def get_ticker_data(start=datetime.datetime(1940, 1, 1), end = datetime.datetime.now()): # get all ticker price data -- we take the window of volatility # in util.get_annualized_volatility_of_series ret = {} for group in TICKERS: for ticker in TICKERS[group]: ret[ticker] = util.get_returns(ticker, start=start, end=end) return ret
def main(): start = datetime.datetime(1940, 1, 1) end = datetime.datetime.now() tickers = sys.argv[1:] # command line arguments for ticker in tickers: tick_df = util.get_returns(ticker, start, end) tick_df['Standard Deviation (60d)'] = pd.rolling_std(tick_df['Returns'], window=60) tick_df['Standard Deviation (200d)'] = pd.rolling_std(tick_df['Returns'], window=200) print(ticker + " Standard Deviation") print(np.std(tick_df['Returns'])) tick_df.to_csv("%s.csv" % ticker)
def main(): start = datetime.datetime(1940, 1, 1) end = datetime.datetime.now() tickers = sys.argv[1:] # command line arguments for ticker in tickers: tick_df = util.get_returns(ticker, start, end) tick_df['Standard Deviation (60d)'] = pd.rolling_std( tick_df['Returns'], window=60) tick_df['Standard Deviation (200d)'] = pd.rolling_std( tick_df['Returns'], window=200) print ticker + " Standard Deviation" print np.std(tick_df['Returns']) tick_df.to_csv("%s.csv" % ticker)