From 86f1f5655e1f00ea8b68a85f60f120f48f920128 Mon Sep 17 00:00:00 2001 From: Christopher Fenaroli Date: Mon, 31 Jul 2017 16:36:40 -0400 Subject: [PATCH 01/11] Added addtional case study ideas. --- .../USD_EUR_exchange_rate/notebook.ipynb | 1417 ++ .../USD_EUR_exchange_rate/preview.html | 20380 ++++++++++++++++ case_studies/google_trends/notebook.ipynb | 131 + case_studies/google_trends/preview.html | 14030 +++++++++++ case_studies/sentiment/notebook.ipynb | 327 + case_studies/sentiment/preview.html | 14800 +++++++++++ case_studies/unemployment/notebook.ipynb | 2131 ++ case_studies/unemployment/preview.html | 13676 +++++++++++ 8 files changed, 66892 insertions(+) create mode 100644 case_studies/USD_EUR_exchange_rate/notebook.ipynb create mode 100644 case_studies/USD_EUR_exchange_rate/preview.html create mode 100644 case_studies/google_trends/notebook.ipynb create mode 100644 case_studies/google_trends/preview.html create mode 100644 case_studies/sentiment/notebook.ipynb create mode 100644 case_studies/sentiment/preview.html create mode 100644 case_studies/unemployment/notebook.ipynb create mode 100644 case_studies/unemployment/preview.html 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..9fd94432 --- /dev/null +++ b/case_studies/USD_EUR_exchange_rate/notebook.ipynb @@ -0,0 +1,1417 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Researching & Developing a Market Neutral Strategy - Case Study\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 and less noisy than 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/quandl/currfx_usdeur), many of which are free to use and all of which have been cleaned and standardized to work both in the research environment and in pipeline.\n", + "\n", + "## Case Study Abstract\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\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", + "** Initial 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 2009-2010, 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: 2017-07-29\n", + "Min Value: 0.627189 Max Value: 1.2064\n", + "Avg Value: 0.834315474675 Median Value: 0.791675\n", + "\n", + "Fields: rate high_est low_est\n", + "Frequency: daily\n", + "\n" + ] + }, + { + "data": { + "image/png": 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Pr6Leu4i2niizyt1ctGwWuXxpav0Vi4rX8TvwOMzG9L6E7FUkxJgkFAkhhBBC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\nVqGwosGtHvEd20nv2Uuwu5tIfz9zj/4IM5utBTfU9xTFYqAo+EtCiE0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2AjA6IdaA0ila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fJH1/bQ4q0uUDSpmsGW3U8in+TJA8+xeURkFj35+xFGo1kuW9nOeZ1i3jc4Ia4vmXyZV/ZM0KIF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\ne0jkhru0l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JqOZj9d7QGe2zdzvAERjRpKjTGUGmNJQzcgRFEmVyKRLrJuWTMWi8SK7gY2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\nv/fp6elUV1ezZcsWlEolGRkZGI1GfvzxRx+P9Nxnt9u967iUSiU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+ "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 = '2009-01-01'\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 important 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 rate, inflation, or gold prices). \n", + "\n", + "An important concept when dealing with macro data like FX rate 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 1500 asset-level ranking values requires some thought. Some approaches include:\n", + "\n", + "* Correlation/beta coefficient (both will produce same ranking)\n", + "* Spearman rank correlation\n", + "* Cointegration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After some experimentation with the data, 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 the risk of overfitting. \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 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 they will 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 2009-2011. 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) 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": 5, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ratios of Europe-Based Equities to US-Based Equities:\n", + "\n", + "US Investor Average: 0.0851800000101\n", + "CAPM Optimal Ratio: 0.407509492777\n", + "\n", + "Difference: 0.322329492767\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]['2009':'2010']\n", + "EMU = mkt_caps.iloc[0]['2009':'2010']\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))['2009':'2010']\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 2009 to 2010, the European equity market cap was 40.7% 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 8.5%, one-fourth of the optimal amount. This discrepancy is a result of home bias, and this test confirms its presence during the research period." + ] + }, + { + "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 can serve as proxies for these international assets, and because they are US equities and subject to US market biases they will be undervalued.*" + ] + }, + { + "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. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "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": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "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)\n", + "\n", + "class Volatility(CustomFactor):\n", + " \"\"\" Custom factor to find volatility \"\"\"\n", + "\n", + " inputs = [Returns(window_length=2)]\n", + " window_length = 10\n", + "\n", + " def compute(self, today, asset_ids, out, returns):\n", + "\n", + " out[:] = np.std(returns)**2" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "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", + "start = '2009-01-01'\n", + "end = '2011-01-01'\n", + "\n", + "# Stores pipeline in result\n", + "result = run_pipeline(pipe, start, end)\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 FX correlations across the Q500US:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "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": [ + "We know 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 can be 'proxies' for international markets. If that is the case, they will behave similarly to international assets and 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 50 stocks with strong negative correlations to FX rate\n", + "* A bucket of 50 stocks with strong positive correlations to FX rate\n", + "\n", + "Pipeline made generating rolling correlations to FX rate for each asset easy. Using the pipeline data, lets find correlations of returns between the portfolios:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "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 0.820017 0.861901\n", + "high_bucket 0.820017 1.000000 0.763954\n", + "vgk 0.861901 0.763954 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", + "# Adjusting end date to get a larger timespan to observe the relationships; \n", + "# Still within research period so does not violate integrity of out-of-sample period\n", + "adj_end = '2011-01-01'\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=adj_end, \n", + " fields = 'price').pct_change().mean(axis=1,skipna=True)[1:]\n", + "returns['high_bucket'] = get_pricing(high_bucket, start_date=start, end_date=adj_end, \n", + " fields = 'price').pct_change().mean(axis=1,skipna=True)[1:]\n", + "returns['vgk'] = get_pricing('vgk', start_date=start, end_date=adj_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. For our hypothesis, we will use the `high_bucket` portfolio of US equities to replicate European equities. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Analyzing our Factor with Alphalens" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we will create a new pipeline and run it over 2009-2010 to find factor data for our research period. [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": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "universe = Q500US()\n", + "\n", + "start = '2009-01-01'\n", + "end = '2011-01-01'\n", + "\n", + "pipe = Pipeline(\n", + " columns={\n", + " 'fx_corr' : FXCorr(mask=universe)\n", + " },\n", + " screen=(universe)\n", + ")\n", + "\n", + "result = run_pipeline(pipe, start, end)\n", + "assets = result.index.levels[1].unique()\n", + "pricing = get_pricing(assets, start_date = start, end_date = end, fields = 'price')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "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", + "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": 15, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 1 10 30\n", + "factor_quantile \n", + "1 -0.000339 -0.003653 -0.011133\n", + "2 -0.000193 -0.001113 -0.000391\n", + "3 0.000007 0.000219 0.001262\n", + "4 0.000172 0.001429 0.002292\n", + "5 0.000353 0.003124 0.007995" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean_return_by_q" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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5IYQQQgghhBDihjGUlbDh+B5Wxm0m6vDfl5yKVKVS0T6wOf1DOtK/eUe6NmqF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5CUrYQYw6ZBSB/aJzuYR5oqEd72CSJ7vlLmhHUkSpByilF89VUoCCZgzdX3onndNqyc/yMU3XAZ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\nfampqSE/Px9vb2/1RkIAnTp1ok+fPujo6KinIYrO+KaGhgaWL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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": "markdown", + "metadata": {}, + "source": [ + "For a full Alphalens tearsheet, run the following cell:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [], + "source": [ + "al.tears.create_full_tear_sheet(factor_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Implement and Backtest the Strategy in the IDE\n", + "Using the [long-short equity algorithm template](https://www.quantopian.com/lectures/example-long-short-equity-algorithm) makes this step simple. To implement a long-short equity strategy with our `fx_corr` factor, we find the custom factor in the algorithm on line 49 and replace it with `fx_corr`. We can also make any other changes we deem suitable. For this algorithm, lets:\n", + "\n", + "* Comment out the other factors ('value' and 'quality') on lines 100 and 101 as we want to isolate our FX factor \n", + "* Comment out lines 145 and 146, as 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" + ] + }, + { + "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 2009-2010. We will run the backtest from 2009-2011, 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", + "$$\n", + " \\\n", + " \\overbrace{\n", + " \\underbrace{\\textit{2009 & 2010}}_\\text{In-Sample}\\:\\:+\n", + " \\underbrace{\\textit{2011}}_\\text{Out-of-Sample}\n", + " }^\\text{Backtest}\n", + " \\\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "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('5970c1174c2dc64e1c3b1d97')" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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h0WhmbE8hg8GAtLQ0yLLs1wiDYgNDEREREdEUtLW1wev1Ii8vT2m3PR6tVuu3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KChoYGcnByGh4fJzMxk48aNKaNFdrsdp9OJzWbDYrGwcuXKGdfozZTTh09jCZ6bpP/15r+esXnC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\nY1Z0urq68Pv9rF27Nnh6tyJ3ZaO3wC9cuMDWrVsZG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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "algo_performance = bt.daily_performance\n", + "benchmark = get_pricing('SPY', start_date='2009-01-01', 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": 46, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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Qq1evWvuta9euFBQUcPDgQVq1alVjn58KBUgiIiIiclH76rcf2Jm5t07r7BLZ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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", + "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).$^3$" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [], + "source": [ + "def find_vifs(data):\n", + " data = pd.DataFrame(data)\n", + " n = len(data.columns)\n", + " VIFs = np.zeros(n)\n", + " for x in range(n):\n", + " # Calculates VIF using steps described here: \n", + " # https://en.wikipedia.org/wiki/Variance_inflation_factor#Calculation_and_Analysis\n", + " VIFs[x] = 1/(1-regression.linear_model.OLS(data.ix[:,x], \n", + " sm.add_constant(np.column_stack((\n", + " [data.ix[:,(x+i+1)%n] for i in range(n-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.ix[:,: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", + " # Create bar graph, with horizontal lines at 0 and annualized algo returns\n", + " ax = returns_decomposition.T.plot(kind='bar', stacked=True, rot=-30)\n", + " ax.plot(ax.get_xlim(),[algo_returns_ann]*len(ax.get_xlim()), linestyle = '--', color='black', label = 'Algo Returns');\n", + " ax.plot(ax.get_xlim(),[0]*len(ax.get_xlim()), color='black');\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": 48, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Variance Inflation Factors:\n", + "[ 2.16028473 1.34813857 2.22835361 1.60996824]\n", + "\n", + "Betas: Mkt-RF SMB HML Mom \n", + "Alpha 0.0537356 0.0528727 0.0432647 0.0275121\n", + "Alpha t-stat 2.01595 2.00192 1.7057 1.15618\n", + "Mkt-RF 0.055417 0.0495098 0.094671 0.0838148\n", + "Mkt-RF t-stat 5.87536 4.41303 6.64337 7.33696\n", + "SMB NaN 0.0346142 -0.00432747 0.0203095\n", + "SMB t-stat NaN 1.54278 -0.205814 1.03043\n", + "HML NaN NaN -0.106863 -0.171577\n", + "HML t-stat NaN NaN -4.33332 -6.03916\n", + "Mom NaN NaN NaN -0.0852706\n", + "Mom t-stat NaN NaN NaN -5.31589\n", + "\n", + "Factor Excess Returns:\n", + "Mkt-RF 0.165067\n", + "SMB 0.053100\n", + "HML -0.039500\n", + "Mom -0.160433\n", + "dtype: float64\n" + ] + }, + { + "data": { + "image/png": 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m5qYePXrU+vuw2Gv6UfL/mThxoqKiovTee+/pb3/7m/bu3atXX31V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\nsv75z38qISFB3333nZ566qk6zW0USis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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "algo_returns = algo_performance['returns']\n", + "decomposition = decompose_returns(algo_returns, plot=True)\n", + "\n", + "print 'Variance Inflation Factors:\\n', decomposition[4]\n", + "print '\\nBetas:', decomposition[0]\n", + "print '\\nFactor Excess Returns:\\n', decomposition[2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The VIFs are all under 5 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", + "One of these factors, momentum, seems to explain much of the algorithim's performance within the fourth model. This is due to the algo having a significant negative exposure (tstat = -5.3) and the momentum factor having negative returns over the time period (-16%). 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": 49, + "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 = '2009-01-01'\n", + "end = '2012-01-01'\n", + "\n", + "alt_result = run_pipeline(pipe, start, end)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "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": 53, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Mkt-RFSMBHMLMomVOLSTMR
Alpha0.05373560.05287270.04326470.02751210.02716020.0295876
Alpha t-stat2.015952.001921.70571.156181.144321.23905
Mkt-RF0.0554170.04950980.0946710.08381480.08376450.0834903
Mkt-RF t-stat5.875364.413036.643377.336967.31087.3148
SMBNaN0.0346142-0.004327470.02030950.02040460.0214652
SMB t-statNaN1.54278-0.2058141.030431.035721.0861
HMLNaNNaN-0.106863-0.171577-0.171465-0.168513
HML t-statNaNNaN-4.33332-6.03916-6.02161-5.91933
MomNaNNaNNaN-0.0852706-0.0854911-0.0875817
Mom t-statNaNNaNNaN-5.31589-5.28629-5.45745
VOLNaNNaNNaNNaN0.003379270.0253341
VOL t-statNaNNaNNaNNaN0.2449691.28122
STMRNaNNaNNaNNaNNaN-0.033891
STMR t-statNaNNaNNaNNaNNaN-0.992107
\n", + "
" + ], + "text/plain": [ + " Mkt-RF SMB HML Mom VOL \\\n", + "Alpha 0.0537356 0.0528727 0.0432647 0.0275121 0.0271602 \n", + "Alpha t-stat 2.01595 2.00192 1.7057 1.15618 1.14432 \n", + "Mkt-RF 0.055417 0.0495098 0.094671 0.0838148 0.0837645 \n", + "Mkt-RF t-stat 5.87536 4.41303 6.64337 7.33696 7.3108 \n", + "SMB NaN 0.0346142 -0.00432747 0.0203095 0.0204046 \n", + "SMB t-stat NaN 1.54278 -0.205814 1.03043 1.03572 \n", + "HML NaN NaN -0.106863 -0.171577 -0.171465 \n", + "HML t-stat NaN NaN -4.33332 -6.03916 -6.02161 \n", + "Mom NaN NaN NaN -0.0852706 -0.0854911 \n", + "Mom t-stat NaN NaN NaN -5.31589 -5.28629 \n", + "VOL NaN NaN NaN NaN 0.00337927 \n", + "VOL t-stat NaN NaN NaN NaN 0.244969 \n", + "STMR NaN NaN NaN NaN NaN \n", + "STMR t-stat NaN NaN NaN NaN NaN \n", + "\n", + " STMR \n", + "Alpha 0.0295876 \n", + "Alpha t-stat 1.23905 \n", + "Mkt-RF 0.0834903 \n", + "Mkt-RF t-stat 7.3148 \n", + "SMB 0.0214652 \n", + "SMB t-stat 1.0861 \n", + "HML -0.168513 \n", + "HML t-stat -5.91933 \n", + "Mom -0.0875817 \n", + "Mom t-stat -5.45745 \n", + "VOL 0.0253341 \n", + "VOL t-stat 1.28122 \n", + "STMR -0.033891 \n", + "STMR t-stat -0.992107 " + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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P8o8TAABw1ViICQAAAADgsphpBQDASWw2m3Jy080uw1Q5uemy2TqZXQYAoAYh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\nGRslJSWSJA8PDyUlJWnp0qU6cOCApk2bptatW2vx4sW6ePGi5syZo7f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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", + "decompose_returns_custom(algo_returns, new_risk_factors, plot=True)[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Of the new factors, none compose more of our algo's returns than momentum." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For a full Pyfolio tearsheet, run the following cell:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [], + "source": [ + "# Create full tear sheets \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": 9, + "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-Domesting 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 we can expect our factor to perform worse in the future as equity home bias fades away." + ] + }, + { + "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", + "* Hedge negative exposure to momentum\n", + "* Adjust algo parameters like `MAX_GROSS_LEVERAGE`, `NUM_LONG_POSITIONS`, and `NUM_SHORT_POSITIONS`\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..9b7ae498 --- /dev/null +++ b/case_studies/USD_EUR_exchange_rate/preview.html @@ -0,0 +1,20380 @@ + + + Using+Alternative+Data%3A+USD-EUR+Exchange+Rate+Case+Study+V3 + + + + + + + + + + + + + + + + +
+
+ +
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+
+
+

Researching & Developing a Market Neutral Strategy - Case Study¶

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 and less noisy than 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 the research environment and in pipeline.

+

Case Study Abstract¶

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+
+
+
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+
In [1]:
+
+
+
import matplotlib.pyplot as plt
+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.

+

Initial 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.

+

To protect against overfitting, we will conduct our research strictly within the interval 2009-2010, 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
+
+ +
+
+
+ +
+
+
+
In [3]:
+
+
+
# 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
+
+ +
+
+
+ +
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In [4]:
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# 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 = '2009-01-01'
+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:          2017-07-29
+Min Value:   0.627189        Max Value:    1.2064
+Avg Value:   0.834315474675  Median Value: 0.791675
+
+Fields: rate high_est low_est
+Frequency: daily
+
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+

Macro vs. Asset-Level Data¶

One important 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 rate, inflation, or gold prices).

+

An important concept when dealing with macro data like FX rate 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 1500 asset-level ranking values requires some thought. Some approaches include:

+
    +
  • Correlation/beta coefficient (both will produce same ranking)
    • +
  • Spearman rank correlation
    • +
  • Cointegration
    • +
+ +
+
+
+
+
+
+
+

After some experimentation with the data, 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 the risk of overfitting.

+

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 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 they will 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 2009-2011. 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 and US/EU market cap data is from the World Bank.

+ +
+
+
+
+
+
In [5]:
+
+
+
# 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]['2009':'2010']
+EMU = mkt_caps.iloc[0]['2009':'2010']
+
+# 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))['2009':'2010']
+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: 0.0851800000101
+CAPM Optimal Ratio: 0.407509492777
+
+Difference: 0.322329492767
+
+
+
+ +
+
+ +
+
+
+
+
+

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 2009 to 2010, the European equity market cap was 40.7% 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 8.5%, one-fourth of the optimal amount. This discrepancy is a result of home bias, and this test confirms its presence during the research period.

+ +
+
+
+
+
+
+
+

Refined Hypothesis: Equity home bias results in international-domiciled equities being undervalued. US equities with strong inverse correlations to the USD-EUR exchange can serve as proxies for these international assets, and because they are US equities and subject to US market biases they will be undervalued.

+ +
+
+
+
+
+
+
+

Designing a Pipeline¶

Let's build a pipeline to pull in rolling USD-EUR rate correlations for every asset in the Q500US universe.

+ +
+
+
+
+
+
In [6]:
+
+
+
# 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 [7]:
+
+
+
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)
+
+class Volatility(CustomFactor):
+    """ Custom factor to find volatility """
+
+    inputs = [Returns(window_length=2)]
+    window_length = 10
+
+    def compute(self, today, asset_ids, out, returns):
+
+        out[:] = np.std(returns)**2
+
+ +
+
+
+ +
+
+
+
In [28]:
+
+
+
# Assigning the Q500US as our universe
+universe = Q500US()
+
+# Buildling our pipeline
+pipe = Pipeline(
+    columns={
+        'fx_corr' : FXCorr(mask=universe),
+    },
+    screen=(universe)
+)
+
+start = '2009-01-01'
+end = '2011-01-01'
+
+# Stores pipeline in result
+result = run_pipeline(pipe, start, end)
+assets = result.index.levels[1].unique()
+pricing = get_pricing(assets, start_date = start, end_date = end, fields = 'price')
+
+ +
+
+
+ +
+
+
+
+
+

The distribution of FX correlations across the Q500US:

+ +
+
+
+
+
+
In [29]:
+
+
+
result.unstack()['fx_corr'].mean().hist(bins=20);
+plt.title('Q500US distribution of USD-EUR correlations');
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+

Further Testing our Hypothesis using Pipeline¶

+
+
+
+
+
+
+
+

We know 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 can be 'proxies' for international markets. If that is the case, they will behave similarly to international assets and 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 50 stocks with strong negative correlations to FX rate
    • +
  • A bucket of 50 stocks with strong positive correlations to FX rate
    • +
+

Pipeline made generating rolling correlations to FX rate for each asset easy. Using the pipeline data, lets find correlations of returns between the portfolios:

+ +
+
+
+
+
+
In [33]:
+
+
+
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()
+
+# Adjusting end date to get a larger timespan to observe the relationships; 
+# Still within research period so does not violate integrity of out-of-sample period
+adj_end = '2011-01-01'
+
+# 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=adj_end, 
+                                    fields = 'price').pct_change().mean(axis=1,skipna=True)[1:]
+returns['high_bucket'] = get_pricing(high_bucket, start_date=start, end_date=adj_end, 
+                                     fields = 'price').pct_change().mean(axis=1,skipna=True)[1:]
+returns['vgk'] = get_pricing('vgk', start_date=start, end_date=adj_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     0.820017  0.861901
+high_bucket    0.820017     1.000000  0.763954
+vgk            0.861901     0.763954  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. For our hypothesis, we will use the high_bucket portfolio of US equities to replicate European equities.

+ +
+
+
+
+
+
+
+

Analyzing our Factor with Alphalens¶

+
+
+
+
+
+
+
+

Now we will create a new pipeline and run it over 2009-2010 to find factor data for our research period. 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 [13]:
+
+
+
universe = Q500US()
+
+start = '2009-01-01'
+end = '2011-01-01'
+
+pipe = Pipeline(
+    columns={
+        'fx_corr' : FXCorr(mask=universe)
+    },
+    screen=(universe)
+)
+
+result = run_pipeline(pipe, start, end)
+assets = result.index.levels[1].unique()
+pricing = get_pricing(assets, start_date = start, end_date = end, fields = 'price')
+
+ +
+
+
+ +
+
+
+
In [14]:
+
+
+
import alphalens as al
+
+# Formats the factor data, pricing data, and group mappings into a DataFrame 
+# Necessary for most Alphalens tearsheets
+factor_data = al.utils.get_clean_factor_and_forward_returns(factor=-result['fx_corr'],
+                                                            prices=pricing,
+                                                            quantiles=5,
+                                                            periods=(1,10,30))
+
+ +
+
+
+ +
+
+
+
In [15]:
+
+
+
al.performance.factor_alpha_beta(factor_data)
+
+ +
+
+
+ +
+
+ + +
+ +
Out[15]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + +
11030
Ann. alpha0.0392920.0281500.020030
beta0.1248050.1428890.134844
+
+
+ +
+ +
+
+ +
+
+
+
In [23]:
+
+
+
mean_return_by_q
+
+ +
+
+
+ +
+
+ + +
+ +
Out[23]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
11030
factor_quantile
1-0.000339-0.003653-0.011133
2-0.000193-0.001113-0.000391
30.0000070.0002190.001262
40.0001720.0014290.002292
50.0003530.0031240.007995
+
+
+ +
+ +
+
+ +
+
+
+
In [35]:
+
+
+
# 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);
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+

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¶

Using the long-short equity algorithm template makes this step simple. To implement a long-short equity strategy with our fx_corr factor, we find the custom factor in the algorithm on line 49 and replace it with fx_corr. We can also make any other changes we deem suitable. For this algorithm, lets:

+
    +
  • Comment out the other factors ('value' and 'quality') on lines 100 and 101 as we want to isolate our FX factor
    • +
  • Comment out lines 145 and 146, as 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
    • +
+ +
+
+
+
+
+
+
+

Analyze Our Backtest Using Pyfolio¶

Our in-sample research up until this point was almost entirely contained within the time period 2009-2010. We will run the backtest from 2009-2011, 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.

+$$ + \ + \overbrace{ + \underbrace{\textit{2009 & 2010}}_\text{In-Sample}\:\:+ + \underbrace{\textit{2011}}_\text{Out-of-Sample} + }^\text{Backtest} + \ +$$ +
+
+
+
+
+
In [44]:
+
+
+
import pyfolio as pf
+from pyfolio import tears
+from pyfolio import timeseries
+import itertools
+import functools
+
+# Get backtest object
+bt = get_backtest('5970c1174c2dc64e1c3b1d97')
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
100% Time: 0:00:03|###########################################################|
+
+
+
+ +
+
+ +
+
+
+
In [45]:
+
+
+
algo_performance = bt.daily_performance
+benchmark = get_pricing('SPY', start_date='2009-01-01', 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 [46]:
+
+
+
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.

+

Below analysis was inspired by Exhibit 3 in a report by AQR on Measuring Factor Exposure.$^3$

+ +
+
+
+
+
+
In [47]:
+
+
+
def find_vifs(data):
+    data = pd.DataFrame(data)
+    n = len(data.columns)
+    VIFs = np.zeros(n)
+    for x in range(n):
+        # Calculates VIF using steps described here: 
+        # https://en.wikipedia.org/wiki/Variance_inflation_factor#Calculation_and_Analysis
+        VIFs[x] = 1/(1-regression.linear_model.OLS(data.ix[:,x], 
+        sm.add_constant(np.column_stack((
+            [data.ix[:,(x+i+1)%n] for i in range(n-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.ix[:,: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:
+        
+        # Create bar graph, with horizontal lines at 0 and annualized algo returns
+        ax = returns_decomposition.T.plot(kind='bar', stacked=True, rot=-30)
+        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');
+        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 [48]:
+
+
+
algo_returns = algo_performance['returns']
+decomposition = decompose_returns(algo_returns, plot=True)
+
+print 'Variance Inflation Factors:\n', decomposition[4]
+print '\nBetas:', decomposition[0]
+print '\nFactor Excess Returns:\n', decomposition[2]
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Variance Inflation Factors:
+[ 2.16028473  1.34813857  2.22835361  1.60996824]
+
+Betas:                   Mkt-RF        SMB         HML     Mom   
+Alpha          0.0537356  0.0528727   0.0432647  0.0275121
+Alpha t-stat     2.01595    2.00192      1.7057    1.15618
+Mkt-RF          0.055417  0.0495098    0.094671  0.0838148
+Mkt-RF t-stat    5.87536    4.41303     6.64337    7.33696
+SMB                  NaN  0.0346142 -0.00432747  0.0203095
+SMB t-stat           NaN    1.54278   -0.205814    1.03043
+HML                  NaN        NaN   -0.106863  -0.171577
+HML t-stat           NaN        NaN    -4.33332   -6.03916
+Mom                  NaN        NaN         NaN -0.0852706
+Mom    t-stat        NaN        NaN         NaN   -5.31589
+
+Factor Excess Returns:
+Mkt-RF    0.165067
+SMB       0.053100
+HML      -0.039500
+Mom      -0.160433
+dtype: float64
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+

The VIFs are all under 5 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.

+

One of these factors, momentum, seems to explain much of the algorithim's performance within the fourth model. This is due to the algo having a significant negative exposure (tstat = -5.3) and the momentum factor having negative returns over the time period (-16%). 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 [49]:
+
+
+
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 = '2009-01-01'
+end = '2012-01-01'
+
+alt_result = run_pipeline(pipe, start, end)
+
+ +
+
+
+ +
+
+
+
In [50]:
+
+
+
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 [53]:
+
+
+
# 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()
+
+decompose_returns_custom(algo_returns, new_risk_factors, plot=True)[0]
+
+ +
+
+
+ +
+
+ + +
+ +
Out[53]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Mkt-RFSMBHMLMomVOLSTMR
Alpha0.05373560.05287270.04326470.02751210.02716020.0295876
Alpha t-stat2.015952.001921.70571.156181.144321.23905
Mkt-RF0.0554170.04950980.0946710.08381480.08376450.0834903
Mkt-RF t-stat5.875364.413036.643377.336967.31087.3148
SMBNaN0.0346142-0.004327470.02030950.02040460.0214652
SMB t-statNaN1.54278-0.2058141.030431.035721.0861
HMLNaNNaN-0.106863-0.171577-0.171465-0.168513
HML t-statNaNNaN-4.33332-6.03916-6.02161-5.91933
MomNaNNaNNaN-0.0852706-0.0854911-0.0875817
Mom t-statNaNNaNNaN-5.31589-5.28629-5.45745
VOLNaNNaNNaNNaN0.003379270.0253341
VOL t-statNaNNaNNaNNaN0.2449691.28122
STMRNaNNaNNaNNaNNaN-0.033891
STMR t-statNaNNaNNaNNaNNaN-0.992107
+
+
+ +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+

Of the new factors, none compose more of our algo's returns than momentum.

+ +
+
+
+
+
+
+
+

For a full Pyfolio tearsheet, run the following cell:

+ +
+
+
+
+
+
In [13]:
+
+
+
# Create full tear sheets 
+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 [9]:
+
+
+
# 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-Domesting 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 we can expect our factor to perform worse in the future as equity home bias fades away.

+ +
+
+
+
+
+
+
+

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
    • +
  • Hedge negative exposure to momentum
    • +
  • Adjust algo parameters like MAX_GROSS_LEVERAGE, NUM_LONG_POSITIONS, and NUM_SHORT_POSITIONS
    • +
  • 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\nI25usGkTjB4NBw9Cly65S2VbUny8ak9egoJUkDR4MLz2mmrLN99krV+5Ut1LYYcKYVdQmFGgNO5R\n0+XdK3s6sXRqH5rWVRfNbs7lszS/p5sji959mEc61QYwBHw1fI0/m/qiB3a2RfozVaoS7+WenFer\n1RGbkMKOI7kn6F249h++WHOcrQdCAXi4fU2j9XWqeTJ5XHvD82qVTVfqGdi1rtHz5vUqM2l01jeY\ndat7MqpfY8NzjUYjgZIQQghRCEXKLA0bNoxhw4blWm6q4oTFXL4M+/bBww/DM8/A1KmwZg3Mn1/w\nOCN9cDF6dN7bODioYMzPDz7/HF54AbZts1z79S5ehH794OZNOH8eatXKvU1g5gSe/fpB9+7w/ffw\nySfqfXt4qHY6OcHQoZZvnyhVG/66xJJNp42W9WhTk6EPN+ReajovfLqT6PgU+jwYwKtDW2FrozFU\nwrMtB0FEYdjb2eDsaEtyinGXQxensjGOMD9f/fpPrmXR8ff4Zv2JfPe7l6rea6MA013iHu9ej+SU\n9Dy7IQ7uVo/Ne68AsHjywzg52tG9TQ26t8m/2IYQQgghzFN+rrZ++kndjxsH9vYwfDhERMDOnfnv\nl56ugiofHxV85MfGRgVfHTvC9u1qDiNLCg6Gzp0hNFR1J/z1V9Pbbd+uCjr06qXa/d57akLZefPg\nyBG4cEFVBPTwML2/KDeyB0rrP3uMTfMGUaWSqjTp5GDHhOGtGT+gCa9lBkoAT/VqAMCgHFmFimDy\n07m71To72hGflMqSTaeMurSVJQdOqL8V3VpnzXU1fsZ2QyGOkX0bsWKa6b8/89/olmd3ymcHNeO1\noa3yfF0/bxdefaol741rV6j5loQQQghhnvIRLOl0Kpvi4qLKaUNWlqigrni7dqmgavhwFWSZY/Ro\n0GpVkGUpO3ZAjx4QGalKldvamg6WoqPh8GFV0twrcxzDG2+Av7/KeM2dq5ZJF7wKpVWDKjjY2+a6\naG73gB9P9mqATbYKa52aV2P1zEfo1LxqaTezxLVp5Gs0vxCobnivztnNhr8u8/q8PcQmpBSq4mZJ\n02qz2vLMwKa8NrRlrm1G9WtMJQ8nVk7vbwiK+3eqTfN6lWlQ0yvX9oXRv1NturQs/QmJhRBCiPtB\n+QiW9u9X2Zgnn1Tji0Blf+rWhd9+g8TEvPfVB1P5dcHLadgwFcyYOyaqIGvWwIABkJqqAqT334ee\nPVWW6No1421371aBWvYsmKurKvKQlATr16vAqXdvy7RNWJWTgy2VPZ348LkOhdrPzcWhXBZ3MIen\nm/GYmkOnbhlN2vrLzgu8ueAvPv3xSGk3zaTAQ1cBqFbZFR9PZ/p1rG20vlurrEDG083REBS/+lRL\nPn2lc4U9j0IIIURFUHaCJa0WXnpJlczO+a3xihXqPns2RaOBUaNUoLRpk+ljJiWpYKpOHejUyfy2\n+PqqYCU4WI0rKo4vv4SRI1UlvsBAFfBB1nijdeuMt9ePV8o5s/Bzz0G9eurx6NFlZj4oUTxp6Voq\nezljb2f9yWXLipOXjef/0Xdl09u09wpXbsRy8OQtJi38myQTxRVKQ2JyGr/uusCizHFJQ3rUN6zr\nkTlmaMYLnXhjRGurtE8IIYQQxVd2gqV9+2DxYvjgA3j9dRU8ASQnq7mPqldX2ZjsCuqKt2kTJCSo\noKqw396a280vLzqdei/6LnR//6264ekNGZK7K55Op4KlSpWgfXvj49nbw9dfQ9Om8PLLRWuTKFO0\nWh0ZWp0ESjm8NbKNyeVebrmLHJwPi2bL/tCSblIuN+8kMGLqVlZsPQtA/Zpe9M+s7gcwYXhrlk7t\nS+tGvjjYy/kVQgghyquyEyzps0dVq6qg4MUXISNDBTyxsTB2rAousmvcGNq0UQHGnTu5j1mULnh6\ngwer7m+rVuXOdBUkPV1lgmbNgvr14cABaJljHEOVKip4OnwYwjJLC1+4oB737p37vYLKdp06lZVh\nquB+3XWBv49ft3YzLC4tXcumvZf5fsNJQFWBE1ma1vVh5oudGNzN+Pf8/fHtmfZcx1zbOzqUfjBy\nISza6HlSsnF2y97OxlCoQwghhBDlV9m4StNnj2rWhJAQFQD98AP85z+wbJnaZuxY0/uOHq2Ck19+\nMV4eGQl//AGtW8MDpuetyZerq5rA9soVFdCYKylJZY2WLoV27dR4q7wmjs3ZFS+vLnj3Ia1Wx4qt\nZ5m7Mph/w+Ot3Ryzrd99kXe+/JujZ8MNy85dvcvwKVs4d+0uny0P4on3NvP9hlP8npkRkWApt1YN\nfenzYIDheYOaXtSr4UW7B/zYPH8wG+cOwtlRdUX18SxaUPLHwav8ffw6Wq2OiLtJhdo3PUNr9Pzt\n0TI7uxBCCFERlY2rtI0b1UStY8eqjMuuXaqAw08/qQCiXTto0sT0viNGqC5233+vCinob9OnqyCq\nKFklPf2++nmaChIdrQKd33+HPn1UsQZf37y3HzJElSvXd8Xbvl3dS7BEalrWXDuvzNlNfFKq4XnS\nvTT+++0BXvpsJwnZllvSvZR07qWmF2qfm5EJ/LjlDOeuRTP9h0PsD7kJwMylh0m6l847X+5l/4mb\nufbr1a5mrmUCalX14NlBzejYzJ+5E7rhmK07m42NhvGPqb8J2ozCV8a7l5rO1+tCmLsymE9/PMKz\nn+xg99Hck8eacuT0bRauzZpXadWMR2iYxzxJQgghhCjfykaVAH0XPH32yMtLBQ4DBsDevTB+fN77\nVqumuq3t2KEKKWRna6uCqaLq00cFb2vXwoIFBZce//BDlUkaORJ+/FFNdJsfX1/VFW/3brh0Cfbs\nUV0LAwLy3+8+oJ+sU2/Uf7cxqGtd+nSoxevz9hiWPz09kOUf9cfN2cyy8GbI0Op4/tOdxCSksPCt\nHtSt7mlyu7DbcZy/Fk3vBwMIuXiHad8fMlr/2YogXJ3tSczRRatF/cr0fjCAnm0lSCrI493r8Xh3\n091ObW3Udz03IxPIyNAWapLeuMSsIFtfQGL19vP0alfwZ2/+z8GGx5vmDZJqdkIIIUQFZv1g6fZt\nlT168EEVKOi5u6vle/cWXCZ76VLYsiX32KLGjVVhiKKys1PzM331lQrGHn00723T0mD1avDzU8Gf\nudXqhg5VwdJbb6kufAVNnHuf0Gd17GxtDF2eNu29wqa9V4y2S03X8tnyI3z8Uudivd7pK1F8vvoY\ntf098K/sQkyCKlU9b1Uwn7z8EJXcnYy2T7qXxqtzVdC240gYZ6/eNayrVtmVm5GqnL0+UHpucDNu\n3ElgQOc61PKXyYQtwd5OBSkr/zjH7/tC+eTlhwgw82e7dseFXMtyBrV5qVvdk1OXo3iyZ30JlIQQ\nQogKzvrB0qpVqvKdqUlWnZ3N65JWo4YqCFESRo9WwdKqVfkHS4GBEBWlqt8Vpqz3E0/Aq6/C5s3q\n+X0YLB0+dYsdR8J4+ckWhvEnx85HADCqXyN6tq3Jf2ZuN9qnb4daPFDbm4VrjxNyMZKE5LRCZ5eS\n7qUxfMpWRvZtxOrtqkR8zrEr/4bHM+6jQL58uwe1q3qw40gYt6MSCTyUNT+WPlCaOLI1V27E0b1N\ndU5eimLZ76fp3roGPdvVoE0jX7mwtjC7bJmkmIQUXp27h7kTutK4lne++x05fZvth6/lWh6flEZC\nUipuLrkzwjfuJODmbI+7iwOnMkubj+7fONd2QgghhKhYrB8srVihurcNH27tlpjWoYOqPrdhgypD\nrp8UN6eiVt7z9YXu3VUXPAcH6NateO0tJ05fiWL19nNERCdzKzMLc/bqXT59pTMBfu78Gayq4PVq\nVxMfT2e+/6A3i387aSic0LVVNVo19OVCWDTbDl5l5NSt9OtYi1efasmvuy5S1ceVrq3zzyrO/uko\ngCFQyq5eDU8e71aP+T8fA2DC/D9zbWNro2HqMx34aetZhvSoR4+2NenVTq1rULMST/Ssn2sfYTm+\nlVxyLZuyaD/rZw8EVNVByF2afebSrIIto/o24ui5cK7eiic1LYOTlyPp1LyaYf2+kBvMXnHU8PyD\n8Vkl/aXkuxBCCFHxWT9YOnFCVZ2rXNnaLTFNo1EB0IwZsH49PP107m3i41WRigYNVDGKwho6VAVL\nXbuqKnz3ganf7ic9x8D8uMRUXpubNR6pRf3KhkyTv48r057ryIWwaGw0GurX9AKgVcMqbDt4FYDA\nQ9e4dD2Gy9djAfh89TFefaoFvR+sBahM0rlr0Uz77iD9OtYiNrOrnd6vswYAcO1WHDX93HFxsqdl\ngyqMmx5otF2dah6E3ozjhSHNafeAH+0e8LPMD0UUSuPa3gzv05A9wdfp2bYGa3dcoGGtrEILr8/b\nQ0R0Ev/LDJ5MGd6nESP7Nc4s5X6KT38M4vM3u9GgpjpO9kAJYMmm02q/3g1L4B0JIYQQoqyxfrAE\npgOQsuTpp+HTT2HmTFW8IWfhht9+U+XPR48u/OS3oIpQ/PILTJhgmfaWA9kDpbrVPHm0c22++jXE\naJsGmQFRdjmrjnVo6k/9Gp5cygyQ9IGSeg0tC9f+Q3qGjht3Etjw12XDOn03Og9XB156ogVVvJxx\nclAfh0bZunFV8nDil08H8PHSw0REJzHtuY7U8HVHq9VhYyPd6qxtTP8HGNP/ARKS01i74wKnLkcx\n56ejvDWqDTfuJABw/tpdwznN0Gb93n39Tk/DOWzX2I/vOQXAW1/8zfQXOrE5x/g4gPC7SWg08FiX\nuiX91oQQQghRBlg/WPL2zn8sUFlQty689JIau/Ttt7mDmuJMfgtQqZLKLFVQOp0u13gdfx8Xbkep\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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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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...........................
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+ From 1c1e2635d2bef81a7f32a4edbcf1122927c83cd7 Mon Sep 17 00:00:00 2001 From: Christopher Fenaroli Date: Tue, 1 Aug 2017 13:46:49 -0400 Subject: [PATCH 02/11] Updated exchange rate case study. --- .../USD_EUR_exchange_rate/notebook.ipynb | 40 ++++++++++++------- .../USD_EUR_exchange_rate/preview.html | 37 ++++++++++------- 2 files changed, 48 insertions(+), 29 deletions(-) diff --git a/case_studies/USD_EUR_exchange_rate/notebook.ipynb b/case_studies/USD_EUR_exchange_rate/notebook.ipynb index 9fd94432..b6a45900 100644 --- a/case_studies/USD_EUR_exchange_rate/notebook.ipynb +++ b/case_studies/USD_EUR_exchange_rate/notebook.ipynb @@ -5,6 +5,7 @@ "metadata": {}, "source": [ "# Researching & Developing a Market Neutral Strategy - Case Study\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", @@ -14,10 +15,21 @@ "\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/quandl/currfx_usdeur), many of which are free to use and all of which have been cleaned and standardized to work both in the research environment and in pipeline.\n", "\n", - "## Case Study Abstract\n", + "### Abstract\n", + "\n", + "Through some preliminary research we arrive at a 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) Attempt to measure the presence of equity home bias within the research time period 2009-2010. To do this we compare optimal portfolio positions in US vs. EU equities to what is observed in US investor portfolios over the research period.\n", "\n", + "2) Try to see if assets with strong negative correlations to the strength of the USD show some similarities to European assets. To do this we compare returns of a strong positive correlation portfolio, strong negative correlation portfolio, and an ETF that tracks top European equities by market cap.\n", "\n", - "\n" + "3) Examine if those assets with an inverse relationship to the USD are in fact undervalued. To do this we create an Alphalens factor which longs strong negative correlation equities and shorts strong positive correlation equities and examine its performance over the research period.\n", + "\n", + "We finish by implementing an algorithm based on the [long short equity template](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." ] }, { @@ -159,7 +171,7 @@ "\n", "One important 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 rate, inflation, or gold prices). \n", "\n", - "An important concept when dealing with macro data like FX rate 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 1500 asset-level ranking values requires some thought. Some approaches include:\n", + "An important concept when dealing with macro data like FX rate 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/beta coefficient (both will produce same ranking)\n", "* Spearman rank correlation\n", @@ -182,13 +194,20 @@ "It is possible (and we will 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 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 2009-2011. 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", + "Because our story is based on the presence of this bias it is important to make sure it exists within our test period 2009-2010. 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) 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). " ] @@ -271,13 +290,6 @@ "However, the US Treasury data shows that during our time period US investor portfolios had a Euro-US equity ratio around 8.5%, one-fourth of the optimal amount. This discrepancy is a result of home bias, and this test confirms its presence during the research period." ] }, - { - "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 can serve as proxies for these international assets, and because they are US equities and subject to US market biases they will be undervalued.*" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -417,8 +429,8 @@ "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 50 stocks with strong negative correlations to FX rate\n", - "* A bucket of 50 stocks with strong positive correlations to FX rate\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\n", "\n", "Pipeline made generating rolling correlations to FX rate for each asset easy. Using the pipeline data, lets find correlations of returns between the portfolios:" ] @@ -1358,7 +1370,7 @@ "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 we can expect our factor to perform worse in the future as equity home bias fades away." + "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 we can maybe expect our factor to perform worse in the future as equity home bias fades away, although in some ways it is dependent on home bias decreasing as well. Without home bias decreasing, those undervalued foreign assets will remain undervalued; it is only through the general decline of home bias that those undervalued assets will approach an efficient price which we need to happen if our algo is to see returns from investing in undervalued assets." ] }, { diff --git a/case_studies/USD_EUR_exchange_rate/preview.html b/case_studies/USD_EUR_exchange_rate/preview.html index 9b7ae498..3477e75d 100644 --- a/case_studies/USD_EUR_exchange_rate/preview.html +++ b/case_studies/USD_EUR_exchange_rate/preview.html @@ -11771,7 +11771,14 @@

Res

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 and less noisy than 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 the research environment and in pipeline.

-

Case Study Abstract¶

+

Abstract¶

Through some preliminary research we arrive at a hypothesis we would like to test:

+

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.

+

To test it we:

+

1) Attempt to measure the presence of equity home bias within the research time period 2009-2010. To do this we compare optimal portfolio positions in US vs. EU equities to what is observed in US investor portfolios over the research period.

+

2) Try to see if assets with strong negative correlations to the strength of the USD show some similarities to European assets. To do this we compare returns of a strong positive correlation portfolio, strong negative correlation portfolio, and an ETF that tracks top European equities by market cap.

+

3) Examine if those assets with an inverse relationship to the USD are in fact undervalued. To do this we create an Alphalens factor which longs strong negative correlation equities and shorts strong positive correlation equities and examine its performance over the research period.

+

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.

+ @@ -13736,7 +13743,7 @@

Researching Alterna

Macro vs. Asset-Level Data¶

One important 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 rate, inflation, or gold prices).

-

An important concept when dealing with macro data like FX rate 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 1500 asset-level ranking values requires some thought. Some approaches include:

+

An important concept when dealing with macro data like FX rate 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/beta coefficient (both will produce same ranking)
  • Spearman rank correlation
    • @@ -13763,7 +13770,16 @@

    Equity Home Bias Puzzle
    -

    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 2009-2011. We will use some of the methods in this paper to estimate home bias.$^1$

    +

    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 2009-2010. 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 and US/EU market cap data is from the World Bank.

@@ -13855,15 +13871,6 @@

Detecting Equity Home Bias
-
-
-
-

Refined Hypothesis: Equity home bias results in international-domiciled equities being undervalued. US equities with strong inverse correlations to the USD-EUR exchange can serve as proxies for these international assets, and because they are US equities and subject to US market biases they will be undervalued.

-

@@ -14259,8 +14266,8 @@

Further Testing our Hypot

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 50 stocks with strong negative correlations to FX rate
    • -
  • A bucket of 50 stocks with strong positive correlations to FX rate
    • +
  • A bucket of 25 stocks with strong negative correlations to FX rate
    • +
  • A bucket of 25 stocks with strong positive correlations to FX rate

Pipeline made generating rolling correlations to FX rate for each asset easy. Using the pipeline data, lets find correlations of returns between the portfolios:

@@ -20341,7 +20348,7 @@

Decay of US-Europe 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 we can expect our factor to perform worse in the future as equity home bias fades away.

+

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 we can maybe expect our factor to perform worse in the future as equity home bias fades away, although in some ways it is dependent on home bias decreasing as well. Without home bias decreasing, those undervalued foreign assets will remain undervalued; it is only through the general decline of home bias that those undervalued assets will approach an efficient price which we need to happen if our algo is to see returns from investing in undervalued assets.

From 2e96bfc16329eddf71afba2594a8248e6d478e5c Mon Sep 17 00:00:00 2001 From: Christopher Fenaroli Date: Wed, 2 Aug 2017 15:12:32 -0400 Subject: [PATCH 03/11] Updated exchange rate case study. --- .../USD_EUR_exchange_rate/notebook.ipynb | 306 +- .../USD_EUR_exchange_rate/preview.html | 2687 ++++++++--------- 2 files changed, 1494 insertions(+), 1499 deletions(-) diff --git a/case_studies/USD_EUR_exchange_rate/notebook.ipynb b/case_studies/USD_EUR_exchange_rate/notebook.ipynb index b6a45900..1b81d6ca 100644 --- a/case_studies/USD_EUR_exchange_rate/notebook.ipynb +++ b/case_studies/USD_EUR_exchange_rate/notebook.ipynb @@ -4,16 +4,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Researching & Developing a Market Neutral Strategy - Case Study\n", + "# 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 and less noisy than traditional data.\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/quandl/currfx_usdeur), many of which are free to use and all of which have been cleaned and standardized to work both in the research environment and in pipeline.\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 the research environment and in pipeline.\n", "\n", "### Abstract\n", "\n", @@ -29,7 +29,7 @@ "\n", "3) Examine if those assets with an inverse relationship to the USD are in fact undervalued. To do this we create an Alphalens factor which longs strong negative correlation equities and shorts strong positive correlation equities and examine its performance over the research period.\n", "\n", - "We finish by implementing an algorithm based on the [long short equity template](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." + "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." ] }, { @@ -41,6 +41,7 @@ "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", @@ -117,9 +118,9 @@ "output_type": "stream", "text": [ "----------- US/Euro Exchange Rate Data -----------\n", - "Start: 1999-09-07 End: 2017-07-29\n", + "Start: 1999-09-07 End: 2017-08-02\n", "Min Value: 0.627189 Max Value: 1.2064\n", - "Avg Value: 0.834315474675 Median Value: 0.791675\n", + "Avg Value: 0.834320811345 Median Value: 0.791706\n", "\n", "Fields: rate high_est low_est\n", "Frequency: daily\n", @@ -130,7 +131,7 @@ "data": { "image/png": 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Pr6Leu4i2niizyt1ctGwWuXxpav0Vi4rX8TvwOMzG9L6E7FUkxJgkFAkhhBBC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\nVqGwosGtHvEd20nv2Uuwu5tIfz9zj/4IM5utBTfU9xTFYqAo+EtCiE0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2AjA6IdaA0ila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fJH1/bQ4q0uUDSpmsGW3U8in+TJA8+xeURkFj35+xFGo1kuW9nOeZ1i3jc4Ia4vmXyZV/ZM0KIF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\ne0jkhru0l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JqOZj9d7QGe2zdzvAERjRpKjTGUGmNJQzcgRFEmVyKRLrJuWTMWi8SK7gY2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\nv/fp6elUV1ezZcsWlEolGRkZGI1GfvzxRx+P9Nxnt9u967iUSiU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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -169,11 +170,12 @@ "source": [ "## Macro vs. Asset-Level Data\n", "\n", - "One important 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 rate, inflation, or gold prices). \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 rate, inflation, or gold prices). \n", "\n", "An important concept when dealing with macro data like FX rate 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/beta coefficient (both will produce same ranking)\n", + "* Correlation\n", + "* Regression beta coefficient\n", "* Spearman rank correlation\n", "* Cointegration" ] @@ -182,7 +184,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "After some experimentation with the data, 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", + "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 the risk of overfitting. \n", "\n", @@ -214,10 +216,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 51, "metadata": { "collapsed": false, - "scrolled": true + "scrolled": false }, "outputs": [ { @@ -226,10 +228,10 @@ "text": [ "Ratios of Europe-Based Equities to US-Based Equities:\n", "\n", - "US Investor Average: 0.0851800000101\n", - "CAPM Optimal Ratio: 0.407509492777\n", + "US Investor Average: 0.0831691329132\n", + "CAPM Optimal Ratio: 0.386750620218\n", "\n", - "Difference: 0.322329492767\n" + "Difference: 0.303581487304\n" ] } ], @@ -261,13 +263,13 @@ "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]['2009':'2010']\n", - "EMU = mkt_caps.iloc[0]['2009':'2010']\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))['2009':'2010']\n", + "holdings_ratio = (euro_investments/(USA-euro_investments))['2009':'2011']\n", "holdings_ratio.index = mkt_ratio.index\n", "diff = mkt_ratio - holdings_ratio\n", "\n", @@ -301,7 +303,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 10, "metadata": { "collapsed": false }, @@ -325,7 +327,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 13, "metadata": { "collapsed": false }, @@ -341,22 +343,12 @@ " 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)\n", - "\n", - "class Volatility(CustomFactor):\n", - " \"\"\" Custom factor to find volatility \"\"\"\n", - "\n", - " inputs = [Returns(window_length=2)]\n", - " window_length = 10\n", - "\n", - " def compute(self, today, asset_ids, out, returns):\n", - "\n", - " out[:] = np.std(returns)**2" + " out[:] = exch_df.corrwith(close_df)" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 52, "metadata": { "collapsed": false, "scrolled": false @@ -392,16 +384,16 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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JGQAAQFel0AAAAIWl0AAAAIWl0AAAAIWl0AAAAIWl0AAAAIWl0AAAAIWl0AAA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\nHZQEACpv4cKFWb16daVjdAk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AFI6gAwAAFI6gAwAAFI6gAwAAFI6gAwAAFI6gAwAAFI6gAwAAFI6g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\nQOEIOgAAQOEIOgAAQOEIOgAAQOH8fwhXGXZwYaMDAAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -437,7 +429,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 31, "metadata": { "collapsed": false, "scrolled": false @@ -449,9 +441,9 @@ "text": [ "Correlations of returns:\n", " low_bucket high_bucket vgk\n", - "low_bucket 1.000000 0.820017 0.861901\n", - "high_bucket 0.820017 1.000000 0.763954\n", - "vgk 0.861901 0.763954 1.000000\n" + "low_bucket 1.000000 0.621341 0.865576\n", + "high_bucket 0.621341 1.000000 0.566791\n", + "vgk 0.865576 0.566791 1.000000\n" ] } ], @@ -468,9 +460,9 @@ "# 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=adj_end, \n", - " fields = 'price').pct_change().mean(axis=1,skipna=True)[1:]\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=adj_end, \n", - " fields = 'price').pct_change().mean(axis=1,skipna=True)[1:]\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=adj_end, fields = 'price').pct_change()[1:]\n", "\n", "print 'Correlations of returns:'\n", @@ -481,7 +473,7 @@ "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. For our hypothesis, we will use the `high_bucket` portfolio of US equities to replicate European equities. " + "Within this time period, it seems like the `low_bucket` portfolio is more closely correlated with the Euro index than the `high_bucket` portfolio. For our hypothesis, we will use the `low_bucket` portfolio of US equities to replicate European equities. " ] }, { @@ -500,7 +492,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": { "collapsed": false }, @@ -525,7 +517,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": { "collapsed": false, "scrolled": false @@ -544,7 +536,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": { "collapsed": false, "scrolled": false @@ -586,7 +578,7 @@ "beta 0.124805 0.142889 0.134844" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -595,11 +587,50 @@ "al.performance.factor_alpha_beta(factor_data)" ] }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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5IYQQQgghhBDihjGUlbDh+B5Wxm0m6vDfl5yKVKVS0T6wOf1DOtK/eUe6NmqF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5CUrYQYw6ZBSB/aJzuYR5oqEd72CSJ7vlLmhHUkSpByilF89VUoCCZgzdX3onndNqyc/yMU3XAZ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\nfampqSE/Px9vb2/1RkIAnTp1ok+fPujo6KinIYrO+KaGhgaWL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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": 23, "metadata": { - "collapsed": false + "collapsed": false, + "scrolled": true }, "outputs": [ { @@ -675,44 +706,6 @@ "mean_return_by_q" ] }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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oo3nkkUeSJDvssEP++Z//OZ///OdTUVFRqpwAAABAG9LobTErKipy6KGH5tBD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\nAABA4RQOAAAAQOEUDgAAAED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5IYQQQgghhBDihjGUlbDh+B5Wxm0m6vDfl5yKVKVS0T6wOf1DOtK/eUe6NmqF\n1tLqBkcrrsXmzZtJTk7mjz/+ID09Ha1Wi7e3N126dLnmNiV5IYQQQgghhBCiTugNhZzJTedMbgaJ\nOWlsOL6bmCM7KS4rMW/j6+RBa7/GeDm6mm4OrgS5+dCzaTvc7J3qMfrbQ009JOrKRx99ZH48f/58\nGjRocF2JC5DkhRBCCCGEEEKIWqIoCtvi9/Pd9lWsOrCNvOL8arcLDwhheJseDGvdndZ+TWQqUnFZ\nkrwQQgghhBBCCHFdCkqK+GVXDJ9vWcbBlH8Ka9paWRPg6kWAizcBrl60bdCMYa274+/qVY/Rihtp\nypTa6fkhyQshhBBCCCGEEFdMURSyC3Ucz0jieMYZdicd5dfdMRSUFAPg5ejKw12Hcn/nwTT19Jde\nFaJWSPJCCCGEEEIIIcRlHU1L4P2Yhfx+aBu5RRcPB+nepC2Te45mZNteWFlY1kOE4nYmyQshhBBC\nCCGEEJcUn3mWN9Z8y6+7/zBPeelgbUuIVxDBXgEEewUwvE0PWvk1qedIxe1MkhdCCCGEEEIIIS6S\nnJfJW1Hf89321RgrjVhqLHi461Cm9ZtAEw8ZDiJuLEleCCGEEEIIUYeKy0o4kpbAgZR4DqeeJkWX\nRao+m8yCPCoqjSiKYrpx/t5UU8BOa42rrSNudk642lV/39K3EV6ObvV9iOI2UG6s4GhaAnHJJ9mf\nfJJtR/eyPzOB0ooy1Co1D3YZwmuDHyLIzbe+QxV3KEleCCGEEEIIcR2S8zKJObKDjcf3cCI5EeUP\nNfklRegNheSXFFNSXlqnrx/o6k2nhi3o0SSMjkGh+Dq74+XgioVG/tQXl2esNLLg7zXMXPUV6fk5\nF60fF96PN4c8SrB3YD1EJ8Q/5F80IYQQQgghLkNRFPJLikjRZZGiyyQ5L4vDaaeJObKTQ6mnatzX\nUmNBM88AWvs1oaVvI4LcfMwJBkuNBSqVChUq070KVJi64heWGsgtyie3OJ+cIj25Rf/c5xbnk67P\nIS75JEm56STlprMkdoP5NVUqFZ4OLvi7eDGoRVcmdOhPiHdQXZ4icQvaejKOZ5fOY9/ZEwA0dPMl\nPDCE1n5NsCtRMa7PYPycPes5SnErKikp4aWXXiInJ4eysjKefPJJevXqdV1tSvJCCCGEEEKIaiiK\nwu6kI/yyK4bf9v5Jqj6r2u3stDb0DW7PwNBOoC+lfeu2OFnb42Rjj6ONHTaW2jqrDWCsNHI0LZG/\nEw6y8Xgfld28AAAgAElEQVQsR9MTSdNnk1WoIyM/l4z8XPYkHWVW1He0bdCMR+4axn2dInCysa+T\neMStITEnlReW/4ele00JrwYunswZOYXx7fubP6uxsbGSuBDXbOPGjbRq1YqHH36Y1NRUHnzwQUle\nCCFqX2FJMWXGctQqNSqVCiuNBVYWlmjUmvoOTQghhKhzKbpMFu6M5qcdURxNTzQvt9Pa4OfkgZ+z\n6Rbk5kOf4Pbc1bi1eVrI2NhYwoNa3LBYNWoNLf0a09KvMY92G2FeXm6sILMgl0Opp1m8Zz3L4zYR\nl3yCKYs/5IUV8xkT1oe2/k1p5hlAU09/Grr7YinDTG5bxWUlHEyJZ++Z4+xOOsqvu/+gtKIMG0st\nLw6YyIwB92FrZV3fYYrbyKBBg8yPU1NT8fHxue425V8oIe4w5cYKdiceYU/SUXKK8skrNnU9zS3K\nJ1mXyZncDPSGwmr31ag15kSGrZU1zb2DaOcfTHhACH2Cw/F0dL3BRyOEEEJcu/M9K46kJRCfmUx8\nVjInM8+yL/mEeTpID3sX7u04kAkd+tMhMPSWmV3BUmOBn7Mnfs6eDAztzBcTXmDl/s18tXUlf56I\n5aedUfy085/tLdQaIlt04YUB99GtSdv6C1zUGkVRWLbvT95Zu4ADKfFUKpVV1k9oP4DZIyfj7+pV\nTxGKO8H48ePJzMzkyy+/vO62JHkhxB1AV1zA8rhNrIjbxKYT+ygsLa5xe2tLLdYWVigoGCsrqag0\nUlpRhrHSiKHSiKG8FL2hkDR9NhuP7wFMY2ubeQbQ0M2HoHM3i6IKWrdtI7/kCCGEuKmUlJfy6+4/\nmLf+vxxOO33ReisLS4a17s79nQcxMLTzbfH/mNbSinHt+zOufX+Opyex9vDfnMw8y4nMM5zIPMvZ\nvAxWH9zG6oPb6NqoFS8MmMjQVt1Qq9X1Hbq4BrFJx5j628dsjY8DTD9AtfJtTFiDYML8m9GrWTva\n+jer5yjFDaPsB3JruVFXULW57FaLFi3i2LFjPP/886xateq6XvHW/5dYCFFFUamBk5lnOZ6RxInM\ns+xOPELM0Z2UVZSbtwn2CqRH07b4OLrjaueIi60DLrYO+Dp5EOjmjZud00W/LCmKgrHSSJmxgtLy\nMvJLijiQEs++syf469QBNp3cy/GMJI5nJFXZ77PYVbwccT8PdB6M1tLqhpwDIYQQojqZ+bl8sWU5\nn29ZRmZBHgA+Tu70btaOxh4NaHLu1sK30W1dEyLYO/CimSMy83OZv/k35m/6je2nDzLiyxcI8Q5k\nRv/7iAjtjI+T+y3T6+ROlqrL4v9WfcmPO6JQFAUPexfeHvY4kzpHYm2pre/wxB3k0KFDuLm54ePj\nQ0hICEajkdzcXFxdr72ntiQvhLhN7Dh9iBdWzDdn2C+kVqnpG9ye8e37E9GiCw1crr74kkqlwkJj\ngYXGAlsra1zsHAl082Fo6+6AaSxlfOZZknLTScxJIzEnjeV7NpKYm84Tv87mnbUL+HD009zdrq/8\n8SOEEKLOVRgrSMhJ5XjGGY5nJLHv7Al+2/snpRVlAIT5N2Na3wmMDe9nrldxJ/N0dGXW0Md4of99\nfPvXKuZt+C/H0pN4eOE7ADha2xHiHUiIVxDdm7Thvk4RcjF8E1AUhTR9NvuTT7I1fj+fblpCUakB\nKwtLnuszjlciHritE3HiCl1BD4natmfPHlJTU3nllVfIzs7GYDBcV+ICJHkhxC0vKSeNF1f8h8Wx\n6wHTGNcmHg1o5hVAsFcAIV5BRLbogreTW53GYWtlTesGTWndoKl52bjAriSq9by99gcOppxi3Lcz\n+a75aj4bN51mXgF1Go8QN6t8QxGxZ46xK/Ewh1JPU1BajKGsFEP5udu/HhsVI/ZaW+ysrLG1ssZO\na4OtpRbbc89dbB1wt3fG3d4ZNztHnGzssba0wtpCa7o/f7PQYmOlxdHa7o64UKusrKSgtJjconyy\nC3VkF+rIKdJTWGowD4erqKygwmikotL4zzLjueXnl51bX1FpxEKtwcrCAiuNJVYWlufuL3xuqgnk\nZGOPr5M7vs7u+Dl73hZDDm4l+84eZ3bMQlbs31yl1yGYEvHDWndnWt8J9GgaJsn0athb2/Jc3/FM\n7jWGRXvW8d1fqziYeorconx2JR5hV+IRftoZxaurv2Z6v3t4vPsIHKzt6jvsO4qx0sivu//gx7/X\nEJd8kpwifZX1o9r2Ys6oKTT2aFBPEQoBEyZM4JVXXuHee++ltLSU119//brblP9NhbhFKYrCTzui\neHrJXApKirG21DKt73heGjjppvkjQqNWMza8H2PC+vDtX6t4aeXn/HF0J8FvjKWxRwM6N2xB54Yt\n6dqoFWH+wfJHpLgtlRsrOJl5lrWHt/O//Vv569SBi4qmXU5BSc11aq6WtaUWJxs781SOTjZ2ONnY\n42xz/rnp5mrniI+jGy62jjhY2+JgbYujtR22VtY3/PtaYawgv6QIXXEhWYV5JOWmk5Cdau7plVdc\nQH5JkflW2+fsWllqLGjmGUCoT0MCXL1wtXXE1c5083P2oENg6B2RTKpriqKw+eRe3o9ZSMyRHebl\n/i5ehHgHEuwVQLBXIANDO9HUU5LnV8JSY8HETpFM7BSJoihkF+o4mp7IodRTfLNtFXHJJ5ix/DPe\njf6RZ3rfzdO9xuJm71TfYd/WjJVGftu7kbeifqhSq8XZxoHWDZrQxq8JY9r1oUfTsHqMUggTrVbL\n3Llza7VNSV4IcQtK1+fwzJJ55rm5R7btycd3TyXA1bueI6ueWq3mse4jGNGmBy//7wsW7VnHqaxk\nTmUl88uuGAD6BLdn4QOv4+vsUc/RCnHtistKWBK7no3HY80X1Sm6rCrJCgu1hnYBzekYGEq7gGBc\n7RyxsdSablbaix5r1BoKS4spLiuhqLSE4rJ/boWlBvKKC0w9C4p05BTmoy8ppLS8jJKKMkrKyyit\nKKekvJSSijKKy0rINxSZnpeXkpF/bcW71Co1Dta2uNk54XGu14eHgzN2VjaoVSo0ag0atdp0U5ke\nq1Vq8zIVKoxKpbmXw/leD2dSkrHc/z/0hiJ0hgJ0xYXoSwrRFRdettBwdey1trjaOeJu53SuZ4oT\n9tY2WKotsNBosDgXp8UFzy3UGiw0Fyw//1ylxqhUUlZRTpmxnLKKiqqPjeWUVZRTWlFOXnEBqfos\nUnSm2+G009UWhQTT1Ju9mrajf/OODGjekRDvIEnkXgVdcQEbj+9hzh8/szPxMGA6p491G87UPhNk\nFoVaolKp8HBwwcPBhR5Nw3iyx2iiD//NO9EL+OvUAd5c8x0frv+VIS3vItSn4bnhJYE08wqQoSW1\nwFhpZPGe9by99gfz1L0Brt68GvkgA0I74e/iJf9uiDuCJC+EuAUYK43sTjzK2sN/E3V4O7FnjqEo\nCvZaW+aPm86kzoNuif+0PB1d+W7i//HVPS9yMPUUO04fYkfCYdYc+ouNx/fQ+u37+H7i/zGsTY/6\nDlWIq5KZn8tnm5byn83LyCvOr7JOpVLRwMWTHk3aMqx1dyJadLnq8ce1OV5ZURQM5aXoigvQGwrR\nG4rQlxSiN5iSBKZlpqRBdqGe9Pwc9IZC8kuKKTjXo+H8jEN6QyGns1NqLbaaqFQqnKztcba1x9XW\nkUA3bxq6+ZpnN/Kwd8HRxg4HrS2ONnbYa23QqDU3JLaaFJUaOJaexJH0BNL1OeQU6c3TUx/POMOh\n1FOsOfQXaw79BUBDN1/6BIfj5eiKh70LHg7O5vvG7g1wtLk5etbVhwpjBSviNrPx+B6OZSRxLD2J\n9Pwc83o3Oyee6T2WyT3HSA+AOqZSqYhs2ZXIll3ZejKOd6MXEH1kh3kI63k2lloe6jqUaX0n0MjD\nr56ivbVUVlaSlJvOsfRE8+f8zxOxnMw8C5iSFq9IIXRxh5LkhRA3uUMppxjy+XSSctPNy7QWVgwM\n7cRHY567Jf8YsNBYEOYfTJh/ME/2HE26Pof7f5zFH0d3MvzLF3iqx2g+HP00NlbW9R2qEDU6nZXC\nh+t/4Ye/11BSXgpAh8BQ7u88iObeQQS6eePv4nVTDQtQqVTmehnX2tPp/BCOnCI9WQWmehJZhToM\n5aUYKysxnqsXUalUmp4r/ywzVlaioFzQ60GDhcYCjUpNZnoGrYNDcbaxx9nGAScbO5xtHXC2ccBe\na3NLTtlop7UhPDCE8MCQaten6rJYf2w3fxzdyR9HdpGQk8p321Or3dZSY8GA5p0YG96XYa2742zr\nUJeh3zSKSg0s+HsNH21cxKms5CrrbCy1NPcO4v7Og3j4rmHYaW3qKco7V/embVnb9GOOpCWwK/Ew\nR9MTOZaexNH0RE5mnuU/m3/jiy3LubtdH2b0v++S34U7na64gO+2r2L+pt9IzEm7aH2Qmw//F/EA\nkzoPuqn+TxHiRpLkhRA3sYMp8fT5eArZhToCXL0Z2qobkS260Ds4HNvb6MLe28mNtVM+4uONi3hp\n5ed8vmUZW+LjWProO4R4B9V3eEIApkTFjoRDpj/MM5I4mpbI0fRE85CQoa268eLAidzV+MZX9L7R\nLDQWuNo54WrnVKv1A2JjYwkPD6+19m4Fvs4eTOo8iEmdB2GsNLIz4TD7k0+SVfhPUiirII/MwjwO\npyaYe2lYaizoG9yeEO8gGrh40MDZkwYunrTybXLb9M5Izstk/qalfL3tf+YeTY09GvBYt+G0adCU\nEK9A/F28bsmk1u0o1KchoT4Nqyw7lHKKD9f/wi+7Ylgcu57Fsevp2TSMB7sMoblPEI3dG+Bq53hL\n9B6tC6XlZZzIPMNXW1eyYMcaikoNAHg6uNDStzEhXoGEeAfS3DuIns3aSfFfcceTb4AQN6n9ySfp\n+/EUcor0RIR2ZsUTs2/rcaNqtZpp/e6hd3A44799lUOpp2j//oOMaNODHk3C6Nk0jGZeAXfsHzii\n/uxOPMKL679hU9J+FEWpss5SY8HEDhHM6H8fLXwb1VOE4nahUWvo2rg1XRu3rnZ9Zn4uy+M2sSR2\nA5tP7iP6yA6iLyhOCabPZO9m4Qxr3Z2hrbvdtLWQLmSsNLIj4RCHUxNIyDEVYU3ITiX2zDEqKo0A\ndG3Uiml9JzCibc+bYjiQuDIt/Rqz4P7XeGvo43zy52K+2rqSzSf3sfnkPvM2jtZ2NPFoQESLztzX\nMYLm/0qA3A7O5Kbz391/cDzjDKn6bFL1WaTqsi+aJaRvcHue7TOOQS27yudciGpI8kKIm9DRtARz\n4mJQy64se+y92zpxcaEw/2D2vPwDj/8ym//u+YNfdsWYi3p6Orhwb8eBvDX0cekaLOqUoiisP7aL\n92MWsvH4HgCsLCwZ1KIrLc4Vo2vubbqXz6K4UTwdXXmixyie6DGKjPwcNp3Yy9m8TJLzMknWZZKY\nk8a+sydMQ1CO7mTK4g9p7deEviHtaerhj4eDC+72TrjbOVNYZqjXYykqNbDx+B7WHv6b/+3fSqo+\n66Jt1Co1Y8P7MrXPBDo3alkPUYra4u/qxYejn+G1QQ/z4441/HkiloTsNE5lJ5NfUsTes8fZe/Y4\n70b/SJh/M+7rGMG49v3wc/as79CvWaoui+Vxm/ht70a2xMddlPwGUwFnHyd3BoZ24pneY2nl16Qe\nIhXi1iHJCyFuMmdzMxjw2bPmxMXyx96/4woyOVjb8evDs3gl4n42n9zHlnjTrzQZ+bl8tGERqw5s\nY8GkmXRr0ra+QxW3icrKSpJ1mRw/Nxzkp51riT1zDAAHa1tGNbuL9+55Fh8n9/oNVIhzvBzdGNe+\n/0XLswt1RB3azqoDW4k+soMDKfEcSImvto2gNT6E+TcjzD+Ytg2a0swzAHd7Z6wsLLDUmG5X++tv\nZWUlJRVlZOTnkqLLJEWXRao+2zzzyvnHZ3LTKTdW/BOLmw89m4bR0M2Xhu6+NHTzJcQ7EA8Hl6s7\nMeKm5mhjx9O9x/J077GAKVGcU6Qn7uwJFu1Zx2/7/mTf2RPsO3uC6cs+pZ1/MAGu3rjZnZ9i2AlX\nW0eKsnQEBjfG3d65Xo9HURTO5KaTqs8mIz+X9Pwc0vQ5bDi+m+2nD5oTFlYWloxs05O+Ie3xc/bE\n18kdXyd33O2dZdiTuK3NmTOHvXv3YjQaeeyxx+jf/+L/t66GJC+EuIlkF+oY8NkzJOdl0q1xG5Y+\n+u4dl7i4UEu/xrT0a8zkXmNQFIU9SUd59Jf32J98kh7znuTpXnfz5pBH75iidaJ2FZeVsHDnWr7f\nvpqDKacwnCu4eZ6ngwtT+07gie4jOXX0hCQuxC3B3d7ZXEOjpLyU7acOsjU+jlR9NlmFeWQX6skq\nzCMhK9U8ne+KuM2XbE+lUmGpscBKY3kuoaHBUmOBWqWm3FhBudE0TWy50UhZRXmVaYFrolKp6BgU\nyqAWXRnUsivtA5vLsMA7kEqlwt3emX7NO9KveUfmj3+eNQf/4pfdMUQf3mHukVGdaeu+pIVPI3o0\nbUv7wOb4OXvgaG2Hk409jtZ2uNk51krhb0VR0BsKyS3KJ7c4n8ScNOLOnmB/ykl2JhwhqzCv2v20\nFlZEtOjMmLDeDG3dvVZnjRLiVrBz507i4+NZtGgROp2OkSNHSvJCiNtBvqGIpXs3MG/DfzmWnkQr\nv8asevKD26oo5/VSqVR0CApl14vfM2vNd7wX8xOf/rmE/+5ex7vDn+DBrkNkfKi4Isl5mfxn8298\nvW0luUX/TGvq6eBCsFcgwV4BdG7Ykns6DJAZb8QtzdpSS5+Q9vQJaX/Rup27d2Hv586+s8fZd/YE\nB1LiOZWVQl5xAeWVFebEhKIolFWUU1ZRfsWvq7WwwsvRFV8nd/ycPf65d/bAz8kDX2d3Gjh7Ym9t\nW5uHK24D1pZaRrfrw+h2fSguK2F34hGyC3XknEsc5BbpySnKJ+70MY7knOFw2mkOp52+ZHse9i4E\nunnj4+hmTmpYWVhSqfwzG1Klopgfl1WUm6cyPp+syCsuwHiu9sqlXiPIzQcvRxe8HFzxcnSllW8T\nBrfqioP17VE8V4hr0aFDB1q3NtVwcnR0xGAwoCjKdSWqJXkhRD1RFIU/j8fyw9+/s2zfn+ZffRu5\n+xE95WNc7BzrOcKbk5WFJW8Pf4Ix7frwzJJ5bI2P49Ff3uOZJfMI8Q4k1KchLXwa0SGwOX2C20t3\nTAFAXlE++1NO8tXWlSzdu9H8h2inoBY822cckS26SA8ecUexUGto4duIFr6NuK9T5CW3M1YaTb0r\nKv5JaJQbK6hUKk09Miwsz/XM+GeYifSgELXB1sqans3aVbsuNjaWlq1bsSvxCH8nHDTP0JNvKEJf\nUki+oZiswjzz7Xo5WNviamsauuLr5EFrvya09W9KO/9gGns0kM+8ENVQq9XY2Jjqgi1dupSePXte\n93dFkhdC1JNXV33FO9ELzM97NWvH/Z0HMSasj/wadQXa+jdj87QvWLxnPa/9/jUnM8+ax8me18Sj\nAU/3upsHuw6RXz/uIIqisOHYbhbHrudYehLHM85U+eNVo9YwLrwfz/UZL0UAhbgMjVqDRq25Y4pG\ni1uH1tKK7k3b0r1p9fWvKisrSc/PISk3ncyCPPSGQvSGQioqjahVatQqFWqVGo1abX5uqbE4V1vD\n0ZyscLVzkilKxS2vfPnbVCbsrdU21Q3bYTlq5mW3W79+PcuXL+e777677teUb6IQ9eCnHVG8E70A\njVrD/0U8wANdBtPQ3be+w7rlqFQqxnfoz/gO/dEVF3AkLYEjaQkcTktgRdwm4rOSeXbpR/zfqq8Y\nF96Xh+8aRueGLeUXktuUsdLIirjNvB/zk7nY5nm2VtY08wxgYGgnJvccg7+rVz1FKYQQ4kZQq9X4\nnhuuJISoH1u3buXrr7/mu+++w97++uu+SPJCiBts27lhDgCfjZ3Gkz1H13NEtwdnWwe6Nm5N18am\nsXUfjJrCqgNb+eTPJWw5uY/vtq/mu+2r8XZ0I8jNhwBXLwJcvAly86F3cDjNvYMkqXGLKi0v46ed\nUXyw7hdOZp4FTGOQJ/cczV2NWxPsFYifs4cMIRJCCCHEHelKekjUtsLCQj744AMWLFiAg0PtDM2V\n5IUQN1BiTiojv3qJsopynu51tyQu6pCFxoJRYb0ZFdabY+mJfL99NT/tXEt6fg7p+TnsSDhUZfsg\nNx8iW3Shb3B7gr0CcbSxw9HaDgdr2xtSCFRRFIpKDeQVF1BUZjAvA1Au2OY8KwsL7LW2aC0sKS4v\noayiHEuNxW2fgCkuK2Hf2eMczzjD8QzTkJAdCYfIyM8FoKGbLzP638sDXQZLsU0hhBBCiHoSFRWF\nTqfjueeeMxfqnDNnDt7e3tfcpiQvhLhB8g1FDPnP82QX6hgY2pl5Y56t75DuGCHeQcwZ9TTvjXiK\n5LxMzuRmcCYvnbN5mRxOPU3MkZ0k5qTxxZblfLFl+UX722ltcLS2o6GbD6E+DWnuHYS/ixdejq64\n2ztjodagKAoKyrl70xAGXXEhecX/VCs33UyPc4suXlZurLj2g/zRdOdu70yAixcBrt4EuHoR6OpN\ngKs3LrYO2GttsNfaYqe1xt3O+ZaqrZKQncqXW5fz1daV6A2FF61v06ApLw2YxJh2vbGQsclCCCGE\nEPVq7NixjB07tlbblL/whLgBjJVGJnz/KofTTtPcO4jFj7wtF1j1QKPWEOjmQ6CbT5XllZWV7Dlz\nlJgjO9kaH0eqLpv8kiL0hkIKSospKjVQVGogTZ/N9tMH6yw+G0stLraO2GttzD0oznekUHH+uem+\ntKKcwtJiyioqMJSVUFFppKLSSHahjuxCHXvPHr/s6/k4uRPsFUAzzwCaefnTzDOABi6e5iSHvdYG\nOyubGzrcorKykrziAlOF+AIdiblpLNy5lnVHd5m3aeXXmFa+jQnxDiLYK4Dm3kG09G182/c6EUII\nIYS4k8nVkxB1TFEUZiz/jKhD23Gzc2L1Ux/iZHP9BWtE7VGr1XQMakHHoBYXrausrKSozICuuJD4\nrGQOp53maFoi6fk5ZBTkklWgo1KpRKVSoUJluleBRqXBycYOl3PVyl1sHc7dTI//WfbPumut5h8b\nG0t4eDjGSiOZBXmcyU3nTG4GSbnppsd5GegNhRSVllBYWkxhqYHMgjzS9Nmk6bPZdOLS1ac1ag3+\nLp40cvejkbuv+d7fxQsHa1vsrGyw01pjr7XF1sr6ihMIiqKQVZDHjoRDbD65j22n9pOYk0ZOUb55\nGtMLaS2sGBvel6d6jJYZQoQQQggh7kCSvBCijugNhfyyK5qvtq7kQEo8lhoLlj/+Po09GtR3aOIq\nqNVqHKztcLC2w9/Vi97B4fUd0iVp1Bp8nNzxcXKnU8OaL/CNlUbO5mVwPOMMJzLOmGtIZBXqKCw1\nUFBSRGGpgeKyEhJz0kjMSWPjZTpz2FpZ43vu9e21NmgtrLCysMBYWUlFpZFyYwUVRiMZBbmczk6h\noKS42nacbOzxsHfGw8EFTwcXejcLZ2KnCFztnK711AghhBBCiFucJC+EqGWns1J4J3oBi/aso7is\nBDDVIZg/bjo9mobVc3RCmGjUGoLcfAly82VgaOdLbldSXkpSTjqns1NIyEnldHYqp7NTSNFlmXty\nFJWVmBMd8VnJxGclX1EMjtZ2tPZrQs+mYfRsFkYLn0a42ztjZWFZW4cphBBCCCFuE5K8EKIWpegy\n6T73CVL1WQD0bhbOY91GMLJtT7SWVvUcnRBXz9pSS7B3IMHegTVupygKBSXFpOqzSNPnUFxWQmlF\nGWUVFVhoNFioNVhqLLDQaHCxdaCxewNc7RylToUQQgghhLgikrwQopYUl5Uw/IsXSNVncVfj1nw/\ncSbNvALqOywhbgiVSmWaXtbGjhDvoPoORwghhBBC3GZuXAl5IW5jiqLw4E9vEXvmGI3c/Vj5xBxJ\nXAghhBBCCCHuaMeOHaN///788ssv192WJC+EqAVvr/2BJbEbcLC2ZfVTH+Ju71zfIQkhhBBCCCFE\nvTEYDMyePZu77rqrVtqT5IUQ12lF3CZeW/01KpWKRQ+/TahPw/oOSQghhBBCCCHqlVar5auvvsLd\n3b1W2pPkhRDX4UDySSYueBOA2SMmM6hl13qOSAghhBBCCCHqn1qtxsqq9iYtkIKdQlyj01kpDPti\nBkWlBiZ2iuT5/vfWd0hCCCGEEEIIUcWmwY+RGrW5Vtv0HdSTXmu+rtU2L0eSF0JchRMZZ1get4nl\n+zaxO+kIAJ2CWvD1vS/JlI9CCCGEEEIIUUckeSHEZRSUFDF3/a/8tvdPDqedNi+3tbJmSKu7+OTu\naVhbausxQiGEEEIIIYSo3o3uIVFXJHkhRA2MlUbu/ub/iDmyAwBnGweGtu7GqLa9GBDaCVsr63qO\nUAghhBBCCCFuPvv372fmzJnk5uai0WhYtGgRP//8M05OTtfUniQvhKjBm2u+I+bIDtztnfn5wTfo\nE9weS418bYQQQgghhBCiJm3atGH16tW11p5chQlxCb8f3MZbUd+jVqlZ9PBb9A3pUN8hCSGEEEII\nIcQdSaZKFaIa8Zlnue+HNwB4d/gTkrgQQgghhBBCiHokyQsh/qW4rITRX7+M3lDIyLY9eWHAxPoO\nSQghhBBCCCHuaJK8EOIcRVHYFh/HsM+f50BKPM08A1gw6TWZAlUIIYQQQggh6pnUvBB3vKJSA7/u\njmH+pt84kBIPgIO1Lcsffx9HG7t6jk4IIYQQQgghrk5paWl9h1Cj0tJStFrtVe0jyQtxR9t39jgD\nPn2W7EIdAJ4OLjx613Ce6DGKBi6e9RydEEIIIYQQQlydq00K1AetVivJCyGulK6kkIe/fIvsQh3h\nASFM7TueMWF90Fpa1XdoQgghhBBCCHFNVCoV1tbW9R1GrZPkhbgjVRgreOX/2XvvcLmO+777M6dt\n3729ouOiEGABCJAEZcoiqGZLluMWxbIVWXkTO3wfucl+3uRNbEeJ4ii2E1u25SeWEudxJL92LMWy\nZY0qb2wAACAASURBVFmFaqQkUiJFASQIohAdF7i9be97zrx/zNlyGy4AAhcXwHzIwZwye3bO3d1z\nznznV57+c4bnJnh40y6+/Wsf16KFRqPRaDQajUaj0dxEpPQou0UqXpmKW6bqlSm7RTKVJOnyLGEG\nln2tFi80dyW/+flP8OLYa/TE2vnsL/yOFi40Go1Go9Fo7iSkBCTg+qXWsuz5+7xliuvvv1qEXwy/\n1JedlmIDFuhA8Jq7iFItz1RxjOniKNOFUVUXR6l4y8fjeKv4+WX3afFCc9fxNy89ze9+9S8whcFn\n/sV/0rEtNBqNRqPRaG4Vsi4WuEAVqLSUKkuJC9uG8iBfoik01PfJBfUaRC4UOuoiRxAI+LUDmP4+\n0y8BEHropllbeNJjqnCZy7mzTOSHyVZTFGt5SrUCJbdAdRmRImAGcYwQjhnANgI4ZpCY3UYi0AET\ny7+f/gVo7iqOj53n/Z/6jwD8yiM/zpu2P3iLe6TRaDQajUazisi6RUFrvdS2K+1baluN5a0YqswX\nJqrMt4K4NuJxgPRVtBQ0B/8mauhjLFPqgoLZsu1qWSia1C0+FooxdYuOheJKCchcxdvYQKilBP1z\naj3HAAj7Gvqu0ayMlB6FWo58NUuhlmEif4nLuTOM5M5SdkvLvs4SNl2hAXrCg3SHVOkJDRK2Y8u+\n5vDE4eWP97rOQqO5jUgXc/z4J/41+XKRn3nobbxn98Fb3SWNRqPRaDR3EtLFND2QZZZ3SWh1X1g4\nmJVXWbwFywstEGB5kWEtUh+AWyx2tWgVE1Q5feYc27ftaNlWbyPm12vNRUMu/MzqIkcFJWCU/Xqh\nuFPz99VFoBWEDhkAwkAUiAAxlFWHdlvRXB1lt8SFzAnOpo4ynDlFrppGLiM0tgW6WB/dxkB0M+2B\nHoJWmJAZIWhFcIwA4gZ+57R4obkruDw3yb/8q9/hzNRl7h8c4n+8999y8tXjt7pbGo1Go1lryIWD\nwNaBYQU1gCjTnGWGxQPCq10XqEex1uL7xS87K2w09+tByI1hUWyE1hgJ1QWldX/rd6Pe1mPPAwDf\nXdVTuD7EEvXVbltqX33mfymrBpumGNES/6He/hq/y9nsMIi2a3rNmkC0uoy0El75tVKirj1FlMBR\nrxd+b4s0r1PJhR0AWb/WBIEE0AbEQZjXc0aaOwRPuqTLc1zIHOdM6iiXsqdxZW1em6AZJmLHidhx\n2gM9bIhtY31sO3GnfdX6qcULzR1Lza3xpWPf5b8/9/d8+fjzeNKjLRTj7/7l7xJ27rzUQRqNRqNZ\nAVk3bW8tFaAA5P1SZO3OTrdSH4QsFDZaZ55bB5Lzl3t7yiBHmT/ANJdsO+/4t0owkQutC9wFy1db\nL9xWFx1u1GduUKtJLMtmefeEuoVBfbAvFhRjiW1XKkt9bnBFAUILX7cfQqAEhxWeYaVEXcfq17Sc\nX+puK3UhrkhT3BAgYygxI4oSl2wcxwPpamHjDiNXTfPa3Etczp0hW0mSrSR9y4rW66BgMLKFobb7\nGWq7j45AL6Zx66WDW98DjeYm8JUTL/DP/+I/MZqaBsA2Ld699838m7e/jy3dg7e4dxqNRqNZFWQZ\nZV5dL1nUw/tKLDeD7KBMr1vN2essHAxezXpdTKnSFFPqywszIbSu160+qldxLkuzbh3A6Wt/oWwV\nSubtuO6+rPxa7yravF4WxkaoF3tBabEWmCcY1PcbvHL0Jfbt23eT+6vRLIMQKEuOMNA9f59sFezy\nqLghKZS4Ub9ONrnvXoBv+8JGW7PowKG3HRW3zJnUEY7Nfo+LmZMLhAoAQcSOMxjZylDbfWxN3EvE\njt+Svl4J/c3T3HF8/+IJfvzj/5pitcy2nvX8wmM/xs8deAfdsdUzadJoNBrNKiAl6qE7TdOVo3VW\ncako5/UBaaubRgjlFx4BwrfHLKO8UsrHhesLrQ08JifH6e3tumKb+SkjF/roX40IdCNZGEzxWuqV\n9tm3x2eu0bxeRGv61gjgZ9yTNdR1NI1yRVFBVSuVPI4DSvjNApf99jEgzvzYJA4Q1b+lNUDNq5Kp\nzJEqz5AqTzOWv8jp5MuN9KSGMNma2M32tj20B3qIO+1E7cSasKxYiRV7eOzYMaampnjiiSf46Ec/\nypEjR/ilX/ol9u/fvxr902iuibNTl/mR//brFKtl/tmjP8L//Ke/cUODxGg0Go3mFtIwh04Cc6gZ\nw9oVXmChAtXFm7UI3Oxerg6iPvC+PkZGk/T27bi2Fy0KNrioU9fdnyu/1nej0PdzjebmICyg0y9N\nXj12mH0P7qFpoZGiacWWXepALe4nbarWmU9WhYpb4vjsixyZeZbJwghLWasNRDZzb+cB7unYR8iK\nrn4nbwArihe//du/ze/8zu9w6NAhXn31VX7rt36LD3/4w3zqU59ajf5pNFfNRHqWt3/sV5nKJnnr\nPQ/ziZ/9f7VwodFoNLcjUqIelpM0A8/Vg2UudJUIoh6SQ8w37w+obfo+cONYNtigRqO5YxEm0OEX\nfKuvNMrqrTUFbhnlilJ3P6lbaURQ1+PW63MIJSoH9TX6dZKpzHF48hmOzHyHslsAQGAQdzppC3TR\nFuiiI9jDtrYH6Aj23uLevn5WFC8CgQCbNm3i05/+NO9+97sZGhrCMPRNS7O2yBTz/PCffJDzM6Ps\n33gPn/2F/4xtrn3TJ41Go9E0CQZdkOeASZZ2+QD14NveLCK0Wt3TaDQazUIxoxVZQwkXrVYa9cCh\nS+GAjKOEjARK1LC068kVcL0ayfI0s6UJXkse5rW5lxopTAcjW9jXc5Dt7XuwjDvT4mXF0V2xWOTL\nX/4yX//61/nABz5AKpUik1kht7BGs4qUqmV+7OP/iiMjp9nWs54vfeAPiAUjt7pbGo1Go7kS89L+\n5YEJdu9qfcgNAl2ooHP1IJl+rWfqNBqNZu0hLBZbaeSpx9BolrqFRgWY8UsLsjW9bryl3J3WdJez\nZ3lp6ptMFi6TLE83xApQVhb3dOznoZ43MxDdfAt7uTqsKF782q/9Gp/61Kf44Ac/SDQa5WMf+xjv\nf//7V6FrGs3KVGpV/un/+g88c/ow/YkuvvrLf6QDc2o0Gs1aQ1ZQLiD1lH0FlHAx3yfXdcE0+4E+\nlK/03feQqtFoNHcMwkSJDkvQiGGUQbmh1MWMKiqmTt1lMAuM+i+yfEuNCPPdUO68YKFSSi5kTvDd\n8S8zkjvbskeQcLroDPbSF9nInu7HiDtLWMHcoawoXhw4cIADBw4gpcTzPD7wgQ+sRr80mmVxPZdn\nTh3mrw99jc++/E1SxSzxYISnfvGjbOocuNXd02g0Go2UqIfQNDCBCq65VKpLB2VhEQI6eeXoZR58\ncOeqdVOj0Wg0t4h5KV37mttlPShwPWtUa7rrCup+MrfUARekdE3clildpfQ4lXyZ5yeeYrKg4oYE\nzDD7eh5nR/uDdAR7sA3nFvfy1rHiJ/pnf/ZnfPzjHyefV2acUkqEEJw8efKmd06jaUVKye9//a/4\nr1//SyYzzYvW/YND/Ol7/hX3r9t2C3un0Wg0dylSonybp1GmwCUWW1UIlBlxDIiiZs2Ci2bJpBxZ\nhQ5rNBqNZs0iBM00xkFUfCNaXA0zKFGj1Q2ljLLqq4scl/zXRFAiubWgOKgYG+FbauHnSZeknODI\n9LPMliaYLU4wVRwhV00DELHiPNz3FvZ0v5GAqeM7wVWIF5/97Gf5/Oc/z8CAntHW3Do8z+OXPv37\n/LdvfxaAoe51vOeht/HT+9/Krv47379Lo9Fo1hTzBItp1GzYQuoR5XuAXhB370yRRqPRaF4nQqDE\njODS+2UVZe1XT+ua5crBQgFskAmUkBFkkSvKTRI2SrUir8w8x+GpZ8gwB8Pz98edDg70vZ37uh69\nq60slmJF8WLjxo1auNDcUiq1Kj/3yQ/z14e+RsBy+Iv3f4ifevAJnQZVo9FobjbSQ81m1a0p6iXF\nfMEiBHSjZsiCQOCO8j3WaDQazRpH2Kggz11qXdZQ8ZVqS5QiSuQos2TAUADMBZlQ4v57XD+p8gyH\nJp/m6Mx3qHgqo1aIGFs7d9MZ7GuU9mAPhtDZPZdiRfFix44d/Pqv/zoPP/wwptl8EPmpn/qpm9ox\njQZgLp/mn/zZb/L1175PLBjm75/8Lxzcse9Wd0uj0WjuXGQV5U8865faMg2DKKuKHlSgNC0oazQa\njWaNICyWDRYKvgVhiWZK14UZUaqoQNPJltdEUDE66tYZFioLVpSFLiilWp5keYZUeZpkeZrx/EXO\npo4ifZfKDbEdPNT7ZtJnK+zfvP/GnPNdwIrixdTUFI7jcOTIkXnbtXihudmcGL/Aj/7p/8O56RF6\nYu186QMfZd9GHchNo9FobgiyinpYK/t1CfWQlmZ+vIoQKkZFoKVE0IKFRqPRaG5bhEDd30JA/+L9\nsh50ul5WckMxcb0wM8UsJ+dOcS59gaJboexWqHouAIYw2d3xEA/1vpne8HoADovDN/a87nBWFC/e\n/va38/jjj69CVzQaqLk1Xrp8im+8doj//JVPki0V2Lt+O5978vfY0NG38gE0Go1GszTSQ80wzaIs\nKwrLNBSoSO2dQBeI8Or0T6PRaDSatYJwUO6Q3WpduigBo0zTMqOGJwu4MoltuJgiS28YesM7eHzd\njsahPClxpcAgimkkUC4padREgOZaWFG8+OQnP8ljjz2GZd1+qWY0tweu5/Lfn/0c//Dqczx37hWy\npeYD9bv3vZk/f99vEXaWCc6j0Wg0mvlIiXL1qPiliBIskoDb0tCgaUnh+CUOdLxuv97VouKWyFZT\n5Cppym6Rqlem6lWouOWW5RJlt0TFK1FxS9S8KkDDdFciQc5bU7Wsr9etUBZvk7K+fOXXgcASNpZR\nLw62X1uGjS382nAIWhGCVhhLWJjCwjT8WlgErBBxu4OQFdFxnzQajWYVcaVkJDfJXGmSbDVJppIk\nW0kxWbhEyS0QtgL0hzvY2bGDTbEBoraDEC5QwxAehgAVQyo377j37hYgD6Gyq9SzoQRQ7ikRIHRb\npny9Waz4l4jFYrzzne9k165d2HbzYeb3fu/3bmrHNHcHI8kp3vvnH+JbZ15ubNvWs57Htz/ID+06\nwI/veVw/oGk0mrsOKT2qXtUfgJepuBU86eJJF4nEk15jGaoEjDJBq0rIquAY9YekxRRrkCpLUmWP\nTKWCR3HesaT0cGWNqlfxS7kx2BcYCCEwhIHAmFcbwsQUJoZfzJZaiIVtDUAg8fCk5wsAanlEXqYw\nOkbNq1DxylS8MlW3KUJUfEGi6papeCWq3lJZTu58LMMmZrcTtRM4ZhDHCChBxHSI2gnaAl20OV20\nB7sJmlro0Gg0mushXZ7jUvYU59LHuJA5TtktLdmuJ7SO3Z2PsLvjYaJOYnED6aIsNQo0BYwcUCAQ\nkCiLjisg62LGUmlf66lfHRpxOO7ga/6K4sXBgwc5ePDgavRFcxchpeQvvvdlfvkzf0C6mKMv3sl/\n/rH/m7fe8zCDbT23unsajUZzQ5FSUqzlSJVnVKmoOlOe9QfkSqCoixWtg3JDGHQGYoTtICHLIWg6\nBC2HjkCUwWgXncHFAcnKbpV8tUS+WiRbLXEpO8X5zDiZynKuImuHc+NX39YUFjGnjajdRtAM45gB\nbMPBNgL+sqodI4hjBgmYQUxhIRAghKqhWQvRuub/P3+beiYUjf+abVra+m3q2yQS16tRlRVqXpWa\nV6XqVah5i9cLtRwlt4AnXVxZo+bV8KRLzatScvNkKknKbpFkeYpkeWrFv1HADJJwughaEWzDwTEC\n2KZDWuYwpwu0BbppD/QQd9oQOrq9RqO5TpT4raz7TGHdFqJp1aswUxxjujhGoZql5BYo1nIUa3mm\niiOkyvOzkHQF+xmIbiHutBOz24k5bbQHemgPdl/5jYSJsqwIAh3N7dLj2PFD3Lt7J/OzoZRQQke9\n1DN9XQ0GSEvVmEvUC4vw9xlLLLe2MxYUv+0qf84rihf79+vop5oby2Rmln/5V7/L37/ybQDedd9j\n/Nl7/y098Y4VXqnRaDRrCyklZbdIrpomU5ljujjGbGmcdHlWuSr47golt9CwYACwhEnAtAlaNgHT\nIWbbBIJhgmaCgOUQsQJ0BOO0B2IknDCmsfygsuZ5zJUKzJSKzJSKpMplPOlbSAgDAwMh+tiSGPCX\nF1tCiJbtpjCxDQfLH+hahrK69KTXsM7w8FQtJR5uwyrE9fxaNrc12zZfJ5FL9mVyYpKB/kE1yPbF\nB9sMqAF3Q4iob3dwjOBt8YB8oym7JbKVOXLVjBK7fOGr4pXJVlJNkaw8TdktMVUcWfI4w8NHG8um\nsGgLdBG12wiYdbEnRNAM0x0aoCe8jvZAtxY4NJo7AE+6FGt5spWU73qn6mwlRcUr4npKPE3JJCdf\nexrXq+H6gqora4396lqvtnnSm/ceprCUm5ywMY3msmXYOEaAkBUlaIUJWRGCZoSQFcE2A9ii6VLX\nKMLBMixMYWMZFoYwkFL69xTp35skVa9MyS1QquX9uuALEmq93LJen1BouvgtJmCGWB/dxqb4Toba\n7qMtsIJIca0Ig3LZBHE1WVEK1ONsNOt6qWdKqaDcQ1fRKlHWrT/q2VdahZNW4cNmvoWIrbZf4z1l\nRfHi537u5xBCqC9EtUoymWRoaIjPfe5z1/RGmrsbz/M4M3WZp08d4rf+4b8zm08TD0b443f/Gu87\n8I678uFTo7nrkZJmlosS6gbsoW683hKlvn2pB43lHz7qM+DLr8/fJlGihCddarJGzavSvznNTOHr\nuFLNkjcf1pS7hQAMIegLCfrDAQSDmIaB2RAEVHFMB8c0Ma95ABhC+cDWHw5sfz2OZUTpiRj03AFx\nvw5PHmbfoE6HvRIBM0ggNEBXaOCK7aSUlNw8qfJsMyaIL3ScHj5JuNMhWZ4mWZ4iX80wW5pgtjSx\n7PFsI0BPaJDe8Ho6Q30EzNA8qxbHCBKxYwStO+DLqNHcRkjpUfHd67KVJOnKLOnyLKnKDOnyLJnK\nnH8NqFLzKrhyuRTYi0nmVm5TxxSW79roixtujTLF6zij1UEg6Ar20x0eJGa3NQUVM0JboJue8Drf\n1fFWdrI1K8pVIFWcjeYz01J1a6k/V3lLLF+pXX291VrkOpB1C4+FFiJLs6J48fTTT89bP3PmDH/z\nN39zfZ3T3FXM5tL8t29/lu+cO8qLF0+QLGQa+956z8P8z/f+Bus7em9hDzUazU1DVmmmFKvfROs3\n1FbBwlvuCLcMgXpWMIR/kzQh2l5/aKibfb5eDJb2W22dwQiifFxDvsmpRnNtCCEIWVFCVnTRvtql\nEPs2N4WiilsiWZ6hUM1Q9oObVtwS+WqGqeIoU4XLZKspRvPnGc2fv+L7JpxO+iIb6AtvpDe8nogd\nb1hxBMygtt7QaK4RT3pkKrPMFCeYLY0zW5pktjhOqjLTCFB8bQiCZpiY00bMbiPq1zFHueDVAwWf\nO3OOnTt2YQrT32ZiCgtDWJiG2QgmrLYZjclIKT1qsobrVal5NWq+8F+vK27Jt4bI+1YQeUq1PBU/\nzlJr24Z7naw2LD486SKEoZz3hFDWgwg/6HGYoBlWwY/NcMt6mKAZ8cUJtZ4IdGEbzo3/wG4lDReV\nVaARILzVIqRV7GgVP+qWIfW6/mxYP0Yry1uiXHPo0m3btnH8+PFrfZnmLkJKyf/+/lf51f/zh0zn\nko3tA4luDmzezT964Af5p4/8sLa20GhuV6QKErn4hlUXK3JcrW+mlBaSABIHT5pIKfAQSOnr+lJS\n81yqXo2yW6HiVqh4FT8OgLKKqMcFKLtFim6eUq1A2S1Sz/JQv9LUrzmL/p0Xs0D9awoLy7QJGiGC\nVpha0aWrvVc9AJlhAv4DUNCMtDz4iJa6XowlakuLEZo1h2MG6Q2vu2KbQjXHVHGEycJlUuVplcml\nReiouGUy1Tk161uZ5VTy5SWOIojZCbpCA3SF+ukK+nWon4B5lTOLGs0dgpSSmqySKk8zW5xoWD8p\n940yFbdI2S1RdosrWkvYhor5E7UTJAKdtAW6SDidJPw6aIX9mEDOVcekSIoS62ND13xeQhjYwrnz\nhAHNfISg6QJyHUiVrWux2HF62ZesKF784R/+4bwv98TEBJlM5gqv0NzNnJoY5gN//V/4xqlDADy+\n/UE+8Kaf4sDme1nXrgNxajS3JbKCR4pSbQJPpgiYNezl0ln41DyPZDnPbClHoVqi7FYpe2VKtQr5\napFUOUe6kqfiXb3p6rUhCFmRFrP2AAFDmbUHzBARO07USRCx4kTtBAEr5MdSCOKYDsYCceHw4cPc\nO6TdGTR3N2E7yiZ7J5viO5dt40mX2dIEE/lLTBQuMV0cpVjLU3aLlN2CitdRVf71FzIn5r3WacQ4\nUb/ZoBlpDMLanC41GAt0ErZiegJEs2bxpEe+mm6JPaPiz6TKM5Tcgh+cV1kUVL3KFWMutBK1E3QG\n++kM9dEV7KMj2EdHsIegqUQJbdGkue0QrRM9V8eK4oVlzW+yY8cOfvVXf/Vau6a5wylWSnzkqU/y\ne1/7/6jUqnRGEvzeT/wi/+zRH9EPGJo1i5QeFHPIuVHk3AgyOYbMTEO1hPRc8FzwPFVLf1m2bDMt\niHQgghFwQggnBE4YEe/GWLcLEr23x/dfSiQ5ctVLVL0MyArCz0vumBCyLAwg3LgdCEq1CoVamZJb\noVSrUHKrJMtZpgoppotpkuXcig9khjCwhO2bmtbTa9YDODaXm4EDQ0qMMIONGSbLsBszSQEzTNRO\nELHjROzYIgFCo9HcfAxh0h0apDs0yH08umi/J13S5Vmmi2PMlMYbkf7nSpON9Lh5WibJlsggaAiT\ngBlSsT/860LADBGyor4JfIKo3UbEivnXjOWFSc3dRT1zjytdPwhlbYkglLVGimbl+lDzXRbUvrJb\npFDLkq9mKdSyFGs5qm6FmqxQ9ap4fsaNq8UQJgmng46gL0qE+pSlhH+/c8wgASOEbWorBo1mRfEi\nGo3y/ve/f962P/7jP+aXf/mXb1afNNeAJz1lKl3LUar56X1a0vyU3WLDL8wUFpawMOb5rdmErAhd\noQE6g72NqPJXQkrJ8Nw4R0fO8urYOV4dPcdz515hNDUNwD9/w7v43R//RTqjS+Q51mhWGSk95PQw\ncvQk3sgJZGoCWc5DOQ/lAsjXGXNh5tLyQ/RoB8aG+zGGHsbYtAdh34hYCa8T3z+x5qXJVYfxZJKo\nDY5pEpv386+nwoKKW2WikGS2VKDkWkii2EYc2wgrX1dDlb6IzWBUXWdMw/YjjVuNNpZQkcJNw2r4\nqWo0mrsLQ5i0B3toD/awnT2N7SrgYJmKW6bilai6ZQp+NoB0I73wrJ9Bpeg/51xDJEEfS9g4ZtDP\ncBBuBOgzheln3amnwG2mw231rVeZDVT2nNYsOp50G5l4ZuUsw2e/72fo8dtIv828dbW/zsJUu/MS\n+S6Z2tfAqIu+GBiGidEi/Kp9LfsbArHaLliYlUg0/gatWYDq29R7G4jWFMItf6d6n+p9bPwtF7Rr\npiSe/xrPdwF0FwgGzVrFUHBljXE5xtTFEw1xwW1xI2xt2xQfKlTc8jUFqrx+BGErqiyGAl20Bbob\nLhxhO9YQ3OtZNExDC2oazdWyrHjxwgsv8MILL/D5z3+edDrd2F6r1fjbv/1bLV7cAnKVNCO5s4zk\nzjFRuMRsaZxircCVo+xfPQKDRKCDsBUjZEWJ2DGidhub4jtZFx3C9Tw+c/jr/Nev/RVHRhb7It03\nuJU/fc+/4ge2PnBD+qPRXA9SepBP4Y2fxjt3CO/8IShewdXNCSPa+xGd6xDtg4hEDwQiCMNQcQkM\nEwzDr02V0qm+r1ZBFlJKBKkUkPV65jLeyAnIzeGd+CbeiW+C5SC6NyHi3YhYlyrt/Yj19yKsmzCb\nIiWF6gS52kUMMtiGxDENAqZ6eLUMaAtAPahTplJgspCm5Bp40gRsDBHENELE7M30hN/Ihrj2R9do\nNDcHIYyGBcVK1Lyq74bSjAdQdosUqlmV9rGaJldJka9lqfpiiBJFyioIYK1KobaESccNZCp1Uw+v\nAS7PXM+rBLYvrjfEd38yrzUAZT1dtOWn+Kxvr6f5DNsxwlaMiB0nZEVwjGAjrefVxpPQaDTXzrLi\nxZYtW5ieVjPpptlUBC3L4g/+4A9ufs/uUspukanCCPlqhlw1zVl5itHzRxjLXyBVXvoqrUwlmzmS\nQ1aEoBUh5AeUq+dCrudjdr1aI4VRTdbIVdLMFMdIlqcavnmtfHf8S+DZnLiU5tljo1yczNMejrNv\nww7uG9zK/YND3DcwxJ7127R6rFlVpPSQl47hnvqOcv3IzkBuVrl0tBLrwli3G2PdPYjuTRCMIgJR\nCEYQN+k7K6WnRIzzh/HOvYgcP90o8wiEMYYewdz5RsSG+665PyrYV4VUeZJyLU2xNoZlZOkIOIRt\nh7ANKqd2k1KtQqZSYLZUoipDBM1++iIPsK294/WdtEaj0awC9UFixF4+Iv1SSCkbwocK7utnOXAL\njbTHyrJCNiws6usg8aTnWyuYvnWC6Vs0+NYLwsDA5MKFCwxt3dZoa/iWG/MtHwzfoqPu6+2/j6wv\n+7VsLDVr2ehRwwLElS6edPGYv960DnFxvVbLDxdXuo3zUlYjUt27/Lp+LM8PqicbfWxdVv2op5hu\nbdfou2/h2Lq9fgbNZRqZLJSlnj2vrgsHdYu+sZFxNm/c0iJANK3+Flv/2ViGhWMGsYSthQWN5jZm\nWfGip6eHd73rXezdu5eBgQFmZ2fp7u5ezb7dNVTdCmfSr3By7hDn08cXm7TNqcoxAgxEt7AuupWB\nyGa6Q4M31K+76lVUbuhSkovJy4ykxzg7d5qiGKcjBrs2hdm1aRumF+HhgTfRH9lAe7CbNqdb++Fp\nVhWZncU99g3cY9+AzPTiBqE4on0AY8s+jK0PITrXr/rDihAGonsjRvdGeOQnkIUUcm4MmZ1GaKVK\nhAAAIABJREFUZmaQ2Rnk2Gnk9AW848/gHX8GQnGM7W/AHHoIkeiDWBfCavpySOkxU7xMqnwOQYqo\nIwiZNg/uDWAbJ1o0CpUWMVspMl0qUnHDmCKBZYRxTGVZ1RZM0BNZA24sGo1Gs0oIIbBNB9t0iHLz\nXFuLFw12tO+9acfXwOHRw+zp1kGUNZq7jRVjXly+fJn3ve99OI7DU089xUc+8hEeffRRDh48uBr9\nu6NJlqZ5afpbHJ35LmW34G8V9IU3EHc6iNoJ0tM5tm/cRW9kPT2hwZsSaKpcrfCl49/lL1/8Ct8f\nPsnl5GRDLa/zzgf28GOP7KZojJCvpnl+4kvz9kftBEOJ+3mo7810BvtueB81tz+edMlWUuSrmUYs\nFrdllqi+Xp8lMoTZyAARqHoE8lmc1Cz22Zcwho/6sRuAeDfmroMqQGa8CxHtRNiBW3uySyDCbYhw\n26Lt3uwI3qnn8F57Dpkcw3vlKbxXnmrsd0MRatEo5fW9GPcM0d07SHdIAO3zjlP1XEq1KvmaS9WL\nELI20hHcSCygraE0Go1Go9FoNLc/K4oXH/3oR/nMZz7DBz/4QQCefPJJnnzySS1eXCOzxQlG8+cb\n+ZtnixMky9PU41X0hzeyu/MRdrQ/SMxpDnAOzxzmgRuoLEspyZYKTOeSXJgZ4zMvfYP/c/hpUsWm\n76chDDZ29jHUvY4dvRv5uQPv4KFNuwA1AD2XPs6F9HFS5RmS5WnSlVly1TRHZp7lyMxzbGu7nzf0\nv4P+yMYb1m/N2qbqVRjPD5OpzFKqFSj5KfFG5DAnX3uGTGWObCU1LzDZlbBdj90TJXZMlWgvuIRq\n88U0V8CFnjAXN/ST71tPLFAlaJzBKgxjl5SfasAMEXfaiTvtxJz2qwpGu2pID6gBVcrxGvk9G3Hv\nbyMwN0no/DBiYgqZzUI2h1nMYxbzBKYn4aWjlAf6KW0forR5N8HIVsJWF8eOnmHv3v3YJsTWnm6j\n0Wg0Go1Go9G8blYUL8LhMF1dXY31jo4ObHsNDQLWOMnSNN8e+3tOzh1atM8UFrs6HuLBnsdvykA/\nU8zzd0e+yd+98i0uzo4znU0xk09RqVUXtd2zbjvvfeTt/Mi9j7G5awDHWvozNoTJtrb72dZ2f2Ob\nJz1miuMcnnqGY7MvcCb1CmdSr3BP+35+cPBHaQ/23PBz0wCyBhSBUktdBZQPa0vDhS9sWa5nlBAt\nywYqiKOJukSYi9q50iVXyTKav8DFzGmGs+co1cpUvNri9JiNYPCCqK3S1wXNkMo44fsHG8LAFCbB\nYpkN5y8yeOEidrX5Pa0ZBtmwQyZsMxZ3ONZjkbMlkIXsiSVT6S0kbMWIOx1E/EjfluHgGAEcM0jc\naScR6KLN6SIR6Lx+oUN6qL9/BSgiKVBxk9S8LIYoYwqwDIHR4sISslSBAEQ2wPoNlGoVKp5Ltebi\n5QqI2RTOmWGci+cRY+MEx8YJPvsCxuYHEV0biSfzeB0OIt4NiZ6bFsdDo9FoNBqNRqO5VawoXgSD\nQV588UUA0uk0X/ziFwkE9NTeSuSqab479iWOzDyLJz1MYbGt7QG6QwN0BvvoDPXRHui54bPBpWqZ\nLx37Ln/1/a/yhVe/Q7lWWdQmEgjRHW2jJ9bOm3c8xM8+/HZ2D2y57vc0hEFPeJAf3vRefnDwR/ne\nxNc4PPUMJ5OHOJV6if7wJnojG+gLq9IV6td51ldCSqCAGpXngDxqQOyiZuxr3KgsM9eDKSARgETA\nYFfHTmBnY58nJZ4fc6xa87AtB4GDKQII4aAuO01RRHogp8ZwX/om3ulD4CnrDDGwHfPBH8YY2IET\n6SQiDPow2C4Eb5KSYi1PtpokU0mSrSSpuCWVk92rUPUqlNwCmUqyYfVRqGWvKrq8bVhErDCOGcAx\nHBzTL4ZD1A4Rc0JE7YAKhmlaBC0TSwhMw5gnSoCSegKmKjSCsoErPSpulVKtQrKcp+waGCJK2O6l\nPbiZqN1JECAARIBeYBfIShHvzAu4J76FvPQq3tnvwdnvsQ6onvqiOngojrH9UcwdjyEGd2ohQ6PR\naDQajUZzR7CiePGhD32If//v/z2vvvoqb3vb23jwwQf58Ic/vBp9uy0o1QrMlSZJlqdIlqdJlqZJ\nlqeYKo5Q86oIBPd1PspjA+8iEbhxkfxdz+Xo6FmGZye4NDfB5eQUF2bH+NrJF8mU8oAKTPWmbXt5\nz0NvY/+Ge+iOtdEdbSPk3LwgfRE7zhPrf5L9vQd5dvQLHJt9ntH8eUbz5xtt4k4Hb1n/bra377nC\nke4SZH2WvuzXJZRgkfLXr4QBBP0S8usA9dSXsDBA5VIBKyXgtdQexVqW8fx5kuUxwCVg2ph+FHVT\nGJiGqm3DJmSFCJohAqaFEMrqwBACw38r2zFRgksRKKqo4qNjeBeGkXNJZDKJTKUbggVCYGwfwty3\nF6O/z+/TyQVdVrnhw5ZB2DLoDVmoEX5swXk2awm4Xo2qV8WTNd99pXneUrqYQuKYJtbrGOx70qPi\n1SjWKiRLWVLlPIVaDUkQ20gQMBPYRoiAFSZgRohYMTYlejCEsfLBAeGEMHcfxNx9EJmdwbvwMjIz\nzezF12i3PWRqQqVnfeUreK98BSLtmNsfxRh6pJllRUdZ12g0Go1Go9HchqwoXvT39/OJT3xiNfpy\nW5GvZnhu7AscmX5uWT/+obb7edPgj9EdGrgh7yml5NDwSf73oa/y14e+znh66dSpD67fwc88/Db+\nyb63sq791rhsxJ0O3rn5fTyx/ieZyA8zWbjMRGGYsfwFMpU5/vbcxxlK3M9bNrybtkDXyge8k5AV\nYBqYQokUy+EAcVTmiChKmJhvtcANGIhKKclXM0wWL/PK9HOcSb3ScP1IOF3s7nyYvvAGwlbUz2se\nJWCGlx4E+2nS6kLI0Vdf4f77diO9Mt7ZF3EPfxU5fmHx62IJzG27MPc+jEjEUIJHvSwUWFrfA1YW\neZSMYRmqLKbuEuOfAgZIA+mLHlIKP7Ub1DxB1ROUPUmp5lF2Ja401WswERgYQqVjawu2sz7ejm3c\nnEw8ItaFef9bARgJHaZ33z4lDk1fxDv1HdxT34H0JO7LX8J92Q+wa9oQaUNEOhBtfRjrdmFsuA8S\nvVrU0Gg0Go1Go9GsaZYVL3K5HB//+Mc5e/Yse/fu5ed//ucxDIPJyUn+3b/7d3etoFHzqhyafJrn\nJ75M2S0hMOgNr6c90E17oIe2oKo7gj1E7defhqvmuRwfO89nDn+Dv/r+Vzg7PdLYt6mzn939W9jQ\n0cv69l7Wt/fw0MZd7OhbO4EyQ1aEzYldbE7UA356vDT1LZ4d+3vOpo9y8fhJfqD/nTzc+xZMY0Ut\n7fZlWcFCoESKgF8clBVBGxC+IeLEoq5Iyfn0MU4mDzNbmmCuNEHZLTX2G8JkV/s+9nS/kXXRoWsb\n1ApBM34GuMUqtZe+gfvSF5opTYNRzHvfgujdgmgfQHQMIOyrtAZqiCN18aIucNRatnOFunGWzI/z\nIVCXQxshTBBL26ms9YTAQghEz2aMns2Yj/0scvIs3pnv4V14CZmegkoBMtPIzDRy/BTeyW+pF8a7\nMdbfi7HhPkTPZkQooaw0zDv4N6nRaDQajUajua1Y9sn0Qx/6EP39/fzjf/yP+cIXvsCf/MmfMDAw\nwMc+9jF+4Rd+YTX7uCaQUnIq+RLPjPwd6YqyeNiauJeD636SrlD/DXmPqcwcf/Py03z3/KsMz04w\nPDfBaGoKryVtaW+8g5/e91Z+5uG38dDGXbfdbKkhDPb3HmRn+4M8PfI3nJj7Pt8a/RzHZl/gQN8P\nMRjdTHugG3GVZvRrDukBMyiBouSXMmpwXaee5rIH6AKxOgFwpZScSb3Cc2NfZKp4ed6+oBmmI9jL\n5vgu9nb/IFHn2oU3mRzHm76ATE0i06rsGDmJ6yrLCNHWj7nvRzB2H7x6sWIhDXEElPWJDh68HEII\nRN82jL5t8Mb3AiCrZcgnkbk5ZaFx+Rje5WOQmcY7/gze8WfmHyQQRoQTiM71iO7NiO5NGD2bIH4b\n/0Y1Go1Go9FoNLcly4oX4+Pj/P7v/z4Ab3rTm3jkkUd4+OGH+fSnP01fX9+qdfBWIaUkVZ5hojDM\nROESw5lTTBSGAegODfDEup9qWBO8Hk5NDPP5o8/y+aPP8t3zr+LJ+S4oAsFgWzdvu+cRfuaht/H4\n9gex7oDZ0KiT4Ee3/HPu73oDXx3+a2ZLE3zx4v8CIGAG6Q1vZH10iP29TxCyIre2syshJZABJlCW\nFbUlGhkoa4rVFSxU9yRn06/y3NgXmCxcAlRskv09T7AuupWOYC9hK3ZdQphMjuOe/g7eqe8ipy8u\n2m8CYv29mPvehbFlnx7w3mKEHYC2PkRbH6zbhbn3HUjpIaeUkCEvv4pMTSCLWSjloFxAlgvI5Dic\nfbF5INOCSDvCcsB0wLLBcsC0EZat3FPmrftt/O3CtP11B4IRJZCE28AJqe2GddsJsxqNRqO5+Ugp\nQXoIz1WCPFLFzZISTFPdd3Sgao3mjmXZUbBpNn/4lmWxa9cu/vRP/3RVOnUrKdZyfGfsS7w6+wJl\ntzBvX9iK8cbBd/FA1w9cU7aMuXya588f4+LsOJPZOaaySaaycxwbO8+ZqeYMuG1a/NA9B/hH9/8g\nQ93r2NjZx9SFER59+MANO7+1xqb4Pfxfu3+TI9PPcjH7GhP5YXLVNJeyp7iUPcXL09/m4Lqf4N7O\nA2tjMCNdVDrSil9yKNGi2NIoihIpwjSDaNo3xQVkKTzpMleaZLo4xnRxlPPp40y0iBaP9v0Qe7rf\neFWZbmS5gBw/rWbqS1koZtXAtphR1hWtgkUgjDG4C1EfHCd6OT6W5P7H3nKTzlRzIxDCQPRuwejd\nAvt/tLFdSg9KefXZz1xETg0ry5rpi1BIK/eTJY53w3LgmHWxw0ZE2hGJHkS8R8XniLYjnBBYQXCC\najnaqYQSjUaj0axp6vcXKgVkpQjlIrKch+wMMj2FzPgll4RS1hcn/OKzG6h8e5k3EEbj/oFpI0Jx\nFe8p3IaItM1frtehmJ5g0WhuA5YVLxYOFNfEwPEmUvUqHJ58hucnnqLsqoFoxIrTF9lAr5/ic2N8\nJwGzae6eKeY5O32ZdDFPvlKkUCmRL5coVErMFTIcHT3LS5dOcX5mdNn37YjEeee9P8C77nuMt+86\nQDw038ogdWny5pzwGsIybPb3PsH+3icAyFZSjOcv8v3Jb3A5d4YvXvwkR2e+y9s2vueGBT+9KqQH\npFGxKuZopipdCgeVz7IPRHR1+reAifwwz479Axczr+HK+dYfESvOgf63s6f7jVcMICndKnL8NN7w\nUbzho8iJM/MeFhbhhDC2Poyx4w0YG/csGjxWk4df1zlpbh1CGOphLhSD7o1wT3OfrBShkEa6NahV\nwK1CrYL0a1q2y1oFalVw63W12a5WQZbyUEgi82moltSxPFfVbhUqIAvpJS175nfYgFiXEs/a+xFt\n/UrwCETADoAdRDhBCLdrkUOj0WhWEZmZwRs/rWItjZ9GTg+r6/31Igw8wDBMde0XQtVeTd1/pAe1\nsiqAzCdhZvjK4row1L2jfztG3xCifzuia4OyFNRoNGuGZcWLkZER/uiP/mjZ9V/5lV+5uT1bJape\nhSPTz/LCxFfIVzMknAh7uvazPf4AlghTdatUa1Uq5TyHLz7NVHaWmVyS2VyaQrWEIQQ1z6Ncq1Gp\nuZRrNbXsuiBrbOoMErT76I8PsK1nI73xDnpi7fTEOljf3sO+DTvvCDeQG0nMaSPm7GFb2wMcm/0e\nz4x8lsu5M/z5id/m4d638ob+d+CYgRv7ptJFxaaolxQqdkV1QcN6gE2bZqDNbqBd3ThvATPFcZ4d\n+zynki83tiWcTrpDg/SEB+kJrWNr4j5sU4kW0q0ipy6oUkgrK4pCGplPISfONm72gLqZ929XA8FQ\nTA1mgzEIxZWpf9+QckXQ3FUIJwROaMmgpjcCKT1f6KhBraysP9JTytonMwX5lDIXrhahWlICSG4O\n6rN1l45eofOGitnR1q8eVNsHVB1OqHNywr77inPHi/YajUZzo5DZWbxLR1Xcq2JaCdyFDDKtUngv\nwgmruEoB/5rrhBGxTkS8B5HoVnW0E0IxMCwlUBgqwLYQgsOHD7Nv376l++K5TSG9WkYW0+p5J+8L\n5b5gLgspdT8ppKCUQ86NIudGm/GfhKGCV4fiEPafe3qHMDY/qIQNfY/QaFadZUfNP/ETP3HF9duZ\n5y59g9HcJVKlSYKBAjs7+vnxLQ/RFUwQbMzI5fzSSghY55froZ7isl5qwHmQEZSLQRhwVs29YK0j\nhOC+rgMMtd3Ht0Y/x5Hp53hh4iscmX6W/sgmesPr6Q2vpy+8kfZg97UdXNZoa6uCPAnMslikqBNC\niRPd/rK1Zj6fVHmG58a+wPHZ7yGRWMLmwZ7HeaTvrUTseKOdLBeQl49TGz2JN/oacvy0uqkvg+ja\noLJObHgAY90u9WCh0awiQhi+tUQAiCCiHdA3dMXXSLeqBI7UuIrbkRxXri3Vkprhq5SQlQLkU+AH\nlJXDR5Y/oBNSD6ddGzG6NhJOlZClnYjgGo/Bo9HcRki3qgaN5YIaaApAmGAYKm6Bv0wgooXyNYas\nVZGjJ/Euvox38WXkzKXlGwciyqKhfzvGwHZEzxYlGN8khGEqQQTUpEu8a8XXyFoFOTOMHD+DN3EG\nOX4GmRxTEzzFDMz5bpGnn8d99i8g2oGxaS/G5gcxNt6vrPw0Gs1NZ1nx4hd/8RdXsx+ryrrENPf2\ndBF3NmAsmC0fS2U4PTXDTK6IlAKEwBAGQhhEnTBt4TgdkQSdkQSJUMx/fT1tY71uXa7QnM2vsTiY\nY3LBugUySF3o2LypAPIUapY/2FICt2ymf7UJWRF+aOPPcn/nG/jKpf/NZOESFzInuJA50WizKX4P\nB9f9JL3hZYSlhmVFEmVRkWTrFkkzVoWgma40iBKSuoDIVYkVsphFzl5CZmeRuTkV7FB6/p1O+kE9\nUZ+Z7ahZXSugghraDlgBFfzQ9re17jdtym6JmeIoydIkc8VJkuUpxtNnCJdrDFUlO+xNbDEHsEfP\nIXOHqJTzaqBWys+3pKifbcegsqiIdvhWFHFEKIHo3oiItF/Fp6LRrC2EaSM6BqFj8IrtZK2qhIvU\nODI5hkwqsYNSDipF9bspF9Ty2Cnk2Ck8YAtQOfIXEIyqmbh6HU4gerdi9G1TD+TaJUWjQXqu+n1N\nnUfOXFaDv1IOWcqpWDrlXOM3d9XYQQgnEP49i5C6bxGO+zPjCUS8W1lTaYvWG4bMp1RA5+wM5OaU\nJVw+iZw8P//5wg5irL9XpdsOt6nPIxyHaIdy51vjz6zCchB926BvG/WodtKtQSmLLGSUiJGdUVm6\nLrwMuTm8Y9/AO/YN9WwXaUNEOxHRDmVB0rMZY+iAFrw1mhvMXXl13xTvBcCTkplckXPTZQrVKN2x\n9fTF9/KD2+IYxg2+yEqJEi7cllIBCn7J+3WNVouPjg6AsWWO6TDfksOkad1Rd2uoFxOV8UKZ3DWX\n144lwUoMRDfz/nv+DenKLJOFy40ykjvDxcxJ/vzEf+LB7jfw2MCbCFtVVAaQuni0OFZFLmcSjW5E\niRThZf8OspxXQSuLORVcqpxvZGEgn8SbOg+Z6Zt34qhPrG7/sTRHgCMsGZ3CtBA9WzAG70EM3oMx\nsOOmznhoNGsZYdmIznXQeWULOllIIacv4c0MI6eHyV06Sbg4qwZcpVzDd1oCnPiWusIYlnpg7d+G\n6N7c4mblix3hNm1mrLntkbUqcvIscnZExb+p+GJfpajujdlpFdPgChZ+Depm+YGIymIEjeCM0nNV\n/BvPhXJOWVClS0p8vNIxDQvROYjo2ojoXKeEjUBUDSKDUTWQ1rPkyyKlh5w8h3f+MN75w8jJc8u2\nFd0blfXBpr2IgZ13nHgr6pm1WiZ1zN0Hm1m6Lr6Md+Fl5NhrTWGn9QBf/wTG5n0Y97wRY/M+bT2k\n0dwA7krxQhhP3OouaG4aH7/VHdBoNBqNRqPRaID/c6s7oNHcdhw6dGjZfVclXniex+zsLN3d1xhX\nYI3yl2xfcntk0yDRrRuIbBwgsmEApyOB0x7HaVd1fMdmAp2ra1K/bEAi6aHiNNStOGoLllvTeVZo\nurO0urTU218tJs3Un0Ga1hx1qw+bpuvFVbq1SK+lv61l4bYyysVjodvNfFzPY6Iwx2h+ltHcHIaI\nELDaiNvddAR7WRcbImorq4PD33+RvT1B5avpp+SS+aTyiV8UtHIbItalgks5EQj4gf1CMUT3JuWG\ncR15xaWUlN0imUqSTGWOdGWGk3OHGcmdrb8593Ts4w39P0x36Mrm8GuRKwXU0tw49N/55nM1f2NZ\nziMnzqlo+nOjUM4pi61STgWwKy2Mo9SCMJRZfD31qxP2g+T6JvGhOCIQUm3soJrBqy/X290Bs3r6\nu7x6tP6tZa3ixy84otw96mbyhbSyrFiA6NqA6N2qLBicsPreBsJghxCRNnVfDMVW+5QAlQ1JzlxS\nJTmmXFXKed9lJat+m+4SzxLBqLKUCkZV32OdGL1DKjB15/prckVZi99jmZvDGzmBN3IcefmY+jss\nR6wLY8s+VdbftyavLWvxb7wQmZvDPfUdvNeeVQHRlyMQwVi3G2P34xhb9q2pDCe3w9/5dkf/jRdz\n+PDy2QpXvBI///zz/MZv/AaO4/DUU0/xkY98hEcffZSDBw/e0E6uJu/4Hz9N9tWj5MdT5KbLzI2U\nSY5WyF8cJX/xChdzIDq0ka5H7qfzkQfofOQB2vfsxHSWTz150xAGSiR4ncgaUEIJAyWaQkerEFL2\n97ko95b8VR7bQokbwDwja9myfoU0nEti0RROrJY6CsQxjQhSXOB85gtczCwOHmVI2F8d4N5Z2Hn+\nNaq1ZVJ1hRMqCNPmB1UK0Bvos1is5TiXPsaZ1FEuZl6j7C5+MHSMIPd2HWBfz+N0Bvtu2HtrNJqb\nhwhEEBvvx9h4/5L7ZT6FnL7YcEWRyVFkeloNDmsV3/y+MN8l5VqoxwQIRFpi5zhK4Ih2QLwLEetS\ncQFiXUrw0G4sdy1OYQ735S8p0/dLx5aMjwSAaSP6tmEM7kQM7sQY2KncoNYowgkhBnbAwI4l90u3\nquJwTJ7DmzyLnDiHnBluxuSg9Qnla2rBchDdmzD6hqCtXz0TBCKIQFRNYrT13fI4G9KtIufGkBNn\nkZlJZDELxaw6p+y0CmLcihVQwkysU7mShhPNLGKdOpPGjUBEO7D2vQv2vQsvOYZ3+nn1ORRS6n5Q\nSEEhDeU83rkX8c69CMEoxs43Yg49gmjrhWjnLf9uaTRriRV/DR/96Ef5zGc+wwc/+EEAnnzySZ58\n8snbWrxo+xf/gYT0kDOXcL/3WbxT38FzJYXYvZTWvZHC2ByFy+NUkhkqqQyVZIbyTJL0sTPkzg6T\nOzvMxb/8BwAM2ybQ1YYdj2LFItjxKHYsgt0WJ3HPFtr37qJ9zz0Eezpv8Vkvg6gP/Fd4EGnE7CjR\nFDncBaVuIVH291/ZSsLvAOpraLfU9hLrDiqIpr1ijI510a389PZfIZ8dJz19kuLcBarJUURqku6J\nSSKVqUbbdDRIdv1WlVEg2oUT68GJ9xEKdxGyIhji2q0pAFzPpeTmKdZyFGt58tUMI7lzXM6dYaow\ngmwZlthGgLjTTtzpIO600x/ZxK6Oh3DM4HW9t0ajWZuISBsisgdj055F+6TnquCFlaLKkFIuqCCH\nxYw/C55VwUSrZZUe1q+pFtUgpZBpxgRoPe6VOmRYKiJ/IIxwgmrWPBBWVh/hBEQSfkDEhLIG8QMK\nN4MNN4MK64HO2kTWKsiREyrgYmZaWRf6VobbK4V5d2nRvRlj8141eK0Hwgwn1CD9Dvp8hWkjerdA\n7xZM3gr4v79SHlnK+pZSWWW1MXlOiQGpceT4adzx00sf1LBUyuWuDYjO9bRPp3CDmfkiohVARNpU\nDIVrsGSQ0lO//+ysEjerJXUdqJRU4MyZSypg+NwYeFd47rKDSnxat1sF1uzdsqZm+O90jPYBjEd+\nctF2KT3IzuGe/g7e8W8iZ4bxjnwZ78iX/RYCou2IeA/Gxgcwth3QaVo1dzUrihfhcJiurmaKoY6O\nDmz79r/YCWEos8Z3/hrepr3Unv4zooXjRCemsX/kVzAGf3rRa7xqldSxM8x+7xVmXniF2e+9Qua1\n8xTHpymOXzlgY2igh+jmddhtceWK0hYn0N1OYvc22u7bTnTrBgzz+gbKq4IQNIWEqzAFbYgddcsK\n0VKLlnXjhgYMlVLinfs+7ktfwLp8jKUko3IswbmeKIfbS8xETGBalXrs1Mlm24AZJGhGCJhhDNF6\nDv6/or4scKVLqZanWMtT8Zax6AAMYbIxuo1t7Q+wNXEfCadT34Q0mrscYZjNTCbX8XoppRrYFNIq\nW0qtArUKslZRgkhuFpmZgeyMCqiYmYFyHkpZZUpfP871dL6elrCeztJywGwdtNlq4OSEVDsn1Gxn\nBZT4YQcg1olwl0tbrbkapJTI5Jhyh7x4BO/ysWUDZ9asEM7WfRib92JsfEBZ59ylCMP0xZr4kvtl\nKacsNSbOIXOz/m8np1zF6umXZy8jZy8DMAjUzjy1/BsGo0okdILqdwDquUl6fi3BrTXEFOViu+JZ\nQKIXo3drM1BpMKZcYMKJa3Z90awOQhgQ78La/4+Q+35UWeed+KZyP8xMQy7ZCAbqjr2G+/ynEe39\nGEMHMIYeVmLjdbgtazS3KytexYLBIC+++CIA6XSaL37xiwQCa8/37XoRQmDe+wRicCe1L34UOXmO\n6l//JsZ9b8EYekjdBPwow4Zt07F3Fx17d7HtyfcAUMsXqKSyVDM5qtk8tUyOaiZHeSZJ6tXTJI+c\nJHnkJMWxKYpjU8v2wwwFSezaStt9O4ht24idiGHFImSnJphIVQiv7yOycRAzcAtcVK4ciy0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5CUrYQYw6ZBSB/aJzuYR5oqEd72CSJ7vlLmhHUkSpByilF89VUoCCZgzdX3onndNqyc/yMU3XAZ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\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=30);" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -763,7 +756,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 5, "metadata": { "collapsed": false, "scrolled": true @@ -790,7 +783,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 6, "metadata": { "collapsed": false, "scrolled": false @@ -800,7 +793,7 @@ "data": { "image/png": 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NdevWYcWKFfjxj38Mn88Hi8WCxx9/HAcOHEBZWRkee+wx3HXXXTh8+DC2bNmihKc1a9bg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KChoYGcnByGh4fJzMxk48aNKaNFdrsdp9OJzWbDYrGwcuXKGdfozZTTh09jCZ6bpP/15r+esXnC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\noa2tDUePHsUNN9yAuLg4tLe349y5c0hKS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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -856,7 +849,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 23, "metadata": { "collapsed": false, "scrolled": false @@ -921,10 +914,13 @@ " # 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)\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 = '--', color='black', label = 'Algo Returns');\n", - " ax.plot(ax.get_xlim(),[0]*len(ax.get_xlim()), color='black');\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", @@ -948,7 +944,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 24, "metadata": { "collapsed": false, "scrolled": false @@ -983,9 +979,9 @@ }, { "data": { - "image/png": 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m5qYePXrU+vuw2Gv6UfL/mThxoqKiovTee+/pb3/7m/bu3atXX31V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\nsv75z38qISFB3333nZ566qk6zW0USis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3OYCh0B/AZfQDuiq2faBzI7Q6gGPHjik/P1/u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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1017,7 +1013,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 11, "metadata": { "collapsed": false, "scrolled": true @@ -1057,7 +1053,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 17, "metadata": { "collapsed": false }, @@ -1079,7 +1075,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 25, "metadata": { "collapsed": false, "scrolled": false @@ -1108,8 +1104,8 @@ " 0.0528727\n", " 0.0432647\n", " 0.0275121\n", - " 0.0271602\n", - " 0.0295876\n", + " 0.0271259\n", + " 0.033393\n", " \n", " \n", " Alpha t-stat\n", @@ -1117,8 +1113,8 @@ " 2.00192\n", " 1.7057\n", " 1.15618\n", - " 1.14432\n", - " 1.23905\n", + " 1.14244\n", + " 1.33864\n", " \n", " \n", " Mkt-RF\n", @@ -1126,8 +1122,8 @@ " 0.0495098\n", " 0.094671\n", " 0.0838148\n", - " 0.0837645\n", - " 0.0834903\n", + " 0.0838225\n", + " 0.0838203\n", " \n", " \n", " Mkt-RF t-stat\n", @@ -1135,8 +1131,8 @@ " 4.41303\n", " 6.64337\n", " 7.33696\n", - " 7.3108\n", - " 7.3148\n", + " 7.32877\n", + " 7.31785\n", " \n", " \n", " SMB\n", @@ -1144,8 +1140,8 @@ " 0.0346142\n", " -0.00432747\n", " 0.0203095\n", - " 0.0204046\n", - " 0.0214652\n", + " 0.0203615\n", + " 0.0211495\n", " \n", " \n", " SMB t-stat\n", @@ -1153,8 +1149,8 @@ " 1.54278\n", " -0.205814\n", " 1.03043\n", - " 1.03572\n", - " 1.0861\n", + " 1.03301\n", + " 1.06842\n", " \n", " \n", " HML\n", @@ -1162,8 +1158,8 @@ " NaN\n", " -0.106863\n", " -0.171577\n", - " -0.171465\n", - " -0.168513\n", + " -0.171515\n", + " -0.168371\n", " \n", " \n", " HML t-stat\n", @@ -1171,8 +1167,8 @@ " NaN\n", " -4.33332\n", " -6.03916\n", - " -6.02161\n", - " -5.91933\n", + " -6.02681\n", + " -5.90937\n", " \n", " \n", " Mom\n", @@ -1180,8 +1176,8 @@ " NaN\n", " NaN\n", " -0.0852706\n", - " -0.0854911\n", - " -0.0875817\n", + " -0.0854547\n", + " -0.0874834\n", " \n", " \n", " Mom t-stat\n", @@ -1189,8 +1185,8 @@ " NaN\n", " NaN\n", " -5.31589\n", - " -5.28629\n", - " -5.45745\n", + " -5.28806\n", + " -5.45412\n", " \n", " \n", " VOL\n", @@ -1198,8 +1194,8 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.00337927\n", - " 0.0253341\n", + " 0.00352071\n", + " 0.0278498\n", " \n", " \n", " VOL t-stat\n", @@ -1207,8 +1203,8 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.244969\n", - " 1.28122\n", + " 0.222961\n", + " 1.15111\n", " \n", " \n", " STMR\n", @@ -1217,7 +1213,7 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.033891\n", + " -0.0359366\n", " \n", " \n", " STMR t-stat\n", @@ -1226,7 +1222,7 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.992107\n", + " -0.92332\n", " \n", " \n", "\n", @@ -1234,47 +1230,47 @@ ], "text/plain": [ " Mkt-RF SMB HML Mom VOL \\\n", - "Alpha 0.0537356 0.0528727 0.0432647 0.0275121 0.0271602 \n", - "Alpha t-stat 2.01595 2.00192 1.7057 1.15618 1.14432 \n", - "Mkt-RF 0.055417 0.0495098 0.094671 0.0838148 0.0837645 \n", - "Mkt-RF t-stat 5.87536 4.41303 6.64337 7.33696 7.3108 \n", - "SMB NaN 0.0346142 -0.00432747 0.0203095 0.0204046 \n", - "SMB t-stat NaN 1.54278 -0.205814 1.03043 1.03572 \n", - "HML NaN NaN -0.106863 -0.171577 -0.171465 \n", - "HML t-stat NaN NaN -4.33332 -6.03916 -6.02161 \n", - "Mom NaN NaN NaN -0.0852706 -0.0854911 \n", - "Mom t-stat NaN NaN NaN -5.31589 -5.28629 \n", - "VOL NaN NaN NaN NaN 0.00337927 \n", - "VOL t-stat NaN NaN NaN NaN 0.244969 \n", + "Alpha 0.0537356 0.0528727 0.0432647 0.0275121 0.0271259 \n", + "Alpha t-stat 2.01595 2.00192 1.7057 1.15618 1.14244 \n", + "Mkt-RF 0.055417 0.0495098 0.094671 0.0838148 0.0838225 \n", + "Mkt-RF t-stat 5.87536 4.41303 6.64337 7.33696 7.32877 \n", + "SMB NaN 0.0346142 -0.00432747 0.0203095 0.0203615 \n", + "SMB t-stat NaN 1.54278 -0.205814 1.03043 1.03301 \n", + "HML NaN NaN -0.106863 -0.171577 -0.171515 \n", + "HML t-stat NaN NaN -4.33332 -6.03916 -6.02681 \n", + "Mom NaN NaN NaN -0.0852706 -0.0854547 \n", + "Mom t-stat NaN NaN NaN -5.31589 -5.28806 \n", + "VOL NaN NaN NaN NaN 0.00352071 \n", + "VOL t-stat NaN NaN NaN NaN 0.222961 \n", "STMR NaN NaN NaN NaN NaN \n", "STMR t-stat NaN NaN NaN NaN NaN \n", "\n", " STMR \n", - "Alpha 0.0295876 \n", - "Alpha t-stat 1.23905 \n", - "Mkt-RF 0.0834903 \n", - "Mkt-RF t-stat 7.3148 \n", - "SMB 0.0214652 \n", - "SMB t-stat 1.0861 \n", - "HML -0.168513 \n", - "HML t-stat -5.91933 \n", - "Mom -0.0875817 \n", - "Mom t-stat -5.45745 \n", - "VOL 0.0253341 \n", - "VOL t-stat 1.28122 \n", - "STMR -0.033891 \n", - "STMR t-stat -0.992107 " + "Alpha 0.033393 \n", + "Alpha t-stat 1.33864 \n", + "Mkt-RF 0.0838203 \n", + "Mkt-RF t-stat 7.31785 \n", + "SMB 0.0211495 \n", + "SMB t-stat 1.06842 \n", + "HML -0.168371 \n", + "HML t-stat -5.90937 \n", + "Mom -0.0874834 \n", + "Mom t-stat -5.45412 \n", + "VOL 0.0278498 \n", + "VOL t-stat 1.15111 \n", + "STMR -0.0359366 \n", + "STMR t-stat -0.92332 " ] }, - "execution_count": 53, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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P8o8TAABw1ViICQAAAADgsphpBQDASWw2m3Jy080uw1Q5uemy2TqZXQYAoAYh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\nGRslJSWSJA8PDyUlJWnp0qU6cOCApk2bptatW2vx4sW6ePGi5syZo7f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l/XXu3LmaP0HV+Nfzl7TTlL6tJCwsTKHXhJpdhmkYBz9iLPibXYIlMA78pTNm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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1299,7 +1295,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Of the new factors, none compose more of our algo's returns than momentum." + "Of the new factors, none compose more of our algo's returns than momentum.\n", + "\n", + "Below is a backtest from 2004 through 20" ] }, { @@ -1354,7 +1352,7 @@ "USA = mkt_caps.iloc[1]['2004':'2015']\n", "EMU = mkt_caps.iloc[0]['2004':'2015']\n", "\n", - "# Finding Euro-USA market cap ratio, Euro-Domesting US investments ratio\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", diff --git a/case_studies/USD_EUR_exchange_rate/preview.html b/case_studies/USD_EUR_exchange_rate/preview.html index 3477e75d..51824939 100644 --- a/case_studies/USD_EUR_exchange_rate/preview.html +++ b/case_studies/USD_EUR_exchange_rate/preview.html @@ -1,6 +1,6 @@ - Using+Alternative+Data%3A+USD-EUR+Exchange+Rate+Case+Study+V3 + Case Study - USD-EUR Exchange Rate V4 + + + + + + + + + + + + + + +
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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.

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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.

+

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¶

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  • 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.
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  • Test the factor on universes larger than the Q500US and add filters.
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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.

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