Python 移动平均交叉回溯测试中的错误_Python_Pandas

Python 移动平均交叉回溯测试中的错误

python pandas

Python 移动平均交叉回溯测试中的错误,python,pandas,Python,Pandas,我试图用熊猫对移动平均线交叉策略进行回溯测试首先，我定义了一个类（书），其中包括股票数量、现金金额和资产总额该类中有3个函数，用于在生成买入或卖出信号时计算账簿状态这是我的代码，但当我测试时，我可能会发现股票和总资产的计算错误有什么问题吗 import pandas as pd from pandas_datareader import data as pdr # download dataframe test = pdr.get_data_yahoo("SPY", start="20

我试图用熊猫对移动平均线交叉策略进行回溯测试

首先，我定义了一个类（书），其中包括股票数量、现金金额和资产总额

该类中有3个函数，用于在生成买入或卖出信号时计算账簿状态

这是我的代码，但当我测试时，我可能会发现股票和总资产的计算错误

有什么问题吗

import pandas as pd
from pandas_datareader import data as pdr

# download dataframe
test = pdr.get_data_yahoo("SPY", start="2000-01-01")

class Book:
    def __init__(self, stocks = 0, money = 100, asset = 0):
        self.stocks = stocks
        self.money = money
        self.asset = asset

    def buy(self, price):
        if self.money == 0:
            return
        self.stocks += self.money/price
        self.money -= (price * self.stocks)
        self.asset = self.stocks * price + self.money

    def sell(self, price):
        if self.stocks == 0:
            return
        self.money += (price * self.stocks)
        self.stocks = 0
        self.asset = self.stocks * price + self.money

    def assetEvaluate(self,price):
        self.asset = self.stocks * price + self.money

test['ma20'] = test.Close.rolling(20).mean()

def macrossover(df, book):
    result = []
    for i, r in df.iterrows():
        if df.Close[i] > df.ma20[i]:
            book.buy(df.Close[i])
        elif df.Close[i] < df.ma20[i]:
            book.sell(df.Close[i])
        else:                        
            book.assetEvaluate(df.Close[i])
        result.append([i,df.Close[i], book.stocks, book.money, book.asset])

    df = pd.DataFrame(result, columns=['date','close','stocks','money','asset'])
    df.set_index('date', inplace=True)
    print(df)

a = Book()
macrossover(test,a)

我已按如下方式更新了您的代码，现在似乎有所改进，我已在

df

中添加了其他信息，用于调试，您可以根据需要删除这些信息：

import pandas as pd
from pandas_datareader import data as pdr

# download dataframe
test = pdr.get_data_yahoo("SPY", start="2000-01-01")

class Book:
    def __init__(self, stocks = 0, money = 100.0, asset = 0.0):
        self.stocks = stocks
        self.money = money
        self.asset = asset

    def buy(self, price):
        if self.money == 0:
            self.asset = price * self.stocks + self.money
            return ''

        stocks = self.money / price
        self.stocks += stocks
        self.money -= (price * stocks)
        self.asset = price * self.stocks + self.money
        return 'buy'

    def sell(self, price):
        if self.stocks == 0:
            return ''

        self.money += price * self.stocks
        self.stocks = 0
        self.asset = self.money
        return 'sell'


    def assetEvaluate(self,price):
        self.asset = self.stocks * price + self.money
        return ''


test['ma20'] = test.Close.rolling(20).mean()

def macrossover(df, book):
    result = []
    trade = ''
    for i, r in df.iterrows():
        if df.Close[i] > df.ma20[i]:
            trade = book.buy(df.Close[i])
        elif df.Close[i] < df.ma20[i]:
            trade = book.sell(df.Close[i])
        else:
            trade = book.assetEvaluate(df.Close[i])
        result.append([i, df.Close[i], df.ma20[i], book.stocks, book.money, book.asset, trade])

    df = pd.DataFrame(result, columns=['date', 'close', 'ma20', 'stocks', 'money', 'asset', 'trade'])
    df.set_index('date', inplace=True)
    print(df)

a = Book()
macrossover(test, a)

我注意到了两件事，首先是在一些计算过程中，

money

变为负值，这是因为Python中的浮点计算限制，请参阅以了解更多详细信息。如果您选择仅将

股票

作为整数，则可以在代码中解析负数

货币

，即使用运算符

。如果您希望将

股票

作为整数，请更新我的上述代码，并将

购买

功能替换为以下内容：

def buy(self, price):
    stocks = self.money // price
    if self.money == 0 | stocks == 0:
        self.asset = price * self.stocks + self.money
        return ''
    self.stocks += stocks
    self.money -= (price * stocks)
    self.asset = price * self.stocks + self.money
    return 'buy'

第二件事是，在

buy

功能中，当我们购买

stocks

时，

money

只需要为新的

stocks

减少，而不是为所有可用的股票减少

此外，对于使用乘法和除法的所有浮点数，可以将其舍入到小数点后两位。

我已更新了您的代码，如下所示，现在它似乎有所改进，我已在

df

中添加了其他信息，以便调试，您可以根据需要删除这些信息：

import pandas as pd
from pandas_datareader import data as pdr

# download dataframe
test = pdr.get_data_yahoo("SPY", start="2000-01-01")

class Book:
    def __init__(self, stocks = 0, money = 100.0, asset = 0.0):
        self.stocks = stocks
        self.money = money
        self.asset = asset

    def buy(self, price):
        if self.money == 0:
            self.asset = price * self.stocks + self.money
            return ''

        stocks = self.money / price
        self.stocks += stocks
        self.money -= (price * stocks)
        self.asset = price * self.stocks + self.money
        return 'buy'

    def sell(self, price):
        if self.stocks == 0:
            return ''

        self.money += price * self.stocks
        self.stocks = 0
        self.asset = self.money
        return 'sell'


    def assetEvaluate(self,price):
        self.asset = self.stocks * price + self.money
        return ''


test['ma20'] = test.Close.rolling(20).mean()

def macrossover(df, book):
    result = []
    trade = ''
    for i, r in df.iterrows():
        if df.Close[i] > df.ma20[i]:
            trade = book.buy(df.Close[i])
        elif df.Close[i] < df.ma20[i]:
            trade = book.sell(df.Close[i])
        else:
            trade = book.assetEvaluate(df.Close[i])
        result.append([i, df.Close[i], df.ma20[i], book.stocks, book.money, book.asset, trade])

    df = pd.DataFrame(result, columns=['date', 'close', 'ma20', 'stocks', 'money', 'asset', 'trade'])
    df.set_index('date', inplace=True)
    print(df)

a = Book()
macrossover(test, a)