def prepare_data(engine: SqlEngine,
factors: Union[Transformer, Iterable[object]],
start_date: str,
end_date: str,
frequency: str,
universe: Universe,
benchmark: int,
warm_start: int = 0):
if warm_start > 0:
p = Period(frequency)
p = Period(length=-warm_start * p.length(), units=p.units())
start_date = advanceDateByCalendar('china.sse', start_date,
p).strftime('%Y-%m-%d')
dates = makeSchedule(start_date,
end_date,
frequency,
calendar='china.sse',
dateRule=BizDayConventions.Following,
dateGenerationRule=DateGeneration.Forward)
dates = [d.strftime('%Y-%m-%d') for d in dates]
horizon = map_freq(frequency)
if isinstance(factors, Transformer):
transformer = factors
else:
transformer = Transformer(factors)
factor_df = engine.fetch_factor_range(universe,
factors=transformer,
dates=dates).sort_values(
['trade_date', 'code'])
alpha_logger.info("factor data loading finished")
return_df = engine.fetch_dx_return_range(universe,
dates=dates,
horizon=horizon)
alpha_logger.info("return data loading finished")
industry_df = engine.fetch_industry_range(universe, dates=dates)
alpha_logger.info("industry data loading finished")
benchmark_df = engine.fetch_benchmark_range(benchmark, dates=dates)
alpha_logger.info("benchmark data loading finished")
df = pd.merge(factor_df, return_df, on=['trade_date', 'code']).dropna()
df = pd.merge(df, benchmark_df, on=['trade_date', 'code'], how='left')
df = pd.merge(df, industry_df, on=['trade_date', 'code'])
df['weight'] = df['weight'].fillna(0.)
return dates, df[['trade_date', 'code', 'dx']], df[[
'trade_date', 'code', 'weight', 'isOpen', 'industry_code', 'industry'
] + transformer.names]
def prepare_data(engine: SqlEngine,
factors: Union[Transformer, Iterable[object]],
start_date: str,
end_date: str,
frequency: str,
universe: Universe,
benchmark: int,
warm_start: int = 0,
fit_target: Union[Transformer, object] = None):
if warm_start > 0:
p = Period(frequency)
p = Period(length=-warm_start * p.length(), units=p.units())
start_date = advanceDateByCalendar('china.sse', start_date,
p).strftime('%Y-%m-%d')
dates = makeSchedule(start_date,
end_date,
frequency,
calendar='china.sse',
dateRule=BizDayConventions.Following,
dateGenerationRule=DateGeneration.Forward)
dates = [d.strftime('%Y-%m-%d') for d in dates]
horizon = map_freq(frequency)
if isinstance(factors, Transformer):
transformer = factors
else:
transformer = Transformer(factors)
factor_df = engine.fetch_factor_range(universe,
factors=transformer,
dates=dates).sort_values(
['trade_date', 'code'])
alpha_logger.info("factor data loading finished")
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'))
alpha_logger.info("fit target data loading finished")
industry_df = engine.fetch_industry_range(universe, dates=dates)
alpha_logger.info("industry data loading finished")
benchmark_df = engine.fetch_benchmark_range(benchmark, dates=dates)
alpha_logger.info("benchmark data loading finished")
df = pd.merge(factor_df, target_df, on=['trade_date', 'code']).dropna()
df = pd.merge(df, benchmark_df, on=['trade_date', 'code'], how='left')
df = pd.merge(df, industry_df, on=['trade_date', 'code'])
df['weight'] = df['weight'].fillna(0.)
df.dropna(inplace=True)
return dates, df[[
'trade_date', 'code', 'dx'
]], df[['trade_date', 'code', 'weight', 'industry_code', 'industry'] +
transformer.names]
