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| """ | |
| This file shows how to calculate the EWMAC trading rule for crude oil futures | |
| As in chapter 7 / appendix B of "Systematic Trading" by Robert Carver (www.systematictrading.org) | |
| Required: pandas, matplotlib | |
| USE AT YOUR OWN RISK! No warranty is provided or implied. | |
| Handling of NAN's and Inf's isn't done here (except within pandas), | |
| And there is no error handling! | |
| """ | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| from common import pd_readcsv, cap_series, ewmac_forecast_scalar, get_price_for_instrument | |
| """ | |
| get some data | |
| """ | |
| ## This is the stitched price series | |
| ## We can't use the price of the contract we're trading, or the volatility will be jumpy | |
| ## And we'll miss out on the rolldown. See http://qoppac.blogspot.co.uk/2015/05/systems-building-futures-rolling.html | |
| code="CRUDE_W" | |
| price=get_price_for_instrument(code) | |
| ## Shouldn't need changing | |
| vol_lookback=25 | |
| """ | |
| Calculate the ewmac trading fule forecast, given a price and EWMA speeds Lfast, Lslow and vol_lookback | |
| Assumes that 'price' is daily data | |
| """ | |
| Lfast=16 | |
| Lslow=4*Lfast | |
| d1=pd.datetime(2007,1,1) | |
| d2=pd.datetime(2009,12,31) | |
| ## We don't need to calculate the decay parameter, just use the span directly | |
| fast_ewma=pd.ewma(price, span=Lfast) | |
| slow_ewma=pd.ewma(price, span=Lslow) | |
| raw_ewmac=fast_ewma - slow_ewma | |
| data_to_plot=pd.concat([price, fast_ewma, slow_ewma], axis=1) | |
| data_to_plot.columns=['Price', 'Fast', 'Slow'] | |
| data_to_plot[d1:d2].plot() | |
| plt.show() | |
| raw_ewmac[d1:d2].plot() | |
| plt.title("Raw EWMAC") | |
| plt.show() | |
| ## volatility adjustment | |
| stdev_returns=pd.ewmstd(price - price.shift(1), span=vol_lookback) | |
| vol_adj_ewmac=raw_ewmac/stdev_returns | |
| vol_adj_ewmac[d1:d2].plot() | |
| plt.title("Vol adjusted") | |
| plt.show() | |
| ## scaling adjustment | |
| f_scalar=ewmac_forecast_scalar(Lfast, Lslow) | |
| forecast=vol_adj_ewmac*f_scalar | |
| cap_forecast=cap_series(forecast, capmin=-20.0,capmax=20.0) | |
| data_to_plot=pd.concat([forecast, cap_forecast], axis=1) | |
| data_to_plot.columns=['Scaled Forecast', 'Capped forecast'] | |
| data_to_plot[d1:d2].plot() | |
| plt.show() | |