{"id":972721,"date":"2024-12-27T05:51:00","date_gmt":"2024-12-26T21:51:00","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/972721.html"},"modified":"2024-12-27T05:51:02","modified_gmt":"2024-12-26T21:51:02","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e8%ae%a1%e7%ae%97macd","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/972721.html","title":{"rendered":"\u5982\u4f55\u7528Python\u8ba1\u7b97MACD"},"content":{"rendered":"

\"\u5982\u4f55\u7528Python\u8ba1\u7b97MACD\"<\/p>\n

\u7528Python\u8ba1\u7b97MACD\u4e3b\u8981\u5305\u62ec\u4ee5\u4e0b\u51e0\u4e2a\u6b65\u9aa4\uff1a\u83b7\u53d6\u6570\u636e\u3001\u8ba1\u7b97EMA\uff08\u6307\u6570\u79fb\u52a8\u5e73\u5747\u7ebf\uff09\u3001\u8ba1\u7b97MACD\u7ebf\u548c\u4fe1\u53f7\u7ebf\u3001\u8ba1\u7b97MACD\u67f1\u72b6\u56fe\u3002\u901a\u8fc7Python\u5e93\u5982Pandas\u3001NumPy\u53caMatplotlib\uff0c\u4f60\u53ef\u4ee5\u8f7b\u677e\u5b9e\u73b0MACD\u7684\u8ba1\u7b97\u4e0e\u53ef\u89c6\u5316\u3002<\/strong>\u5728\u8fd9\u7bc7\u6587\u7ae0\u4e2d\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u63a2\u8ba8\u6bcf\u4e2a\u6b65\u9aa4\uff0c\u5e76\u63d0\u4f9b\u4ee3\u7801\u793a\u4f8b\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u5982\u4f55\u7528Python\u8ba1\u7b97MACD\u3002<\/p>\n<\/p>\n

\n

\u4e00\u3001\u83b7\u53d6\u6570\u636e<\/h3>\n<\/p>\n

\u5728\u8ba1\u7b97MACD\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u83b7\u53d6\u80a1\u7968\u6216\u5176\u4ed6\u91d1\u878d\u8d44\u4ea7\u7684\u4ef7\u683c\u6570\u636e\u3002\u901a\u5e38\uff0c\u8fd9\u4e9b\u6570\u636e\u5305\u62ec\u5f00\u76d8\u4ef7\u3001\u6700\u9ad8\u4ef7\u3001\u6700\u4f4e\u4ef7\u3001\u6536\u76d8\u4ef7\u53ca\u4ea4\u6613\u91cf\u3002\u83b7\u53d6\u6570\u636e\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5305\u62ec\u4f7f\u7528\u5728\u7ebfAPI\u3001\u4e0b\u8f7dCSV\u6587\u4ef6\u7b49\u3002<\/p>\n<\/p>\n

1. \u4f7f\u7528\u5728\u7ebfAPI\u83b7\u53d6\u6570\u636e<\/h4>\n<\/p>\n

Python\u7684yfinance<\/code>\u5e93\u63d0\u4f9b\u4e86\u65b9\u4fbf\u7684\u63a5\u53e3\u6765\u83b7\u53d6\u96c5\u864e\u8d22\u7ecf\u7684\u6570\u636e\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff0c\u5c55\u793a\u5982\u4f55\u4f7f\u7528yfinance<\/code>\u83b7\u53d6\u82f9\u679c\u516c\u53f8\u80a1\u7968\u7684\u5386\u53f2\u6570\u636e\uff1a<\/p>\n<\/p>\n

import yfinance as yf<\/p>\n
\u83b7\u53d6\u82f9\u679c\u516c\u53f8\u80a1\u7968\u6570\u636e<\/strong><\/h2>\n
data = yf.download('AAPL', start='2022-01-01', end='2022-12-31')<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u8bfb\u53d6CSV\u6587\u4ef6<\/h4>\n<\/p>\n
\u5982\u679c\u4f60\u5df2\u7ecf\u6709\u4e86CSV\u683c\u5f0f\u7684\u5386\u53f2\u6570\u636e\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u6765\u8bfb\u53d6\u8fd9\u4e9b\u6570\u636e\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u4eceCSV\u6587\u4ef6\u4e2d\u8bfb\u53d6\u6570\u636e<\/strong><\/h2>\n
data = pd.read_csv('stock_data.csv')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u8ba1\u7b97EMA\uff08\u6307\u6570\u79fb\u52a8\u5e73\u5747\u7ebf\uff09<\/h3>\n<\/p>\n
MACD\u7684\u8ba1\u7b97\u4f9d\u8d56\u4e8e\u6307\u6570\u79fb\u52a8\u5e73\u5747\u7ebf\uff08EMA\uff09\u3002\u6211\u4eec\u9700\u8981\u8ba1\u7b9712\u65e5\u548c26\u65e5\u7684EMA\u3002Pandas\u63d0\u4f9b\u4e86\u65b9\u4fbf\u7684\u65b9\u6cd5\u6765\u8ba1\u7b97EMA\u3002<\/p>\n<\/p>\n
1. \u5b9a\u4e49EMA\u8ba1\u7b97\u51fd\u6570<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u5b9a\u4e49\u4e00\u4e2a\u51fd\u6570\u6765\u8ba1\u7b97EMA\uff1a<\/p>\n<\/p>\n
def calculate_ema(data, span):<\/p>\n
    return data.ewm(span=span, adjust=False).mean()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u8ba1\u7b9712\u65e5\u548c26\u65e5EMA<\/h4>\n<\/p>\n
\u4f7f\u7528\u4e0a\u9762\u5b9a\u4e49\u7684\u51fd\u6570\uff0c\u6211\u4eec\u53ef\u4ee5\u8ba1\u7b97\u51fa12\u65e5\u548c26\u65e5\u7684EMA\uff1a<\/p>\n<\/p>\n
# \u8ba1\u7b9712\u65e5EMA<\/p>\n
ema_12 = calculate_ema(data['Close'], span=12)<\/p>\n
\u8ba1\u7b9726\u65e5EMA<\/strong><\/h2>\n
ema_26 = calculate_ema(data['Close'], span=26)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u8ba1\u7b97MACD\u7ebf\u548c\u4fe1\u53f7\u7ebf<\/h3>\n<\/p>\n
MACD\u7ebf\u662f12\u65e5EMA\u548c26\u65e5EMA\u7684\u5dee\u503c\uff0c\u800c\u4fe1\u53f7\u7ebf\u662fMACD\u7ebf\u76849\u65e5EMA\u3002<\/p>\n<\/p>\n
1. \u8ba1\u7b97MACD\u7ebf<\/h4>\n<\/p>\n
# \u8ba1\u7b97MACD\u7ebf<\/p>\n
macd_line = ema_12 - ema_26<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u8ba1\u7b97\u4fe1\u53f7\u7ebf<\/h4>\n<\/p>\n
# \u8ba1\u7b97\u4fe1\u53f7\u7ebf<\/p>\n
signal_line = calculate_ema(macd_line, span=9)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u8ba1\u7b97MACD\u67f1\u72b6\u56fe<\/h3>\n<\/p>\n
MACD\u67f1\u72b6\u56fe\uff08Histogram\uff09\u662fMACD\u7ebf\u4e0e\u4fe1\u53f7\u7ebf\u7684\u5dee\u503c\uff0c\u5b83\u53ef\u4ee5\u5e2e\u52a9\u8bc6\u522b\u8d8b\u52bf\u7684\u5f3a\u5f31\u3002<\/p>\n<\/p>\n
# \u8ba1\u7b97MACD\u67f1\u72b6\u56fe<\/p>\n
macd_histogram = macd_line - signal_line<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u6570\u636e\u53ef\u89c6\u5316<\/h3>\n<\/p>\n
\u4e3a\u4e86\u66f4\u76f4\u89c2\u5730\u5206\u6790MACD\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\u6765\u7ed8\u5236\u56fe\u8868\u3002<\/p>\n<\/p>\n
1. \u7ed8\u5236\u6536\u76d8\u4ef7\u548cMACD\u7ebf<\/h4>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
plt.figure(figsize=(14, 7))<\/p>\n
\u7ed8\u5236\u6536\u76d8\u4ef7<\/strong><\/h2>\n
plt.subplot(2, 1, 1)<\/p>\n
plt.plot(data.index, data['Close'], label='Close Price')<\/p>\n
plt.title('Stock Price and MACD')<\/p>\n
plt.legend()<\/p>\n
\u7ed8\u5236MACD\u7ebf\u548c\u4fe1\u53f7\u7ebf<\/strong><\/h2>\n
plt.subplot(2, 1, 2)<\/p>\n
plt.plot(data.index, macd_line, label='MACD Line', color='r')<\/p>\n
plt.plot(data.index, signal_line, label='Signal Line', color='b')<\/p>\n
plt.bar(data.index, macd_histogram, label='Histogram', color='g')<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001MACD\u7684\u5e94\u7528<\/h3>\n<\/p>\n
MACD\u662f\u6280\u672f\u5206\u6790\u4e2d\u975e\u5e38\u91cd\u8981\u7684\u5de5\u5177\uff0c\u5b83\u4e3b\u8981\u7528\u4e8e\u8bc6\u522b\u4ef7\u683c\u8d8b\u52bf\u7684\u65b9\u5411\u548c\u53d8\u5316\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u5e94\u7528\u7b56\u7565\uff1a<\/p>\n<\/p>\n
1. \u9ec4\u91d1\u4ea4\u53c9\u4e0e\u6b7b\u4ea1\u4ea4\u53c9<\/h4>\n<\/p>\n
    \n
  • \u9ec4\u91d1\u4ea4\u53c9<\/strong>\uff1a\u5f53MACD\u7ebf\u4ece\u4e0b\u65b9\u7a7f\u8fc7\u4fe1\u53f7\u7ebf\u65f6\uff0c\u901a\u5e38\u88ab\u89c6\u4e3a\u4e70\u5165\u4fe1\u53f7\u3002<\/li>\n
  • \u6b7b\u4ea1\u4ea4\u53c9<\/strong>\uff1a\u5f53MACD\u7ebf\u4ece\u4e0a\u65b9\u7a7f\u8fc7\u4fe1\u53f7\u7ebf\u65f6\uff0c\u901a\u5e38\u88ab\u89c6\u4e3a\u5356\u51fa\u4fe1\u53f7\u3002<\/li>\n<\/ul>\n
    2. \u80cc\u79bb<\/h4>\n<\/p>\n
    MACD\u80cc\u79bb\u662f\u6307\u4ef7\u683c\u548cMACD\u7ebf\u7684\u8d70\u52bf\u51fa\u73b0\u4e0d\u4e00\u81f4\u7684\u60c5\u51b5\uff0c\u901a\u5e38\u9884\u793a\u7740\u8d8b\u52bf\u53ef\u80fd\u4f1a\u53cd\u8f6c\u3002<\/p>\n<\/p>\n
    3. \u8d8b\u52bf\u5f3a\u5ea6<\/h4>\n<\/p>\n
    MACD\u67f1\u72b6\u56fe\u7684\u5927\u5c0f\u8868\u793a\u8d8b\u52bf\u7684\u5f3a\u5ea6\u3002\u67f1\u72b6\u56fe\u8f83\u5927\u65f6\uff0c\u8868\u793a\u5f53\u524d\u8d8b\u52bf\u8f83\u5f3a\uff1b\u67f1\u72b6\u56fe\u8f83\u5c0f\u65f6\uff0c\u8868\u793a\u8d8b\u52bf\u8f83\u5f31\u3002<\/p>\n<\/p>\n
    \u4e03\u3001\u4f18\u5316\u4e0e\u6ce8\u610f\u4e8b\u9879<\/h3>\n<\/p>\n
    \u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0cMACD\u7684\u53c2\u6570\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u7684\u5e02\u573a\u548c\u8d44\u4ea7\u8fdb\u884c\u8c03\u6574\u3002\u5e38\u7528\u7684\u53c2\u6570\u662f12\u300126\u30019\uff0c\u4f46\u8fd9\u5e76\u4e0d\u662f\u56fa\u5b9a\u7684\u3002\u6295\u8d44\u8005\u53ef\u4ee5\u6839\u636e\u81ea\u8eab\u7684\u4ea4\u6613\u98ce\u683c\u548c\u5e02\u573a\u7279\u70b9\u8fdb\u884c\u4f18\u5316\u3002<\/p>\n<\/p>\n
    \u6b64\u5916\uff0cMACD\u662f\u4e00\u79cd\u6ede\u540e\u6307\u6807\uff0c\u53ef\u80fd\u5728\u5e02\u573a\u5feb\u901f\u53d8\u5316\u65f6\u4ea7\u751f\u6ede\u540e\u4fe1\u53f7\u3002\u56e0\u6b64\uff0c\u5efa\u8bae\u5c06MACD\u4e0e\u5176\u4ed6\u6280\u672f\u6307\u6807\u7ed3\u5408\u4f7f\u7528\uff0c\u4ee5\u63d0\u9ad8\u4ea4\u6613\u4fe1\u53f7\u7684\u51c6\u786e\u6027\u3002<\/p>\n<\/p>\n
    \u516b\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
    \u901a\u8fc7\u8fd9\u7bc7\u6587\u7ae0\uff0c\u6211\u4eec\u8be6\u7ec6\u63a2\u8ba8\u4e86\u5982\u4f55\u7528Python\u8ba1\u7b97MACD\uff0c\u5305\u62ec\u6570\u636e\u83b7\u53d6\u3001EMA\u8ba1\u7b97\u3001MACD\u7ebf\u548c\u4fe1\u53f7\u7ebf\u7684\u8ba1\u7b97\u4ee5\u53ca\u6570\u636e\u53ef\u89c6\u5316\u3002\u6211\u4eec\u8fd8\u63a2\u8ba8\u4e86MACD\u5728\u6280\u672f\u5206\u6790\u4e2d\u7684\u5e94\u7528\u7b56\u7565\u3002\u5e0c\u671b\u8fd9\u7bc7\u6587\u7ae0\u80fd\u591f\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u548c\u5e94\u7528MACD\u6307\u6807\u3002<\/p>\n<\/p>\n
    \u5982\u9700\u8fdb\u4e00\u6b65\u63d0\u9ad8\u81ea\u5df1\u7684\u6280\u672f\u5206\u6790\u80fd\u529b\uff0c\u53ef\u4ee5\u8003\u8651\u5b66\u4e60\u66f4\u591a\u7684\u6280\u672f\u6307\u6807\uff0c\u5e76\u5c1d\u8bd5\u5c06\u4e0d\u540c\u7684\u6307\u6807\u7ed3\u5408\u4f7f\u7528\uff0c\u4ee5\u83b7\u5f97\u66f4\u51c6\u786e\u7684\u5e02\u573a\u5206\u6790\u7ed3\u679c\u3002<\/p>\n<\/p>\n
    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
     \u5982\u4f55\u5728Python\u4e2d\u83b7\u53d6\u80a1\u5e02\u6570\u636e\u4ee5\u8ba1\u7b97MACD\uff1f<\/strong>
    \u8981\u8ba1\u7b97MACD\uff0c\u9996\u5148\u9700\u8981\u83b7\u53d6\u80a1\u5e02\u7684\u5386\u53f2\u4ef7\u683c\u6570\u636e\u3002\u53ef\u4ee5\u4f7f\u7528\u50cfyfinance<\/code>\u8fd9\u6837\u7684\u5e93\u6765\u8f7b\u677e\u83b7\u53d6\u6570\u636e\u3002\u901a\u8fc7\u4ee5\u4e0b\u6b65\u9aa4\u83b7\u53d6\u6570\u636e\uff1a\u5b89\u88c5yfinance<\/code>\u5e93\u5e76\u4f7f\u7528download()<\/code>\u51fd\u6570\u83b7\u53d6\u6240\u9700\u7684\u80a1\u7968\u6570\u636e\uff0c\u901a\u5e38\u662f\u6536\u76d8\u4ef7\u3002\u8fd9\u6837\u53ef\u4ee5\u4e3a\u540e\u7eed\u7684MACD\u8ba1\u7b97\u63d0\u4f9b\u57fa\u7840\u6570\u636e\u3002<\/p>\n
    MACD\u7684\u8ba1\u7b97\u516c\u5f0f\u662f\u4ec0\u4e48\uff0c\u5982\u4f55\u5728Python\u4e2d\u5b9e\u73b0\uff1f<\/strong>
    MACD\u662f\u901a\u8fc7\u8ba1\u7b97\u77ed\u671f\u548c\u957f\u671f\u6307\u6570\u79fb\u52a8\u5e73\u5747\u7ebf\uff08EMA\uff09\u6765\u5f97\u51fa\u7684\u3002\u4e00\u822c\u6765\u8bf4\uff0c\u5e38\u7528\u7684\u53c2\u6570\u662f12\u65e5EMA\u548c26\u65e5EMA\u3002\u8ba1\u7b97\u6b65\u9aa4\u5982\u4e0b\uff1a\u9996\u5148\u8ba1\u7b97\u8fd9\u4e24\u4e2aEMA\uff0c\u7136\u540e\u901a\u8fc7\u77ed\u671fEMA\u51cf\u53bb\u957f\u671fEMA\u5f97\u5230MACD\u7ebf\u3002\u63a5\u7740\uff0c\u8ba1\u7b97MACD\u7ebf\u76849\u65e5EMA\uff0c\u79f0\u4e3a\u4fe1\u53f7\u7ebf\u3002\u53ef\u4ee5\u4f7f\u7528pandas<\/code>\u5e93\u4e2d\u7684ewm()<\/code>\u51fd\u6570\u6765\u5b9e\u73b0EMA\u7684\u8ba1\u7b97\u3002<\/p>\n
    \u5728\u4f7f\u7528Python\u8ba1\u7b97MACD\u65f6\uff0c\u5982\u4f55\u53ef\u89c6\u5316\u7ed3\u679c\uff1f<\/strong>
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