def _get_combined_forecast(system, instrument_code, this_stage):
this_stage.log.msg("Calculating combined forecast for %s" %
(instrument_code),
instrument_code=instrument_code)
forecast_weights = this_stage.get_forecast_weights(instrument_code)
rule_variation_list = list(forecast_weights.columns)
forecasts = this_stage.get_all_forecasts(instrument_code,
rule_variation_list)
forecast_div_multiplier = this_stage.get_forecast_diversification_multiplier(
instrument_code)
forecast_cap = this_stage.get_forecast_cap()
# multiply weights by forecasts
combined_forecast = multiply_df(forecast_weights, forecasts)
# sum
combined_forecast = combined_forecast.sum(
axis=1).to_frame("comb_forecast")
# apply fdm
# (note in this simple version we aren't adjusting FDM if forecast_weights change)
forecast_div_multiplier = forecast_div_multiplier.reindex(
forecasts.index, method="ffill")
raw_combined_forecast = multiply_df(combined_forecast,
forecast_div_multiplier)
combined_forecast = apply_cap(raw_combined_forecast, forecast_cap)
combined_forecast[combined_forecast > 0.0] = 10.0
combined_forecast[combined_forecast < 0.0] = 10.0
return combined_forecast
def _get_combined_forecast(system, instrument_code, this_stage):
this_stage.log.msg(
"Calculating combined forecast for %s" % (instrument_code),
instrument_code=instrument_code)
forecast_weights = this_stage.get_forecast_weights(instrument_code)
rule_variation_list = list(forecast_weights.columns)
forecasts = this_stage.get_all_forecasts(instrument_code,
rule_variation_list)
forecast_div_multiplier = this_stage.get_forecast_diversification_multiplier(
instrument_code)
forecast_cap = this_stage.get_forecast_cap()
# multiply weights by forecasts
combined_forecast = multiply_df(forecast_weights, forecasts)
# sum
combined_forecast = combined_forecast.sum(
axis=1).to_frame("comb_forecast")
# apply fdm
# (note in this simple version we aren't adjusting FDM if forecast_weights change)
forecast_div_multiplier = forecast_div_multiplier.reindex(
forecasts.index, method="ffill")
raw_combined_forecast = multiply_df(combined_forecast,
forecast_div_multiplier)
combined_forecast = apply_cap(raw_combined_forecast, forecast_cap)
combined_forecast[combined_forecast > 0.0] = 10.0
combined_forecast[combined_forecast < 0.0] = 10.0
return combined_forecast
def _get_combined_forecast(system, instrument_code, this_stage):
this_stage.log.msg("Calculating combined forecast for %s" %
(instrument_code),
instrument_code=instrument_code)
forecast_weights = this_stage.get_forecast_weights(instrument_code)
rule_variation_list = list(forecast_weights.columns)
forecasts = this_stage.get_all_forecasts(instrument_code,
rule_variation_list)
forecast_div_multiplier = this_stage.get_forecast_diversification_multiplier(
instrument_code)
forecast_cap = this_stage.get_forecast_cap()
# multiply weights by forecasts
combined_forecast = multiply_df(forecast_weights, forecasts)
# sum
combined_forecast = combined_forecast.sum(
axis=1).to_frame("comb_forecast")
# apply fdm
# (note in this simple version we aren't adjusting FDM if forecast_weights change)
forecast_div_multiplier = forecast_div_multiplier.reindex(
forecasts.index, method="ffill")
raw_combined_forecast = multiply_df(combined_forecast,
forecast_div_multiplier)
def map_forecast_value(x):
x = float(x)
if x < -20.0:
return -30.0
if x >= -20.0 and x < -10.0:
return -(abs(x) - 10.0) * 3
if x >= -10.0 and x <= 10.0:
return 0.0
if x > 10.0 and x <= 20.0:
return (abs(x) - 10.0) * 3
return 30.0
combined_forecast = pd.DataFrame(
[map_forecast_value(x) for x in combined_forecast.values],
combined_forecast.index)
return combined_forecast
def _get_combined_forecast(system, instrument_code, this_stage):
this_stage.log.msg(
"Calculating combined forecast for %s" % (instrument_code),
instrument_code=instrument_code)
forecast_weights = this_stage.get_forecast_weights(instrument_code)
rule_variation_list = list(forecast_weights.columns)
forecasts = this_stage.get_all_forecasts(instrument_code,
rule_variation_list)
forecast_div_multiplier = this_stage.get_forecast_diversification_multiplier(
instrument_code)
forecast_cap = this_stage.get_forecast_cap()
# multiply weights by forecasts
combined_forecast = multiply_df(forecast_weights, forecasts)
# sum
combined_forecast = combined_forecast.sum(
axis=1).to_frame("comb_forecast")
# apply fdm
# (note in this simple version we aren't adjusting FDM if forecast_weights change)
forecast_div_multiplier = forecast_div_multiplier.reindex(
forecasts.index, method="ffill")
raw_combined_forecast = multiply_df(combined_forecast,
forecast_div_multiplier)
def map_forecast_value(x):
x = float(x)
if x < -20.0:
return -30.0
if x >= -20.0 and x < -10.0:
return -(abs(x) - 10.0) * 3
if x >= -10.0 and x <= 10.0:
return 0.0
if x > 10.0 and x <= 20.0:
return (abs(x) - 10.0) * 3
return 30.0
combined_forecast = pd.DataFrame(
[map_forecast_value(x)
for x in combined_forecast.values], combined_forecast.index)
return combined_forecast