Permalink
Cannot retrieve contributors at this time
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
85 lines (54 sloc)
1.99 KB
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
""" | |
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() | |