{"id":1536,"date":"2024-10-22T07:59:00","date_gmt":"2024-10-21T23:59:00","guid":{"rendered":"https:\/\/blog.laoyulaoyu.top\/?p=1536"},"modified":"2024-09-27T16:00:15","modified_gmt":"2024-09-27T08:00:15","slug":"%e6%88%91%e4%bb%a5%e7%be%8e%e8%82%a1-50-%e5%bc%ba%e5%85%ac%e5%8f%b8%e6%95%b0%e6%8d%ae%e5%ae%9e%e6%b5%8b%e5%9d%87%e5%80%bc%e5%9b%9e%e5%bd%92%e7%ad%96%e7%95%a5%e7%9a%84%e5%8f%af%e8%a1%8c%e6%80%a7","status":"publish","type":"post","link":"https:\/\/laoyulaoyu.com\/index.php\/2024\/10\/22\/%e6%88%91%e4%bb%a5%e7%be%8e%e8%82%a1-50-%e5%bc%ba%e5%85%ac%e5%8f%b8%e6%95%b0%e6%8d%ae%e5%ae%9e%e6%b5%8b%e5%9d%87%e5%80%bc%e5%9b%9e%e5%bd%92%e7%ad%96%e7%95%a5%e7%9a%84%e5%8f%af%e8%a1%8c%e6%80%a7\/","title":{"rendered":"\u6211\u4ee5\u7f8e\u80a1 50 \u5f3a\u516c\u53f8\u6570\u636e\u5b9e\u6d4b\u5747\u503c\u56de\u5f52\u7b56\u7565\u7684\u53ef\u884c\u6027"},"content":{"rendered":"\n

\u4f5c\u8005\uff1a\u8001\u4f59\u635e\u9c7c<\/a><\/p>\n\n\n\n

\u539f\u521b\u4e0d\u6613\uff0c\u8f6c\u8f7d\u8bf7\u6807\u660e\u51fa\u5904\u53ca\u539f\u4f5c\u8005\u3002<\/mark><\/strong><\/p>\n\n\n\n

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\u5199\u5728\u524d\u9762\u7684\u8bdd\uff1a<\/strong>\u672c\u6587\u662f\u4e00\u7bc7\u5747\u503c\u56de\u5f52\u7b56\u7565\u539f\u7406\u4e0e\u5b9e\u65bd\u8be6\u8ff0\u3002<\/mark>\u8be5\u7b56\u7565\u57fa\u4e8e\u8d44\u4ea7\u4ef7\u683c\u56de\u5f52\u5386\u53f2\u5e73\u5747\u6c34\u5e73\uff0c\u901a\u8fc7\u5b9e\u9645\u64cd\u4f5c\uff0c\u5229\u7528 Python \u548c yfinance \u5e93\u83b7\u53d6\u6807\u666e 500 \u6307\u6570\u4e2d\u6700\u5927 50 \u5bb6\u516c\u53f8\u7684\u5386\u53f2\u80a1\u7968\u6570\u636e\uff0c\u5e76\u4ee5 20 \u5929\u79fb\u52a8\u5e73\u5747\u7ebf\u4e3a\u57fa\u7840\u3002\u5f53\u80a1\u4ef7\u4f4e\u4e8e\u5747\u7ebf\u9608\u503c\uff08\u5982 2%\uff09\u4e3a\u4e70\u5165\u4fe1\u53f7\uff0c\u9ad8\u4e8e\u9608\u503c\u4e3a\u5356\u51fa\u4fe1\u53f7\u3002\u4ee3\u7801\u5b9e\u73b0\u7b56\u7565\u5e76\u7ecf\u56de\u6eaf\u6d4b\u8bd5\u540e\uff0c\u6536\u96c6\u80a1\u7968\u6027\u80fd\u6307\u6807\uff0c\u6574\u7406\u7ed3\u679c\u81f3 DataFrame \uff0c\u5e76\u5206\u6790\u4e86\u8be5\u7b56\u7565\u7684\u5b9e\u9645\u8868\u73b0\u3002<\/pre>\n<\/blockquote>\n\n\n\n
\u4e00\u3001\u4ec0\u4e48\u662f\u5747\u503c\u56de\u5f52\uff1f<\/strong><\/h3>\n\n\n\n
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\u4e8c\u3001\u5b9e\u65bd\u5747\u503c\u56de\u5f52\u6218\u7565<\/strong><\/h3>\n\n\n\n
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2.1 \u4ee3\u7801\u6267\u884c<\/strong><\/p>\n\n\n\n
import yfinance as yf\nimport pandas as pd\nfrom backtesting import Backtest, Strategy\nfrom backtesting.lib import crossover\nfrom backtesting.test import SMA\nimport openpyxl\n\n# List of top 50 S&P 500 companies by market cap (as of a specific date)\ntop_50_sp500 = [\n    'AAPL', 'MSFT', 'GOOGL', 'AMZN', 'META', 'BRK-B', 'TSLA', 'NVDA', 'JPM', 'JNJ',\n    'V', 'UNH', 'HD', 'PG', 'DIS', 'MA', 'PYPL', 'VZ', 'ADBE', 'NFLX',\n    'INTC', 'CMCSA', 'KO', 'PFE', 'PEP', 'T', 'XOM', 'CSCO', 'ABT', 'MRK',\n    'NKE', 'ABBV', 'CRM', 'AVGO', 'MCD', 'QCOM', 'TXN', 'ACN', 'MDT', 'COST',\n    'NEE', 'DHR', 'WMT', 'AMGN', 'HON', 'IBM', 'GE', 'LOW', 'CAT', 'BA'\n]\n\n# Parameters\nstart_date = '2020-01-01'\nend_date = '2023-01-01'\nmoving_average_window = 20\nthreshold = 0.05  # 2% deviation from the mean\ncash = 10000\ncommission = 0.002\n\n# Define the mean reversion strategy\nclass MeanReversion(Strategy):\n    def init(self):\n        self.sma = self.I(SMA, self.data.Close, moving_average_window)\n\n    def next(self):\n        price_sma_diff = (self.data.Close[-1] - self.sma[-1]) \/ self.sma[-1]\n        \n        if price_sma_diff < -threshold:\n            if not self.position:\n                self.buy()\n        elif price_sma_diff > threshold:\n            if not self.position:\n                self.sell()\n        elif abs(price_sma_diff) < threshold:\n            self.position.close()\n\n# Function to run the backtest for a list of tickers\ndef run_backtests(tickers, start_date, end_date, moving_average_window, threshold, cash, commission):\n    all_metrics = []\n\n    for ticker in tickers:\n        try:\n            # Fetch historical data\n            data = yf.download(ticker, start=start_date, end=end_date)\n\n            if data.empty:\n                print(f\"No data for {ticker}. Skipping...\")\n                continue\n\n            # Prepare the data for backtesting\n            data['SMA'] = data['Close'].rolling(window=moving_average_window).mean()\n            data = data.dropna()\n\n            if data.empty:\n                print(f\"Not enough data after rolling mean calculation for {ticker}. Skipping...\")\n                continue\n\n            # Run the backtest\n            bt = Backtest(data, MeanReversion, cash=cash, commission=commission)\n            stats = bt.run()\n\n            # Collect metrics\n            metrics = {\n                \"Stock\": ticker,\n                \"Start\": stats['Start'],\n                \"End\": stats['End'],\n                \"Duration\": stats['Duration'],\n                \"Equity Final [$]\": stats['Equity Final [$]'],\n                \"Equity Peak [$]\": stats['Equity Peak [$]'],\n                \"Return [%]\": stats['Return [%]'],\n                \"Buy & Hold Return [%]\": stats['Buy & Hold Return [%]'],\n                \"Max. Drawdown [%]\": stats['Max. Drawdown [%]'],\n                \"Avg. Drawdown [%]\": stats['Avg. Drawdown [%]'],\n                \"Max. Drawdown Duration\": stats['Max. Drawdown Duration'],\n                \"Trades\": stats['# Trades'],\n                \"Win Rate [%]\": stats['Win Rate [%]'],\n                \"Best Trade [%]\": stats['Best Trade [%]'],\n                \"Worst Trade [%]\": stats['Worst Trade [%]'],\n                \"Avg. Trade [%]\": stats['Avg. Trade [%]'],\n                \"Max. Trade Duration\": stats['Max. Trade Duration'],\n                \"Avg. Trade Duration\": stats['Avg. Trade Duration'],\n                \"Profit Factor\": stats['Profit Factor'],\n                \"Expectancy [%]\": stats['Expectancy [%]'],\n                \"Sharpe Ratio\": stats['Sharpe Ratio'],\n                \"Sortino Ratio\": stats['Sortino Ratio'],\n            }\n\n            all_metrics.append(metrics)\n        except Exception as e:\n            print(f\"Error processing {ticker}: {e}\")\n            continue\n\n    # Convert to DataFrame\n    metrics_df = pd.DataFrame(all_metrics)\n\n    # Save to Excel\n    metrics_df.to_excel(\"top_50_sp500_mean_reversion_metrics.xlsx\", index=False)\n\n    return metrics_df\n\n# Run the backtests\nmetrics_df = run_backtests(top_50_sp500, start_date, end_date, moving_average_window, threshold, cash, commission)\n\n# Print the results\nprint(metrics_df)<\/code><\/code><\/pre>\n\n\n\n
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2.2 \u76f8\u5173\u5e93\u8bf4\u660e<\/strong><\/p>\n\n\n\n
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