testing out sqlite caching, adjusting queries and result formatting t…

…o be consistent with how applicaiton expects date to be structured, add caches for all calculations, abstract stat manager and div manager into new classes

README.md

@@ -262,12 +262,12 @@ The four functions of interest in this module are:
     <b>Returns:</b><br>
     a dictionary of prices with the `YYYY-MM-DD` formatted date as key. The dictionary is sorted latest price to earliest price.<br>
 
-2. `scrilla.services.get_daily_stat_history(statistic, start_date=None, end_date=None)`<br>
+2. `scrilla.services.get_daily_stat_history(symbol, start_date=None, end_date=None)`<br>
     <b>Description:</b><br>
         This function will retrieve the price history for the financial statistic specifed by the `statistic` argument. 
     <br><br>
     <b>Arguments:</b><br>
-    - `statistic : str`: Required. Statistic symbol for quantity of interest. A list of allowable values can be found [here](https://www.quandl.com/data/FRED-Federal-Reserve-Economic-Data/documentation)<br>
+    - `symbol : str`: Required. Statistic symbol for quantity of interest. A list of allowable values can be found [here](https://www.quandl.com/data/FRED-Federal-Reserve-Economic-Data/documentation)<br>
     - `start_date: datetime.date` : Optional. Start date of analysis range. Defaults to `None`<br> 
     - `end_date: datetime.date` : Optional. End date of analysis range. Defaults to `None`<br>
 

src/scrilla/analysis/markets.py

@@ -36,13 +36,21 @@ def sharpe_ratio(ticker, start_date=None, end_date=None, risk_free_rate=None, ti
         Risk-return profile for the supplied ticker. If provided, start_date and end_date are ignored and the values in ticker_profile are used to calculate the Sharpe ratio.
 
     """
+    try:
+        start_date, end_date = errors.validate_dates(start_date=start_date, end_date=end_date, 
+                                                        asset_type=static.keys['ASSETS']['EQUITY'])
+    except errors.InputValidationError as ive:
+        raise ive
 
+    print('inside markets.sharpe_ratio')
     result = profile_cache.filter_profile_cache(ticker=ticker, start_date=start_date, end_date=end_date)
 
-    if result[static.keys['STATISTICS']['SHARPE']] is not None:
-        return result
+    print('result', result)
+    if result is not None and result[static.keys['STATISTICS']['SHARPE']] is not None:
+        return result[static.keys['STATISTICS']['SHARPE']]
 
-    if ticker_profile is None:
+    if ticker_profile is None:  
+        print('calcuatling rr from markets.sharpe_ratio')
         ticker_profile = statistics.calculate_risk_return(ticker=ticker, start_date=start_date,
                                                         end_date=end_date)
 
@@ -51,6 +59,7 @@ def sharpe_ratio(ticker, start_date=None, end_date=None, risk_free_rate=None, ti
 
     sharpe_ratio = (ticker_profile['annual_return'] - risk_free_rate)/ticker_profile['annual_volatility']
 
+    print('RRRRRRIGGGHHHHTTT FUCKING HERE!!!')
     profile_cache.save_or_update_row(ticker=ticker, start_date=start_date,
                                         end_date=end_date,sharpe_ratio=sharpe_ratio)
 
@@ -131,8 +140,8 @@ def market_beta(ticker, start_date=None, end_date=None, market_profile=None, mar
 
     result = profile_cache.filter_profile_cache(ticker=ticker, start_date=start_date, end_date=end_date)
 
-    if result['STATISTICS']['BETA'] is not None:
-        return result
+    if result is not None and result[static.keys['STATISTICS']['BETA']] is not None:
+        return result[static.keys['STATISTICS']['BETA']]
 
     try:
         if market_profile is None:
@@ -195,8 +204,8 @@ def cost_of_equity(ticker, start_date=None, end_date=None, market_profile=None,
 
     result = profile_cache.filter_profile_cache(ticker=ticker, start_date=start_date, end_date=end_date)
 
-    if result['STATISTICS']['EQUITY'] is not None:
-        return result
+    if result is not None and result[static.keys['STATISTICS']['EQUITY']] is not None:
+        return result[static.keys['STATISTICS']['EQUITY']]
 
     try:
         beta = market_beta(ticker=ticker, start_date=start_date, end_date=end_date,
@@ -209,6 +218,9 @@ def cost_of_equity(ticker, start_date=None, end_date=None, market_profile=None,
     except errors.APIResponseError as api:
         raise api
 
+    print('prem', premium)
+    print('beta', beta)
+    print('risk_free', services.get_risk_free_rate())
     equity_cost = (premium*beta + services.get_risk_free_rate())
 
     profile_cache.save_or_update_row(ticker=ticker, start_date=start_date, end_date=end_date, equity_cost=equity_cost)

src/scrilla/analysis/statistics.py

@@ -502,11 +502,11 @@ def calculate_risk_return(ticker, start_date=None, end_date=None, sample_prices=
     1. ticker : str \n
         Required. Ticker symbol whose risk-return profile is to be calculated. \n \n 
     2. start_date : datetime.date \n 
-        Optional. Start date of the time period over which the risk-return profile is to be calculated. Defaults to `None`, in which case the calculation proceeds as if `start_date` were set to 100 trading days prior to `end_date`. If `get_asset_type(ticker)=static.keys['ASSET']['CRYPTO']`, this means 100 days regardless. If `get_asset_type(ticker)=static.keys['ASSET']['EQUITY']`, this excludes weekends and holidays and decrements the `end_date` by 100 trading days.\n \n
+        Optional. Start date of the time period over which the risk-return profile is to be calculated. Defaults to `None`, in which case the calculation proceeds as if `start_date` were set to 100 trading days prior to `end_date`. If `get_asset_type(ticker)=static.keys['ASSETS']['CRYPTO']`, this means 100 days regardless. If `get_asset_type(ticker)=static.keys['ASSETS']['EQUITY']`, this excludes weekends and holidays and decrements the `end_date` by 100 trading days.\n \n
     3. end_date : datetime.date \n 
-        Optional. End date of the time period over which the risk-return profile is to be calculated. Defaults to `None`, in which the calculation proceeds as if `end_date` were set to today. If the `get_asset_type(ticker)==static.keys['ASSETS']['CRYPTO']` this means today regardless. If `get_asset_type(ticker)=static.keys['ASSET']['EQUITY']` this excludes holidays and weekends and sets the end date to the last valid trading date. \n \n
-    4. sample_prices : { 'date' (str) : 'price' (str) } \n
-        Optional. A list of the asset prices for which correlation will be calculated. Overrides calls to service and calculates correlation for sample of prices supplied. Function will disregard start_date and end_date if sample_price is specified:  { 'date_1' : { 'open' : number, 'close' : number}, 'date_2': { 'open': number, 'close': number} ... }  and ordered from latest date to earliest date.  \n \n
+        Optional. End date of the time period over which the risk-return profile is to be calculated. Defaults to `None`, in which the calculation proceeds as if `end_date` were set to today. If the `get_asset_type(ticker)==static.keys['ASSETS']['CRYPTO']` this means today regardless. If `get_asset_type(ticker)=static.keys['ASSETS']['EQUITY']` this excludes holidays and weekends and sets the end date to the last valid trading date. \n \n
+    4. sample_prices : { 'date_1' : { 'open' : number, 'close' : number}, 'date_2': { 'open': number, 'close': number} ... } \n
+        Optional. A list of the asset prices for which correlation will be calculated. Overrides calls to service and forces calculation of correlation for sample of prices supplied. Function will disregard `start_date` and `end_date` and use the first and last key as the latest and earliest date, respectively. In other words, the `sample_prices` dictionary must be ordered from latest to earliest.   \n \n
     5. asset_type : str
          Optional. Specify asset type to prevent overusing redundant calculations. Allowable values: settings.ASSET_TYPE_EQUITY, settings.ASSET_TYPE_CRYPTO \n \n
 
@@ -535,9 +535,12 @@ def calculate_risk_return(ticker, start_date=None, end_date=None, sample_prices=
         except errors.InputValidationError as ive:
            raise ive
 
+        print('inside statistics.calculate_risk_return')
         results = profile_cache.filter_profile_cache(ticker=ticker, start_date=start_date, end_date=end_date)
 
-        if results[static.keys['STATISTICS']['RETURN']] is not None \
+        print('results', results)
+        if results is not None \
+                and results[static.keys['STATISTICS']['RETURN']] is not None \
                 and results[static.keys['STATISTICS']['VOLATILITY']] is not None:
             return results
 
@@ -549,13 +552,18 @@ def calculate_risk_return(ticker, start_date=None, end_date=None, sample_prices=
     else:
         logger.debug(f'{ticker} sample prices provided, skipping service call.')
         prices = sample_prices
+        try:
+           asset_type = errors.validate_asset_type(ticker, asset_type)
+           trading_period = static.get_trading_period(asset_type)
+        except errors.InputValidationError as ive:
+           raise ive
 
     if not prices:
         raise errors.PriceError(f'No prices could be retrieved for {ticker}')
 
     # Log of difference loses a sample
     sample = len(prices) - 1
-    logger.debug(f'Calculating mean annual return over last {sample+1} days for {ticker}')
+    logger.debug(f'Calculating mean annual return over last {sample} days for {ticker}')
 
     ### MEAN CALCULATION
     # NOTE: mean return is a telescoping series, i.e. sum of log(x1/x0) only depends on the first and
@@ -571,7 +579,7 @@ def calculate_risk_return(ticker, start_date=None, end_date=None, sample_prices=
     today, variance, tomorrows_price, tomorrows_date = False, 0, 0, None
         # adjust the random variable being measured so expectation is easier to calculate. 
     mean_mod_return = mean_return*sqrt(trading_period)
-    logger.debug(f'Calculating mean annual volatility over last {sample+1} days for {ticker}')
+    logger.debug(f'Calculating mean annual volatility over last {sample} days for {ticker}')
 
     for date in prices:
         todays_price = prices[date][static.keys['PRICES']['CLOSE']]
@@ -682,9 +690,9 @@ def calculate_ito_correlation(ticker_1, ticker_2, asset_type_1=None, asset_type_
             raise api
 
     if (len(sample_prices[ticker_1]) == 0) \
-        or (len(sample_prices[ticker_2]) == 0 \
-        or len(sample_prices[ticker_1]) != len(sample_prices[ticker_2]) \
-        or len(helper.intersect_dict_keys(sample_prices[ticker_1], sample_prices[ticker_2]) == 0)):
+        or (len(sample_prices[ticker_2]) == 0) \
+        or (len(sample_prices[ticker_1]) != len(sample_prices[ticker_2])) \
+        or (len(helper.intersect_dict_keys(sample_prices[ticker_1], sample_prices[ticker_2])) == 0):
         raise errors.PriceError("Prices cannot be retrieved for correlation calculation")
 
     if asset_type_1 != asset_type_2: