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alpha-mind
Commit ed1f44d9 authored by Dr.李's avatar Dr.李
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fixed bug and add y in predict data

parent 31805b28
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Showing with 65 additions and 35 deletions
alphamind/model/composer.py
View file @ ed1f44d9
......@@ -8,6 +8,7 @@ Created on 2017-9-27
import copy
import bisect
from typing import Iterable
import numpy as np
import pandas as pd
from simpleutils.miscellaneous import list_eq
from alphamind.model.modelbase import ModelBase
......@@ -133,7 +134,8 @@ class DataMeta(object):
self.pre_process,
self.post_process,
self.warm_start,
fillna=True)
fillna=True,
fit_target=alpha_model.fit_target)
def train_model(ref_date: str,
......@@ -186,6 +188,15 @@ class Composer(object):
codes = x.index
return pd.DataFrame(model.predict(x_values).flatten(), index=codes)
def score(self, ref_date: str, x: pd.DataFrame = None, y: np.ndarray = None) -> float:
model = self._fetch_latest_model(ref_date)
if x is None:
predict_data = self.data_meta.fetch_predict_data(ref_date, model)
x = predict_data['predict']['x']
if y is None:
y = predict_data['predict']['y']
return model.score(x, y)
def _fetch_latest_model(self, ref_date) -> ModelBase:
if self.is_updated:
sorted_keys = self.sorted_keys
......@@ -211,35 +222,33 @@ class Composer(object):
if __name__ == '__main__':
import numpy as np
from alphamind.data.standardize import standardize
from alphamind.data.winsorize import winsorize_normal
from alphamind.data.engines.sqlengine import industry_styles
from alphamind.model.linearmodel import ConstLinearModel
data_source = "postgres+psycopg2://postgres:we083826@localhost/alpha"
alpha_model = ConstLinearModel(['EPS'], np.array([1.]))
alpha_factors = ['EPS']
freq = '1w'
universe = Universe('zz500', ['zz500'])
batch = 4
neutralized_risk = ['SIZE'] + industry_styles
from PyFin.api import LAST
from alphamind.data.engines.sqlengine import risk_styles, industry_styles
from alphamind.model.linearmodel import LinearRegression
universe = Universe('custom', ['ashare_ex'])
freq = '20b'
batch = 0
neutralized_risk = risk_styles + industry_styles
risk_model = 'short'
pre_process = [winsorize_normal, standardize]
pos_process = [winsorize_normal, standardize]
data_meta = DataMeta(freq,
universe,
batch,
neutralized_risk,
risk_model,
pre_process,
pos_process,
post_process = [standardize]
warm_start = 0
data_source = "postgres+psycopg2://postgres:we083826@localhost/alpha"
data_meta = DataMeta(freq=freq,
universe=universe,
batch=batch,
neutralized_risk=neutralized_risk,
risk_model=risk_model,
pre_process=pre_process,
post_process=post_process,
warm_start=warm_start,
data_source=data_source)
composer = Composer(alpha_model, data_meta)
alpha_model = LinearRegression({'roe_q': LAST('roe_q')}, fit_target='roe_q')
composer = Composer(alpha_model=alpha_model, data_meta=data_meta)
composer.train('2017-09-20')
composer.train('2017-09-22')
composer.train('2017-09-25')
composer.predict('2017-09-21')
ref_date = '2018-01-30'
composer.train(ref_date)
res = composer.predict(ref_date)
\ No newline at end of file
alphamind/model/data_preparing.py
View file @ ed1f44d9
......@@ -310,10 +310,10 @@ def fetch_train_phase(engine,
if dates[-1] == dt.datetime.strptime(ref_date, '%Y-%m-%d'):
pyFinAssert(len(dates) >= 2, ValueError, "No previous data for training for the date {0}".format(ref_date))
end = dates[-2]
start = dates[-batch - 1] if batch <= len(dates) - 1 else dates[0]
start = dates[-batch - 2] if batch <= len(dates) - 2 else dates[0]
else:
end = dates[-1]
start = dates[-batch] if batch <= len(dates) else dates[0]
start = dates[-batch - 1] if batch <= len(dates) else dates[0]
index = (date_label >= start) & (date_label <= end)
this_raw_x = x_values[index]
......@@ -352,7 +352,8 @@ def fetch_predict_phase(engine,
pre_process: Iterable[object] = None,
post_process: Iterable[object] = None,
warm_start: int = 0,
fillna: str = None):
fillna: str = None,
fit_target: Union[Transformer, object] = None):
if isinstance(alpha_factors, Transformer):
transformer = alpha_factors
else:
......@@ -369,6 +370,8 @@ def fetch_predict_phase(engine,
dateRule=BizDayConventions.Following,
dateGenerationRule=DateGeneration.Backward)
horizon = map_freq(frequency)
factor_df = engine.fetch_factor_range(universe, factors=transformer, dates=dates)
if fillna:
......@@ -377,6 +380,14 @@ def fetch_predict_phase(engine,
else:
factor_df = factor_df.dropna()
if fit_target is None:
target_df = engine.fetch_dx_return_range(universe, dates=dates, horizon=horizon)
else:
one_more_date = advanceDateByCalendar('china.sse', dates[-1], frequency)
target_df = engine.fetch_factor_range_forward(universe, factors=fit_target, dates=dates + [one_more_date])
target_df = target_df[target_df.trade_date.isin(dates)]
target_df = target_df.groupby('code').apply(lambda x: x.fillna(method='pad'))
names = transformer.names
if neutralized_risk:
......@@ -384,13 +395,15 @@ def fetch_predict_phase(engine,
used_neutralized_risk = list(set(neutralized_risk).difference(names))
risk_df = risk_df[['trade_date', 'code'] + used_neutralized_risk].dropna()
train_x = pd.merge(factor_df, risk_df, on=['trade_date', 'code'])
train_x = pd.merge(train_x, target_df, on=['trade_date', 'code'], how='left')
risk_exp = train_x[neutralized_risk].values.astype(float)
else:
train_x = factor_df.copy()
train_x = pd.merge(factor_df, target_df, on=['trade_date', 'code'], how='left')
risk_exp = None
x_values = train_x[names].values.astype(float)
y_values = train_x['dx'].values.astype(float)
date_label = pd.DatetimeIndex(factor_df.trade_date).to_pydatetime()
date_label = pd.DatetimeIndex(train_x.trade_date).to_pydatetime()
dates = np.unique(date_label)
if dates[-1] == dt.datetime.strptime(ref_date, '%Y-%m-%d'):
......@@ -400,6 +413,7 @@ def fetch_predict_phase(engine,
left_index = bisect.bisect_left(date_label, start)
right_index = bisect.bisect_right(date_label, end)
this_raw_x = x_values[left_index:right_index]
this_raw_y = y_values[left_index:right_index]
sub_dates = date_label[left_index:right_index]
if risk_exp is not None:
......@@ -412,10 +426,16 @@ def fetch_predict_phase(engine,
risk_factors=this_risk_exp,
post_process=post_process)
ne_y = factor_processing(this_raw_y,
pre_process=pre_process,
risk_factors=this_risk_exp,
post_process=post_process)
inner_left_index = bisect.bisect_left(sub_dates, end)
inner_right_index = bisect.bisect_right(sub_dates, end)
ne_x = ne_x[inner_left_index:inner_right_index]
ne_y = ne_y[inner_left_index:inner_right_index]
left_index = bisect.bisect_left(date_label, end)
right_index = bisect.bisect_right(date_label, end)
......@@ -423,11 +443,12 @@ def fetch_predict_phase(engine,
codes = train_x.code.values[left_index:right_index]
else:
ne_x = None
ne_y = None
codes = None
ret = dict()
ret['x_names'] = transformer.names
ret['predict'] = {'x': pd.DataFrame(ne_x, columns=transformer.names), 'code': codes}
ret['predict'] = {'x': pd.DataFrame(ne_x, columns=transformer.names), 'code': codes, 'y': ne_y.flatten()}
return ret
......@@ -437,7 +458,7 @@ if __name__ == '__main__':
engine = SqlEngine('postgresql+psycopg2://postgres:we083826@localhost/alpha')
universe = Universe('zz500', ['hs300', 'zz500'])
neutralized_risk = risk_styles + industry_styles
res = fetch_train_phase(engine, ['ep_q'],
res = fetch_predict_phase(engine, ['ep_q'],
'2012-01-05',
'5b',
universe,
......
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