{"id":1379,"date":"2024-09-01T07:48:00","date_gmt":"2024-08-31T23:48:00","guid":{"rendered":"https:\/\/blog.laoyulaoyu.top\/?p=1379"},"modified":"2024-08-28T17:09:12","modified_gmt":"2024-08-28T09:09:12","slug":"%e7%94%a8-python-%e5%92%8c%e5%9d%87%e5%80%bc%e5%9b%9e%e5%bd%92%e7%ad%96%e7%95%a5%ef%bc%8c%e8%bd%bb%e6%9d%be%e8%b7%91%e8%b5%a2%e6%a0%87%e5%87%86%e6%99%ae%e5%b0%94-500-%e6%8c%87%e6%95%b0%ef%bc%81","status":"publish","type":"post","link":"https:\/\/laoyulaoyu.com\/index.php\/2024\/09\/01\/%e7%94%a8-python-%e5%92%8c%e5%9d%87%e5%80%bc%e5%9b%9e%e5%bd%92%e7%ad%96%e7%95%a5%ef%bc%8c%e8%bd%bb%e6%9d%be%e8%b7%91%e8%b5%a2%e6%a0%87%e5%87%86%e6%99%ae%e5%b0%94-500-%e6%8c%87%e6%95%b0%ef%bc%81\/","title":{"rendered":"\u7528 Python \u548c\u5747\u503c\u56de\u5f52\u7b56\u7565\uff0c\u8f7b\u677e\u8dd1\u8d62\u6807\u51c6\u666e\u5c14 500 \u6307\u6570\uff01"},"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

\"\"<\/figure>\n\n\n\n
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\u4e0d\u53ef\u5426\u8ba4\uff0c\u6218\u80dc\u5e02\u573a\u662f\u4e00\u4ef6\u975e\u5e38\u56f0\u96be\u7684\u4e8b\u60c5\u3002\u8f83\u4e3a\u7b80\u5355\u7684\u6295\u8d44\u65b9\u5f0f\u6216\u8bb8\u662f\u8d2d\u5165\u5e76\u6301\u6709\u6807\u51c6\u666e\u5c14 500 \u6307\u6570\u3002\u8d2d\u4e70\u57fa\u91d1\u8d39\u7528\u6781\u4e3a\u4f4e\u5ec9\uff0c\u80a1\u606f\u7a33\u5b9a\u4e14\u6301\u7eed\u9012\u589e\uff0c\u5e76\u4e14\u4f60\u7684\u6295\u8d44\u7ec4\u5408\u6709\u4e16\u754c\u9886\u5148\u7ecf\u6d4e\u4f53\u7684\u5b9e\u529b\u4f5c\u4e3a\u575a\u5b9e\u540e\u76fe\uff0c\u6545\u800c\u80fd\u5e26\u6765\u5b89\u5168\u611f\u3002<\/p>\n\n\n\n
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\"\"<\/figure>\n\n\n\n
<\/p>\n\n\n\n
\u4e00\u3001\u4ec0\u4e48\u662f\u5747\u503c\u56de\u5f52\uff1f<\/strong><\/h3>\n\n\n\n
\u5747\u503c\u56de\u5f52\u7406\u8bba\u7684\u6838\u5fc3\u662f\uff1a\u968f\u7740\u65f6\u95f4\u7684\u63a8\u79fb\uff0c\u4ef7\u683c\u548c\u6536\u76ca\u5f80\u5f80\u4f1a\u56de\u5f52\u5747\u503c\u6216\u5e73\u5747\u503c\u3002\u4e0e\u5e03\u6797\u7a81\u7834\u7c7b\u4f3c\uff0c\u5747\u503c\u56de\u5f52\u4e5f\u80fd\u8ba9\u4eba\u6d1e\u5bdf\u5e02\u573a\u5468\u671f\uff0c\u4ee5\u53ca\u4eba\u7c7b\u548c\u7b97\u6cd5\u4ea4\u6613\u8005\u5728\u52aa\u529b\u786e\u5b9a\u8d44\u4ea7\u771f\u6b63\u4ef7\u503c\u65f6\u7684\u5fc3\u7406\u3002\u7531\u4e8e\u5e02\u573a\u5e73\u8861\u5f88\u5c11\u80fd\u957f\u671f\u4fdd\u6301\uff0c\u8fd9\u4e9b\u5f62\u5f0f\u7684\u6280\u672f\u5206\u6790\u53ef\u4f5c\u4e3a\u4ece\u5386\u53f2\u6a21\u5f0f\u4e2d\u5b66\u4e60\u7684\u5de5\u5177\uff0c\u5e76\u6709\u53ef\u80fd\u5728\u672a\u6765\u83b7\u5f97\u6536\u76ca\u3002<\/p>\n\n\n\n
\u6709\u591a\u79cd\u6307\u6807\u53ef\u4ee5\u8bc6\u522b\u6f5c\u5728\u7684\u5747\u7ebf\u53cd\u8f6c\uff0c\u5e03\u6797\u7ebf\u5c31\u662f\u5176\u4e2d\u4e4b\u4e00\u3002\u5728\u672c\u7b56\u7565\u4e2d\uff0c\u6211\u4eec\u5c06\u4f7f\u7528\u5e03\u6797\u7ebf\u7684\u4e00\u79cd\u53d8\u4f53\u3002\u6b64\u5916\uff0c\u6211\u4eec\u8fd8\u5c06\u5173\u6ce8\u5185\u90e8\u6760\u6760\u5f3a\u5ea6\uff08IBS\uff09\u3002IBS \u901a\u5e38\u7528\u4e8e\u77ed\u671f\u4ea4\u6613\u51b3\u7b56\uff0c\u5b83\u80fd\u5e2e\u52a9\u6211\u4eec\u6d1e\u5bdf\u5f53\u65e5\u6536\u76d8\u4ef7\u662f\u770b\u6da8\u8fd8\u662f\u770b\u8dcc\u3002\u8fd9\u662f\u4e00\u4e2a\u7b80\u5355\u6709\u6548\u7684\u8ba1\u7b97\u6307\u6807\uff1a<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u5982\u679c\u4ef7\u683c\u6536\u76d8\u4ef7\u63a5\u8fd1\u65e5\u6700\u4f4e\u70b9\u800c\u975e\u65e5\u6700\u9ad8\u70b9\uff0c\u5219\u5176 IBS \u503c\u5c06\u5c0f\u4e8e 0.5\uff0c\u503c\u4e3a 0 \u8868\u793a\u4ef7\u683c\u6536\u76d8\u4e8e\u5f53\u65e5\u6700\u4f4e\u70b9\u3002\u5728\u4eca\u5929\u7684\u5206\u6790\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u5c06\u4efb\u4f55\u4f4e\u4e8e 0.6 \u7684 IBS \u503c\u89c6\u4e3a\u6f5c\u5728\u770b\u8dcc\u60c5\u7eea\u7684\u4fe1\u53f7\u3002\u5728\u6807\u51c6\u666e\u5c14 500 \u6307\u6570\u7b49\u7a33\u5b9a\u8d44\u4ea7\u7684\u5747\u503c\u56de\u5f52\u6846\u67b6\u5185\uff0c\u8fd9\u8868\u660e\u4ef7\u683c\u53ef\u80fd\u5f88\u5feb\u4f1a\u518d\u6b21\u4e0a\u6da8\u3002<\/p>\n\n\n\n
\u6211\u4eec\u7684\u5e03\u6797\u5e26\u53d8\u4f53\u5c06\u6574\u5408\u8fd1\u671f\u7684\u5386\u53f2\u6570\u636e\uff0c\u800c\u4e0d\u662f\u4ec5\u4ec5\u96c6\u4e2d\u5728\u67d0\u4e00\u5929\uff0c\u4ee5\u786e\u5b9a\u4f55\u65f6\u53ef\u80fd\u51fa\u73b0\u5747\u503c\u53cd\u8f6c\u3002\u8be5\u6ce2\u6bb5\u5c06\u91c7\u7528 25 \u5929\u79fb\u52a8\u5e73\u5747\u9ad8\u4ef7\u53ca\u5176\u4e0e\u79fb\u52a8\u5e73\u5747\u9ad8\u4ef7\u548c\u79fb\u52a8\u5e73\u5747\u4f4e\u4ef7\u7f29\u653e\u8303\u56f4\u7684\u504f\u5dee\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u5176\u4e2d<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
<\/p>\n\n\n\n
\u4e8c\u3001\u5747\u503c\u53cd\u8f6c\u4ea4\u6613<\/strong><\/h3>\n\n\n\n
\u60a8\u53ef\u4ee5\u8ba1\u7b97\u6307\u6807\uff1a<\/p>\n\n\n\n
data['IBS'] = (data['Close'] - data['Low']) \/ (data['High'] - data['Low'])\ndata['HL_avg'] = data['High'].rolling(window=25).mean() - data['Low'].rolling(window=25).mean()\ndata['Band'] = data['High'].rolling(window=25).mean() - (data['HL_avg'] * 2.25)<\/code><\/pre>\n\n\n\n
2.1 \u8981\u5b9e\u73b0\u4ee5\u4e0b\u7b97\u6cd5\uff0c\u4f60\u9700\u8981<\/strong><\/h4>\n\n\n\n
    \n
  1. \u83b7\u53d6\u6bcf\u4e2a\u80a1\u7968\u6700\u8fd1\u51e0\u5e74\u7684\u6bcf\u65e5\u8721\u70db\u56fe\u4ef7\u683c\uff0c\u786e\u4fdd\u4fdd\u7559\u6700\u9ad8\u4ef7\u3001\u6700\u4f4e\u4ef7\u548c\u6536\u76d8\u4ef7\u3002<\/li>\n\n\n\n
  2. \u4e3a\u7b80\u5355\u8d77\u89c1\uff0c\u5047\u8bbe\u76d8\u540e\u4ef7\u683c\u4e0e\u6536\u76d8\u4ef7\u683c\u76f8\u540c\uff08\u4f46\u5728\u5b9e\u9645\u4ea4\u6613\u4e2d\uff0c\u6700\u597d\u5728\u6536\u76d8\u524d\u4e70\u5165\uff09\u3002<\/li>\n\n\n\n
  3. \u5728\u6536\u76d8\u4ef7\u9ad8\u4e8e\u524d\u4e00\u5929\u6700\u9ad8\u4ef7\u7684\u4efb\u4f55\u4e00\u5929\u6a21\u62df\u5356\u51fa\uff0c\u5e76\u8ba1\u5165\u8d39\u7528\u3002<\/li>\n\n\n\n
  4. \u6d4b\u8bd5\u4e0d\u540c\u7684\u65f6\u95f4\u6846\u67b6\uff0c\u4ee5\u786e\u5b9a\u8be5\u7b56\u7565\u7684\u957f\u671f\u8868\u73b0\u662f\u5426\u4f18\u4e8e\u8d44\u4ea7\u4ef7\u683c\u3002\u4e00\u65e6\u786e\u5b9a\u4e86\u7a33\u5b9a\u6536\u76ca\uff0c\u60a8\u5c31\u53ef\u4ee5\u6839\u636e\u5b9e\u65f6\u53cd\u8f6c\u4fe1\u53f7\u8fdb\u884c\u4ea4\u6613\u4e86\u3002<\/li>\n<\/ol>\n\n\n\n
    2.2 \u6211\u7684\u57fa\u7840\u65b9\u6cd5\uff08\u4ee3\u7801\uff09<\/strong><\/h4>\n\n\n\n
    import yfinance as yf\nimport pandas as pd\n\n# Backtesting parameters (adjust as needed)\ntickers = ['^GSPC']\nperiods = ['5y', '10y', '25y']\ninit_portfolio_value = 1000 # USD\nfor period in periods:\n    for ticker in tickers:\n        try:\n            data = yf.download(ticker, period=period, progress=False)\n            # Calculate indicators\n            data['HL_avg'] = data['High'].rolling(window=25).mean() - data['Low'].rolling(window=25).mean()\n            data['IBS'] = (data['Close'] - data['Low']) \/ (data['High'] - data['Low'])\n            data['Band'] = data['High'].rolling(window=25).mean() - (data['HL_avg'] * 2.25)\n        except:\n            continue\n        # Initialize variables for trading strategy\n        portfolio_value, portfolio_values = init_portfolio_value, [init_portfolio_value]\n        trades = {'Entry': [], 'Exit': []}\n        fees = 0\n        position = False\n        entry_price = 0\n        # Simulate buying and selling using mentioned strategy\n        for i in range(25, len(data)):\n            if not position and data['Close'][i] < data['Band'][i] and data['IBS'][i] < 0.6: # Buy\n                position = True\n                entry_price = data['Close'][i]\n                shares = portfolio_values[-1] \/ entry_price\n                trades['Entry'].append(data.index[i])\n            elif position and data['Close'][i] > data['High'][i - 1]: # Sell\n                position = False\n                exit_price = data['Close'][i]\n                sale_value = shares * exit_price\n                fee = max(.01, round((sale_value \/ 1000) * .01, 2))\n                fees += fee\n                trade_profit = shares * (exit_price - entry_price) - fee\n                portfolio_value += trade_profit\n                trades['Exit'].append(data.index[i])\n            portfolio_values.append(portfolio_value)\n        mean_reversal_return = portfolio_values[-1] \/ init_portfolio_value\n        buy_and_hold_return = data['Close'].iloc[-1] \/ data['Close'].iloc[0]\n        print(f'{ticker} made {mean_reversal_return*100:.1f}% return vs. {buy_and_hold_return*100:.1f}% in {period} time')<\/code><\/pre>\n\n\n\n
    2.3 \u6210\u679c<\/strong><\/h4>\n\n\n\n
    ^GSPC made 157.7% return vs. 180.6% in 5y time\n^GSPC made 226.8% return vs. 275.3% in 10y time\n^GSPC made 471.3% return vs. 391.7% in 25y time<\/code><\/pre>\n\n\n\n
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    \"\"<\/figure>\n\n\n\n
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