def alpha_factor_function():
return 10
def make_pipeline():
return Pipeline(columns={'column_name': alpha_factor_function()})
my_pipe = make_pipeline()
pipeline_data = run_pipeline(my_pipe,
start_date='2014-1-1',
end_date='2016-1-1').dropna()
# Alphalens (second cell)
from alphalens.utils import get_clean_factor_and_forward_returns
from alphalens.tears import create_full_tear_sheet
pricing_data = get_pricing(
symbols=pipeline_data.index.
levels[1], # Finds all assets that appear at least once in the pipeline
start_date='2014-1-1',
end_date='2016-2-1', #1 trading day after end date of pipeline
fields='open_price')
merged_data = get_clean_factor_and_forward_returns(
factor=pipeline_data['column_name'], prices=pricing_data)
create_full_tear_sheet(merged_data)
# 申万行业分类
industry_dict = sw_industry.set_index('code')['index_code'].to_dict()
industry_labels = sw_industry.set_index('index_code')['index_name'].to_dict()
# 分析因子
factor_data = get_clean_factor_and_forward_returns(stock_factor,
stock_close,
groupby=industry_dict,
quantiles=5,
binning_by_group=False,
periods=(1, 5, 10),
filter_zscore=None)
create_full_tear_sheet(factor_data,
long_short=False,
group_neutral=False,
by_group=False)
# create_summary_tear_sheet(factor_data, long_short=True, group_neutral=True)
# create_event_returns_tear_sheet(factor_data, stock_close, avgretplot=(3, 11),
# long_short=False, group_neutral=True, by_group=True)
# create_event_study_tear_sheet(factor_data, stock_close)
# returns, positions, benchmark_rets = create_pyfolio_input(factor_data,
# period='1D',
# capital=None,
# long_short=False,
# group_neutral=True,
# equal_weight=False,
# quantiles=None,
# groups=None,
# benchmark_period='1D')
factor_data
def create_tear_sheet(self):
return create_full_tear_sheet(self.factor_and_forward_returns)
import pandas as pd
import tushare as ts
from alphalens.utils import get_clean_factor_and_forward_returns
from alphalens.tears import create_full_tear_sheet
pro = ts.pro_api()
df = pro.daily(ts_code='000001.SZ,600982.SH',
start_date='20200101',
end_date='20211122')
df.index = pd.to_datetime(df['trade_date'])
df.index.name = None
df.sort_index(inplace=True)
# 多索引的因子列，第一个索引为日期，第二个索引为股票代码
assets = df.set_index([df.index, df['ts_code']], drop=True)
# column为股票代码，index为日期，值为收盘价
close = df.pivot_table(index='trade_date', columns='ts_code', values='close')
close.index = pd.to_datetime(close.index)
from alphalens.utils import get_clean_factor_and_forward_returns
from alphalens.tears import create_full_tear_sheet
ret = get_clean_factor_and_forward_returns(assets[['pct_chg']], close)
create_full_tear_sheet(ret, long_short=False)
# https://github.com/Ckend/alphalens