def calc_ewmac_forecast(price, Lfast, Lslow=None, usescalar=True):
"""
Calculate the ewmac trading fule forecast, given a price and EWMA speeds Lfast, Lslow and vol_lookback
Assumes that 'price' is daily data
"""
## price: 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
if Lslow is None:
Lslow=4*Lfast
## 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
## volatility adjustment
stdev_returns=volatility(price)
vol_adj_ewmac=raw_ewmac/stdev_returns
## scaling adjustment
if usescalar:
f_scalar=ewmac_forecast_scalar(Lfast, Lslow)
forecast=vol_adj_ewmac*f_scalar
else:
forecast=vol_adj_ewmac
cap_forecast=cap_series(forecast, capmin=-20.0,capmax=20.0)
return cap_forecast
def calc_ewmac_forecast(price, Lfast, Lslow=None, usescalar=True):
"""
Calculate the ewmac trading fule forecast, given a price and EWMA speeds Lfast, Lslow and vol_lookback
Assumes that 'price' is daily data
"""
## price: 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
if Lslow is None:
Lslow = 4 * Lfast
## 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
## volatility adjustment
stdev_returns = volatility(price)
vol_adj_ewmac = raw_ewmac / stdev_returns
## scaling adjustment
if usescalar:
f_scalar = ewmac_forecast_scalar(Lfast, Lslow)
forecast = vol_adj_ewmac * f_scalar
else:
forecast = vol_adj_ewmac
cap_forecast = cap_series(forecast, capmin=-20.0, capmax=20.0)
return cap_forecast
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()
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()
