{"id":2095,"date":"2025-04-15T07:15:00","date_gmt":"2025-04-14T23:15:00","guid":{"rendered":"https:\/\/blog.laoyulaoyu.top\/?p=2095"},"modified":"2025-03-27T15:25:13","modified_gmt":"2025-03-27T07:25:13","slug":"%e6%8e%8c%e6%8f%a1%e8%bf%995%e7%a7%8d%e4%b8%93%e5%ae%b6%e7%ba%a7ma%e7%a7%bb%e5%8a%a8%e5%b9%b3%e5%9d%87%e7%ba%bf%e6%8a%80%e6%9c%af%ef%bc%8c%e8%ae%a9%e6%82%a8%e7%9a%84%e4%ba%a4%e6%98%93%e5%a6%82","status":"publish","type":"post","link":"https:\/\/laoyulaoyu.com\/index.php\/2025\/04\/15\/%e6%8e%8c%e6%8f%a1%e8%bf%995%e7%a7%8d%e4%b8%93%e5%ae%b6%e7%ba%a7ma%e7%a7%bb%e5%8a%a8%e5%b9%b3%e5%9d%87%e7%ba%bf%e6%8a%80%e6%9c%af%ef%bc%8c%e8%ae%a9%e6%82%a8%e7%9a%84%e4%ba%a4%e6%98%93%e5%a6%82\/","title":{"rendered":"\u638c\u63e1\u8fd95\u79cd\u4e13\u5bb6\u7ea7MA(\u79fb\u52a8\u5e73\u5747\u7ebf)\u6280\u672f\uff0c\u8ba9\u60a8\u7684\u4ea4\u6613\u5982\u9c7c\u5f97\u6c34"},"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>\u4eca\u5929\u7ed9\u5927\u4f19\u5206\u4eab 5 \u79cd\u7528\u79fb\u52a8\u5e73\u5747\u7ebf\u63d0\u5347\u4ea4\u6613\u8868\u73b0\u7684\u65b9\u6cd5<\/mark>\uff0c\u50cf\u79fb\u52a8\u5e73\u5747\u7ebf\u659c\u7387\u3001\u5305\u7edc\u7ebf\u8fd9\u4e9b\u9ad8\u7ea7\u6280\u672f\uff0c\u6211\u8fd8\u4e3a\u6b64\u642d\u914d\u4e86\u5b8c\u6574\u7684Python\u4ee3\u7801\u3002\u4e0d\u7ba1\u4f60\u662f\u4ea4\u6613\u5c0f\u767d\u8fd8\u662f\u8001\u624b\uff0c\u90fd\u80fd\u4ece\u4e2d\u5b66\u5230\u65b0\u4e1c\u897f\uff0c\u8d76\u7d27\u6765\u770b\u770b\u5427\uff01<\/pre>\n<\/blockquote>\n\n\n\n
\u4ec0\u4e48\u662f\u79fb\u52a8\u5e73\u5747\u7ebf \uff1f<\/strong><\/h2>\n\n\n\n
\u79fb\u52a8\u5e73\u5747\u7ebf\uff08Moving Average\uff0c\u7b80\u79f0MA\uff09\u662f\u4e00\u79cd\u5e38\u7528\u7684\u6280\u672f\u5206\u6790\u5de5\u5177\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u91d1\u878d\u5e02\u573a\uff0c\u5c24\u5176\u662f\u5728\u80a1\u7968\u3001\u5916\u6c47\u548c\u671f\u8d27\u4ea4\u6613\u4e2d\u3002<\/p>\n\n\n\n
\u5b83\u901a\u8fc7\u8ba1\u7b97\u4e00\u5b9a\u65f6\u95f4\u6bb5\u5185\u7684\u4ef7\u683c\u5e73\u5747\u503c\uff0c\u5e2e\u52a9\u4ea4\u6613\u8005\u5e73\u6ed1\u4ef7\u683c\u6ce2\u52a8\uff0c\u8bc6\u522b\u8d8b\u52bf\u65b9\u5411\uff0c\u4ece\u800c\u505a\u51fa\u66f4\u660e\u667a\u7684\u4ea4\u6613\u51b3\u7b56\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u79fb\u52a8\u5e73\u5747\u7ebf\u5728\u4ea4\u6613\u4e2d\u7684\u5e94\u7528\u4e3b\u8981\u5305\u62ec\u4ee5\u4e0b\u51e0\u4e2a\u65b9\u9762\uff1a<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u79fb\u52a8\u5e73\u5747\u7ebf\u5e94\u7528\u5e7f\u6cdb\uff0c\u5f88\u591a\u4eba\u90fd\u9760\u7740\u7814\u7a76\u5b83\u5f00\u53d1\u51fa\u5404\u79cd\u4ea4\u6613\u65b9\u6cd5\u3002\u6211\u81ea\u5df1\u4e5f\u7528\u79fb\u52a8\u5e73\u5747\u7ebf\u6784\u5efa\u4e86\u4e0d\u5c11\u7684\u4ea4\u6613\u7cfb\u7edf\uff0c\u4eca\u5929\u5c31\u7ed9\u5927\u5bb6\u5206\u4eab5\u79cd\u66f4\u9ad8\u7ea7\u7684\u5e94\u7528\u79fb\u52a8\u5e73\u5747\u7ebf\u4ea4\u6613\u7cfb\u7edf\u3002<\/p>\n\n\n\n
\u4e00\u3001\u79fb\u52a8\u5e73\u5747\u7ebf\u659c\u7387(MAS)<\/strong><\/h2>\n\n\n\n
\u79fb\u52a8\u5e73\u5747\u7ebf\u659c\u7387\uff08Moving Average Slope\uff09\uff0c\u7b80\u5355\u6765\u8bf4\uff0c\u5c31\u662f\u770b\u79fb\u52a8\u5e73\u5747\u7ebf\u5728\u4e00\u6bb5\u65f6\u95f4\u91cc\u4e0a\u5347\u6216\u4e0b\u964d\u7684\u9661\u5ced\u7a0b\u5ea6\u3002\u4ee5\u524d\u54b1\u4eec\u53ef\u80fd\u53ea\u5173\u6ce8\u5b83\u662f\u6da8\u8fd8\u662f\u8dcc\uff0c\u73b0\u5728\u7528\u659c\u7387\uff0c\u80fd\u66f4\u51c6\u786e\u5224\u65ad\u8d8b\u52bf\u7684\u5f3a\u5f31\u3002<\/strong><\/p>\n\n\n\n
\u4e3a\u4ec0\u4e48\u5b83\u8fd9\u4e48\u6709\u7528\u5462\uff1f\u5f53\u79fb\u52a8\u5e73\u5747\u7ebf\u659c\u7387\u5f88\u9661\uff0c\u5411\u4e0a\u7684\u8bdd\uff0c\u8bf4\u660e\u5e02\u573a\u4e0a\u6da8\u7684\u52b2\u5934\u7279\u522b\u8db3\uff1b\u8981\u662f\u5411\u4e0b\u5f88\u9661\uff0c\u90a3\u5c31\u662f\u5e02\u573a\u4e0b\u8dcc\u7684\u529b\u91cf\u5f88\u5f3a\u3002\u54b1\u4eec\u4ea4\u6613\u8005\u53ef\u4ee5\u501f\u52a9\u659c\u7387\uff0c\u786e\u8ba4\u5f53\u524d\u5e02\u573a\u8d8b\u52bf\u662f\u4e0d\u662f\u771f\u7684\u5f3a\uff0c\u8fd8\u80fd\u63d0\u524d\u53d1\u73b0\u8d8b\u52bf\u53ef\u80fd\u53cd\u8f6c\u7684\u4fe1\u53f7\u3002<\/p>\n\n\n\n
\u5b83\u7684\u8ba1\u7b97\u65b9\u6cd5\u662f\u7b97\u51fa\u79fb\u52a8\u5e73\u5747\u7ebf\u4e0a\u4e24\u4e2a\u70b9\u7684\u4ef7\u683c\u5dee\uff0c\u518d\u9664\u4ee5\u8fd9\u4e24\u4e2a\u70b9\u4e4b\u95f4\u7684\u65f6\u95f4\u95f4\u9694\u3002\u89d2\u5ea6\u8d8a\u9661\uff0c\u8d8b\u52bf\u5c31\u8d8a\u5f3a\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u4e0a\u56fe\u662f\u4e00\u5f20\u79fb\u52a8\u5e73\u5747\u7ebf\u659c\u7387\u8ba1\u7b97\u8fc7\u7a0b\u7684\u793a\u610f\u56fe\uff0c\u6a2a\u5750\u6807\u4e3a\u65f6\u95f4\uff0c\u7eb5\u5750\u6807\u4e3a\u4ef7\u683c\uff0c\u79fb\u52a8\u5e73\u5747\u7ebf\u660e\u663e\u7684\u659c\u7387\u5c55\u793a\u4e86\u5176\u53d8\u5316\u3002<\/p>\n\n\n\n
\u4e0b\u9762\u662f\u7528 Python \u4ee3\u7801\u8ba1\u7b97\u79fb\u52a8\u5e73\u5747\u7ebf\u659c\u7387\u7684\u793a\u4f8b\uff1a<\/p>\n\n\n\n
# \u8ba1\u7b97\u79fb\u52a8\u5e73\u5747\u7ebf\u659c\u7387\u7684\u51fd\u6570\ndef calculate_ma_slope(df, period=50):\n    ma = df['Close'].rolling(window=period).mean()\n    first_derivative = ma.diff()\n    second_derivative = first_derivative.diff()\n    return first_derivative, second_derivative<\/code><\/pre>\n\n\n\n
\u6211\u4eec\u5c06\u79fb\u52a8\u5e73\u5747\u7ebf\u659c\u7387\u7528\u4e8eSPY(\u6807\u666e500)\u7684\u5b9e\u6218\u4ea4\u6613\u7b56\u7565<\/strong>\uff0c\u5e76\u4f7f\u7528\u4e86 vectorbt<\/code> \u8fdb\u884c\u56de\u6d4b\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u7b56\u7565\u7684\u6838\u5fc3\u601d\u60f3\u662f\u5728\u79fb\u52a8\u5e73\u5747\u7ebf\u659c\u7387\u8f83\u5f3a\u65f6\u5165\u573a\uff0c\u659c\u7387\u8f83\u5f31\u65f6\u51fa\u573a\u3002\u901a\u8fc7\u56de\u6d4b\u53ef\u4ee5\u8bc4\u4f30\u7b56\u7565\u7684\u8868\u73b0\uff0c\u5e76\u7ed8\u5236\u4e86\u8d44\u91d1\u66f2\u7ebf\u3002<\/p>\n\n\n\n
\u4e8c<\/strong>\u3001\u79fb\u52a8\u5e73\u5747\u7ebf\u5305\u7edc\u7ebf(MAE)<\/strong><\/h2>\n\n\n\n
\u79fb\u52a8\u5e73\u5747\u7ebf\u5305\u7edc\u7ebf\uff08Moving Average Envelope\uff09\u5b83\u5c31\u50cf\u662f\u7ed9\u79fb\u52a8\u5e73\u5747\u7ebf\u7a7f\u4e0a\u4e86\u4e00\u4ef6 \u201c\u5916\u5957\u201d\u3002<\/p>\n\n\n\n
\u5728\u79fb\u52a8\u5e73\u5747\u7ebf\u7684\u4e0a\u65b9\u548c\u4e0b\u65b9\uff0c\u5404\u6709\u4e00\u6761\u5e73\u884c\u7684\u7ebf\uff0c\u8fd9\u4e24\u6761\u7ebf\u548c\u79fb\u52a8\u5e73\u5747\u7ebf\u4e4b\u95f4\u7684\u8ddd\u79bb\u662f\u56fa\u5b9a\u6bd4\u4f8b\u7684\uff0c\u5f62\u6210\u4e86\u4e00\u4e2a\u7c7b\u4f3c \u201c\u901a\u9053\u201d \u7684\u4e1c\u897f\uff0c\u8fd9\u4e2a\u901a\u9053\u5c31\u53eb \u201c\u5305\u7edc\u7ebf\u201d\u3002<\/p>\n\n\n\n
\u5b83\u6709\u5565\u7528\u5462\uff1f\u5f53\u4ef7\u683c\u8dd1\u5230\u5305\u7edc\u7ebf\u5916\u9762\u53bb\u4e86\uff0c\u5c31\u8bf4\u660e\u5e02\u573a\u53ef\u80fd\u6709\u70b9 \u201c\u8fc7\u5934\u201d \u4e86\uff0c\u4ef7\u683c\u53ef\u80fd\u8981\u5f80\u56de\u8d70\uff1b\u8981\u662f\u4ef7\u683c\u5728\u5305\u7edc\u7ebf\u9644\u8fd1\u6643\u60a0\uff0c\u90a3\u5c31\u53ef\u80fd\u662f\u5e02\u573a\u5728\u76d8\u6574<\/strong>\u3002<\/p>\n\n\n\n
\u4ea4\u6613\u8005\u8fd8\u80fd\u81ea\u5df1\u8c03\u6574\u5305\u7edc\u7ebf\u548c\u79fb\u52a8\u5e73\u5747\u7ebf\u4e4b\u95f4\u7684\u8ddd\u79bb\u6bd4\u4f8b\u3002\u8ddd\u79bb\u5c0f\uff0c\u8bf4\u660e\u5e02\u573a\u6ce2\u52a8\u5c0f\uff1b\u8ddd\u79bb\u5927\uff0c\u5e02\u573a\u6ce2\u52a8\u5c31\u5927\uff0c\u4ef7\u683c\u53d8\u5316\u66f4\u5267\u70c8\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u8fd9\u662f\u4e00\u5f20\u5c55\u793a\u79fb\u52a8\u5e73\u5747\u7ebf\u5305\u7edc\u7ebf\u7684 K \u7ebf\u56fe\uff0cK \u7ebf\u5728\u5305\u7edc\u7ebf\u5185\u6ce2\u52a8\uff0c\u5076\u5c14\u7a81\u7834\u5305\u7edc\u7ebf\uff0c\u6e05\u6670\u5c55\u793a\u5305\u7edc\u7ebf\u4f5c\u4e3a\u52a8\u6001\u652f\u6491\u548c\u963b\u529b\u4f4d\u7684\u4f5c\u7528\u3002<\/p>\n\n\n\n
Python \u4ee3\u7801\u793a\u4f8b\u5982\u4e0b\uff1a<\/p>\n\n\n\n
# \u8ba1\u7b97\u79fb\u52a8\u5e73\u5747\u7ebf\u5305\u7edc\u7ebf\uff08\u4e0a\u4e0b\u8fb9\u754c\uff09\u7684\u51fd\u6570\ndef calculate_ma_envelope(df, ma_period=20, envelope_pct=0.02):\n    ma = df['Close'].rolling(window=ma_period).mean()  # \u8ba1\u7b97\u79fb\u52a8\u5e73\u5747\u7ebf\n    upper_envelope = ma * (1 + envelope_pct)  # \u4e0a\u5305\u7edc\u7ebf\uff08\u9ad8\u4e8e\u79fb\u52a8\u5e73\u5747\u7ebf\u7684\u767e\u5206\u6bd4\uff09\n    lower_envelope = ma * (1 - envelope_pct)  # \u4e0b\u5305\u7edc\u7ebf\uff08\u4f4e\u4e8e\u79fb\u52a8\u5e73\u5747\u7ebf\u7684\u767e\u5206\u6bd4\uff09\n    return upper_envelope, lower_envelope<\/code><\/pre>\n\n\n\n
\u4e09\u3001\u987e\u6bd4\u590d\u5408\u79fb\u52a8\u5e73\u5747\u7ebf\uff08GMMA\uff09<\/strong><\/h2>\n\n\n\n
\u987e\u6bd4\u590d\u5408\u79fb\u52a8\u5e73\u5747\u7ebf\uff08Guppy Multiple Moving Averages\uff09\u662f\u628a\u597d\u591a\u6761\u79fb\u52a8\u5e73\u5747\u7ebf\u7ec4\u5408\u5728\u4e00\u8d77\uff0c\u5e2e\u52a9\u54b1\u4eec\u4ea4\u6613\u8005\u66f4\u597d\u5730\u4e86\u89e3\u77ed\u671f\u548c\u957f\u671f\u7684\u5e02\u573a\u8d8b\u52bf<\/strong>\u3002<\/p>\n\n\n\n
\u5b83\u5206\u6210\u4e24\u7ec4\u79fb\u52a8\u5e73\u5747\u7ebf\uff0c\u4e00\u7ec4\u7528\u6765\u53cd\u6620\u77ed\u671f\u8d8b\u52bf\uff08\u4e00\u822c\u662f 3 – 6 \u6761\u6307\u6570\u79fb\u52a8\u5e73\u5747\u7ebf\uff09\uff0c\u53e6\u4e00\u7ec4\u53cd\u6620\u957f\u671f\u8d8b\u52bf\uff08\u4e00\u822c\u662f 6 – 12 \u6761\u6307\u6570\u79fb\u52a8\u5e73\u5747\u7ebf\uff09 \u3002<\/p>\n\n\n\n
\u4e3a\u5565\u8981\u7528 GMMA \u5462\uff1f\u5b83\u80fd\u8ba9\u6211\u4eec\u5168\u9762\u5730\u770b\u6e05\u5e02\u573a\uff0c\u901a\u8fc7\u89c2\u5bdf\u77ed\u671f\u548c\u957f\u671f\u79fb\u52a8\u5e73\u5747\u7ebf\u4e4b\u95f4\u7684\u4ea4\u53c9\u60c5\u51b5\uff0c\u5c31\u80fd\u627e\u5230\u5408\u9002\u7684\u4e70\u5356\u65f6\u673a\u3002\u8981\u662f\u77ed\u671f\u7684\u79fb\u52a8\u5e73\u5747\u7ebf\u8dd1\u5230\u957f\u671f\u79fb\u52a8\u5e73\u5747\u7ebf\u4e0a\u9762\u53bb\u4e86\uff0c\u8fd9\u5c31\u662f\u5e02\u573a\u8981\u4e0a\u6da8\u7684\u4fe1\u53f7\uff1b\u53cd\u8fc7\u6765\uff0c\u5c31\u662f\u5e02\u573a\u8981\u4e0b\u8dcc\u7684\u4fe1\u53f7\u3002<\/p>\n\n\n\n
\u4f7f\u7528\u7684\u65f6\u5019\uff0c\u6211\u4eec\u8981\u7559\u610f\u77ed\u671f\u548c\u957f\u671f\u79fb\u52a8\u5e73\u5747\u7ebf\u662f\u9760\u8fd1\u8fd8\u662f\u8fdc\u79bb\u3002\u5b83\u4eec\u79bb\u5f97\u8fdc\uff0c\u8bf4\u660e\u5e02\u573a\u8d8b\u52bf\u5f88\u5f3a\uff1b\u8981\u662f\u8d8a\u6765\u8d8a\u8fd1\uff0c\u90a3\u5c31\u53ef\u80fd\u5e02\u573a\u8981\u53d8\u76d8\u4e86\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u4e0a\u56fe\u662f SPY \u5e26\u6709 GMMA \u6307\u6807\u7684 K \u7ebf\u56fe\uff0c\u6e05\u6670\u5c55\u793a\u77ed\u671f\u548c\u957f\u671f\u79fb\u52a8\u5e73\u5747\u7ebf\u7684\u4ea4\u53c9\u3001\u6536\u655b\u548c\u53d1\u6563\u60c5\u51b5\uff0c\u4ee5\u53ca\u5bf9\u5e94\u7684\u4ef7\u683c\u8d70\u52bf\u53d8\u5316\u3002<\/p>\n\n\n\n
Python \u4ee3\u7801\u793a\u4f8b\uff1a<\/p>\n\n\n\n
# \u8ba1\u7b97\u987e\u6bd4\u590d\u5408\u79fb\u52a8\u5e73\u5747\u7ebf\uff08GMMA\uff09\u7684\u51fd\u6570\ndef calculate_gmma(df, short_periods=range(3, 16, 3), long_periods=range(30, 51, 5)):\n    for period in short_periods:\n        df[f'GMMA_Short_{period}'] = df['Close'].ewm(span=period).mean()\n    for period in long_periods:\n        df[f'GMMA_Long_{period}'] = df['Close'].ewm(span=period).mean()\n    return df<\/code><\/pre>\n\n\n\n
\u6211\u4eec\u8fd8\u662f\u5c06 GMMA \u7528\u4e8e SPY (\u6807\u666e500)\u7684\u5b9e\u6218\u4ea4\u6613\u7b56\u7565<\/strong>\uff0c\u5e76\u7ee7\u7eed\u4f7f\u7528\u4e86 vectorbt<\/code> \u8fdb\u884c\u56de\u6d4b\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u7b56\u7565\u7684\u6838\u5fc3\u662f\u5728\u77ed\u671f GMMA \u5747\u7ebf\u538b\u7f29\u4f4e\u4e8e\u957f\u671f GMMA \u5747\u7ebf\u65f6\u5165\u573a\uff0c\u77ed\u671f GMMA \u5747\u7ebf\u4f4e\u4e8e\u957f\u671f GMMA \u5747\u7ebf\u65f6\u51fa\u573a\u3002<\/p>\n\n\n\n
\u56db<\/strong>\u3001\u79fb\u4f4d\u79fb\u52a8\u5e73\u5747\u7ebf\uff08DMA\uff09<\/strong><\/h2>\n\n\n\n
\u79fb\u4f4d\u79fb\u52a8\u5e73\u5747\u7ebf\uff08Displaced Moving Average\uff09\uff0c\u7b80\u5355\u8bb2\u5c31\u662f\u628a\u79fb\u52a8\u5e73\u5747\u7ebf\u5728\u65f6\u95f4\u4e0a\u5f80\u524d\u6216\u8005\u5f80\u540e\u632a\u4e00\u632a\u3002\u8fd9\u6837\u505a\u80fd\u8ba9\u6211\u4eec\u4ece\u4e0d\u540c\u7684\u89d2\u5ea6\u770b\u5e02\u573a\u8d8b\u52bf\u3002<\/p>\n\n\n\n
\u5f80\u524d\u632a\uff0c\u80fd\u5e2e\u6211\u4eec\u9884\u5224\u672a\u6765\u4ef7\u683c\u53ef\u80fd\u8d70\u5230\u54ea\u91cc\uff1b\u5f80\u540e\u632a\uff0c\u53ef\u4ee5\u770b\u770b\u5386\u53f2\u8d8b\u52bf\u548c\u672a\u6765\u4ef7\u683c\u4e4b\u95f4\u6709\u5565\u5173\u7cfb\u3002<\/p>\n\n\n\n
\u5b83\u7684\u4f5c\u7528\u53ef\u4e0d\u5c0f\uff0c\u901a\u8fc7\u628a\u79fb\u52a8\u5e73\u5747\u7ebf\u5f80\u524d\u79fb\uff0c\u6211\u4eec\u5c31\u80fd\u63d0\u524d\u77e5\u9053\u4ef7\u683c\u53ef\u80fd\u7684\u8d70\u52bf\uff0c\u5728\u8d8b\u52bf\u53cd\u8f6c\u4e4b\u524d\u6536\u5230\u4fe1\u53f7\u3002<\/strong><\/p>\n\n\n\n
\u8ba1\u7b97\u7684\u65f6\u5019\uff0c\u5c31\u662f\u628a\u79fb\u52a8\u5e73\u5747\u7ebf\u6309\u7167\u8bbe\u5b9a\u7684\u65f6\u95f4\u5f80\u524d\u6216\u5f80\u540e\u5e73\u79fb\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u4e0a\u56fe\u662f\u82f1\u4f1f\u8fbe\u80a1\u7968\uff08NVDA\uff09\u79fb\u4f4d\u79fb\u52a8\u5e73\u5747\u7ebf\u7684\u793a\u610f\u56fe\uff0c\u5c55\u793a\u4e86\u79fb\u52a8\u5e73\u5747\u7ebf\u79fb\u4f4d\u524d\u540e\u7684\u5bf9\u6bd4\uff0c\u4ee5\u53ca\u5bf9\u4ef7\u683c\u8d70\u52bf\u5224\u65ad\u7684\u5f71\u54cd\u3002<\/p>\n\n\n\n
Python \u4ee3\u7801\u793a\u4f8b\uff1a<\/p>\n\n\n\n
# \u8ba1\u7b97\u79fb\u4f4d\u79fb\u52a8\u5e73\u5747\u7ebf\uff08DMA\uff09\u7684\u51fd\u6570\ndef calculate_dma(df, period, shift):\n    return df['Close'].rolling(window=period).mean().shift(shift)\n<\/code><\/pre>\n\n\n\n
\u7528\u82f1\u4f1f\u8fbe\u80a1\u7968\uff08NVDA\uff09\u5b9e\u73b0\u57fa\u4e8e \u4f4d\u79fb\u79fb\u52a8\u5e73\u5747\u7ebf\uff08DMA\uff09<\/strong> \u7684\u4ea4\u6613\u7b56\u7565\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u6211\u4eec\u4f7f\u7528\u5feb\u901f DMA\uff0850 \u65e5\u5747\u7ebf\uff0c\u4f4d\u79fb 10 \u5929\uff09\u4e0a\u7a7f\u6162\u901f DMA\uff08200 \u65e5\u5747\u7ebf\uff0c\u4f4d\u79fb 20 \u5929\uff09\u4f5c\u4e3a\u5165\u573a\u4fe1\u53f7\uff0c\u4e0b\u7a7f\u4f5c\u4e3a\u51fa\u573a\u4fe1\u53f7\uff0c\u5e76\u901a\u8fc7 vectorbt<\/code> \u8fdb\u884c\u56de\u6d4b\u3002<\/p>\n\n\n\n
\u4e94<\/strong>\u3001\u79fb\u52a8\u5e73\u5747\u7ebf\u6c47\u805a\u533a\uff08MACZ\uff09<\/strong><\/h2>\n\n\n\n
\u79fb\u52a8\u5e73\u5747\u7ebf\u6c47\u805a\u533a\uff08Moving Average Confluence Zones\uff09\u6307\u7684\u662f\u597d\u591a\u6761\u4e0d\u540c\u5468\u671f\u7684\u79fb\u52a8\u5e73\u5747\u7ebf\u5728\u540c\u4e00\u4e2a\u4ef7\u683c\u4f4d\u7f6e\u6c47\u805a\u5230\u4e00\u8d77\uff0c\u8fd9\u4e2a\u6c47\u805a\u7684\u533a\u57df\u5c31\u6709\u53ef\u80fd\u6210\u4e3a\u4ef7\u683c\u7684\u652f\u6491\u4f4d\u6216\u8005\u963b\u529b\u4f4d\u3002\u9053\u7406\u5f88\u7b80\u5355\uff0c\u6c47\u805a\u7684\u79fb\u52a8\u5e73\u5747\u7ebf\u8d8a\u591a\uff0c\u8fd9\u4e2a\u4ef7\u683c\u4f4d\u7f6e\u7684\u652f\u6491\u6216\u8005\u963b\u529b\u4f5c\u7528\u5c31\u8d8a\u5f3a\u3002<\/p>\n\n\n\n
\u5bf9\u6211\u4eec\u4ea4\u6613\u8005\u6765\u8bf4\uff0c\u8fd9\u4e2a\u6c47\u805a\u533a\u53ef\u592a\u91cd\u8981\u4e86\u3002\u5f53\u4ef7\u683c\u9760\u8fd1\u6216\u8005\u78b0\u5230\u8fd9\u4e2a\u533a\u57df\u7684\u65f6\u5019\uff0c\u4ef7\u683c\u53ef\u80fd\u5c31\u4f1a\u53cd\u5f39\uff08\u5982\u679c\u662f\u652f\u6491\u533a\uff09\uff0c\u6216\u8005\u53cd\u8f6c\uff08\u5982\u679c\u662f\u963b\u529b\u533a\uff09<\/strong>\u3002<\/p>\n\n\n\n
\u4e00\u822c\u6765\u8bf4\uff0c\u6211\u4eec\u4f1a\u5173\u6ce8\u50cf 50 \u5468\u671f\u3001100 \u5468\u671f\u3001200 \u5468\u671f\u8fd9\u4e9b\u4e0d\u540c\u5468\u671f\u7684\u79fb\u52a8\u5e73\u5747\u7ebf\u662f\u4e0d\u662f\u6c47\u805a\u5728\u4e00\u8d77\u3002\u4e00\u65e6\u5b83\u4eec\u6c47\u805a\u5728\u67d0\u4e2a\u4ef7\u683c\u70b9\uff0c\u5c31\u8bf4\u660e\u4e0d\u540c\u65f6\u95f4\u6bb5\u7684\u5e02\u573a\u770b\u6cd5\u8fbe\u6210\u4e86\u4e00\u81f4\uff0c\u8fd9\u4e2a\u4ef7\u683c\u533a\u57df\u5f88\u5173\u952e\uff0c\u4e0d\u7ba1\u662f\u51b3\u5b9a\u4ec0\u4e48\u65f6\u5019\u4e70\u5165\u3001\u5356\u51fa\uff0c\u8fd8\u662f\u8bbe\u7f6e\u6b62\u635f\u70b9\uff0c\u90fd\u80fd\u53c2\u8003\u5b83\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u8fd9\u662f\u4e00\u5f20\u5c55\u793ameta\u80a1\u7968\u7684\u79fb\u52a8\u5e73\u5747\u7ebf\u6c47\u805a\u533a\u7684 K \u7ebf\u56fe\uff0c\u6807\u6ce8\u51fa\u4e86\u4e0d\u540c\u5468\u671f\u79fb\u52a8\u5e73\u5747\u7ebf\u6c47\u805a\u7684\u4f4d\u7f6e\uff0c\u4ee5\u53ca\u4ef7\u683c\u5728\u6c47\u805a\u533a\u9644\u8fd1\u7684\u53cd\u5e94\u3002<\/p>\n\n\n\n
Python \u4ee3\u7801\u793a\u4f8b\uff1a<\/p>\n\n\n\n
# \u68c0\u6d4b\u6c47\u805a\u533a\u7684\u51fd\u6570\uff08\u53ef\u590d\u7528\u5468\u671f\uff09\ndef detect_confluence(df, periods=None):\n    if periods is None:\n        periods = [20, 50, 100, 200]  # \u5982\u679c\u6ca1\u6709\u63d0\u4f9b\u5468\u671f\uff0c\u5219\u4f7f\u7528\u9ed8\u8ba4\u5468\u671f\n    ema_columns = [f'EMA_{period}' for period in periods]\n    df['Confluence_Upper'] = df[ema_columns].max(axis=1)\n    df['Confluence_Lower'] = df[ema_columns].min(axis=1)\n    return df<\/code><\/pre>\n\n\n\n
\u5176\u5b9e\uff0c\u79fb\u52a8\u5e73\u5747\u7ebf\u7684\u7528\u6cd5\u8fd8\u6709\u5f88\u591a\uff0c\u50cf\u79fb\u52a8\u5e73\u5747\u7ebf\u6324\u538b\u7b49\u3002\u4e0d\u8fc7\u6211\u8fd8\u6ca1\u57fa\u4e8e\u5b83\u597d\u597d\u6784\u5efa\u4ea4\u6613\u7cfb\u7edf\uff0c\u6240\u4ee5\u4eca\u5929\u5c31\u6ca1\u8be6\u7ec6\u8bb2\u3002<\/p>\n\n\n\n
\u800c\u4e14\u79fb\u52a8\u5e73\u5747\u7ebf\u7684\u7c7b\u578b\u7279\u522b\u591a\uff0c\u50cf\u7b80\u5355\u79fb\u52a8\u5e73\u5747\u7ebf\uff08SMA\uff09\u3001\u8d6b\u5c14\u79fb\u52a8\u5e73\u5747\u7ebf\uff08HMA\uff09\u3001\u4e09\u89d2\u79fb\u52a8\u5e73\u5747\u7ebf\uff08TMA\uff09\u7b49\u7b49\u3002\u5927\u5bb6\u53ef\u4ee5\u6839\u636e\u4eca\u5929\u8bb2\u7684\u8fd9\u4e9b\u8ba1\u7b97\u65b9\u6cd5\uff0c\u81ea\u5df1\u52a8\u624b\u6784\u5efa\u9002\u5408\u81ea\u5df1\u7684\u4ea4\u6613\u7cfb\u7edf\u3002<\/p>\n\n\n\n
\u89c2\u70b9\u603b\u7ed3<\/strong><\/h2>\n\n\n\n
\u672c\u6587\u4e3b\u8981\u5206\u4eab\u4e86 5 \u79cd\u5229\u7528\u79fb\u52a8\u5e73\u5747\u7ebf\u63d0\u5347\u4ea4\u6613\u8868\u73b0\u7684\u65b9\u6cd5\uff0c\u65e0\u8bba\u662f\u4ea4\u6613\u65b0\u624b\u8fd8\u662f\u6709\u7ecf\u9a8c\u7684\u6295\u8d44\u8005\uff0c\u90fd\u80fd\u4ece\u4e2d\u83b7\u53d6\u65b0\u7684\u4ea4\u6613\u601d\u8def\uff0c\u6709\u52a9\u4e8e\u5728\u4ea4\u6613\u5e02\u573a\u4e2d\u505a\u51fa\u66f4\u660e\u667a\u7684\u51b3\u7b56\u3002<\/p>\n\n\n\n
    \n
  1. \u79fb\u52a8\u5e73\u5747\u7ebf\u659c\u7387\uff08MAS\uff09<\/strong>\uff1a\u901a\u8fc7\u8ba1\u7b97\u659c\u7387\u5224\u65ad\u8d8b\u52bf\u5f3a\u5f31\uff0c\u659c\u7387\u8d8a\u9661\uff0c\u8d8b\u52bf\u8d8a\u5f3a\u3002<\/li>\n\n\n\n
  2. \u79fb\u52a8\u5e73\u5747\u7ebf\u5305\u7edc\u7ebf(MAE)<\/strong>\uff1a\u53ef\u8bc6\u522b\u5e02\u573a\u8d85\u4e70\u8d85\u5356\u548c\u76d8\u6574\u72b6\u6001\uff0c\u901a\u8fc7\u8c03\u6574\u5305\u7edc\u7ebf\u4e0e\u5747\u7ebf\u8ddd\u79bb\u5224\u65ad\u5e02\u573a\u6ce2\u52a8\u6027\u3002<\/li>\n\n\n\n
  3. \u987e\u6bd4\u590d\u5408\u79fb\u52a8\u5e73\u5747\u7ebf\uff08GMMA\uff09<\/strong>\uff1a\u7531\u77ed\u671f\u548c\u957f\u671f\u4e24\u7ec4\u5747\u7ebf\u6784\u6210\uff0c\u901a\u8fc7\u89c2\u5bdf\u4ea4\u53c9\u3001\u6536\u655b\u548c\u53d1\u6563\u5224\u65ad\u4e70\u5356\u70b9\u548c\u5e02\u573a\u8d8b\u52bf\u3002<\/li>\n\n\n\n
  4. \u79fb\u4f4d\u79fb\u52a8\u5e73\u5747\u7ebf\uff08DMA\uff09<\/strong>\uff1a\u5c06\u5747\u7ebf\u5728\u65f6\u95f4\u4e0a\u79fb\u4f4d\uff0c\u63d0\u524d\u9884\u5224\u4ef7\u683c\u8d70\u52bf\uff0c\u83b7\u5f97\u8d8b\u52bf\u53cd\u8f6c\u4fe1\u53f7\u3002<\/li>\n\n\n\n
  5. \u79fb\u52a8\u5e73\u5747\u7ebf\u6c47\u805a\u533a\uff08MACZ\uff09<\/strong>\uff1a\u591a\u6761\u5747\u7ebf\u6c47\u805a\u5f62\u6210\u652f\u6491\u6216\u963b\u529b\u533a\uff0c\u5e2e\u52a9\u786e\u5b9a\u4ea4\u6613\u7684\u5173\u952e\u70b9\u4f4d\u3002<\/li>\n<\/ol>\n\n\n\n
    \u6e90\u4ee3\u7801\u4e0b\u8f7d<\/strong><\/h2>\n\n\n\n
    \u4ee5\u4e0a\u7684\u6e90\u4ee3\u7801\u8bf7\u5728\u6211\u7684Google Colab\u4e2d\u7533\u8bf7\u4e0b\u8f7d\uff0c\u5185\u9644\u4e2d\u6587\u6784\u5efa\u8bf4\u660e\u3002<\/p>\n\n\n\n
    \"\"<\/figure>\n\n\n\n
    \u5982\u89c9\u5f97\u5bf9\u60a8\u6709\u6240\u5e2e\u52a9\uff0c\u8bf7\u7559\u8a00\u6216\u8005\u79c1\u4fe1\u5411\u6211\u7d22\u8981\uff0c\u540c\u65f6\u8bf7\u6211\u559d\u676f\u5496\u5561\u5c31\u597d\u3002<\/p>\n\n\n\n
    \u611f\u8c22\u60a8\u9605\u8bfb\u5230\u6700\u540e\uff0c\u5e0c\u671b\u8fd9\u7bc7\u6587\u7ae0\u4e3a\u60a8\u5e26\u6765\u4e86\u65b0\u7684\u542f\u53d1\u548c\u5b9e\u7528\u7684\u77e5\u8bc6\uff01\u5982\u679c\u89c9\u5f97\u6709\u5e2e\u52a9\uff0c\u8bf7\u4e0d\u541d\u70b9\u8d5e\u548c\u5206\u4eab\uff0c\u60a8\u7684\u652f\u6301\u662f\u6211\u6301\u7eed\u521b\u4f5c\u7684\u52a8\u529b\u3002\u795d\u60a8\u6295\u8d44\u987a\u5229\uff0c\u6536\u76ca\u957f\u8679\uff01\u5982\u679c\u5bf9\u6587\u4e2d\u5185\u5bb9\u6709\u4efb\u4f55\u7591\u95ee\uff0c\u6b22\u8fce\u7559\u8a00\uff0c\u6211\u4f1a\u5c3d\u5feb\u56de\u590d\uff01<\/em><\/p>\n\n\n\n
    \n\n\n\n
    \u672c\u6587\u5185\u5bb9\u4ec5\u9650\u6280\u672f\u63a2\u8ba8\u548c\u5b66\u4e60\uff0c\u4e0d\u6784\u6210\u4efb\u4f55\u6295\u8d44\u5efa\u8bae\u3002<\/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
    Continue reading\u638c\u63e1\u8fd95\u79cd\u4e13\u5bb6\u7ea7MA(\u79fb\u52a8\u5e73\u5747\u7ebf)\u6280\u672f\uff0c\u8ba9\u60a8\u7684\u4ea4\u6613\u5982\u9c7c\u5f97\u6c34<\/span><\/a><\/div>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[4,5,6],"class_list":{"0":"post-2095","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"hentry","6":"category-aiinvest","7":"tag-ai","9":"tag-6","10":"entry"},"_links":{"self":[{"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/posts\/2095","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/comments?post=2095"}],"version-history":[{"count":8,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/posts\/2095\/revisions"}],"predecessor-version":[{"id":2139,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/posts\/2095\/revisions\/2139"}],"wp:attachment":[{"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/media?parent=2095"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/categories?post=2095"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/tags?post=2095"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}