{"id":311,"date":"2024-04-28T16:14:14","date_gmt":"2024-04-28T08:14:14","guid":{"rendered":"https:\/\/blog.alltick.co\/?p=311"},"modified":"2025-04-28T15:45:23","modified_gmt":"2025-04-28T07:45:23","slug":"algo-trading-python-code-examples","status":"publish","type":"post","link":"https:\/\/blog.alltick.co\/zh-CN\/algo-trading-python-code-examples\/","title":{"rendered":"Python\u91cf\u5316\u4ea4\u6613\u7b56\u7565"},"content":{"rendered":"\n

\u968f\u7740\u7b97\u6cd5\u4ea4\u6613\u7684\u5174\u8d77\uff0cPython\u5df2\u7ecf\u6210\u4e3a\u91cf\u5316\u5f00\u53d1\u4ece\u4e1a\u8005\u7684\u5fc5\u5907\u5de5\u5177\u3002\u8fd9\u5f97\u76ca\u4e8ePython\u5728\u79d1\u5b66\u8ba1\u7b97\u548c\u6570\u636e\u5206\u6790\u9886\u57df\u7684\u5f3a\u5927\u751f\u6001\u7cfb\u7edf\uff0c\u4ee5\u53ca\u5176\u4f18\u79c0\u7684\u7b2c\u4e09\u65b9\u5e93\u7684\u652f\u6301\u3002\u5176\u4e2d\uff0c\u50cfPandas\u3001Numpy\u548cScipy\u7b49\u5e93\u4e3a\u91cf\u5316\u4ea4\u6613\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u5904\u7406\u3001\u6570\u503c\u8ba1\u7b97\u548c\u79d1\u5b66\u8ba1\u7b97\u529f\u80fd\uff0c\u4f7f\u5f97\u5f00\u53d1\u8005\u80fd\u591f\u66f4\u9ad8\u6548\u5730\u8fdb\u884c\u91cf\u5316\u5206\u6790\u548c\u7b56\u7565\u5f00\u53d1\u3002\u4eca\u5929\u4e3a\u5927\u5bb6\u4ecb\u7ecd\u4e94\u4e2a\u7ecf\u5178\u7684\u91cf\u5316\u4ea4\u6613\u7b56\u7565\uff0c\u4ee5\u53ca\u5bf9\u5e94\u7684Python\u4ee3\u7801\u793a\u4f8b\u3002<\/p>\n\n\n\n

#1 \u5747\u503c\u56de\u5f52\u7b56\u7565<\/strong><\/h2>\n\n\n\n

\u5747\u503c\u56de\u5f52\u7b56\u7565\u662f\u4e00\u79cd\u7edf\u8ba1\u5957\u5229\u7b56\u7565\uff0c\u5b83\u662f\u57fa\u4e8e\u8fd9\u6837\u4e00\u79cd\u5047\u8bbe\uff0c\u5373\uff1a\u957f\u671f\u6765\u770b\uff0c\u8d44\u4ea7\u4ef7\u683c\u4f1a\u56f4\u7ed5\u4e00\u4e2a\u5e73\u5747\u4ef7\u503c\u4e0a\u4e0b\u6ce2\u52a8\uff0c\u4f46\u65e0\u8bba\u5982\u4f55\u6ce2\u52a8\uff0c\u4ef7\u683c\u6700\u7ec8\u90fd\u4f1a\u56de\u5f52\u5230\u5b83\u7684\u957f\u671f\u5747\u503c\u4e0a\u6765\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u5747\u503c\u56de\u5f52\u7b56\u7565\u7684Python\u4ee3\u7801\u793a\u4f8b\uff0c\u4f7f\u7528\u7b80\u5355\u7684\u79fb\u52a8\u5e73\u5747\u7ebf\u6765\u5b9a\u4e49\u8d44\u4ea7\u4ef7\u683c\u7684\u201c\u5747\u503c\u201d\uff0c\u5e76\u4f7f\u7528\u6807\u51c6\u5dee\u6765\u786e\u5b9a\u4e70\u5165\u548c\u5356\u51fa\u7684\u4fe1\u53f7\u3002\u4e0a\u4ee3\u7801\uff1a<\/p>\n\n\n\n

import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\ndata = pd.DataFrame({\n    'Date': pd.date_range(start='2023-01-01', periods=100),\n    'Close': np.random.normal(100, 10, 100)  # \u751f\u6210\u4e00\u4e9b\u6a21\u62df\u6570\u636e\n})\ndata.set_index('Date', inplace=True)\n\n# \u8ba1\u7b9720\u65e5\u79fb\u52a8\u5747\u7ebf\u548c\u6807\u51c6\u5dee\nwindow = 20\ndata['Moving Average'] = data['Close'].rolling(window=window).mean()\ndata['Standard Deviation'] = data['Close'].rolling(window=window).std()\n\n# \u5b9a\u4e49\u4e70\u5165\u548c\u5356\u51fa\u7684\u4fe1\u53f7\u9608\u503c\ndata['Upper Bound'] = data['Moving Average'] + data['Standard Deviation']\ndata['Lower Bound'] = data['Moving Average'] - data['Standard Deviation']\n\n# \u751f\u6210\u4ea4\u6613\u4fe1\u53f7\n# \u5f53\u4ef7\u683c\u4f4e\u4e8e\u5747\u503c\u65f6\u4e70\u5165\uff0c\u9ad8\u4e8e\u5747\u503c\u65f6\u5356\u51fa\ndata['Position'] = 0\ndata.loc[data['Close'] < data['Lower Bound'], 'Position'] = 1  # \u4e70\u5165\u4fe1\u53f7\ndata.loc[data['Close'] > data['Upper Bound'], 'Position'] = -1  # \u5356\u51fa\u4fe1\u53f7\n\n# \u7ed8\u5236\u4ef7\u683c\u548c\u5747\u503c\u56de\u5f52\u5e26\nplt.figure(figsize=(14, 7))\nplt.plot(data['Close'], label='Close Price')\nplt.plot(data['Moving Average'], label='Moving Average')\nplt.fill_between(data.index, data['Upper Bound'], data['Lower Bound'], color='gray', alpha=0.3, label='Mean Reversion Band')\nplt.plot(data.index, data['Position'] * 50, label='Trading Signal', color='magenta')\nplt.legend()\nplt.show()<\/pre>\n\n\n\n
#2 \u8d8b\u52bf\u8ddf\u8e2a\u7b56\u7565<\/strong><\/h2>\n\n\n\n
\u8d8b\u52bf\u8ddf\u8e2a\u7b56\u7565\u662f\u6307\u5bf9\u6bd4\u8d44\u4ea7\u4ef7\u683c\u7684\u77ed\u671f\u5747\u4ef7\u4e0e\u957f\u671f\u5747\u4ef7\uff0c\u627e\u51fa\u76ee\u524d\u5e02\u573a\u7684\u4e3b\u5bfc\u8d8b\u52bf\uff0c\u5e76\u8ddf\u968f\u8fd9\u4e00\u8d8b\u52bf\uff0c\u76f4\u5230\u8d8b\u52bf\u626d\u8f6c\u3002\u7b80\u5355\u6765\u8bf4\u5c31\u662f\u627e\u51fa\u4e00\u652f\u80a1\u7968\u73b0\u5728\u7684\u5e02\u573a\u4e3b\u6d41\u770b\u6cd5\uff0c\u5982\u679c\u5927\u5bb6\u90fd\u4e70\u5165\uff0c\u6211\u4eec\u4e5f\u987a\u52bf\u800c\u4e3a\uff0c\u4e70\u5165\u8fd9\u652f\u80a1\u7968\u5e76\u6301\u6709\uff0c\u76f4\u5230\u5e02\u573a\u8d8b\u52bf\u5f00\u59cb\u53cd\u8f6c\u3002\u5728Python\u4ee3\u7801\u7684\u5b9e\u73b0\u4e0a\uff0c\u662f\u5229\u7528\u79fb\u52a8\u5e73\u5747\u6536\u655b\/\u53d1\u6563\uff08MACD\uff09\u6765\u5224\u65ad\u77ed\u671f\u7684\u4ef7\u683c\u8d8b\u52bf\uff0c\u5e76\u6839\u636e\u8d8b\u52bf\u6765\u751f\u6210\u5bf9\u5e94\u7684\u4e70\u5165\/\u5356\u51fa\u4fe1\u53f7\u3002<\/p>\n\n\n\n
import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\ndata = pd.DataFrame({\n    'Date': pd.date_range(start='2023-01-01', periods=200),\n    'Close': np.random.normal(100, 15, 200)  # \u751f\u6210\u4e00\u4e9b\u6a21\u62df\u6570\u636e\n})\ndata.set_index('Date', inplace=True)\n\n# \u8ba1\u7b97\u7b80\u5355\u79fb\u52a8\u5e73\u5747\u7ebf\nshort_window = 40\nlong_window = 100\ndata['Short MA'] = data['Close'].rolling(window=short_window).mean()\ndata['Long MA'] = data['Close'].rolling(window=long_window).mean()\n\n# \u751f\u6210\u4ea4\u6613\u4fe1\u53f7\n# \u5f53\u77ed\u671f\u79fb\u52a8\u5e73\u5747\u7ebf\u7a7f\u8d8a\u957f\u671f\u79fb\u52a8\u5e73\u5747\u7ebf\u65f6\u4ea7\u751f\u4fe1\u53f7\ndata['Signal'] = 0\ndata['Signal'][short_window:] = np.where(data['Short MA'][short_window:] > data['Long MA'][short_window:], 1, 0)\ndata['Position'] = data['Signal'].diff()\n\n# \u7ed8\u5236\u4ef7\u683c\u548c\u79fb\u52a8\u5e73\u5747\u7ebf\nplt.figure(figsize=(14, 7))\nplt.plot(data['Close'], label='Close Price')\nplt.plot(data['Short MA'], label='40-Day Moving Average')\nplt.plot(data['Long MA'], label='100-Day Moving Average')\nplt.plot(data.index, data['Position'] * 50, label='Trading Signal', color='magenta', marker='o', linestyle='None')\nplt.legend()\nplt.show()<\/pre>\n\n\n\n
#3 \u914d\u5bf9\u4ea4\u6613\uff08Pair Trading\uff09<\/strong><\/h2>\n\n\n\n
\u914d\u5bf9\u4ea4\u6613\u4e3b\u8981\u57fa\u4e8e\u4e24\u79cd\u4e0d\u540c\u8d44\u4ea7\u4e4b\u95f4\u4ef7\u683c\u5dee\u5f02\u7684\u7edf\u8ba1\u5957\u5229\uff0c\u524d\u63d0\u662f\u8fd9\u4e24\u79cd\u8d44\u4ea7\u5728\u4ef7\u683c\u4e0a\u6709\u975e\u5e38\u5f3a\u7684\u76f8\u5173\u6027\u3002\u5f53\u4e24\u8005\u7684\u4ef7\u683c\u5dee\u5f02\u8d85\u51fa\u6b63\u5e38\u8303\u56f4\u65f6\uff0c\u6211\u4eec\u4e70\u5165\u88ab\u4f4e\u4f30\u7684\u8d44\u4ea7\uff0c\u540c\u65f6\u5356\u51fa\u88ab\u9ad8\u4f30\u7684\u8d44\u4ea7\u3002\u4ece\u957f\u671f\u6765\u770b\uff0c\u8fd9\u4e24\u4e2a\u8d44\u4ea7\u7684\u4ef7\u683c\u90fd\u5e94\u8be5\u56de\u5f52\u5230\u957f\u671f\u5747\u503c\uff0c\u4f46\u77ed\u671f\u5185\u53ef\u80fd\u4f1a\u51fa\u73b0\u5957\u5229\u673a\u4f1a\u3002<\/p>\n\n\n\n
\u6211\u4eec\u53ef\u4ee5\u5206\u6790\u4e24\u4e2a\u8d44\u4ea7\u4e4b\u95f4\u7684\u5386\u53f2\u4ef7\u683c\u5173\u7cfb\uff0c\u5e76\u6839\u636e\u5b83\u4eec\u9884\u671f\u5dee\u4ef7\u7684\u504f\u79bb\u521b\u9020\u4ea4\u6613\u4fe1\u53f7\uff1a<\/p>\n\n\n\n
import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n# \u521b\u5efa\u4e24\u4e2a\u9ad8\u5ea6\u76f8\u5173\u7684\u8d44\u4ea7\u7684\u6a21\u62df\u4ef7\u683c\u6570\u636e\nnp.random.seed(42)\ndata = pd.DataFrame({\n    'Date': pd.date_range(start='2023-01-01', periods=180),\n    'Asset_A': np.random.normal(100, 10, 180).cumsum() + 100,\n    'Asset_B': np.random.normal(100, 10, 180).cumsum() + 120\n})\ndata.set_index('Date', inplace=True)\n\n# \u8ba1\u7b97\u4e24\u4e2a\u8d44\u4ea7\u7684\u4ef7\u683c\u5dee\uff08\u4ef7\u5dee\uff09\ndata['Price_Diff'] = data['Asset_A'] - data['Asset_B']\n\n# \u8ba1\u7b97\u4ef7\u5dee\u7684\u79fb\u52a8\u5e73\u5747\u548c\u6807\u51c6\u5dee\nwindow = 30\ndata['Mean_Diff'] = data['Price_Diff'].rolling(window=window).mean()\ndata['Std_Diff'] = data['Price_Diff'].rolling(window=window).std()\n\n# \u8bbe\u7f6e\u5165\u5e02\u548c\u6e05\u4ed3\u7684\u95e8\u69db\ndata['Upper_Bound'] = data['Mean_Diff'] + data['Std_Diff']\ndata['Lower_Bound'] = data['Mean_Diff'] - data['Std_Diff']\n\n# \u751f\u6210\u4ea4\u6613\u4fe1\u53f7\n# \u4ef7\u5dee\u5927\u4e8e\u4e0a\u754c\u65f6\u505a\u7a7aAsset A\uff0c\u505a\u591aAsset B\n# \u4ef7\u5dee\u5c0f\u4e8e\u4e0b\u754c\u65f6\u505a\u591aAsset A\uff0c\u505a\u7a7aAsset B\ndata['Position'] = 0\ndata.loc[data['Price_Diff'] > data['Upper_Bound'], 'Position'] = -1  # \u505a\u7a7aAsset A\uff0c\u505a\u591aAsset B\ndata.loc[data['Price_Diff'] < data['Lower_Bound'], 'Position'] = 1   # \u505a\u591aAsset A\uff0c\u505a\u7a7aAsset B\n\n# \u7ed8\u5236\u8d44\u4ea7\u4ef7\u683c\u548c\u4ea4\u6613\u4fe1\u53f7\nplt.figure(figsize=(14, 7))\nplt.subplot(211)\nplt.plot(data['Asset_A'], label='Asset A')\nplt.plot(data['Asset_B'], label='Asset B')\nplt.legend()\n\nplt.subplot(212)\nplt.plot(data['Price_Diff'], label='Price Difference')\nplt.plot(data['Mean_Diff'], label='Mean Difference')\nplt.fill_between(data.index, data['Upper_Bound'], data['Lower_Bound'], color='gray', alpha=0.3, label='Trading Zone')\nplt.plot(data.index, data['Position'] * 20, label='Trading Signal', color='magenta', marker='o', linestyle='None')\nplt.legend()\nplt.show()\n<\/pre>\n\n\n\n
#4 \u7edf\u8ba1\u5957\u5229<\/strong><\/h2>\n\n\n\n
\u7edf\u8ba1\u5957\u5229\u662f\u5229\u7528\u591a\u79cd\u8d44\u4ea7\u4e4b\u95f4\u7684\u4ef7\u683c\u5dee\u5f02\u8fdb\u884c\u5957\u5229\u3002\u5176\u4e2d\u4e00\u79cd\u5e38\u89c1\u7684\u65b9\u6cd5\u662f\u901a\u8fc7\u5bfb\u627e\u4ef7\u503c\u504f\u79bb\u6b63\u5e38\u8303\u56f4\u7684\u80a1\u7968\u5bf9\u6216\u8d44\u4ea7\u7ec4\u5408\uff0c\u5e76\u8fdb\u884c\u76f8\u5e94\u7684\u4e70\u5356\u4ee5\u8d5a\u53d6\u5229\u6da6\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528Python\u5b9e\u73b0\u7684\u7b80\u5355\u7edf\u8ba1\u5957\u5229\u7b56\u7565\u793a\u4f8b\uff0c\u8be5\u7b56\u7565\u57fa\u4e8e\u4e24\u4e2a\u80a1\u7968\u95f4\u7684\u4ef7\u5dee\u8fdb\u884c\u5957\u5229\u4ea4\u6613\u3002<\/p>\n\n\n\n
import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nnp.random.seed(42)\ndata = pd.DataFrame({\n    'Date': pd.date_range(start='2023-01-01', periods=250),\n    'Stock_A': np.random.normal(0, 1, 250).cumsum() + 50,\n    'Stock_B': np.random.normal(0, 1, 250).cumsum() + 50\n})\ndata.set_index('Date', inplace=True)\n\n# \u8ba1\u7b97\u4e24\u4e2a\u80a1\u7968\u7684\u4ef7\u5dee\ndata['Spread'] = data['Stock_A'] - data['Stock_B']\n\n# \u8ba1\u7b97\u4ef7\u5dee\u7684\u79fb\u52a8\u5e73\u5747\u548c\u6807\u51c6\u5dee\nwindow = 20\ndata['Spread Mean'] = data['Spread'].rolling(window=window).mean()\ndata['Spread Std'] = data['Spread'].rolling(window=window).std()\n\n# \u8bbe\u7f6e\u4e70\u5165\u548c\u5356\u51fa\u7684\u9608\u503c\nentry_z = 2  # \u4e70\u5165Z\u5206\u6570\nexit_z = 0   # \u5356\u51faZ\u5206\u6570\ndata['Upper Threshold'] = data['Spread Mean'] + entry_z * data['Spread Std']\ndata['Lower Threshold'] = data['Spread Mean'] - entry_z * data['Spread Std']\ndata['Exit Threshold'] = data['Spread Mean'] + exit_z * data['Spread Std']\n\n# \u751f\u6210\u4ea4\u6613\u4fe1\u53f7\ndata['Position'] = 0\ndata.loc[data['Spread'] > data['Upper Threshold'], 'Position'] = -1  # \u505a\u7a7aStock A\uff0c\u505a\u591aStock B\ndata.loc[data['Spread'] < data['Lower Threshold'], 'Position'] = 1   # \u505a\u591aStock A\uff0c\u505a\u7a7aStock B\ndata.loc[data['Spread'] * data['Position'] < data['Exit Threshold'], 'Position'] = 0  # \u9000\u51fa\u4fe1\u53f7\n\n# \u7ed8\u5236\u80a1\u7968\u4ef7\u683c\u548c\u4ea4\u6613\u4fe1\u53f7\nplt.figure(figsize=(14, 7))\nplt.subplot(211)\nplt.plot(data['Stock_A'], label='Stock A')\nplt.plot(data['Stock_B'], label='Stock B')\nplt.title('Stock Prices')\nplt.legend()\n\nplt.subplot(212)\nplt.plot(data['Spread'], label='Spread')\nplt.plot(data['Spread Mean'], label='Mean Spread')\nplt.fill_between(data.index, data['Upper Threshold'], data['Lower Threshold'], color='gray', alpha=0.3, label='Entry Zone')\nplt.plot(data.index, data['Position'] * 10, label='Trading Signal', color='magenta', marker='o', linestyle='None')\nplt.title('Spread and Trading Signals')\nplt.legend()\nplt.show()\n<\/pre>\n\n\n\n
#5 \u6ce2\u52a8\u6027\u4ea4\u6613<\/strong><\/h2>\n\n\n\n
\u6ce2\u52a8\u7b56\u7565\u5229\u7528\u5e02\u573a\u7684\u6ce2\u52a8\u6027\u53d8\u5316\u6765\u8fdb\u884c\u4ea4\u6613\uff0c\u4ece\u800c\u5728\u6ce2\u52a8\u6027\u7684\u5347\u9ad8\u6216\u964d\u4f4e\u4e2d\u83b7\u5229\u3002\u6bd4\u5982\u6211\u4eec\u53ef\u4ee5\u5148\u8ba1\u7b97\u80a1\u7968\u7684\u65e5\u6536\u76ca\u7387\u548c\u5386\u53f2\u6ce2\u52a8\u6027\uff08\u5176\u5b9e\u5c31\u662f\u5e74\u5316\u6807\u51c6\u5dee\uff09\uff0c\u7136\u540e\u8bbe\u7f6e\u4e00\u4e2a\u6761\u4ef6\uff1a\u5f53\u6ce2\u52a8\u6027\u9ad8\u4e8e\u5e73\u5747\u6c34\u5e731.2\u500d\u65f6\u5356\u51fa\uff0c\u4f4e\u4e8e\u5747\u503c0.8\u500d\u65f6\u4e70\u5165\uff0c\u5177\u4f53\u770b\u4e0b\u9762\u7684\u4ee3\u7801\uff1a<\/p>\n\n\n\n
import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nnp.random.seed(42)\ndates = pd.date_range(start='2023-01-01', periods=250)\nprices = np.random.normal(0, 1, 250).cumsum() + 100\ndata = pd.DataFrame({\n    'Date': dates,\n    'Price': prices\n})\ndata.set_index('Date', inplace=True)\n\n# \u8ba1\u7b97\u65e5\u6536\u76ca\u7387\ndata['Returns'] = data['Price'].pct_change()\ndata.dropna(inplace=True)\n\n# \u8ba1\u7b97\u5386\u53f2\u6ce2\u52a8\u6027\uff08\u8fd9\u91cc\u6211\u4eec\u4f7f\u7528\u6807\u51c6\u5dee\u4f5c\u4e3a\u6ce2\u52a8\u6027\u7684\u5ea6\u91cf\uff09\nwindow = 20\ndata['Volatility'] = data['Returns'].rolling(window=window).std() * np.sqrt(252)  # \u5e74\u5316\u6ce2\u52a8\u6027\n\n# \u5b9a\u4e49\u4ea4\u6613\u7b56\u7565\n# \u6ce2\u52a8\u6027\u9ad8\u4e8e\u67d0\u4e2a\u503c\u65f6\u5356\u51fa\uff0c\u4f4e\u4e8e\u67d0\u4e2a\u503c\u65f6\u4e70\u5165\nthreshold_high = data['Volatility'].mean() * 1.2\nthreshold_low = data['Volatility'].mean() * 0.8\ndata['Position'] = 0\ndata.loc[data['Volatility'] > threshold_high, 'Position'] = -1  # \u9ad8\u6ce2\u52a8\u6027\u65f6\u5356\u51fa\ndata.loc[data['Volatility'] < threshold_low, 'Position'] = 1   # \u4f4e\u6ce2\u52a8\u6027\u65f6\u4e70\u5165\n\n# \u7ed8\u5236\u4ef7\u683c\u56fe\u548c\u6ce2\u52a8\u6027\u56fe\nplt.figure(figsize=(14, 10))\nplt.subplot(211)\nplt.plot(data['Price'], label='Price')\nplt.title('Stock Price')\nplt.legend()\n\nplt.subplot(212)\nplt.plot(data['Volatility'], label='Volatility')\nplt.axhline(y=threshold_high, color='r', linestyle='--', label='High Threshold')\nplt.axhline(y=threshold_low, color='g', linestyle='--', label='Low Threshold')\nplt.plot(data.index, data['Position'] * 0.01, label='Trading Signal', color='magenta', marker='o', linestyle='None')\nplt.title('Volatility and Trading Signals')\nplt.legend()\nplt.show()<\/pre>\n\n\n\n
Python\u91cf\u5316\u4ea4\u6613\u4e66\u7c4d\u63a8\u8350<\/strong><\/h2>\n\n\n\n
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