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
def calc_carry_forecast(carrydata, price, f_scalar=30.0):
"""
Carry calculation
Formulation here will work whether we are trading the nearest contract or not
For other asset classes you will have to work out nerpu (net expected return in price units) yourself
"""
nerpu = carrydata.apply(find_datediff, axis=1)
stdev_returns = volatility(price)
ann_stdev = stdev_returns * ROOT_DAYS_IN_YEAR
raw_carry = nerpu / ann_stdev
forecast = raw_carry * f_scalar
cap_forecast = cap_series(forecast)
return cap_forecast
def calc_carry_forecast(carrydata, price, f_scalar=30.0):
"""
Carry calculation
Formulation here will work whether we are trading the nearest contract or not
For other asset classes you will have to work out nerpu (net expected return in price units) yourself
"""
nerpu=carrydata.apply(find_datediff, axis=1)
stdev_returns=volatility(price)
ann_stdev=stdev_returns*ROOT_DAYS_IN_YEAR
raw_carry=nerpu/ann_stdev
forecast=raw_carry*f_scalar
cap_forecast=cap_series(forecast)
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()
#d1=pd.datetime(2007,1,1)
#d2=pd.datetime(2009,12,31)
nerpu = data.apply(find_datediff, axis=1)
nerpu.plot()
plt.title("Nerpu")
plt.show()
## Shouldn't need changing
vol_lookback = 25
stdev_returns = pd.ewmstd(price - price.shift(1), span=vol_lookback)
ann_stdev = stdev_returns * ROOT_DAYS_IN_YEAR
raw_carry = nerpu / ann_stdev
f_scalar = 30.0
raw_carry.plot()
plt.title("Raw carry")
plt.show()
forecast = raw_carry * f_scalar
c_forecast = cap_series(forecast)
data_to_plot = pd.concat([forecast, c_forecast], axis=1)
data_to_plot.columns = ['Forecast', 'Capped forecast']
data_to_plot.plot()
plt.show()
#d1=pd.datetime(2007,1,1)
#d2=pd.datetime(2009,12,31)
nerpu=data.apply(find_datediff, axis=1)
nerpu.plot()
plt.title("Nerpu")
plt.show()
## Shouldn't need changing
vol_lookback=25
stdev_returns=pd.ewmstd(price - price.shift(1), span=vol_lookback)
ann_stdev=stdev_returns*ROOT_DAYS_IN_YEAR
raw_carry=nerpu/ann_stdev
f_scalar=30.0
raw_carry.plot()
plt.title("Raw carry")
plt.show()
forecast=raw_carry*f_scalar
c_forecast=cap_series(forecast)
data_to_plot=pd.concat([forecast,c_forecast], axis=1)
data_to_plot.columns=['Forecast','Capped forecast']
data_to_plot.plot()
plt.show()
price = get_price_for_instrument(code)
carrydata = get_carry_data(code)
current_price = carrydata.TRADED
if Lfast is None:
forecast = calc_carry_forecast(carrydata, price)
## carry
else:
forecast = calc_ewmac_forecast(price, Lfast)
stdev = volatility(price) * root_days_in_week
price_volatility = 100 * stdev / current_price
next_weeks_return = price.shift(-5) - price
next_weeks_vol_norm_return = next_weeks_return / stdev
next_weeks_vol_norm_return = cap_series(next_weeks_vol_norm_return,
capmin=-50.0,
capmax=50.0)
fcastStack.append(forecast)
vnStack.append(next_weeks_vol_norm_return)
binary1forecast = make_binary(forecast, rounded=False)
binary2forecast = make_binary(forecast, rounded=True)
binary1Stack.append(binary1forecast)
binary2Stack.append(binary2forecast)
volStack.append(price_volatility)
priceStack.append(price)
currentpriceStack.append(current_price)
for code in code_list:
price=get_price_for_instrument(code)
carrydata=get_carry_data(code)
current_price=carrydata.TRADED
if Lfast is None:
forecast=calc_carry_forecast(carrydata, price)
## carry
else:
forecast=calc_ewmac_forecast(price, Lfast)
stdev=volatility(price)*root_days_in_week
price_volatility=100*stdev/current_price
next_weeks_return =price.shift(-5) - price
next_weeks_vol_norm_return =next_weeks_return/stdev
next_weeks_vol_norm_return=cap_series(next_weeks_vol_norm_return, capmin=-50.0,capmax=50.0)
fcastStack.append(forecast)
vnStack.append(next_weeks_vol_norm_return)
binary1forecast=make_binary(forecast, rounded=False)
binary2forecast=make_binary(forecast, rounded=True)
binary1Stack.append(binary1forecast)
binary2Stack.append(binary2forecast)
volStack.append(price_volatility)
priceStack.append(price)
currentpriceStack.append(current_price)
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()
#d1=pd.datetime(2007,1,1)
#d2=pd.datetime(2009,12,31)
nerpu=data.apply(find_datediff, axis=1)
nerpu.plot()
plt.title("Nerpu")
plt.show()
## Shouldn't need changing
vol_lookback=25
stdev_returns=pd.ewmstd(price - price.shift(1), span=vol_lookback)
ann_stdev=stdev_returns*ROOT_DAYS_IN_YEAR
raw_carry=nerpu/ann_stdev
f_scalar=30.0
raw_carry.plot()
plt.title("Raw carry")
plt.show()
forecast=raw_carry*f_scalar
c_forecast=cap_series(forecast).to_frame()
data_to_plot=pd.concat([forecast,c_forecast], axis=1)
data_to_plot.columns=['Forecast','Capped forecast']
data_to_plot.plot()
plt.show()
