算法交易脚本运行Sharpe/Sortino函数触发KeyError: 'Close'求助

问题现象 #

CAGR和Volatility函数可正常运行，但执行Sharpe和Sortino函数时出现KeyError: 'Close'错误，错误定位到CAGR函数中的df["return"] = DF["Close"].pct_change()代码行。

原代码及错误栈 #

import yfinance as yf
import numpy as np
import pandas as pd


tickers = ["AAPL","AMC","SPY","BBBY"]

ohlcv_data = {}

for ticker in tickers:
    temp = yf.download(ticker, period="7mo", interval="1d")
    temp.dropna(how="any", inplace=True)
    ohlcv_data[ticker] = temp

def CAGR(DF):
    
    df = DF.copy()
    df["return"] = DF["Close"].pct_change()
    df["cum_return"] = (1+df["return"]).cumprod()
    n = len(df)/252 
    CAGR = (df["cum_return"][-1])**(1/n) - 1
    
    return CAGR

for ticker in ohlcv_data:
    print("GAGR for {} = {}".format(ticker, CAGR(ohlcv_data[ticker])))


def Volatility(DF):

    df = DF.copy()
    df["return"] = DF["Close"].pct_change()
    vol = df["return"].std() * np.sqrt(252)

    return vol

for ticker in ohlcv_data:
    print("Volatility of {} = {}".format(ticker, Volatility(ohlcv_data[ticker])))


def Sharpe(DF, rf=0.03):

    df = DF.copy()
    return (CAGR(df)- rf)/Volatility(df)

for ticker in ohlcv_data:
    print("Sharpe for {} = {}".format(ticker, Sharpe(ohlcv_data, 0.03)))

def Sortino(DF, rf=0.03):

    df= DF.copy()
    df["return"] = df["Close"].pct_change()
    neg_return = np.where(df["return"]>0,0,df["return"])
    neg_vol = pd.Series(neg_return[neg_return!=0]).std()

    return (CAGR(df)- rf)/neg_vol

for ticker in ohlcv_data:
    print("Sortino for {} = {}".format(ticker, Sortino(ohlcv_data, 0.03)))

错误栈：

File "c:\users\cryst\onedrive\documents\algotradingcode\untitled11.py", line 56, in <module>
print("Sharpe for {} = {}".format(ticker, Sharpe(ohlcv_data, 0.03)))

File "c:\users\cryst\onedrive\documents\algotradingcode\untitled11.py", line 53, in Sharpe
return (CAGR(df)- rf)/Volatility(df)

File "c:\users\cryst\onedrive\documents\algotradingcode\untitled11.py", line 26, in CAGR
df["return"] = DF["Close"].pct_change()

KeyError: 'Close'

错误原因 #

调用Sharpe和Sortino函数时，错误地传入了整个ohlcv_data字典（键为ticker，值为对应股票的OHLCV DataFrame），而非单个ticker对应的DataFrame。字典本身没有"Close"键，只有DataFrame中才包含"Close"列，因此触发KeyError。

修正后的代码 #

只需修改Sharpe和Sortino的循环调用部分，将传入的ohlcv_data改为ohlcv_data[ticker]，同时优化Sortino函数中负波动率的计算逻辑：

import yfinance as yf
import numpy as np
import pandas as pd


tickers = ["AAPL","AMC","SPY","BBBY"]

ohlcv_data = {}

for ticker in tickers:
    temp = yf.download(ticker, period="7mo", interval="1d")
    temp.dropna(how="any", inplace=True)
    ohlcv_data[ticker] = temp

def CAGR(DF):
    
    df = DF.copy()
    df["return"] = DF["Close"].pct_change()
    df["cum_return"] = (1+df["return"]).cumprod()
    n = len(df)/252 
    CAGR = (df["cum_return"][-1])**(1/n) - 1
    
    return CAGR

for ticker in ohlcv_data:
    print("GAGR for {} = {}".format(ticker, CAGR(ohlcv_data[ticker])))


def Volatility(DF):

    df = DF.copy()
    df["return"] = DF["Close"].pct_change()
    vol = df["return"].std() * np.sqrt(252)

    return vol

for ticker in ohlcv_data:
    print("Volatility of {} = {}".format(ticker, Volatility(ohlcv_data[ticker])))


def Sharpe(DF, rf=0.03):

    df = DF.copy()
    return (CAGR(df)- rf)/Volatility(df)

# 修正：传入单个ticker的DataFrame
for ticker in ohlcv_data:
    print("Sharpe for {} = {}".format(ticker, Sharpe(ohlcv_data[ticker], 0.03)))

def Sortino(DF, rf=0.03):

    df= DF.copy()
    df["return"] = df["Close"].pct_change()
    # 优化负波动率计算：直接筛选负收益后取标准差，再年化
    neg_returns = df["return"][df["return"] < 0]
    neg_vol = neg_returns.std() * np.sqrt(252)

    return (CAGR(df)- rf)/neg_vol

# 修正：传入单个ticker的DataFrame
for ticker in ohlcv_data:
    print("Sortino for {} = {}".format(ticker, Sortino(ohlcv_data[ticker], 0.03)))

内容的提问来源于stack exchange，提问作者Rene Rodriguez