要使用Python进行量化回测多只股票，你可以按照以下步骤进行操作：

导入所需的库：

import pandas as pd

import numpy as np

import baostock as bs

import tushare as ts

import talib

import matplotlib.pyplot as plt

设置股票池：

stock_list = ['000001.SZ', '000002.SZ', '600000.SH'] # 设置股票代码列表

获取股票数据：

def get_stock_data(stock_code, start_date, end_date):

# 使用baostock获取股票数据

lg = bs.login()

rs = bs.query_history_k_data_plus(stock_code,

"date,code,open,high,low,close,volume",

start_date=start_date, end_date=end_date,

frequency="d", adjustflag="3")

data_list = []

while rs.next():

data_list.append(rs.get_row_data())

bs.logout()

# 将数据转换为DataFrame格式

df = pd.DataFrame(data_list, columns=rs.fields)

df['date'] = pd.to_datetime(df['date'])

df.set_index('date', inplace=True)

df.sort_index(ascending=True, inplace=True)

df['close'] = df['close'].astype(float)

return df

# 获取股票数据

start_date = '2021-01-01'

end_date = '2021-12-31'

stock_data = {}

for stock_code in stock_list:

stock_data[stock_code] = get_stock_data(stock_code, start_date, end_date)

定义策略函数：

def strategy(df):

# 在这里编写你的策略逻辑

# 返回一个包含买入和卖出信号的DataFrame，如：

# df['buy_signal'] = ...

# df['sell_signal'] = ...

return df

回测策略：

def backtest(stock_data):

portfolio_value = 1000000 # 设置初始投资金额

positions = {}

cash = portfolio_value

portfolio_values = []

for stock_code, df in stock_data.items():

df = strategy(df) # 对每只股票应用策略

df['position'] = 0 # 初始化仓位列

# 根据买入信号和卖出信号进行交易

for i in range(len(df)):

if df['buy_signal'].iloc[i] == 1:

# 买入股票

shares = cash / df['close'].iloc[i]

positions[stock_code] = shares

cash -= shares * df['close'].iloc[i]

df['position'].iloc[i] = shares

elif df['sell_signal'].iloc[i] == 1:

# 卖出股票

shares = positions[stock_code]

cash += shares * df['close'].iloc[i]

positions.pop(stock_code)

df['position'].iloc[i] = -shares

# 计算投资组合价值

df['portfolio_value'] = df['position'] * df['close']

df['cash'] = cash

df['total_value'] = df['portfolio_value'] + df['cash']

portfolio_values.append(df['total_value'])

# 合并所有股票的投资组合价值

portfolio_values = pd.concat(portfolio_values, axis=1)

portfolio_values.columns = stock_list

portfolio_values['total_value'] = portfolio_values.sum(axis=1)

return portfolio_values

# 进行回测

portfolio_values = backtest(stock_data)

绘制回测结果：

# 绘制投资组合价值曲线

plt.plot(portfolio_values['total_value'])

plt.xlabel('Date')

plt.ylabel('Portfolio Value')

plt.title('Backtest Result')

plt.show()

以上是一个简单的示例，你可以根据自己的需求对策略函数进行修改和优化，增加其他指标、止损止盈条件等。同时，你也可以使用其他量化回测框架，如vnpy、jqdata等。

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