def get_scatter_data_for_code_forecast(system,
instrument_code,
rule_name,
startdate=None,
enddate=None,
return_period=5):
norm_data = system.rawdata.norm_returns(instrument_code)
forecast = system.rules.get_raw_forecast(instrument_code, rule_name)
if startdate is None:
startdate = forecast.index[0]
if enddate is None:
enddate = forecast.index[-1]
(forecast, norm_data) = align_to_joint(
forecast[startdate:enddate],
norm_data[startdate:enddate],
ffill=(True, False))
# work out return for the N days after the forecast
period_returns = pd.rolling_sum(norm_data, return_period, min_periods=1)
ex_post_returns = period_returns.shift(-return_period)
lagged_forecast = forecast.shift(1)
return (list(ex_post_returns.iloc[:, 0].values),
list(lagged_forecast.iloc[:, 0].values))
def get_scatter_data_for_code_vol(system,
instrument_code,
rule_name,
return_period=5,
days=64):
denom_price = system.rawdata.daily_denominator_price(instrument_code)
x = system.rawdata.daily_returns(instrument_code)
vol = robust_vol_calc(x, days)
perc_vol = 100.0 * divide_df_single_column(vol, denom_price.shift(1))
volavg = pd.rolling_median(perc_vol, 1250, min_periods=10)
vol_qq = (perc_vol - volavg) / volavg
## work out return for the N days after the forecast
norm_data = system.accounts.pandl_for_instrument_forecast(
instrument_code, rule_name)
(vol_qq, norm_data) = align_to_joint(vol_qq,
norm_data,
ffill=(True, False))
period_returns = pd.rolling_sum(norm_data, return_period, min_periods=1)
ex_post_returns = period_returns.shift(-return_period)
lagged_vol = vol_qq.shift(1)
return (list(ex_post_returns.iloc[:, 0].values),
list(lagged_vol.iloc[:, 0].values))
def get_scatter_data_for_code_vol(
system, instrument_code, rule_name, return_period=5, days=64):
denom_price = system.rawdata.daily_denominator_price(instrument_code)
x = system.rawdata.daily_returns(instrument_code)
vol = robust_vol_calc(x, days)
perc_vol = 100.0 * divide_df_single_column(vol, denom_price.shift(1))
volavg = pd.rolling_median(perc_vol, 1250, min_periods=10)
vol_qq = (perc_vol - volavg) / volavg
# work out return for the N days after the forecast
norm_data = system.accounts.pandl_for_instrument_forecast(
instrument_code, rule_name)
(vol_qq, norm_data) = align_to_joint(
vol_qq, norm_data, ffill=(True, False))
period_returns = pd.rolling_sum(norm_data, return_period, min_periods=1)
ex_post_returns = period_returns.shift(-return_period)
lagged_vol = vol_qq.shift(1)
return (list(ex_post_returns.iloc[:, 0].values), list(
lagged_vol.iloc[:, 0].values))
def get_scatter_data_for_code_forecast(system, instrument_code, rule_name, startdate=None, enddate=None, return_period=5):
norm_data=system.rawdata.norm_returns(instrument_code)
forecast=system.rules.get_raw_forecast(instrument_code, rule_name)
if startdate is None:
startdate=forecast.index[0]
if enddate is None:
enddate=forecast.index[-1]
(forecast, norm_data) = align_to_joint(forecast[startdate:enddate], norm_data[startdate:enddate], ffill=(True, False))
## work out return for the N days after the forecast
period_returns = pd.rolling_sum(norm_data, return_period, min_periods=1)
ex_post_returns = period_returns.shift(-return_period)
lagged_forecast = forecast.shift(1)
return (list(ex_post_returns.iloc[:,0].values),list(lagged_forecast.iloc[:,0].values))
ans = load(open("/home/rob/data.pck", "rb"))
[roll_acc, idm, acc_curve, mkt_counters] = ans
# plot IDM against market count, scattered
mkt_count_for_scatter = []
idm_for_scatter = []
roll_acc_for_scatter = []
risk_for_scatter = []
for (roll_acc_item, idm_item, mkt_count_item) in zip(
roll_acc, idm, mkt_counters):
(roll_acc_item, mkt_count_item) = align_to_joint(
roll_acc_item, mkt_count_item, ffill=(True, True))
(idm_item, mkt_count_item) = align_to_joint(
idm_item, mkt_count_item, ffill=(True, True))
norm_risk = .2 / (roll_acc_item.resample("A", how="mean").iloc[
:, 0] / idm_item.resample("A", how="mean").iloc[:, 0])
roll_acc_item_rs = list(
roll_acc_item.resample(
"A", how="mean").iloc[
:, 0].values)
idm_item_rs = list(idm_item.resample("A", how="mean").iloc[:, 0].values)
mktcount_item_rs = list(mkt_count_item.resample("A", how="mean").values)
mkt_count_for_scatter = mkt_count_for_scatter + mktcount_item_rs
roll_acc_for_scatter = roll_acc_for_scatter + roll_acc_item_rs
idm_for_scatter = idm_for_scatter + idm_item_rs
ans = load(open("/home/rob/data.pck", "rb"))
[roll_acc, idm, acc_curve, mkt_counters] = ans
### plot IDM against market count, scattered
mkt_count_for_scatter = []
idm_for_scatter = []
roll_acc_for_scatter = []
risk_for_scatter = []
for (roll_acc_item, idm_item, mkt_count_item) in zip(roll_acc, idm,
mkt_counters):
(roll_acc_item, mkt_count_item) = align_to_joint(roll_acc_item,
mkt_count_item,
ffill=(True, True))
(idm_item, mkt_count_item) = align_to_joint(idm_item,
mkt_count_item,
ffill=(True, True))
norm_risk = .2 / (roll_acc_item.resample("A", how="mean").iloc[:, 0] /
idm_item.resample("A", how="mean").iloc[:, 0])
roll_acc_item_rs = list(
roll_acc_item.resample("A", how="mean").iloc[:, 0].values)
idm_item_rs = list(idm_item.resample("A", how="mean").iloc[:, 0].values)
mktcount_item_rs = list(mkt_count_item.resample("A", how="mean").values)
mkt_count_for_scatter = mkt_count_for_scatter + mktcount_item_rs
roll_acc_for_scatter = roll_acc_for_scatter + roll_acc_item_rs
idm_for_scatter = idm_for_scatter + idm_item_rs
