", + " '03-01-2001',\n", + " '12-01-2007']\n", + "\n", + "troughs = [\n", + " '07-01-1980',\n", + " '11-01-1982',\n", + " '03-01-1991',\n", + " '11-01-2001',\n", + " '06-01-2009']\n", + "\n", + "data['contraction'].loc[peaks[0]:troughs[4]] = False\n", + "\n", + "for i in range(len(peaks)):\n", + " data['contraction'].loc[peaks[i]:troughs[i]] = True" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Testing w Spy" + ] + }, + { + "cell_type": "code", + "execution_count": 487, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "benchmark = get_pricing('SPY',\n", + " fields = 'price',\n", + " start_date = '2002-01-01',\n", + " end_date = '2017-01-01')" + ] + }, + { + "cell_type": "code", + "execution_count": 511, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean returns after unrate up & expansion: 0.00803704867715\n", + "Mean return: 0.000319791691461\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.0028309883031021272,\n", + " -0.0056189927116586927,\n", + " -0.0018284301981009044,\n", + " 0.014776646936081377,\n", + " -0.007657171331284753,\n", + " 0.029376274887235349,\n", + " 0.03493548130680163,\n", + " 0.00095801911125290159,\n", + " 0.015787131379851486,\n", + " -0.0031894609117697607]" + ] + }, + "execution_count": 511, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Store returns for day after unemployment news\n", + "post_strike_returns = []\n", + "\n", + "for date in data['2002-01-01':'2016-12-30'].index:\n", + " if (data.loc[date]['contraction'] & (data.loc[date]['unrate_change'] > 0)):\n", + " price_spot = benchmark.iloc[benchmark.index.get_loc(date,method='nearest')]\n", + " price_next = benchmark.iloc[benchmark.index.get_loc(date,method='nearest')+1]\n", + " post_strike_returns.append((price_next - price_spot)/price_spot)\n", + "\n", + "print 'Mean returns after unrate up & expansion:', np.mean(post_strike_returns)\n", + "print 'Mean return:',np.mean(benchmark.pct_change(1))\n", + "post_strike_returns" + ] + }, + { + "cell_type": "code", + "execution_count": 461, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "benchmark = get_pricing('SPY',\n", + " fields = 'price',\n", + " start_date = '2002-01-01',\n", + " end_date = '2017-01-01')" + ] + }, + { + "cell_type": "code", + "execution_count": 462, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean returns after unrate up & expansion: 0.00386474448032\n", + "Mean return: 0.000319791691461\n" + ] + } + ], + "source": [ + "post_strike_returns = []\n", + "for date in data['2002-01-01':'2017-01-01'].index:\n", + " if (data.loc[date]['contraction'] & (data.loc[date]['unrate_change'] > 0)):\n", + " price_spot = benchmark.iloc[benchmark.index.get_loc(date,method='nearest')]\n", + " price_next = benchmark.iloc[benchmark.index.get_loc(date,method='nearest')+1]\n", + " post_strike_returns.append((price_next - price_spot)/price_spot)\n", + "\n", + "print 'Mean returns after unrate rises & expansion:', np.mean(post_strike_returns)\n", + "print 'Mean return:',np.mean(benchmark.pct_change(1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Trying to replicate the paper's unemployment model \n", + "\n", + "We need the historical model predictions to determine whether the unemployment news was a 'surprise' or not." + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + " This is separate from the ipykernel package so we can avoid doing imports until\n" + ] + } + ], + "source": [ + "ind_pro = local_csv('industrial_prod_rate.csv').set_index([pd.date_range(start='1919-01-01', end = '2017-05-01', freq = 'MS')])['1948-01-02':'2017-06-02'].drop('DATE', 1).astype(float)\n", + "data['ind_pro']= data['unrate']\n", + "data['ind_pro'][1:]=ind_pro['INDPRO'].astype(float)\n", + "data['ind_pro_change']=data['ind_pro'].pct_change(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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unrateGDPunrate_changeGDP_changefixed datescontractionind_proind_pro_change
fixed_dates
1948-02-063.4266.2NaNNaN1948-02-06NaN3.4000NaN
1948-03-053.8266.20.117647NaN1948-03-05NaN14.85353.368676
1948-04-024.0266.20.052632NaN1948-04-02NaN14.6866-0.011236
1948-05-073.9272.9-0.0250000.0251691948-05-07False14.71440.001893
1948-06-043.5272.9-0.1025640.0251691948-06-04False14.96470.017011
1948-07-023.6272.90.0285710.0251691948-07-02False15.15940.013011
1948-08-063.6279.50.0000000.0241851948-08-06False15.15940.000000
1948-09-033.9279.50.0833330.0241851948-09-03False15.1038-0.003668
1948-10-013.8279.5-0.0256410.0241851948-10-01False14.9926-0.007362
1948-11-053.7280.7-0.0263160.0042931948-11-05False15.10380.007417
1948-12-033.8280.70.0270270.0042931948-12-03False14.9091-0.012891
1949-01-074.0280.70.0526320.0042931949-01-07False14.7700-0.009330
1949-02-044.3275.40.075000-0.0188811949-02-04False14.6310-0.009411
1949-03-044.7275.40.093023-0.0188811949-03-04False14.4919-0.009507
1949-04-015.0275.40.063830-0.0188811949-04-01False14.2137-0.019197
1949-05-065.3271.70.060000-0.0134351949-05-06True14.1303-0.005868
1949-06-036.1271.70.150943-0.0134351949-06-03True13.9356-0.013779
1949-07-016.2271.70.016393-0.0134351949-07-01True13.9078-0.001995
1949-08-056.7273.30.0806450.0058891949-08-05True13.8799-0.002006
1949-09-026.8273.30.0149250.0058891949-09-02True14.01900.010022
1949-10-076.6273.3-0.0294120.0058891949-10-07True14.15810.009922
1949-11-047.9271.00.196970-0.0084161949-11-04False13.6296-0.037328
1949-12-026.4271.0-0.189873-0.0084161949-12-02False13.99120.026530
1950-01-066.6271.00.031250-0.0084161950-01-06False14.24150.017890
1950-02-036.5281.2-0.0151520.0376381950-02-03True14.49190.017582
1950-03-036.4281.2-0.0153850.0376381950-03-03True14.54750.003837
1950-04-076.3281.2-0.0156250.0376381950-04-07True15.02040.032507
1950-05-055.8290.7-0.0793650.0337841950-05-05False15.52100.033328
1950-06-025.5290.7-0.0517240.0337841950-06-02False15.88260.023297
1950-07-075.4290.7-0.0181820.0337841950-07-07False16.35550.029775
...........................
2015-01-025.617615.9-0.0344830.0053532015-01-02False106.3797-0.002192
2015-02-065.717649.30.0178570.0018962015-02-06False105.6148-0.007190
2015-03-065.517649.3-0.0350880.0018962015-03-06False105.4321-0.001730
2015-04-035.517649.30.0000000.0018962015-04-03False105.0745-0.003392
2015-05-015.417913.7-0.0181820.0149812015-05-01False104.6624-0.003922
2015-06-055.517913.70.0185190.0149812015-06-05False104.2843-0.003613
2015-07-035.317913.7-0.0363640.0149812015-07-03False103.9927-0.002796
2015-08-075.318064.70.0000000.0084292015-08-07False104.51500.005022
2015-09-045.118064.7-0.0377360.0084292015-09-04False104.5091-0.000056
2015-10-025.118064.70.0000000.0084292015-10-02False104.2038-0.002921
2015-11-065.018128.2-0.0196080.0035152015-11-06False104.0045-0.001913
2015-12-045.018128.20.0000000.0035152015-12-04False103.3965-0.005846
2016-01-015.018128.20.0000000.0035152016-01-01False102.9179-0.004629
2016-02-054.918221.1-0.0200000.0051252016-02-05False103.48220.005483
2016-03-044.918221.10.0000000.0051252016-03-04False103.2685-0.002065
2016-04-015.018221.10.0204080.0051252016-04-01False102.5263-0.007187
2016-05-065.018437.60.0000000.0118822016-05-06False102.86970.003349
2016-06-034.718437.6-0.0600000.0118822016-06-03False102.7552-0.001113
2016-07-014.918437.60.0425530.0118822016-07-01False103.12490.003598
2016-08-054.918651.20.0000000.0115852016-08-05False103.21730.000896
2016-09-024.918651.20.0000000.0115852016-09-02False103.1459-0.000692
2016-10-075.018651.20.0204080.0115852016-10-07False102.9898-0.001513
2016-11-044.918860.8-0.0200000.0112382016-11-04False103.17420.001790
2016-12-024.618860.8-0.0612240.0112382016-12-02False102.9478-0.002194
2017-01-064.718860.80.0217390.0112382017-01-06False103.76750.007962
2017-02-034.819007.30.0212770.0077672017-02-03False103.4647-0.002918
2017-03-034.719007.3-0.0208330.0077672017-03-03False103.74160.002676
2017-04-074.519007.3-0.0425530.0077672017-04-07False103.86070.001148
2017-05-054.419007.3-0.0222220.0077672017-05-05False105.03290.011286
2017-06-024.319007.3-0.0227270.0077672017-06-02False105.0284-0.000043
\n", + "

833 rows × 8 columns

\n", + "
" + ], + "text/plain": [ + " unrate GDP unrate_change GDP_change fixed dates \\\n", + "fixed_dates \n", + "1948-02-06 3.4 266.2 NaN NaN 1948-02-06 \n", + "1948-03-05 3.8 266.2 0.117647 NaN 1948-03-05 \n", + "1948-04-02 4.0 266.2 0.052632 NaN 1948-04-02 \n", + "1948-05-07 3.9 272.9 -0.025000 0.025169 1948-05-07 \n", + "1948-06-04 3.5 272.9 -0.102564 0.025169 1948-06-04 \n", + "1948-07-02 3.6 272.9 0.028571 0.025169 1948-07-02 \n", + "1948-08-06 3.6 279.5 0.000000 0.024185 1948-08-06 \n", + "1948-09-03 3.9 279.5 0.083333 0.024185 1948-09-03 \n", + "1948-10-01 3.8 279.5 -0.025641 0.024185 1948-10-01 \n", + "1948-11-05 3.7 280.7 -0.026316 0.004293 1948-11-05 \n", + "1948-12-03 3.8 280.7 0.027027 0.004293 1948-12-03 \n", + "1949-01-07 4.0 280.7 0.052632 0.004293 1949-01-07 \n", + "1949-02-04 4.3 275.4 0.075000 -0.018881 1949-02-04 \n", + "1949-03-04 4.7 275.4 0.093023 -0.018881 1949-03-04 \n", + "1949-04-01 5.0 275.4 0.063830 -0.018881 1949-04-01 \n", + "1949-05-06 5.3 271.7 0.060000 -0.013435 1949-05-06 \n", + "1949-06-03 6.1 271.7 0.150943 -0.013435 1949-06-03 \n", + "1949-07-01 6.2 271.7 0.016393 -0.013435 1949-07-01 \n", + "1949-08-05 6.7 273.3 0.080645 0.005889 1949-08-05 \n", + "1949-09-02 6.8 273.3 0.014925 0.005889 1949-09-02 \n", + "1949-10-07 6.6 273.3 -0.029412 0.005889 1949-10-07 \n", + "1949-11-04 7.9 271.0 0.196970 -0.008416 1949-11-04 \n", + "1949-12-02 6.4 271.0 -0.189873 -0.008416 1949-12-02 \n", + "1950-01-06 6.6 271.0 0.031250 -0.008416 1950-01-06 \n", + "1950-02-03 6.5 281.2 -0.015152 0.037638 1950-02-03 \n", + "1950-03-03 6.4 281.2 -0.015385 0.037638 1950-03-03 \n", + "1950-04-07 6.3 281.2 -0.015625 0.037638 1950-04-07 \n", + "1950-05-05 5.8 290.7 -0.079365 0.033784 1950-05-05 \n", + "1950-06-02 5.5 290.7 -0.051724 0.033784 1950-06-02 \n", + "1950-07-07 5.4 290.7 -0.018182 0.033784 1950-07-07 \n", + "... ... ... ... ... ... \n", + "2015-01-02 5.6 17615.9 -0.034483 0.005353 2015-01-02 \n", + "2015-02-06 5.7 17649.3 0.017857 0.001896 2015-02-06 \n", + "2015-03-06 5.5 17649.3 -0.035088 0.001896 2015-03-06 \n", + "2015-04-03 5.5 17649.3 0.000000 0.001896 2015-04-03 \n", + "2015-05-01 5.4 17913.7 -0.018182 0.014981 2015-05-01 \n", + "2015-06-05 5.5 17913.7 0.018519 0.014981 2015-06-05 \n", + "2015-07-03 5.3 17913.7 -0.036364 0.014981 2015-07-03 \n", + "2015-08-07 5.3 18064.7 0.000000 0.008429 2015-08-07 \n", + "2015-09-04 5.1 18064.7 -0.037736 0.008429 2015-09-04 \n", + "2015-10-02 5.1 18064.7 0.000000 0.008429 2015-10-02 \n", + "2015-11-06 5.0 18128.2 -0.019608 0.003515 2015-11-06 \n", + "2015-12-04 5.0 18128.2 0.000000 0.003515 2015-12-04 \n", + "2016-01-01 5.0 18128.2 0.000000 0.003515 2016-01-01 \n", + "2016-02-05 4.9 18221.1 -0.020000 0.005125 2016-02-05 \n", + "2016-03-04 4.9 18221.1 0.000000 0.005125 2016-03-04 \n", + "2016-04-01 5.0 18221.1 0.020408 0.005125 2016-04-01 \n", + "2016-05-06 5.0 18437.6 0.000000 0.011882 2016-05-06 \n", + "2016-06-03 4.7 18437.6 -0.060000 0.011882 2016-06-03 \n", + "2016-07-01 4.9 18437.6 0.042553 0.011882 2016-07-01 \n", + "2016-08-05 4.9 18651.2 0.000000 0.011585 2016-08-05 \n", + "2016-09-02 4.9 18651.2 0.000000 0.011585 2016-09-02 \n", + "2016-10-07 5.0 18651.2 0.020408 0.011585 2016-10-07 \n", + "2016-11-04 4.9 18860.8 -0.020000 0.011238 2016-11-04 \n", + "2016-12-02 4.6 18860.8 -0.061224 0.011238 2016-12-02 \n", + "2017-01-06 4.7 18860.8 0.021739 0.011238 2017-01-06 \n", + "2017-02-03 4.8 19007.3 0.021277 0.007767 2017-02-03 \n", + "2017-03-03 4.7 19007.3 -0.020833 0.007767 2017-03-03 \n", + "2017-04-07 4.5 19007.3 -0.042553 0.007767 2017-04-07 \n", + "2017-05-05 4.4 19007.3 -0.022222 0.007767 2017-05-05 \n", + "2017-06-02 4.3 19007.3 -0.022727 0.007767 2017-06-02 \n", + "\n", + " contraction ind_pro ind_pro_change \n", + "fixed_dates \n", + "1948-02-06 NaN 3.4000 NaN \n", + "1948-03-05 NaN 14.8535 3.368676 \n", + "1948-04-02 NaN 14.6866 -0.011236 \n", + "1948-05-07 False 14.7144 0.001893 \n", + "1948-06-04 False 14.9647 0.017011 \n", + "1948-07-02 False 15.1594 0.013011 \n", + "1948-08-06 False 15.1594 0.000000 \n", + "1948-09-03 False 15.1038 -0.003668 \n", + "1948-10-01 False 14.9926 -0.007362 \n", + "1948-11-05 False 15.1038 0.007417 \n", + "1948-12-03 False 14.9091 -0.012891 \n", + "1949-01-07 False 14.7700 -0.009330 \n", + "1949-02-04 False 14.6310 -0.009411 \n", + "1949-03-04 False 14.4919 -0.009507 \n", + "1949-04-01 False 14.2137 -0.019197 \n", + "1949-05-06 True 14.1303 -0.005868 \n", + "1949-06-03 True 13.9356 -0.013779 \n", + "1949-07-01 True 13.9078 -0.001995 \n", + "1949-08-05 True 13.8799 -0.002006 \n", + "1949-09-02 True 14.0190 0.010022 \n", + "1949-10-07 True 14.1581 0.009922 \n", + "1949-11-04 False 13.6296 -0.037328 \n", + "1949-12-02 False 13.9912 0.026530 \n", + "1950-01-06 False 14.2415 0.017890 \n", + "1950-02-03 True 14.4919 0.017582 \n", + "1950-03-03 True 14.5475 0.003837 \n", + "1950-04-07 True 15.0204 0.032507 \n", + "1950-05-05 False 15.5210 0.033328 \n", + "1950-06-02 False 15.8826 0.023297 \n", + "1950-07-07 False 16.3555 0.029775 \n", + "... ... ... ... \n", + "2015-01-02 False 106.3797 -0.002192 \n", + "2015-02-06 False 105.6148 -0.007190 \n", + "2015-03-06 False 105.4321 -0.001730 \n", + "2015-04-03 False 105.0745 -0.003392 \n", + "2015-05-01 False 104.6624 -0.003922 \n", + "2015-06-05 False 104.2843 -0.003613 \n", + "2015-07-03 False 103.9927 -0.002796 \n", + "2015-08-07 False 104.5150 0.005022 \n", + "2015-09-04 False 104.5091 -0.000056 \n", + "2015-10-02 False 104.2038 -0.002921 \n", + "2015-11-06 False 104.0045 -0.001913 \n", + "2015-12-04 False 103.3965 -0.005846 \n", + "2016-01-01 False 102.9179 -0.004629 \n", + "2016-02-05 False 103.4822 0.005483 \n", + "2016-03-04 False 103.2685 -0.002065 \n", + "2016-04-01 False 102.5263 -0.007187 \n", + "2016-05-06 False 102.8697 0.003349 \n", + "2016-06-03 False 102.7552 -0.001113 \n", + "2016-07-01 False 103.1249 0.003598 \n", + "2016-08-05 False 103.2173 0.000896 \n", + "2016-09-02 False 103.1459 -0.000692 \n", + "2016-10-07 False 102.9898 -0.001513 \n", + "2016-11-04 False 103.1742 0.001790 \n", + "2016-12-02 False 102.9478 -0.002194 \n", + "2017-01-06 False 103.7675 0.007962 \n", + "2017-02-03 False 103.4647 -0.002918 \n", + "2017-03-03 False 103.7416 0.002676 \n", + "2017-04-07 False 103.8607 0.001148 \n", + "2017-05-05 False 105.0329 0.011286 \n", + "2017-06-02 False 105.0284 -0.000043 \n", + "\n", + "[833 rows x 8 columns]" + ] + }, + "execution_count": 171, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/case_studies/unemployment/preview.html b/case_studies/unemployment/preview.html new file mode 100644 index 00000000..f332e0bb --- /dev/null +++ b/case_studies/unemployment/preview.html @@ -0,0 +1,13676 @@ + + + Boyd Paper Strategy + + + + + + + + + + + + + + + + +
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+

Boyd, John H., et al. “The Stock Market's Reaction to Unemployment News: Why Bad News Is Usually Good for Stocks.” The Journal of Finance, vol. 60, no. 2, 2005, pp. 649–672. JSTOR, www.jstor.org/stable/3694763

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In [2]:
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import matplotlib.pyplot as plt
+from odo import odo
+import pandas as pd
+import blaze as bz
+import numpy as np
+import scipy.stats as stats
+import datetime
+import statsmodels.tsa as tsa
+from statsmodels import regression
+import statsmodels.api as sm
+from quantopian.interactive.data.quandl import fred_unrate, fred_gdp
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+
In [19]:
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fred_unrate.sort('asof_date',ascending=True)
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Out[19]:
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valueasof_datetimestamp
03.41948-01-011948-01-02
13.81948-02-011948-02-02
24.01948-03-011948-03-02
33.91948-04-011948-04-02
43.51948-05-011948-05-02
53.61948-06-011948-06-02
63.61948-07-011948-07-02
73.91948-08-011948-08-02
83.81948-09-011948-09-02
93.71948-10-011948-10-02
103.81948-11-011948-11-02
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In [16]:
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unemployment = bz.compute(fred_unrate).set_index(['asof_date']).sort_index()#.drop('timestamp', 1)
+# data.columns = ['unrate']
+# data['GDP'] = bz.compute(fred_gdp).set_index(['asof_date']).sort_index().drop('timestamp', 1)
+# data['unrate_change'] = data['unrate'].pct_change(1)
+# data['GDP_change'] = data['GDP'].pct_change(1)
+
+ +
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+ +
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+
In [17]:
+
+
+
dates = pd.to_datetime(data.index, yearfirst = True)
+
+new_index = []
+
+for date in dates:
+    next_month = datetime.datetime(date.year + (date.month / 12),
+                                  (date.month % 12) + 1, 1)
+    for i in range(7):
+        test_date = datetime.datetime(next_month.year, next_month.month, i+1)
+        if (test_date.weekday() == 5):
+            annc_date = test_date
+            
+    new_index.append(annc_date)
+    
+unemployment['new_dates'] = new_index
+
+# data = data.set_index(['fixed_dates'])
+
+ +
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+ +
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+
In [18]:
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+
+
unemployment.loc['2015-05-01':'2016-03-01']
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Out[18]:
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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
valuetimestampnew_dates
asof_date
2015-05-015.52015-05-02 00:00:00.0000002015-06-06
2015-06-015.32015-06-02 00:00:00.0000002015-07-04
2015-07-015.32015-07-02 00:00:00.0000002015-08-01
2015-08-015.12015-08-02 00:00:00.0000002015-09-05
2015-09-015.12015-09-02 00:00:00.0000002015-10-03
2015-10-015.02015-10-02 00:00:00.0000002015-11-07
2015-11-015.02015-11-02 00:00:00.0000002015-12-05
2015-12-015.02016-01-09 04:08:01.4321942016-01-02
2016-01-014.92016-02-06 04:02:32.4608302016-02-06
2016-02-014.92016-03-05 05:01:14.2790022016-03-05
2016-03-015.02016-04-02 05:00:44.6653632016-04-02
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In [166]:
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+
# Getting an understanding of the size and structure of the data
+print "-------- FRED Unemployment Rate Data --------"
+print "%-12s %-15s %-13s %s" % ('Start Date:', data['unrate'].index[0].date(), 'End Date:', data['unrate'].index[-1].date())
+print "%-12s %-15s %-13s %s" % ('Min Value:', data['unrate'].min(), 'Max Value:', data['unrate'].max())
+print "%-12s %-15s %-13s %s" % ('Avg Value:', data['unrate'].mean(), 'Median Value:', data['unrate'].median())
+print "Frequency: monthly\n"
+
+print "-------- FRED GDP Data --------"
+print "%-12s %-15s %-13s %s" % ('Start Date:', data['GDP'].index[0].date(), 'End Date:', data['GDP'].index[-1].date())
+print "%-12s %-15s %-13s %s" % ('Min Value:', data['GDP'].min(), 'Max Value:', data['GDP'].max())
+print "%-12s %-15s %-13s %s" % ('Avg Value:', data['GDP'].mean(), 'Median Value:', data['GDP'].median())
+print "Frequency: quarterly"
+
+# Fill monthly data down through days
+data = data.ffill()
+
+# Add boolean column denoting a contraction(true)/expansion(false)
+data['contraction'] = data['GDP_change'].map(lambda x: x<0).shift(3)
+
+data
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
-------- FRED Unemployment Rate Data --------
+Start Date:  1948-02-06      End Date:     2017-06-02
+Min Value:   2.5             Max Value:    10.8
+Avg Value:   5.80300120048   Median Value: 5.6
+Frequency: monthly
+
+-------- FRED GDP Data --------
+Start Date:  1948-02-06      End Date:     2017-06-02
+Min Value:   266.2           Max Value:    19007.3
+Avg Value:   5706.81732852   Median Value: 3367.1
+Frequency: quarterly
+
+
+
+ +
+ +
Out[166]:
+ + + +
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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
unrateGDPunrate_changeGDP_changefixed datescontraction
fixed_dates
1948-02-063.4266.2NaNNaN1948-02-06NaN
1948-03-053.8266.20.117647NaN1948-03-05NaN
1948-04-024.0266.20.052632NaN1948-04-02NaN
1948-05-073.9272.9-0.0250000.0251691948-05-07False
1948-06-043.5272.9-0.1025640.0251691948-06-04False
1948-07-023.6272.90.0285710.0251691948-07-02False
1948-08-063.6279.50.0000000.0241851948-08-06False
1948-09-033.9279.50.0833330.0241851948-09-03False
1948-10-013.8279.5-0.0256410.0241851948-10-01False
1948-11-053.7280.7-0.0263160.0042931948-11-05False
1948-12-033.8280.70.0270270.0042931948-12-03False
1949-01-074.0280.70.0526320.0042931949-01-07False
1949-02-044.3275.40.075000-0.0188811949-02-04False
1949-03-044.7275.40.093023-0.0188811949-03-04False
1949-04-015.0275.40.063830-0.0188811949-04-01False
1949-05-065.3271.70.060000-0.0134351949-05-06True
1949-06-036.1271.70.150943-0.0134351949-06-03True
1949-07-016.2271.70.016393-0.0134351949-07-01True
1949-08-056.7273.30.0806450.0058891949-08-05True
1949-09-026.8273.30.0149250.0058891949-09-02True
1949-10-076.6273.3-0.0294120.0058891949-10-07True
1949-11-047.9271.00.196970-0.0084161949-11-04False
1949-12-026.4271.0-0.189873-0.0084161949-12-02False
1950-01-066.6271.00.031250-0.0084161950-01-06False
1950-02-036.5281.2-0.0151520.0376381950-02-03True
1950-03-036.4281.2-0.0153850.0376381950-03-03True
1950-04-076.3281.2-0.0156250.0376381950-04-07True
1950-05-055.8290.7-0.0793650.0337841950-05-05False
1950-06-025.5290.7-0.0517240.0337841950-06-02False
1950-07-075.4290.7-0.0181820.0337841950-07-07False
.....................
2015-01-025.617615.9-0.0344830.0053532015-01-02False
2015-02-065.717649.30.0178570.0018962015-02-06False
2015-03-065.517649.3-0.0350880.0018962015-03-06False
2015-04-035.517649.30.0000000.0018962015-04-03False
2015-05-015.417913.7-0.0181820.0149812015-05-01False
2015-06-055.517913.70.0185190.0149812015-06-05False
2015-07-035.317913.7-0.0363640.0149812015-07-03False
2015-08-075.318064.70.0000000.0084292015-08-07False
2015-09-045.118064.7-0.0377360.0084292015-09-04False
2015-10-025.118064.70.0000000.0084292015-10-02False
2015-11-065.018128.2-0.0196080.0035152015-11-06False
2015-12-045.018128.20.0000000.0035152015-12-04False
2016-01-015.018128.20.0000000.0035152016-01-01False
2016-02-054.918221.1-0.0200000.0051252016-02-05False
2016-03-044.918221.10.0000000.0051252016-03-04False
2016-04-015.018221.10.0204080.0051252016-04-01False
2016-05-065.018437.60.0000000.0118822016-05-06False
2016-06-034.718437.6-0.0600000.0118822016-06-03False
2016-07-014.918437.60.0425530.0118822016-07-01False
2016-08-054.918651.20.0000000.0115852016-08-05False
2016-09-024.918651.20.0000000.0115852016-09-02False
2016-10-075.018651.20.0204080.0115852016-10-07False
2016-11-044.918860.8-0.0200000.0112382016-11-04False
2016-12-024.618860.8-0.0612240.0112382016-12-02False
2017-01-064.718860.80.0217390.0112382017-01-06False
2017-02-034.819007.30.0212770.0077672017-02-03False
2017-03-034.719007.3-0.0208330.0077672017-03-03False
2017-04-074.519007.3-0.0425530.0077672017-04-07False
2017-05-054.419007.3-0.0222220.0077672017-05-05False
2017-06-024.319007.3-0.0227270.0077672017-06-02False
+

833 rows × 6 columns

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+
In [167]:
+
+
+
# Fill contraction column with NBER data (www.nber.org/cycles.html)
+
+peaks = [
+    '01-01-1980',
+    '07-01-1981',
+    '07-01-1990',
+    '03-01-2001',
+    '12-01-2007']
+
+troughs = [
+    '07-01-1980',
+    '11-01-1982',
+    '03-01-1991',
+    '11-01-2001',
+    '06-01-2009']
+
+data['contraction'].loc[peaks[0]:troughs[4]] = False
+
+for i in range(len(peaks)):
+    data['contraction'].loc[peaks[i]:troughs[i]] = True
+
+ +
+
+
+ +
+
+
+
+
+

Testing w Spy¶

+
+
+
+
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+
In [487]:
+
+
+
benchmark = get_pricing('SPY',
+                        fields = 'price',
+                        start_date = '2002-01-01',
+                        end_date = '2017-01-01')
+
+ +
+
+
+ +
+
+
+
In [511]:
+
+
+
# Store returns for day after unemployment news
+post_strike_returns = []
+
+for date in data['2002-01-01':'2016-12-30'].index:
+    if (data.loc[date]['contraction'] & (data.loc[date]['unrate_change'] > 0)):
+        price_spot = benchmark.iloc[benchmark.index.get_loc(date,method='nearest')]
+        price_next = benchmark.iloc[benchmark.index.get_loc(date,method='nearest')+1]
+        post_strike_returns.append((price_next - price_spot)/price_spot)
+
+print 'Mean returns after unrate up & expansion:', np.mean(post_strike_returns)
+print 'Mean return:',np.mean(benchmark.pct_change(1))
+post_strike_returns
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Mean returns after unrate up & expansion: 0.00803704867715
+Mean return: 0.000319791691461
+
+
+
+ +
+ +
Out[511]:
+ + + + +
+
[0.0028309883031021272,
+ -0.0056189927116586927,
+ -0.0018284301981009044,
+ 0.014776646936081377,
+ -0.007657171331284753,
+ 0.029376274887235349,
+ 0.03493548130680163,
+ 0.00095801911125290159,
+ 0.015787131379851486,
+ -0.0031894609117697607]
+
+ +
+ +
+
+ +
+
+
+
In [461]:
+
+
+
benchmark = get_pricing('SPY',
+                        fields = 'price',
+                        start_date = '2002-01-01',
+                        end_date = '2017-01-01')
+
+ +
+
+
+ +
+
+
+
In [462]:
+
+
+
post_strike_returns = []
+for date in data['2002-01-01':'2017-01-01'].index:
+    if (data.loc[date]['contraction'] & (data.loc[date]['unrate_change'] > 0)):
+        price_spot = benchmark.iloc[benchmark.index.get_loc(date,method='nearest')]
+        price_next = benchmark.iloc[benchmark.index.get_loc(date,method='nearest')+1]
+        post_strike_returns.append((price_next - price_spot)/price_spot)
+
+print 'Mean returns after unrate rises & expansion:', np.mean(post_strike_returns)
+print 'Mean return:',np.mean(benchmark.pct_change(1))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Mean returns after unrate up & expansion: 0.00386474448032
+Mean return: 0.000319791691461
+
+
+
+ +
+
+ +
+
+
+
+
+

Trying to replicate the paper's unemployment model¶

We need the historical model predictions to determine whether the unemployment news was a 'surprise' or not.

+ +
+
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+
In [170]:
+
+
+
ind_pro = local_csv('industrial_prod_rate.csv').set_index([pd.date_range(start='1919-01-01', end = '2017-05-01', freq = 'MS')])['1948-01-02':'2017-06-02'].drop('DATE', 1).astype(float)
+data['ind_pro']= data['unrate']
+data['ind_pro'][1:]=ind_pro['INDPRO'].astype(float)
+data['ind_pro_change']=data['ind_pro'].pct_change(1)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  This is separate from the ipykernel package so we can avoid doing imports until
+
+
+
+ +
+
+ +
+
+
+
In [171]:
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+
+
data
+
+ +
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+
+ +
+
+ + +
+ +
Out[171]:
+ + + +
+
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unrateGDPunrate_changeGDP_changefixed datescontractionind_proind_pro_change
fixed_dates
1948-02-063.4266.2NaNNaN1948-02-06NaN3.4000NaN
1948-03-053.8266.20.117647NaN1948-03-05NaN14.85353.368676
1948-04-024.0266.20.052632NaN1948-04-02NaN14.6866-0.011236
1948-05-073.9272.9-0.0250000.0251691948-05-07False14.71440.001893
1948-06-043.5272.9-0.1025640.0251691948-06-04False14.96470.017011
1948-07-023.6272.90.0285710.0251691948-07-02False15.15940.013011
1948-08-063.6279.50.0000000.0241851948-08-06False15.15940.000000
1948-09-033.9279.50.0833330.0241851948-09-03False15.1038-0.003668
1948-10-013.8279.5-0.0256410.0241851948-10-01False14.9926-0.007362
1948-11-053.7280.7-0.0263160.0042931948-11-05False15.10380.007417
1948-12-033.8280.70.0270270.0042931948-12-03False14.9091-0.012891
1949-01-074.0280.70.0526320.0042931949-01-07False14.7700-0.009330
1949-02-044.3275.40.075000-0.0188811949-02-04False14.6310-0.009411
1949-03-044.7275.40.093023-0.0188811949-03-04False14.4919-0.009507
1949-04-015.0275.40.063830-0.0188811949-04-01False14.2137-0.019197
1949-05-065.3271.70.060000-0.0134351949-05-06True14.1303-0.005868
1949-06-036.1271.70.150943-0.0134351949-06-03True13.9356-0.013779
1949-07-016.2271.70.016393-0.0134351949-07-01True13.9078-0.001995
1949-08-056.7273.30.0806450.0058891949-08-05True13.8799-0.002006
1949-09-026.8273.30.0149250.0058891949-09-02True14.01900.010022
1949-10-076.6273.3-0.0294120.0058891949-10-07True14.15810.009922
1949-11-047.9271.00.196970-0.0084161949-11-04False13.6296-0.037328
1949-12-026.4271.0-0.189873-0.0084161949-12-02False13.99120.026530
1950-01-066.6271.00.031250-0.0084161950-01-06False14.24150.017890
1950-02-036.5281.2-0.0151520.0376381950-02-03True14.49190.017582
1950-03-036.4281.2-0.0153850.0376381950-03-03True14.54750.003837
1950-04-076.3281.2-0.0156250.0376381950-04-07True15.02040.032507
1950-05-055.8290.7-0.0793650.0337841950-05-05False15.52100.033328
1950-06-025.5290.7-0.0517240.0337841950-06-02False15.88260.023297
1950-07-075.4290.7-0.0181820.0337841950-07-07False16.35550.029775
...........................
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2015-11-065.018128.2-0.0196080.0035152015-11-06False104.0045-0.001913
2015-12-045.018128.20.0000000.0035152015-12-04False103.3965-0.005846
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833 rows × 8 columns

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