import pandas as pd
import os, sys

sys.path.append("/home/lenovo/quant/tools/get_factor_tools/")
from db_tushare import get_factor_tools
gft = get_factor_tools()

if __name__ == '__main__':
    data_dir = '/home/lenovo/quant/tools/detail_testing/basic_data'
    save_dir = '/home/lenovo/quant/data/backtest/basic_data'

    for i,f in enumerate(['open_post','close_post','down_limit','up_limit','size','amount_20','opening_info','ipo_days','margin_list',
                          'abnormal', 'recession']):
        if f in ['margin_list']:
            tmp = gft.get_stock_factor(f, start='2012-01-01').fillna(0)
        else:
            tmp = pd.read_csv(f'{data_dir}/{f}.csv', index_col=0)
        tmp = tmp.unstack().reset_index()
        tmp.columns = ['stock_code', 'date', f]
        if i == 0:
            df = tmp
        else:
            df = df.merge(tmp, on=['stock_code', 'date'], how="left")
    df = df.set_index(['date']).sort_index()
    
    existed = os.listdir(save_dir)
    for d in sorted(df.index.unique()):
        if (d+'.csv' in existed) and (d+'.csv' != max(existed)):
            continue
        else:
            df.loc[d].sort_values(by=['stock_code']).to_csv(f'{save_dir}/{d}.csv', index=False)
    
    # 更新下一日的数据用于筛选
    next_date = gft.days_after(df.index.max(), 1)
    next_list = []
    for i,f in enumerate(['amount_20','opening_info','ipo_days','margin_list','abnormal','recession']):
        if f in ['margin_list']:
            next_list.append(pd.Series(gft.get_stock_factor(f, start='2012-01-01').fillna(0).iloc[-1], name=f))
        else:
            next_list.append(pd.Series(pd.read_csv(f'{data_dir}/{f}.csv', index_col=0).iloc[-1], name=f))
    df = pd.concat(next_list, axis=1)
    df.index.name = 'stock_code'
    df = df.reset_index()

    df.sort_values(by=['stock_code']).to_csv(f'{save_dir}/{next_date}.csv', index=False)