全球大类资风险平价月度再平衡年化78最大回撤95

一.效果

二.实现过程

1.现在回测需要的数据

1.1etf和lof数据

import akshare as ak
import time
from config import DATA_DIR, DATA_DIR_QUOTES
import pandas as pd
def download_data(symbol, start_date="20000101"):
    print(symbol)
    # 全部转换成大写
    symbol = symbol.upper()
    code = symbol[:6]
    # 回测最好用后复权数据，前复权会有负数的问题
    try:
        # lof基金
        if "16" == code[:2]:
            df = ak.fund_lof_hist_em(
                symbol=code, period="daily", start_date=start_date, adjust="hfq"
            )
        else:
            df = ak.fund_etf_hist_em(
                symbol=code, period="daily", start_date=start_date, adjust="hfq"
            )
    except Exception as e:
        print(f"Error fetching data for code {code}: {e}")
        return pd.DataFrame()
    df.rename(
        columns={
            "日期": "date",
            "开盘": "open",
            "收盘": "close",
            "最高": "high",
            "最低": "low",
            "成交量": "volume",
            "换手率": "turn_over",
            "成交额": "amount",
        },
        inplace=True,
    )
    # 只取cols这几列数据
    df = df[["date", "open", "high", "low", "close", "volume"]]
    df["symbol"] = symbol
    print(df)
    csv_path = DATA_DIR.joinpath(DATA_DIR_QUOTES, f"{symbol}.csv")
    with open(csv_path, "w") as f:
        df.to_csv(f, index=False)
    print(df)
if __name__ == "__main__":
    # symbols = [
    #     '511220.SH',  # 城投债
    #     '512010.SH',  # 医药
    #     '518880.SH',  # 黄金
    #     '163415.SZ',  # 兴全商业
    #     '159928.SZ',  # 消费
    #     '161903.SZ',  # 万家行业优选
    #     '513100.SH'  # 纳指
    # ]  # 证券池列表
    symbols = [
        "511220.SH",  # 城投债
        "512010.SH",  # 医药
        "518880.SH",  # 黄金ETF
        "163415.SZ",  # 兴全商业
        "159928.SZ",  # 消费
        "161903.SZ",  # 万家行业优选
        "513100.SH",  # 纳指
        "159929.SZ",  # 消费
        "511010.SH",  # 国债
        "510300.SH",  # 沪深300ETF
    ]  # 证券池列表
    symbols += ["513100.SH", "159934.SZ", "510880.SH", "159915.SZ"]
    symbols += [
        "513100.SH",  # 纳指ETF
        "515300.SH",  # 红利地波
        "511880.SH",  # 银河利华
        "159949.SZ",  # 创业板50
        "159985.SZ",  # 豆粕ETF
        "512100.SH",  # 中证1000
    ]
    # 下载标的数据
    symbols = [
        "511880.SH",  # 银河利华
        "511990.SH",  # 华宝添益
    ]
    symbols = [
        "000300.SH",  # 沪深300
        "159915.SZ",  # 创业板
        "000012.SH",  # 国债指数
        "518880.SH",  # 黄金ETF
        "162411.SZ",  # 华宝油气
    ]
    symbols = list(set(symbols))
    for symbol in symbols:
        download_data(symbol)

2.香港和纳值指数数据

import akshare as ak
import time
import pandas as pd
from config import DATA_DIR, DATA_DIR_QUOTES
def download_data(symbol):
    print(symbol)
    # 全部转换成大写
    symbol = symbol.upper()
    code = symbol
    # 回测最好用后复权数据，前复权会有负数的问题
    try:
        if "HSI" == code:
            # 香港恒生
            df = ak.stock_hk_index_daily_sina(symbol=code)
        elif "NDX" in code:
            # 美国 纳值
            df = ak.index_us_stock_sina(symbol=".NDX")
        else:
            df = pd.DataFrame()
    except Exception as e:
        print(f"Error fetching data for code {code}: {e}")
        return pd.DataFrame()
    df.rename(
        columns={
            "日期": "date",
            "开盘": "open",
            "收盘": "close",
            "最高": "high",
            "最低": "low",
            "成交量": "volume",
            "换手率": "turn_over",
            "成交额": "amount",
        },
        inplace=True,
    )
    # 只取cols这几列数据
    df = df[["date", "open", "high", "low", "close", "volume"]]
    df["symbol"] = symbol
    print(df)
    csv_path = DATA_DIR.joinpath(DATA_DIR_QUOTES, f"{symbol}.csv")
    with open(csv_path, "w") as f:
        df.to_csv(f, index=False)
    print(df)
if __name__ == "__main__":
    symbols = []
    # 香港
    # 恒生指数
    symbols = [
        "HSI",  # 香港恒生
    ]
    # 美国
    #  {".IXIC", ".DJI", ".INX", ".NDX"}
    symbols += [
        ".NDX",  # 纳指
    ]
    symbols = list(set(symbols))
    for symbol in symbols:
        download_data(symbol)

二.运行策即可