def sync_equity_to_sw_industry(sw_industry_lv: str, start_t: date, end_t: date, force_reinit: bool = False):
lv_to_lib_name = {"L1": TuShareProData.DERIVED_TS_EQUITY_SW_INDUSTRY_L1,
"L2": TuShareProData.DERIVED_TS_EQUITY_SW_INDUSTRY_L2,
"L3": TuShareProData.DERIVED_TS_EQUITY_SW_INDUSTRY_L3}
assert sw_industry_lv in lv_to_lib_name.keys()
tushare = TuShareProData(use_l3_cache=False)
req_freq_controller = TushareReqSleepController(tushare)
df_sw_index = tushare.index_classify(level=sw_industry_lv, src="SW")
df_sw_index = df_sw_index.reset_index(drop=True)
ls_df_index_equities = []
i_num = 0
for idx_num, row in df_sw_index.iterrows():
i_num += 1
lv = row["level"]
ind_name = row["industry_name"]
ind_code = row["index_code"]
req_freq_controller.begin_internal_check()
df_index_equities = tushare.period_index_member(index_code=ind_code, resample_freq="B", start=start_t,
end=end_t)
req_freq_controller.end_internal_check()
df_index_equities["index_code"] = ind_code
ls_df_index_equities.append(df_index_equities)
df_all_index_equities = pd.concat(ls_df_index_equities)
for i, symbol in enumerate(df_all_index_equities["symbol"].unique()):
df_symbol_in_industry = df_all_index_equities[df_all_index_equities["symbol"] == symbol]
df_symbol_in_industry.drop(columns=["symbol"], inplace=True)
print(f"\rsync_equity_to_sw_industry {i}:{symbol}-{df_symbol_in_industry.index.min()}-{df_symbol_in_industry.index.max()} ", end="")
tushare.ts_upsert_arctic_storage(lv_to_lib_name[sw_industry_lv], symbol, df_symbol_in_industry,
force_reinit=force_reinit)
def _calc_sw_industry_data(tushare: TuShareProData, sw_industry_code: str, start_t: date, end_t: date,
f_equity_quotation_daily=None, f_equity_basic_daily=None) -> pd.DataFrame:
# 行业指数在 date 的持仓
df_stocks_in_industry = tushare.period_index_member(sw_industry_code, start=start_t, end=end_t,
resample_freq="B")
if df_stocks_in_industry is None: # 区间内没有股票，直接返回
return
df_stocks_in_industry = df_stocks_in_industry.reset_index().set_index(keys=["date", "symbol"])
if f_equity_quotation_daily is None:
f_equity_quotation_daily = tushare.cs_equity_quotation_daily
if f_equity_basic_daily is None:
f_equity_basic_daily = functools.partial(tushare.cs_equity_basic_daily,
cols=["turnover_rate", "turnover_rate_f",
"volume_ratio", "pe", "pe_ttm", "pb",
"ps", "ps_ttm", "dv_ratio", "dv_ttm",
"total_share", "float_share",
"free_share", "total_mv", "circ_mv"])
# 每日指标
mv_weighted_cols = ["open", "high", "low", "close", "pre_close", "change", "pct_chg", "turnover_rate",
"turnover_rate_f", "volume_ratio", "pe", "pe_ttm", "pb", "ps", "ps_ttm", "dv_ratio",
"dv_ttm"] # 总市值加权的列
# sum_cols = ["vol", "amount", "total_share", "float_share", "free_share", "total_mv", "circ_mv"]
# 得到个股的区间数据
df_cs_daily_industry_membership = df_stocks_in_industry.join(
f_equity_quotation_daily(start=start_t, end=end_t), how="left").join(
f_equity_basic_daily(start=start_t, end=end_t), how="left")
# 市值加权列的加总计算
for col in mv_weighted_cols:
df_cs_daily_industry_membership[f"_{col}"] = df_cs_daily_industry_membership[col] * \
df_cs_daily_industry_membership["total_mv"]
df_cs_daily_industry_membership.drop(columns=mv_weighted_cols, inplace=True)
df_cs_daily_industry_membership.rename(columns={f"_{col}": col for col in mv_weighted_cols}, inplace=True)
df_cs_daily_industry_membership = df_cs_daily_industry_membership.reset_index().groupby("date").sum()
for col in mv_weighted_cols:
df_cs_daily_industry_membership[f"_{col}"] = df_cs_daily_industry_membership[col] / \
df_cs_daily_industry_membership["total_mv"]
df_cs_daily_industry_membership.drop(columns=mv_weighted_cols, inplace=True)
df_cs_daily_industry_membership.rename(columns={f"_{col}": col for col in mv_weighted_cols}, inplace=True)
return df_cs_daily_industry_membership