MoFin/venv/lib/python3.12/site-packages/akshare/futures/futures_roll_yield.py

#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Date: 2025/7/16 17:40
Desc: 中国期货各合约展期收益率
日线数据从 daily_bar 函数获取, 需要在收盘后运行
"""

import datetime
import re
import warnings

import math
import pandas as pd

from akshare.futures import cons
from akshare.futures.futures_daily_bar import get_futures_daily
from akshare.futures.symbol_var import symbol_market, symbol_varieties

calendar = cons.get_calendar()


def get_roll_yield(date=None, var="BB", symbol1=None, symbol2=None, df=None):
    """
    指定交易日指定品种（主力和次主力）或任意两个合约的展期收益率
    Parameters
    ------
    date: string 某一天日期 format： YYYYMMDD
    var: string 合约品种如 RB、AL 等
    symbol1: string 合约 1 如 rb1810
    symbol2: string 合约 2 如 rb1812
    df: DataFrame或None 从dailyBar得到合约价格，如果为空就在函数内部抓dailyBar，直接喂给数据可以让计算加快
    """
    # date = "20100104"
    date = cons.convert_date(date) if date is not None else datetime.date.today()
    if date.strftime("%Y%m%d") not in calendar:
        warnings.warn("%s非交易日" % date.strftime("%Y%m%d"))
        return None
    if symbol1:
        var = symbol_varieties(symbol1)
    if not isinstance(df, pd.DataFrame):
        market = symbol_market(var)
        df = get_futures_daily(start_date=date, end_date=date, market=market)
    if var:
        df = df[
            ~df["symbol"].str.contains("efp")
        ]  # 20200304 由于交易所获取的数据中会有比如 "CUefp"，所以在这里过滤
        df = df[df["variety"] == var].sort_values(by=["open_interest"], ascending=False)
        # df["close"] = df["close"].astype("float")
        df["close"] = pd.to_numeric(df["close"])
        if len(df["close"]) < 2:
            return None
        symbol1 = df["symbol"].tolist()[0]
        symbol2 = df["symbol"].tolist()[1]

    close1 = df["close"][df["symbol"] == symbol1].tolist()[0]
    close2 = df["close"][df["symbol"] == symbol2].tolist()[0]

    a = re.sub(r"\D", "", symbol1)
    a_1 = int(a[:-2])
    a_2 = int(a[-2:])
    b = re.sub(r"\D", "", symbol2)
    b_1 = int(b[:-2])
    b_2 = int(b[-2:])
    c = (a_1 - b_1) * 12 + (a_2 - b_2)
    if close1 == 0 or close2 == 0:
        return False
    if c > 0:
        return math.log(close2 / close1) / c * 12, symbol2, symbol1
    else:
        return math.log(close2 / close1) / c * 12, symbol1, symbol2


def get_roll_yield_bar(
    type_method: str = "var",
    var: str = "RB",
    date: str = "20201030",
    start_day: str = None,
    end_day: str = None,
):
    """
    展期收益率
    :param type_method: 'symbol': 获取指定交易日指定品种所有交割月合约的收盘价; 'var': 获取指定交易日所有品种两个主力合约的展期收益率(展期收益率横截面); 'date': 获取指定品种每天的两个主力合约的展期收益率(展期收益率时间序列)
    :param var: 合约品种如 "RB", "AL" 等
    :param date: 指定交易日 format： YYYYMMDD
    :param start_day: 开始日期 format：YYYYMMDD
    :param end_day: 结束日期 format：YYYYMMDD
    :return: pandas.DataFrame
    展期收益率数据(DataFrame)
    ry      展期收益率
    index   日期或品种
    """
    date = cons.convert_date(date) if date is not None else datetime.date.today()
    start_day = (
        cons.convert_date(start_day) if start_day is not None else datetime.date.today()
    )
    end_day = (
        cons.convert_date(end_day)
        if end_day is not None
        else cons.convert_date(cons.get_latest_data_date(datetime.datetime.now()))
    )

    if type_method == "symbol":
        df = get_futures_daily(
            start_date=date, end_date=date, market=symbol_market(var)
        )
        df = df[df["variety"] == var]
        return df

    if type_method == "var":
        df = pd.DataFrame()
        for market in ["dce", "cffex", "shfe", "czce", "gfex"]:
            df = pd.concat(
                [
                    df,
                    get_futures_daily(start_date=date, end_date=date, market=market),
                ]
            )
        var_list = list(set(df["variety"]))
        for i_remove in ["IO", "MO", "HO"]:
            if i_remove in var_list:
                var_list.remove(i_remove)
        df_l = pd.DataFrame()
        for var in var_list:
            ry = get_roll_yield(date, var, df=df)
            if ry:
                df_l = pd.concat(
                    [
                        df_l,
                        pd.DataFrame(
                            [ry],
                            index=[var],
                            columns=["roll_yield", "near_by", "deferred"],
                        ),
                    ]
                )
        df_l["date"] = date
        df_l = df_l.sort_values("roll_yield")
        return df_l

    if type_method == "date":
        df_l = pd.DataFrame()
        while start_day <= end_day:
            try:
                ry = get_roll_yield(start_day, var)
                if ry:
                    df_l = pd.concat(
                        [
                            df_l,
                            pd.DataFrame(
                                [ry],
                                index=[start_day],
                                columns=["roll_yield", "near_by", "deferred"],
                            ),
                        ]
                    )
            except:  # noqa: E722
                pass
            start_day += datetime.timedelta(days=1)
        return df_l


if __name__ == "__main__":
    get_roll_yield_bar_range_df = get_roll_yield_bar(
        type_method="date",
        var="RB",
        start_day="20230801",
        end_day="20230810",
    )
    print(get_roll_yield_bar_range_df)

    get_roll_yield_bar_range_df = get_roll_yield_bar(
        type_method="var",
        date="20191008",
    )
    print(get_roll_yield_bar_range_df)

    get_roll_yield_bar_symbol = get_roll_yield_bar(type_method="var", date="20210201")
    print(get_roll_yield_bar_symbol)