python分仓管理策略选股的代码

Python中分仓管理策略选股通常涉及到数据处理、机器学习或统计分析，以及股票市场分析库如pandas, yfinance等。这里是一个简单的示例，展示如何使用这些工具进行基础的策略设计，但请注意，实际应用会更复杂，并且需要结合实时数据和风险管理：

import pandas as pd
import yfinance as yf
from sklearn.linear_model import LinearRegression

# 下载股票历史数据
def download_data(tickers, start_date, end_date):
    data = yf.download(tickers, start=start_date, end=end_date)
    return data

# 计算技术指标（例如简单移动平均线）
def calculate_indicators(data, n_day):
    data['SMA'] = data['Close'].rolling(n_day).mean()
    return data

# 策略示例：基于过去股价走势预测未来涨跌并分配资金
def stock_allocation(data, n_day, weight):
    model = LinearRegression()  # 使用简单线性回归作为示例
    X = data['SMA'][n_day:].values.reshape(-1, 1)
    Y = data['Close'][n_day:].pct_change().shift(-1).values
    model.fit(X, Y)

    predictions = model.predict(X)
    ranked_predictions = sorted(zip(predictions, tickers), reverse=True)

    total_weight = sum(weight)
    allocated_weights = [weight * prediction for prediction in ranked_predictions]

    return allocated_weights

# 示例用法
tickers = ['AAPL', 'GOOGL']
start_date = '2020-01-01'
end_date = '2022-12-31'
n_day = 30  # 短期均线天数
weight = [0.4, 0.6]  # 指定每只股票的权重比例

data = download_data(tickers, start_date, end_date)
indicators = calculate_indicators(data, n_day)
allocations = stock_allocation(indicators, n_day, weight)

print("分配策略:")
for allocation, ticker in zip(allocations, tickers):
    print(f"{ticker}: {allocation[0]*100}%")