{"id":1544,"date":"2024-10-26T07:07:00","date_gmt":"2024-10-25T23:07:00","guid":{"rendered":"https:\/\/blog.laoyulaoyu.top\/?p=1544"},"modified":"2024-09-30T11:07:55","modified_gmt":"2024-09-30T03:07:55","slug":"%e6%89%8b%e6%8a%8a%e6%89%8b%e5%b8%a6%e4%bd%a0%e7%94%a8-python-%e7%94%bb%e5%87%ba%e5%8f%af%e8%a7%86%e5%8c%96%e4%ba%a4%e6%98%93%e4%bf%a1%e5%8f%b7","status":"publish","type":"post","link":"https:\/\/laoyulaoyu.com\/index.php\/2024\/10\/26\/%e6%89%8b%e6%8a%8a%e6%89%8b%e5%b8%a6%e4%bd%a0%e7%94%a8-python-%e7%94%bb%e5%87%ba%e5%8f%af%e8%a7%86%e5%8c%96%e4%ba%a4%e6%98%93%e4%bf%a1%e5%8f%b7\/","title":{"rendered":"\u624b\u628a\u624b\u5e26\u4f60\u7528 Python \u753b\u51fa\u53ef\u89c6\u5316\u4ea4\u6613\u4fe1\u53f7"},"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
\n
\u5199\u5728\u524d\u9762\u7684\u8bdd\uff1a<\/strong>\u672c\u6587\u4ecb\u7ecd\u4e86\u5982\u4f55\u5728 Python \u4e2d\u4f7f\u7528 Jupyter \u7b14\u8bb0\u672c\u548c EODHD API \u6765\u53ef\u89c6\u5316\u4ea4\u6613\u4fe1\u53f7<\/mark>\uff0c\u5e76\u901a\u8fc7\u5b9e\u4f8b\u6f14\u793a\u4e86\u5982\u4f55\u7ed8\u5236\u7b80\u5355\u79fb\u52a8\u5e73\u5747\u7ebf (SMA)\u3001MACD \u548c RSI \u6307\u6807\uff0c\u4ee5\u53ca\u5982\u4f55\u7ed3\u5408\u8fd9\u4e9b\u6307\u6807\u6765\u8bc6\u522b\u4e70\u5356\u4ea4\u6613\u4fe1\u53f7\u3002<\/pre>\n<\/blockquote>\n\n\n\n
\u672c\u6587\u7684\u5f00\u59cb\uff0c\u6211\u4eec\u9700\u8981\u5047\u8bbe\u60a8\u719f\u6089\u4ea4\u6613\u6280\u672f\u5206\u6790\u7684\u4e00\u4e9b\u57fa\u7840\u77e5\u8bc6\u3002\u5982\u679c\u4e0d\u719f\u6089\uff0c\u6211\u4eec\u5efa\u8bae\u60a8\u9605\u8bfb\u6211\u7684\u6587\u7ae0\u300a2025 \u5df2\u8fd1\uff0c\u4f60\u60f3\u8fdb\u5165\u91cf\u5316\u91d1\u878d\u884c\u4e1a\u5417\uff1f\u770b\u8fd9\u4e00\u7bc7\u5c31\u591f\uff01<\/a>\u300b\uff0c\u4efb\u4f55\u65f6\u5019\u5f00\u59cb\u5b66\u4e60\u90fd\u4e0d\u665a\u3002<\/p>\n\n\n\n
\u5982\u679c\u60a8\u60f3\u4e86\u89e3\u5982\u4f55\u5b89\u88c5 EODHD APIs Python Financial Official Library \u5e76\u6fc0\u6d3b\u60a8\u7684 API \u5bc6\u94a5\uff0c\u8bf7\u4ece\u6d4f\u89c8\u6211\u7684\u8fd9\u7bc7\u6587\u6863\u5f00\u59cb\u300a\u624b\u628a\u624b\u6559\u4f1a\u4f60\u7528 AI \u548c Python \u8fdb\u884c\u80a1\u7968\u4ea4\u6613\u9884\u6d4b\uff08\u5b8c\u6574\u4ee3\u7801\u5e72\u8d27\uff09<\/a>\u300b\u3002<\/p>\n\n\n\n
\u4e00\u3001\u5efa\u7acb\u7528\u4f8b<\/strong><\/h2>\n\n\n\n
\u6211\u4eec\u7684\u76ee\u6807\u662f\u786e\u5b9a\u4e70\u5165\u6216\u5356\u51fa\u80a1\u7968\u6216\u52a0\u5bc6\u8d27\u5e01\u7684\u6700\u4f73\u65f6\u673a<\/strong>\u3002\u6211\u4eec\u8981\u5982\u4f55\u505a\u5230\u8fd9\u4e00\u70b9\u5462\uff1f\u6709\u4e00\u79cd\u65b9\u6cd5\u662f\u901a\u8fc7\u79fb\u52a8\u5e73\u5747\u7ebf\u6765\u8fa8\u522b\u5e02\u573a\u65b9\u5411\u3002<\/p>\n\n\n\n
\u7b80\u5355\u79fb\u52a8\u5e73\u5747\u7ebf (SMA) \u662f\u8fc7\u53bb ‘n’ \u671f\u5386\u53f2\u4ea4\u6613\u6570\u636e\u6536\u76d8\u4ef7\u7684\u5e73\u5747\u503c\u3002\u4f8b\u5982\uff0c\u5728\u65e5\u7ebf\u56fe\u4e2d\uff0c\u6bcf\u4e2a\u8721\u70db\u56fe\u6216\u6570\u636e\u70b9\u4ee3\u8868\u4e00\u5929\u3002SMA50 \u662f\u8fc7\u53bb 50 \u5929\u6536\u76d8\u4ef7\u7684\u5e73\u5747\u503c\uff0c\u6709\u52a9\u4e8e\u6d88\u9664\u5e02\u573a “\u566a\u97f3”\u3002\u4e0d\u8fc7\uff0c\u5f53\u6211\u4eec\u5c06\u5176\u4e0e\u53e6\u4e00\u6761\u79fb\u52a8\u5e73\u5747\u7ebf\uff08\u5982 SMA200\uff09\u8fdb\u884c\u6bd4\u8f83\u65f6\uff0c\u5b83\u5c31\u4f1a\u53d8\u5f97\u66f4\u52a0\u6709\u7528\u3002<\/p>\n\n\n\n
\u5f53 SMA50 \u5728 SMA200 \u4e0a\u65b9\u4ea4\u53c9\u65f6\uff0c\u9884\u793a\u7740\u5e02\u573a\u8d8b\u52bf\u5411\u4e0a\u3002\u76f8\u53cd\uff0c\u5f53 SMA50 \u7a7f\u8fc7 SMA200 \u4e0b\u65b9\u65f6\uff0c\u5219\u9884\u793a\u7740\u5e02\u573a\u8d8b\u52bf\u5411\u4e0b\u3002\u867d\u7136\u53ef\u4ee5\u4f7f\u7528\u4efb\u4f55\u79fb\u52a8\u5e73\u5747\u7ebf\uff0c\u4f46\u8fd9\u79cd\u7279\u6b8a\u7684\u7ec4\u5408\u662f\u72ec\u4e00\u65e0\u4e8c\u7684\u3002\u5f53 SMA50 \u5728 SMA200 \u4e0a\u65b9\u4ea4\u53c9\u65f6\uff0c\u5b83\u88ab\u79f0\u4e3a “\u9ec4\u91d1\u4ea4\u53c9”\uff0c\u800c\u5f53\u5b83\u8dcc\u7834 SMA200 \u65f6\uff0c\u5b83\u88ab\u79f0\u4e3a “\u6b7b\u4ea1\u4ea4\u53c9”\u3002\u8fd9\u4e9b\u4e8b\u4ef6\u5f80\u5f80\u53d1\u751f\u5728\u91cd\u5927\u4ef7\u683c\u53d8\u52a8\u4e4b\u524d\u3002<\/p>\n\n\n\n
\u4e8c\u3001\u51c6\u5907\u5de5\u4f5c<\/strong><\/h2>\n\n\n\n
\u6211\u4eec\u4f7f\u7528\u7684\u662f Google Colab<\/a> \u4e2d\u7684 Jupyter \u7b14\u8bb0\u672c\uff0c\u8fd9\u662f\u4e00\u4e2a\u514d\u8d39\u4e14\u65b9\u4fbf\u7684\u5de5\u5177\uff0c\u5982\u679c\u60a8\u60f3\u8ddf\u8fdb\u7684\u8bdd\u3002\u51fa\u4e8e\u6f14\u793a\u76ee\u7684\uff0c\u6211\u4eec\u8fd8\u4f7f\u7528\u4e86  EODHD API<\/a> \u7684 end-of-day demo key\u5bc6\u94a5\u3002<\/p>\n\n\n\n
\u6b65\u9aa4 1\uff1a\u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/strong><\/h3>\n\n\n\n
\u9996\u5148\uff0c\u5bfc\u5165\u4ee5\u4e0b\u5e93\uff1a<\/p>\n\n\n\n
import json\nimport requests\nimport pandas as pd\nimport matplotlib.pyplot as plt<\/code><\/code><\/pre>\n\n\n\n
\u7b2c 2 \u6b65\uff1a\u5904\u7406\u6570\u636e<\/strong><\/h3>\n\n\n\n
\u63a5\u4e0b\u6765\uff0c\u4f7f\u7528 API \u83b7\u53d6\u82f9\u679c\u516c\u53f8 (AAPL) \u7684\u6bcf\u65e5\u6570\u636e\uff0c\u5e76\u5c06\u5176\u5b58\u50a8\u5230 Pandas DataFrame \u4e2d\uff1a<\/p>\n\n\n\n
resp = requests.get(\"https:\/\/eodhistoricaldata.com\/api\/eod\/AAPL?api_token=demo&fmt=json\")\njson_data = json.loads(resp.content)\ndf = pd.DataFrame(json_data)\ndf<\/code><\/code><\/pre>\n\n\n\n
\u5982\u679c\u4e00\u5207\u6b63\u5e38\uff0c\u751f\u6210\u7684 DataFrame \u5e94\u5305\u542b\u82f9\u679c\u516c\u53f8\u7684\u6bcf\u65e5\u80a1\u7968\u6570\u636e\u3002\u8fd9\u4e9b\u6570\u636e\u5c06\u6210\u4e3a\u5206\u6790\u548c\u53ef\u89c6\u5316\u4ea4\u6613\u4fe1\u53f7\u7684\u57fa\u7840\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u82f9\u679c\u516c\u53f8\u6709 10,534 \u5929\u7684\u5386\u53f2\u6570\u636e\u53ef\u7528\uff0c\u503c\u5f97\u6ce8\u610f\u7684\u662f\uff0c\u867d\u7136 “close “\u548c “adjusted_close “\u503c\u975e\u5e38\u76f8\u4f3c\uff0c\u4f46\u672c\u6559\u7a0b\u4e2d\u6211\u4eec\u5c06\u4f7f\u7528 “close”\u3002<\/p>\n\n\n\n
\u73b0\u5728\uff0c\u8ba9\u6211\u4eec\u8ba1\u7b97 SMA50 \u548c SMA200 \u5217\u5e76\u5c06\u5176\u6dfb\u52a0\u5230 DataFrame \u4e2d\uff1a<\/p>\n\n\n\n
df[\"sma50\"] = df.close.rolling(50, min_periods=50).mean()\ndf[\"sma200\"] = df.close.rolling(200, min_periods=200).mean()\ndf<\/code><\/code><\/pre>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u5728\u66f4\u65b0\u540e\u7684 DataFrame \u4e2d\uff0c\u60a8\u4f1a\u6ce8\u610f\u5230\u6807\u6709 “sma50 “\u548c “sma200 “\u7684\u4e24\u4e2a\u65b0\u5217\u3002\u4e4b\u6240\u4ee5\u4f1a\u51fa\u73b0 “NaN “\u503c\uff0c\u662f\u56e0\u4e3a\u6bcf\u4e2a SMA \u90fd\u9700\u8981\u6700\u5c11\u7684\u6570\u636e\u70b9\u6570\u624d\u80fd\u8ba1\u7b97\u51fa\u7b2c\u4e00\u4e2a\u503c\u3002\u4f8b\u5982\uff0cSMA50 \u9700\u8981 50 \u4e2a\u6536\u76d8\u4ef7\u624d\u80fd\u751f\u6210\u521d\u59cb\u5e73\u5747\u503c\u3002<\/p>\n\n\n\n
\u6211\u4eec\u6709\u4e24\u79cd\u65b9\u6cd5\u6765\u5904\u7406\u8fd9\u4e9b “NaN “\u503c\uff1a\u7528\u5176\u4ed6\u503c\uff08\u5982\u6536\u76d8\u4ef7\uff09\u66ff\u6362\uff0c\u6216\u8005\u76f4\u63a5\u5220\u9664\u6570\u636e\u7f3a\u5931\u7684\u884c\u3002\u7531\u4e8e\u6211\u4eec\u6709\u5927\u91cf\u53ef\u7528\u6570\u636e\uff0c\u5220\u9664\u524d 200 \u884c\u4e0d\u4f1a\u5f71\u54cd\u6211\u4eec\u7684\u5206\u6790\u3002<\/p>\n\n\n\n
\u5220\u9664\u5305\u542b “NaN “\u7684\u884c\uff1a<\/p>\n\n\n\n
df.dropna(inplace=True)\ndf<\/code><\/code><\/pre>\n\n\n\n
\u5220\u9664\u7f3a\u5931\u503c\u7684\u884c\u540e\uff0c\u6211\u4eec\u7684 DataFrame \u5c06\u53ea\u5305\u542b SMA50 \u548c SMA200 \u503c\u5df2\u586b\u5145\u5b8c\u6574\u7684\u884c\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n

\u4e09\u3001\u521b\u5efa\u53ef\u89c6\u5316\u56fe<\/strong><\/h2>\n\n\n\n
\u4e0b\u4e00\u6b65\uff0c\u6211\u4eec\u5c06\u4f7f\u7528 Matplotlib \u5e93\uff0c\u5b83\u5728\u6570\u636e\u79d1\u5b66\u4e2d\u88ab\u5e7f\u6cdb\u7528\u4e8e\u6570\u636e\u53ef\u89c6\u5316\uff08\u4f60\u53ef\u80fd\u8fd8\u8bb0\u5f97\uff0c\u6211\u4eec\u5728\u524d\u9762\u5bfc\u5165\u8fc7\u5b83\uff09\u3002<\/p>\n\n\n\n
\u4e0b\u9762\u4ecb\u7ecd\u5982\u4f55\u7ed8\u5236\u4ea4\u6613\u4fe1\u53f7\uff1a<\/p>\n\n\n\n
plt.figure(figsize=(30,10))\nplt.plot(df[\"close\"], color=\"black\", label=\"Price\")\nplt.plot(df[\"sma50\"], color=\"blue\", label=\"SMA50\")\nplt.plot(df[\"sma200\"], color=\"green\", label=\"SMA200\")\nplt.ylabel(\"Price\")\nplt.xticks(rotation=90)\nplt.title(\"APPL Daily SMA50\/SMA200\")\nplt.legend()\nplt.show()<\/code><\/code><\/pre>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u867d\u7136\u56fe\u8868\u663e\u793a\u6210\u529f\uff0c\u4f46\u5374\u4e0d\u5bb9\u6613\u89e3\u8bfb\uff0c\u56e0\u4e3a\u5b83\u53ef\u89c6\u5316\u4e86\u8d85\u8fc7 28 \u5e74\u7684\u6570\u636e\uff0c\u8d85\u51fa\u4e86\u6211\u4eec\u7684\u9700\u8981\u3002\u4e3a\u4e86\u8ba9\u56fe\u8868\u66f4\u6e05\u6670\uff0c\u6211\u4eec\u53ef\u4ee5\u5c06\u6570\u636e\u96c6\u9650\u5236\u5728\u8fc7\u53bb 365 \u5929\u5185\uff0c\u53ea\u663e\u793a\u8fc7\u53bb\u4e00\u5e74\u7684\u6570\u636e\uff1a<\/p>\n\n\n\n
df = df.tail(365)<\/code><\/code><\/pre>\n\n\n\n
\u8fd9\u5c06\u4ee5\u66f4\u805a\u7126\u7684\u89c6\u89d2\u663e\u793a\u56fe\u8868\u7684\u5916\u89c2\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u60a8\u53ef\u80fd\u4f1a\u6ce8\u610f\u5230\uff0cX \u8f74\u76ee\u524d\u663e\u793a\u7684\u662f\u6307\u6570\u6570\u5b57\uff0c\u800c\u4e0d\u662f\u5b9e\u9645\u65e5\u671f\u3002\u867d\u7136\u8fd9\u5e76\u4e0d\u4e00\u5b9a\u6709\u95ee\u9898\uff0c\u4f46\u5728\u89c6\u89c9\u4e0a\u5e76\u4e0d\u7279\u522b\u5438\u5f15\u4eba\u3002\u4e3a\u4e86\u6539\u5584\u8fd9\u79cd\u60c5\u51b5\uff0c\u6211\u4eec\u5c06\u7528\u5b9e\u9645\u65e5\u671f\u53d6\u4ee3 x \u8f74\u4e0a\u7684\u523b\u5ea6\u3002<\/p>\n\n\n\n
df.set_index(['date'], inplace=True)\ndf<\/code><\/code><\/pre>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u73b0\u5728\uff0c\u66f4\u65b0\u540e\u7684\u56fe\u8868\u663e\u793a\u5982\u4e0b\uff1a<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u76ee\u524d\uff0cX \u8f74\u663e\u793a\u6240\u6709 365 \u5929\uff0c\u5bfc\u81f4\u56fe\u8868\u6742\u4e71\u65e0\u7ae0\uff0c\u96be\u4ee5\u9605\u8bfb\u3002\u4e0d\u8fc7\u4e0d\u7528\u62c5\u5fc3\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574 x \u8f74\u6807\u7b7e\u6765\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff0c\u5373\u53ea\u663e\u793a\u7b2c\u4e03\u5929\u3002<\/p>\n\n\n\n
\u4ee3\u7801\u5de5\u4f5c\u539f\u7406\u5982\u4e0b\uff1a<\/p>\n\n\n\n
ax = plt.gca()\nfor index, label in enumerate(ax.xaxis.get_ticklabels()):\n    if index % 7 != 0:\n        label.set_visible(False)<\/code><\/code><\/pre>\n\n\n\n
\u5728\u8fd9\u6bb5\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f1a\u68c0\u67e5\u4e00\u4e2a\u6307\u6570\u662f\u5426\u80fd\u88ab 7 \u6574\u9664\uff0c\u5982\u679c\u4e0d\u80fd\uff0c\u76f8\u5e94\u7684 x \u8f74\u6807\u7b7e\u5c31\u4f1a\u88ab\u9690\u85cf\uff0c\u53ea\u6709\u6bcf\u9694 7 \u5929\u624d\u4f1a\u663e\u793a\u51fa\u6765\u3002\u8fd9\u4f7f\u5f97\u56fe\u8868\u66f4\u52a0\u6e05\u6670\u6613\u61c2\u3002<\/p>\n\n\n\n
\u4e0b\u9762\u662f\u5b8c\u6574\u7684\u4ee3\u7801\uff1a<\/p>\n\n\n\n
plt.figure(figsize=(30,10))\nplt.plot(df[\"close\"], color=\"black\", label=\"Price\")\nplt.plot(df[\"sma50\"], color=\"blue\", label=\"SMA50\")\nplt.plot(df[\"sma200\"], color=\"green\", label=\"SMA200\")\nplt.ylabel(\"Price\")\nplt.xticks(rotation=90)\nplt.title(\"APPL Daily SMA50\/SMA200\")\nax = plt.gca()\nfor index, label in enumerate(ax.xaxis.get_ticklabels()):\n  if index % 7 != 0:\n    label.set_visible(False)\nplt.legend()\nplt.show()<\/code><\/code><\/pre>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u5728\u56fe\u8868\u4e2d\uff0c\u60a8\u4f1a\u770b\u5230\u4e00\u6761\u9ed1\u7ebf\u4ee3\u8868\u6bcf\u65e5\u6536\u76d8\u4ef7\uff0c\u4e00\u6761\u84dd\u7ebf\u4ee3\u8868 SMA50\uff0c\u4e00\u6761\u7eff\u7ebf\u4ee3\u8868 SMA200\u3002<\/p>\n\n\n\n
\u89c2\u5bdf\u56fe\u8868\uff0c\u5728\u8fc7\u53bb\u4e00\u5e74\u4e2d\uff0c\u84dd\u8272 SMA50 \u7ebf\u9996\u6b21\u8d8a\u8fc7\u7eff\u8272 SMA200 \u7ebf\u65f6\uff0c\u5e02\u573a\u5c31\u4f1a\u8fdb\u5165\u4e0a\u5347\u8d8b\u52bf\u3002\u8fd9\u672c\u6765\u662f\u4e00\u4e2a\u6781\u597d\u7684\u4e70\u5165\u65f6\u673a\uff0c\u4e5f\u662f\u4e00\u4e2a\u6709\u5229\u53ef\u56fe\u7684\u65f6\u673a\u3002\u968f\u540e\uff0c\u84dd\u7ebf\u8dcc\u7834\u7eff\u7ebf\uff0c\u4ef7\u683c\u968f\u4e4b\u5927\u5e45\u4e0b\u8dcc\u3002\u7406\u60f3\u60c5\u51b5\u4e0b\uff0c\u6211\u4eec\u4e0d\u5e0c\u671b\u5728\u5356\u51fa\u524d\u7b49\u5f85\u592a\u957f\u65f6\u95f4\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u4e00\u4e9b\u65b9\u6cd5\u6765\u5b8c\u5584\u8fd9\u4e00\u70b9\u3002<\/p>\n\n\n\n
\u56db\u3001\u786e\u5b9a\u5747\u7ebf\u4ea4\u53c9\u70b9<\/strong><\/h2>\n\n\n\n
\u5728 Pandas \u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u6839\u636e\u7279\u5b9a\u6761\u4ef6\u521b\u5efa\u5305\u542b\u5e03\u5c14\u503c\uff08\u771f\u6216\u5047\uff09\u7684\u65b0\u7279\u5f81\u6216\u5217\u3002<\/p>\n\n\n\n
pd.options.mode.chained_assignment = None\ndf.loc[df[\"sma50\"] > df[\"sma200\"], \"sma50gtsma200\"] = True\ndf[\"sma50gtsma200\"].fillna(False, inplace=True)\ndf.loc[df[\"sma50\"] < df[\"sma200\"], \"sma50ltsma200\"] = True\ndf[\"sma50ltsma200\"].fillna(False, inplace=True)\ndf<\/code><\/code><\/pre>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u5728\u6211\u4eec\u7684 DataFrame \u4e2d\uff0c\u60a8\u4f1a\u6ce8\u610f\u5230\u4e24\u4e2a\u65b0\u5217\uff1a”SMA50GTSMA200 “\u548c “SMA50LTSMA200″\u3002\u5f53 SMA50 \u5927\u4e8e SMA200 \u65f6\uff0c\u7b2c\u4e00\u5217\u8fd4\u56de “\u771f”\uff1b\u5f53 SMA50 \u5c0f\u4e8e SMA200 \u65f6\uff0c\u7b2c\u4e8c\u5217\u8fd4\u56de “\u771f”\u3002\u5982\u679c\u4e24\u4e2a\u6761\u4ef6\u90fd\u4e0d\u6ee1\u8db3\uff0c\u5219\u503c\u4e3a “\u5047”\u3002\u6211\u4eec\u8981\u786e\u5b9a\u53d1\u751f\u4ea4\u53c9\u7684\u7cbe\u786e\u70b9\u3002\u60a8\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u65b9\u6cd5\u6765\u5b9e\u73b0\u8fd9\u4e00\u76ee\u6807\uff1a<\/p>\n\n\n\n
df[\"sma50gtsma200co\"] = df.sma50gtsma200.ne(df.sma50gtsma200.shift())\ndf.loc[df[\"sma50gtsma200\"] == False, \"sma50gtsma200co\"] = False\n\ndf[\"sma50ltsma200co\"] = df.sma50ltsma200.ne(df.sma50ltsma200.shift())\ndf.loc[df[\"sma50ltsma200\"] == False, \"sma50ltsma200co\"] = False\n\ndf<\/code><\/code><\/pre>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u65b0\u7684\u529f\u80fd\u5217 “SMA50GTSMA200CO “\u548c “SMA50LTSMA200CO “\u5c06\u663e\u793a\u4f55\u65f6\u51fa\u73b0\u4ea4\u53c9\u3002\u8ba9\u6211\u4eec\u786e\u8ba4\u4e70\u5165\u4fe1\u53f7\uff1a<\/p>\n\n\n\n
buysignals = df[df[\"sma50gtsma200co\"] == True]\nbuysignals<\/code><\/code><\/pre>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u522b\u5fd8\u8bb0\u8fd8\u6709\u5356\u51fa\u4fe1\u53f7\uff1a<\/p>\n\n\n\n
sellsignals = df[df[\"sma50ltsma200co\"] == True]\nsellsignals<\/code><\/code><\/pre>\n\n\n\n
\"\"<\/figure>\n\n\n\n
<\/p>\n\n\n\n
\u4e94\u3001\u7ed8\u5236\u4e70\u5356\u4ea4\u6613\u4fe1\u53f7\u56fe<\/strong><\/h2>\n\n\n\n
\u4e0b\u4e00\u6b65\u6211\u4eec\u9700\u8981\u7528 Python \u5728\u56fe\u8868\u4e0a\u7ed8\u5236\u6211\u4eec\u7684\u4e70\u5165\u548c\u5356\u51fa\u4ea4\u6613\u4fe1\u53f7\u3002\u4e0b\u9762\u662f\u5b9e\u73b0\u8fd9\u4e00\u76ee\u6807\u7684\u65b9\u6cd5\uff1a<\/p>\n\n\n\n
for idx in buysignals.index.tolist():\n  plt.plot(\n      idx,\n      df.loc[idx][\"close\"],\n      \"g*\",\n      markersize=25\n  )\n\nfor idx in sellsignals.index.tolist():\n  plt.plot(\n      idx,\n      df.loc[idx][\"close\"],\n      \"r*\",\n      markersize=25\n  )<\/code><\/code><\/pre>\n\n\n\n
\u8fd9\u6bb5\u4ee3\u7801\u5c06\u4e3a “buysignals “\u6570\u636e\u5e27\u4e2d\u7684\u6761\u76ee\u6dfb\u52a0\u7eff\u661f\uff0c\u4e3a “sellsignals “\u6570\u636e\u5e27\u4e2d\u7684\u6761\u76ee\u6dfb\u52a0\u7ea2\u661f\u3002\u5b83\u5c06\u76f4\u89c2\u663e\u793a\u6570\u636e\u5e27\u4e2d\u7684\u6240\u6709\u4e70\u5165\u548c\u5356\u51fa\u4fe1\u53f7\uff0c\u4ee5\u4fbf\u7ed3\u5408\u4fe1\u53f7\u5f97\u51fa\u66f4\u9ad8\u7ea7\u7684\u7ed3\u679c\u3002\u5b8c\u6574\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n\n\n\n
plt.figure(figsize=(30,10))\nplt.plot(df[\"close\"], color=\"black\", label=\"Price\")\nplt.plot(df[\"sma50\"], color=\"blue\", label=\"SMA50\")\nplt.plot(df[\"sma200\"], color=\"green\", label=\"SMA200\")\nplt.ylabel(\"Price\")\nplt.xticks(rotation=90)\nplt.title(\"APPL Daily SMA50\/SMA200\")\nfor idx in buysignals.index.tolist():\n  plt.plot(\n      idx,\n      df.loc[idx][\"close\"],\n      \"g*\",\n      markersize=25\n  )\n\nfor idx in sellsignals.index.tolist():\n  plt.plot(\n      idx,\n      df.loc[idx][\"close\"],\n      \"r*\",\n      markersize=25\n  )\n\nax = plt.gca()\nfor index, label in enumerate(ax.xaxis.get_ticklabels()):\n  if index % 7 != 0:\n    label.set_visible(False)\nplt.legend()\nplt.show()<\/code><\/code><\/pre>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u7528 Python \u7ed8\u5236\u4e70\u5165\u548c\u5356\u51fa\u4ea4\u6613\u4fe1\u53f7\u7684\u597d\u5904\u662f\uff0c\u5b83\u80fd\u6e05\u6670\u76f4\u89c2\u5730\u663e\u793a\u5e02\u573a\u8d70\u52bf\uff0c\u4ece\u800c\u66f4\u5bb9\u6613\u5b8c\u5584\u4ea4\u6613\u7b56\u7565\u3002\u4f8b\u5982\uff0c\u867d\u7136\u6700\u521d\u7684\u4e70\u5165\u4fe1\u53f7\u53ef\u80fd\u770b\u8d77\u6765\u5f88\u6709\u5e0c\u671b\uff0c\u4f46\u7531\u4e8e\u79fb\u52a8\u5e73\u5747\u7ebf\u56fa\u6709\u7684\u6ede\u540e\u6027\uff0c\u968f\u540e\u7684\u5356\u51fa\u4fe1\u53f7\u4f1a\u88ab\u5ef6\u8fdf\u3002<\/p>\n\n\n\n
\u90a3\u8ba9\u6211\u4eec\u7ed3\u5408\u79fb\u52a8\u5e73\u5747\u6536\u655b\u80cc\u79bb (MACD) \u6307\u6807\uff0c\u770b\u770b\u5b83\u80fd\u5426\u6539\u5584\u6211\u4eec\u7684\u4fe1\u53f7\u3002<\/p>\n\n\n\n
df[\"ema12\"] = df[\"close\"].ewm(span=12, adjust=False).mean()\ndf[\"ema26\"] = df[\"close\"].ewm(span=26, adjust=False).mean()\n\ndf[\"macd\"] = df[\"ema12\"] - df[\"ema26\"]        \ndf[\"signal\"] = df[\"macd\"].ewm(span=9, adjust=False).mean()\n\ndf.loc[df[\"macd\"] > df[\"signal\"], \"macdgtsignal\"] = True\ndf[\"macdgtsignal\"].fillna(False, inplace=True)\n\ndf.loc[df[\"macd\"] < df[\"signal\"], \"macdltsignal\"] = True\ndf[\"macdltsignal\"].fillna(False, inplace=True)\n\ndf<\/code><\/code><\/pre>\n\n\n\n
\"\"<\/figure>\n\n\n\n
MACD \u7684\u8ba1\u7b97\u65b9\u6cd5\u662f\u4ece 12 \u671f EMA \u51cf\u53bb 26 \u671f\u6307\u6570\u79fb\u52a8\u5e73\u5747\u7ebf (EMA)\u3002\u4fe1\u53f7\u662f\u53e6\u4e00\u4e2a\u4f7f\u7528\u6700\u8fd1 9 \u4e2a MACD \u503c\u7684\u6307\u6570\u79fb\u52a8\u5e73\u5747\u7ebf\u3002\u5f53 MACD \u5728\u4fe1\u53f7\u4e0a\u65b9\u4ea4\u53c9\u65f6\uff0c\u5373\u4e3a\u4e70\u5165\u4fe1\u53f7\uff0c\u53cd\u4e4b\u5219\u4e3a\u5356\u51fa\u4fe1\u53f7\u3002\u6211\u4eec\u901a\u8fc7\u5728\u6570\u636e\u5e27\u4e2d\u6dfb\u52a0\u6355\u6349\u7cbe\u786e\u4ea4\u53c9\u70b9\u7684\u7279\u5f81\u5217\u6765\u8bc6\u522b\u8fd9\u4e9b\u4fe1\u53f7\u3002<\/p>\n\n\n\n
\u5c06 SMA50\/200 \u548c MACD\/\u4fe1\u53f7\u6307\u6807\u7ec4\u5408\u6210\u7efc\u5408\u4e70\u5356\u4ea4\u6613\u4fe1\u53f7\uff1a<\/p>\n\n\n\n
buysignals = df[(df[\"sma50gtsma200co\"] == 1) & (df[\"macdgtsignal\"] == 1)]\n\nsellsignals = df[(df[\"sma50ltsma200co\"] == 1) & (df[\"macdltsignal\"] == 1)]<\/code><\/code><\/pre>\n\n\n\n
\u6839\u636e\u8fd9\u4e00\u903b\u8f91\uff0c\u5f53 SMA50 \u7a7f\u8fc7 SMA200 \u4e14 MACD \u5927\u4e8e\u4fe1\u53f7\u65f6\uff0c\u5c31\u4f1a\u89e6\u53d1\u4e70\u5165\u4fe1\u53f7\u3002\u53cd\u4e4b\uff0c\u5f53 SMA50 \u7a7f\u8fc7 SMA200 \u4e0b\u65b9\u4e14 MACD \u5c0f\u4e8e\u4fe1\u53f7\u65f6\uff0c\u5356\u51fa\u4fe1\u53f7\u5c31\u4f1a\u89e6\u53d1\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u4f60\u4f1a\u53d1\u73b0\uff0c\u7ec4\u5408\u7b56\u7565\u4ecd\u80fd\u51c6\u786e\u8bc6\u522b\u6211\u4eec\u7684\u4e70\u5165\u4fe1\u53f7\uff0c\u4f46\u4e24\u4e2a\u5356\u51fa\u4fe1\u53f7\u5df2\u88ab\u5220\u9664\u3002\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u5b83\u53ef\u80fd\u4f1a\u5efa\u8bae\u6301\u6709\u5934\u5bf8\u800c\u4e0d\u662f\u5356\u51fa\u3002\u4e0d\u8fc7\uff0c\u5728\u8fd9\u79cd\u7279\u5b9a\u60c5\u51b5\u4e0b\uff0c\u8fd8\u4e0d\u80fd\u786e\u5b9a\u8fd9\u662f\u5426\u662f\u6b63\u786e\u7684\u65b9\u6cd5\u3002\u53ef\u80fd\u9700\u8981\u8fdb\u4e00\u6b65\u5206\u6790\u6765\u5b8c\u5584\u4fe1\u53f7\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u5982\u679c\u6211\u4eec\u4fee\u6539\u5356\u51fa\u4fe1\u53f7\uff0c\u4ee5\u8868\u660e\u5373\u4f7f SMA50 \u4ecd\u9ad8\u4e8e SMA200\uff0c\u5f53 MACD \u7a7f\u8fc7\u4fe1\u53f7\u4e0b\u65b9\u65f6\u4e5f\u5e94\u5356\u51fa\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u8fd9\u6837\u8c03\u6574\u6211\u4eec\u7684\u6761\u4ef6\uff1a<\/p>\n\n\n\n
df[\"macdgtsignalco\"] = df.macdgtsignal.ne(df.macdgtsignal.shift())\ndf.loc[df[\"macdgtsignal\"] == False, \"macdgtsignalco\"] = False\n\ndf[\"macdltsignalco\"] = df.macdltsignal.ne(df.macdltsignal.shift())\ndf.loc[df[\"macdltsignal\"] == False, \"macdltsignalco\"] = False\n\nbuysignals = df[(df[\"sma50gtsma200co\"] == 1) & (df[\"macdgtsignal\"] == 1)]\n\nsellsignals = df[(df[\"sma50gtsma200\"] == 1) & (df[\"macdltsignalco\"] == 1)]<\/code><\/code><\/pre>\n\n\n\n
\u6839\u636e\u8fd9\u4e00\u8c03\u6574\u6761\u4ef6\uff0c\u6211\u4eec\u7684 DataFrame \u4e2d\u73b0\u5728\u6709\u4e94\u4e2a\u5356\u51fa\u4fe1\u53f7\uff0c\u8fd9\u6709\u52a9\u4e8e\u6211\u4eec\u901a\u8fc7\u8003\u8651\u79fb\u52a8\u5e73\u5747\u7ebf\u548c MACD \u4ea4\u53c9\u6765\u5b8c\u5584\u4ea4\u6613\u7b56\u7565\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u8fd9\u4e2a\u8bbe\u7f6e\u73b0\u5728\u770b\u8d77\u6765\u597d\u591a\u4e86\u3002\u4e70\u5165\u4fe1\u53f7\u4f3c\u4e4e\u662f\u6b63\u786e\u7684\uff0c\u6211\u4eec\u6709\u51e0\u4e2a\u5356\u51fa\u4fe1\u53f7\u53ef\u4f9b\u9009\u62e9\u3002\u5728\u4e70\u5165\u540e\uff0c\u5229\u7528\u6760\u6746\u5728\u6700\u521d\u7684\u5356\u51fa\u4fe1\u53f7\u4e0a\u5356\u51fa\uff0c\u5f88\u53ef\u80fd\u4f1a\u83b7\u5f97\u53ef\u89c2\u7684\u5229\u6da6\u3002<\/p>\n\n\n\n
\u53ef\u80fd\u4f1a\u6709\u8bfb\u8005\u8981\u95ee\uff0c\u4e3a\u4ec0\u4e48\u4e0d\u5c1d\u8bd5\u6dfb\u52a0\u76f8\u5bf9\u5f3a\u5f31\u6307\u6570 (RSI)\uff1f<\/strong>\u5f53 RSI14 \u4f4e\u4e8e 30\uff0c\u901a\u5e38\u8868\u793a\u4e70\u5165\u4fe1\u53f7\uff1b\u5982\u679c RSI14 \u9ad8\u4e8e 70\uff0c\u5219\u8868\u793a\u5356\u51fa\u4fe1\u53f7\u3002<\/p>\n\n\n\n
\u8fd9\u7bc7\u6587\u7ae0\u7684\u76ee\u7684\u662f\u4e3a\u4e86\u5e2e\u52a9\u60a8\u5165\u95e8\uff01\u8bf7\u8bb0\u4f4f RSI \u7684\u8ba1\u7b97\u8981\u6bd4\u79fb\u52a8\u5e73\u5747\u7ebf\u6216 MACD \u590d\u6742\u4e00\u4e9b\u3002\u4f7f\u7528 `pandas_ta` \u5e93\u53ef\u4ee5\u7b80\u5316\u8fd9\u4e00\u8fc7\u7a0b\u3002\u6211\u5c31\u987a\u4fbf\u7ed9\u5927\u5bb6\u4ecb\u7ecd\u4e0b\u3002<\/p>\n\n\n\n
\u5728 Google Colab \u4e2d\uff0c\u4f7f\u7528\u5355\u72ec\u7684\u4ee3\u7801\u5355\u5143\u5b89\u88c5 `pandas_ta` \u5e93\uff1a<\/p>\n\n\n\n
pip install pandas_ta<\/code><\/code><\/pre>\n\n\n\n
\u7136\u540e\uff0c\u5bfc\u5165\u7a0b\u5e8f\u5e93\uff1a<\/p>\n\n\n\n
import pandas_ta as ta<\/pre>\n\n\n\n
\u6700\u540e\uff0c\u8ba1\u7b97 RSI14\uff1a<\/p>\n\n\n\n
df[\"rsi14\"] = ta.rsi(df[\"close\"], length=14, fillna=50)<\/code><\/code><\/pre>\n\n\n\n
\u8bf7\u8bb0\u4f4f\uff0cRSI14 \u7684\u524d 14 \u4e2a\u6761\u76ee\u5c06\u5305\u542b “NaN “\u503c\u3002\u901a\u8fc7\u4f7f\u7528 `fillna`\uff0c\u6211\u4eec\u5c06\u9ed8\u8ba4\u503c\u8bbe\u7f6e\u4e3a 50\u3002<\/p>\n\n\n\n
<\/p>\n\n\n\n
\u516d\u3001\u89c2\u70b9\u56de\u987e<\/strong><\/h2>\n\n\n\n
\u901a\u8fc7\u8fd9\u4e2a\u4f8b\u5b50\uff0c\u6211\u4eec\u53ef\u4ee5\u5f88\u597d\u5730\u4e86\u89e3\u5404\u79cd\u53ef\u80fd\u6027\u4ee5\u53ca\u5b9e\u73b0\u8fd9\u4e9b\u53ef\u80fd\u6027\u6240\u9700\u7684\u5de5\u5177\u3002\u5c1d\u8bd5\u4f7f\u7528\u5404\u79cd\u6280\u672f\u6307\u6807\u548c\u8721\u70db\u56fe\u5f62\u6001\uff0c\u521b\u9020\u5c5e\u4e8e\u81ea\u5df1\u7684 “\u795e\u5947\u7b56\u7565”\u3002\u603b\u7ed3\u8981\u70b9\u5982\u4e0b\uff1a<\/p>\n\n\n\n
    \n
  1. \u6280\u672f\u5206\u6790\u662f\u4ea4\u6613\u51b3\u7b56\u7684\u91cd\u8981\u5de5\u5177\uff0cSMA\u3001MACD \u548c RSI \u662f\u5e38\u7528\u7684\u6280\u672f\u6307\u6807\u3002<\/li>\n\n\n\n
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  5. \u6301\u7eed\u5b66\u4e60\u548c\u5b9e\u8df5\u662f\u63d0\u9ad8\u4ea4\u6613\u6280\u80fd\u548c\u5e02\u573a\u7406\u89e3\u7684\u5173\u952e\u3002<\/li>\n<\/ol>\n\n\n\n
    <\/p>\n\n\n\n
    \u611f\u8c22\u60a8\u9605\u8bfb\u5230\u6700\u540e\u3002\u5982\u679c\u5bf9\u6587\u4e2d\u7684\u5185\u5bb9\u6709\u4efb\u4f55\u7591\u95ee\uff0c\u8bf7\u7ed9\u6211\u7559\u8a00\uff0c\u5fc5\u590d\u3002<\/strong><\/p>\n\n\n\n
    \n\n\n\n
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    <\/p>\n\n\n\n
    \u672c<\/mark><\/strong>\u6587\u5185\u5bb9\u4ec5\u4ec5\u662f\u6280\u672f\u63a2\u8ba8\u548c\u5b66\u4e60\uff0c\u5e76\u4e0d\u6784\u6210\u4efb\u4f55\u6295\u8d44\u5efa\u8bae\u3002<\/mark><\/strong><\/p>\n\n\n\n
    \u8f6c\u53d1\u8bf7\u6ce8\u660e\u539f\u4f5c\u8005\u548c\u51fa\u5904\u3002<\/mark><\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"
    \u4f5c\u8005\uff1a\u8001\u4f59\u635e\u9c7c \u539f\u521b\u4e0d\u6613\uff0c\u8f6c\u8f7d\u8bf7\u6807\u660e\u51fa\u5904\u53ca\u539f\u4f5c\u8005\u3002…<\/p>\n
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