{"id":1151163,"date":"2025-01-13T17:10:16","date_gmt":"2025-01-13T09:10:16","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1151163.html"},"modified":"2025-01-13T17:10:18","modified_gmt":"2025-01-13T09:10:18","slug":"python%e5%a6%82%e4%bd%95%e5%ae%9e%e7%8e%b0%e7%a7%bb%e5%8a%a8%e5%b9%b3%e5%9d%87","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1151163.html","title":{"rendered":"python\u5982\u4f55\u5b9e\u73b0\u79fb\u52a8\u5e73\u5747"},"content":{"rendered":"

\"python\u5982\u4f55\u5b9e\u73b0\u79fb\u52a8\u5e73\u5747\"<\/p>\n

Python\u5b9e\u73b0\u79fb\u52a8\u5e73\u5747\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5305\u62ec\u7b80\u5355\u79fb\u52a8\u5e73\u5747\u6cd5\uff08SMA\uff09\u3001\u52a0\u6743\u79fb\u52a8\u5e73\u5747\u6cd5\uff08WMA\uff09\u3001\u6307\u6570\u79fb\u52a8\u5e73\u5747\u6cd5\uff08EMA\uff09\u7b49\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528NumPy\u3001Pandas\u7b49\u5e93\u5b9e\u73b0\u8fd9\u4e9b\u65b9\u6cd5\u3002\u901a\u8fc7\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u4f60\u53ef\u4ee5\u5e73\u6ed1\u6570\u636e\u5e76\u53d1\u73b0\u957f\u671f\u8d8b\u52bf\u3002<\/strong>\u672c\u6587\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u51e0\u79cd\u5e38\u89c1\u7684\u5b9e\u73b0\u65b9\u6cd5\uff0c\u5e76\u63d0\u4f9b\u4ee3\u7801\u793a\u4f8b\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u7b80\u5355\u79fb\u52a8\u5e73\u5747\u6cd5\uff08SMA\uff09<\/h3>\n<\/p>\n

\u7b80\u5355\u79fb\u52a8\u5e73\u5747\u6cd5\uff08SMA\uff09\u662f\u6700\u57fa\u672c\u7684\u79fb\u52a8\u5e73\u5747\u65b9\u6cd5\u3002\u5b83\u901a\u8fc7\u8ba1\u7b97\u7279\u5b9a\u65f6\u95f4\u7a97\u53e3\u5185\u6570\u636e\u70b9\u7684\u5e73\u5747\u503c\u6765\u5e73\u6ed1\u6570\u636e\u3002<\/strong><\/p>\n<\/p>\n

1. \u4f7f\u7528\u7eafPython\u5b9e\u73b0SMA<\/h4>\n<\/p>\n

\u7b80\u5355\u79fb\u52a8\u5e73\u5747\u6cd5\u7684\u5b9e\u73b0\u53ef\u4ee5\u901a\u8fc7\u7eafPython\u4ee3\u7801\u6765\u5b8c\u6210\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u5b9e\u73b0SMA\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n

def simple_moving_average(data, window):<\/p>\n
    if len(data) < window:<\/p>\n
        return []<\/p>\n
    sma = []<\/p>\n
    for i in range(len(data) - window + 1):<\/p>\n
        window_data = data[i:i + window]<\/p>\n
        window_average = sum(window_data) \/ window<\/p>\n
        sma.append(window_average)<\/p>\n
    return sma<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]<\/p>\n
window = 3<\/p>\n
print(simple_moving_average(data, window))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u51fd\u6570\u4e2d\uff0cdata<\/code> \u662f\u8f93\u5165\u7684\u65f6\u95f4\u5e8f\u5217\u6570\u636e\uff0cwindow<\/code> \u662f\u79fb\u52a8\u7a97\u53e3\u7684\u5927\u5c0f\u3002\u51fd\u6570\u901a\u8fc7\u8ba1\u7b97\u7a97\u53e3\u5185\u6570\u636e\u7684\u5e73\u5747\u503c\u6765\u751f\u6210SMA\u3002<\/p>\n<\/p>\n
2. \u4f7f\u7528NumPy\u5b9e\u73b0SMA<\/h4>\n<\/p>\n
NumPy\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u503c\u8ba1\u7b97\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u7ec4\u8fd0\u7b97\u529f\u80fd\u3002\u4f7f\u7528NumPy\uff0c\u53ef\u4ee5\u66f4\u9ad8\u6548\u5730\u5b9e\u73b0SMA\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528NumPy\u7684SMA\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
def numpy_sma(data, window):<\/p>\n
    if len(data) < window:<\/p>\n
        return np.array([])<\/p>\n
    data = np.array(data)<\/p>\n
    weights = np.ones(window) \/ window<\/p>\n
    sma = np.convolve(data, weights, mode='valid')<\/p>\n
    return sma<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]<\/p>\n
window = 3<\/p>\n
print(numpy_sma(data, window))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u52a0\u6743\u79fb\u52a8\u5e73\u5747\u6cd5\uff08WMA\uff09<\/h3>\n<\/p>\n
\u52a0\u6743\u79fb\u52a8\u5e73\u5747\u6cd5\uff08WMA\uff09\u662f\u5bf9\u7a97\u53e3\u5185\u7684\u6570\u636e\u8d4b\u4e88\u4e0d\u540c\u7684\u6743\u91cd\u6765\u8ba1\u7b97\u5e73\u5747\u503c\u3002\u6743\u91cd\u901a\u5e38\u968f\u65f6\u95f4\u9012\u51cf\uff0c\u8f83\u65b0\u7684\u6570\u636e\u70b9\u6743\u91cd\u66f4\u9ad8\u3002<\/strong><\/p>\n<\/p>\n
1. \u4f7f\u7528\u7eafPython\u5b9e\u73b0WMA<\/h4>\n<\/p>\n
\u4ee5\u4e0b\u662f\u4e00\u4e2a\u5b9e\u73b0WMA\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
def weighted_moving_average(data, window):<\/p>\n
    if len(data) < window:<\/p>\n
        return []<\/p>\n
    wma = []<\/p>\n
    weights = list(range(1, window + 1))<\/p>\n
    weights_sum = sum(weights)<\/p>\n
    for i in range(len(data) - window + 1):<\/p>\n
        window_data = data[i:i + window]<\/p>\n
        weighted_sum = sum(w * d for w, d in zip(weights, window_data))<\/p>\n
        wma.append(weighted_sum \/ weights_sum)<\/p>\n
    return wma<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]<\/p>\n
window = 3<\/p>\n
print(weighted_moving_average(data, window))<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u4f7f\u7528NumPy\u5b9e\u73b0WMA<\/h4>\n<\/p>\n
\u540c\u6837\uff0cNumPy\u53ef\u4ee5\u7b80\u5316WMA\u7684\u5b9e\u73b0\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528NumPy\u7684WMA\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
def numpy_wma(data, window):<\/p>\n
    if len(data) < window:<\/p>\n
        return np.array([])<\/p>\n
    data = np.array(data)<\/p>\n
    weights = np.arange(1, window + 1)<\/p>\n
    weights_sum = np.sum(weights)<\/p>\n
    weighted_data = np.convolve(data, weights[::-1], mode='valid')<\/p>\n
    wma = weighted_data \/ weights_sum<\/p>\n
    return wma<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]<\/p>\n
window = 3<\/p>\n
print(numpy_wma(data, window))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u6307\u6570\u79fb\u52a8\u5e73\u5747\u6cd5\uff08EMA\uff09<\/h3>\n<\/p>\n
\u6307\u6570\u79fb\u52a8\u5e73\u5747\u6cd5\uff08EMA\uff09\u5bf9\u8f83\u65b0\u7684\u6570\u636e\u70b9\u8d4b\u4e88\u66f4\u9ad8\u7684\u6743\u91cd\uff0c\u6743\u91cd\u968f\u7740\u65f6\u95f4\u6307\u6570\u9012\u51cf\u3002EMA\u5bf9\u7a81\u53d8\u6570\u636e\u7684\u53cd\u5e94\u901f\u5ea6\u6bd4SMA\u548cWMA\u5feb\u3002<\/strong><\/p>\n<\/p>\n
1. \u4f7f\u7528\u7eafPython\u5b9e\u73b0EMA<\/h4>\n<\/p>\n
\u4ee5\u4e0b\u662f\u4e00\u4e2a\u5b9e\u73b0EMA\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
def exponential_moving_average(data, span):<\/p>\n
    if not data:<\/p>\n
        return []<\/p>\n
    ema = [data[0]]  # \u521d\u59cb\u503c<\/p>\n
    alpha = 2 \/ (span + 1)<\/p>\n
    for price in data[1:]:<\/p>\n
        ema.append(alpha * price + (1 - alpha) * ema[-1])<\/p>\n
    return ema<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]<\/p>\n
span = 3<\/p>\n
print(exponential_moving_average(data, span))<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u4f7f\u7528Pandas\u5b9e\u73b0EMA<\/h4>\n<\/p>\n
Pandas\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u63d0\u4f9b\u4e86\u5185\u7f6e\u7684EMA\u8ba1\u7b97\u51fd\u6570\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528Pandas\u7684EMA\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
def pandas_ema(data, span):<\/p>\n
    series = pd.Series(data)<\/p>\n
    ema = series.ewm(span=span, adjust=False).mean()<\/p>\n
    return ema<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]<\/p>\n
span = 3<\/p>\n
print(pandas_ema(data, span))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u79fb\u52a8\u5e73\u5747\u7684\u5e94\u7528<\/h3>\n<\/p>\n
\u79fb\u52a8\u5e73\u5747\u5728\u91d1\u878d\u5e02\u573a\u4e2d\u88ab\u5e7f\u6cdb\u5e94\u7528\u4e8e\u6280\u672f\u5206\u6790\uff0c\u4f8b\u5982\u80a1\u7968\u4ef7\u683c\u7684\u8d8b\u52bf\u5206\u6790\u3002<\/strong><\/p>\n<\/p>\n
1. \u8d8b\u52bf\u5206\u6790<\/h4>\n<\/p>\n
\u79fb\u52a8\u5e73\u5747\u53ef\u4ee5\u5e2e\u52a9\u8bc6\u522b\u6570\u636e\u4e2d\u7684\u957f\u671f\u8d8b\u52bf\u3002\u4f8b\u5982\uff0c\u901a\u8fc7\u8ba1\u7b97\u80a1\u7968\u4ef7\u683c\u7684SMA\uff0c\u53ef\u4ee5\u5e73\u6ed1\u77ed\u671f\u6ce2\u52a8\uff0c\u8bc6\u522b\u51fa\u957f\u671f\u4e0a\u6da8\u6216\u4e0b\u8dcc\u8d8b\u52bf\u3002<\/p>\n<\/p>\n
2. \u566a\u58f0\u8fc7\u6ee4<\/h4>\n<\/p>\n
\u79fb\u52a8\u5e73\u5747\u53ef\u4ee5\u8fc7\u6ee4\u6389\u6570\u636e\u4e2d\u7684\u566a\u58f0\u3002\u539f\u59cb\u6570\u636e\u53ef\u80fd\u5305\u542b\u8bb8\u591a\u77ed\u671f\u6ce2\u52a8\uff0c\u79fb\u52a8\u5e73\u5747\u901a\u8fc7\u8ba1\u7b97\u7a97\u53e3\u5185\u6570\u636e\u7684\u5e73\u5747\u503c\uff0c\u53ef\u4ee5\u51cf\u5c11\u8fd9\u4e9b\u77ed\u671f\u6ce2\u52a8\u7684\u5f71\u54cd\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u4ee3\u7801\u4f18\u5316\u4e0e\u6027\u80fd\u6bd4\u8f83<\/h3>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6570\u636e\u91cf\u901a\u5e38\u8f83\u5927\uff0c\u8ba1\u7b97\u6548\u7387\u975e\u5e38\u91cd\u8981\u3002\u4e0b\u9762\u6211\u4eec\u5bf9\u6bd4\u51e0\u79cd\u5b9e\u73b0\u65b9\u6cd5\u7684\u6027\u80fd\uff1a<\/p>\n<\/p>\n
1. \u7eafPython vs NumPy<\/h4>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528timeit<\/code>\u6a21\u5757\u6765\u6bd4\u8f83\u7eafPython\u548cNumPy\u5b9e\u73b0\u7684\u6027\u80fd\uff1a<\/p>\n<\/p>\n
import timeit<\/p>\n
data = list(range(10000))<\/p>\n
window = 50<\/p>\n
\u7eafPython SMA<\/strong><\/h2>\n
sma_python_time = timeit.timeit(lambda: simple_moving_average(data, window), number=10)<\/p>\n
print(f"SMA (\u7eafPython)\u8017\u65f6: {sma_python_time:.4f}\u79d2")<\/p>\n
NumPy SMA<\/strong><\/h2>\n
sma_numpy_time = timeit.timeit(lambda: numpy_sma(data, window), number=10)<\/p>\n
print(f"SMA (NumPy)\u8017\u65f6: {sma_numpy_time:.4f}\u79d2")<\/p>\n
\u7eafPython WMA<\/strong><\/h2>\n
wma_python_time = timeit.timeit(lambda: weighted_moving_average(data, window), number=10)<\/p>\n
print(f"WMA (\u7eafPython)\u8017\u65f6: {wma_python_time:.4f}\u79d2")<\/p>\n
NumPy WMA<\/strong><\/h2>\n
wma_numpy_time = timeit.timeit(lambda: numpy_wma(data, window), number=10)<\/p>\n
print(f"WMA (NumPy)\u8017\u65f6: {wma_numpy_time:.4f}\u79d2")<\/p>\n
\u7eafPython EMA<\/strong><\/h2>\n
ema_python_time = timeit.timeit(lambda: exponential_moving_average(data, window), number=10)<\/p>\n
print(f"EMA (\u7eafPython)\u8017\u65f6: {ema_python_time:.4f}\u79d2")<\/p>\n
Pandas EMA<\/strong><\/h2>\n
ema_pandas_time = timeit.timeit(lambda: pandas_ema(data, window), number=10)<\/p>\n
print(f"EMA (Pandas)\u8017\u65f6: {ema_pandas_time:.4f}\u79d2")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u79fb\u52a8\u5e73\u5747\u7684\u4f18\u7f3a\u70b9<\/h3>\n<\/p>\n
\u79fb\u52a8\u5e73\u5747\u6709\u52a9\u4e8e\u5e73\u6ed1\u6570\u636e\u3001\u8bc6\u522b\u8d8b\u52bf\uff0c\u4f46\u4e5f\u6709\u5176\u5c40\u9650\u6027\u3002<\/strong><\/p>\n<\/p>\n
1. \u4f18\u70b9<\/h4>\n<\/p>\n
    \n
  • \u5e73\u6ed1\u6570\u636e\uff1a<\/strong> \u79fb\u52a8\u5e73\u5747\u53ef\u4ee5\u5e73\u6ed1\u6570\u636e\u4e2d\u7684\u77ed\u671f\u6ce2\u52a8\uff0c\u4f7f\u957f\u671f\u8d8b\u52bf\u66f4\u52a0\u660e\u663e\u3002<\/li>\n
  • \u7b80\u5355\u6613\u7528\uff1a<\/strong> \u5b9e\u73b0\u548c\u7406\u89e3\u79fb\u52a8\u5e73\u5747\u6bd4\u8f83\u7b80\u5355\uff0c\u9002\u5408\u521d\u5b66\u8005\u548c\u5feb\u901f\u5206\u6790\u3002<\/li>\n<\/ul>\n
    2. \u7f3a\u70b9<\/h4>\n<\/p>\n
      \n
    • \u6ede\u540e\u6027\uff1a<\/strong> \u79fb\u52a8\u5e73\u5747\u4f1a\u4ea7\u751f\u6ede\u540e\u6548\u5e94\uff0c\u53cd\u6620\u8d8b\u52bf\u7684\u901f\u5ea6\u8f83\u6162\uff0c\u7279\u522b\u662fSMA\u3002<\/li>\n
    • \u5bf9\u7a81\u53d8\u4e0d\u654f\u611f\uff1a<\/strong> \u79fb\u52a8\u5e73\u5747\u5bf9\u6570\u636e\u7684\u7a81\u7136\u53d8\u5316\u53cd\u5e94\u8f83\u6162\uff0c\u7279\u522b\u662fSMA\u548cWMA\u3002<\/li>\n<\/ul>\n
      \u4e03\u3001\u7efc\u5408\u5e94\u7528\u793a\u4f8b<\/h3>\n<\/p>\n
      \u7ed3\u5408\u524d\u9762\u7684\u5185\u5bb9\uff0c\u6211\u4eec\u53ef\u4ee5\u5b9e\u73b0\u4e00\u4e2a\u7efc\u5408\u5e94\u7528\u793a\u4f8b\uff0c\u4f7f\u7528\u79fb\u52a8\u5e73\u5747\u5206\u6790\u80a1\u7968\u4ef7\u683c\u8d8b\u52bf\uff1a<\/p>\n<\/p>\n
      import pandas as pd<\/p>\n
      import numpy as np<\/p>\n
      import matplotlib.pyplot as plt<\/p>\n
      \u4e0b\u8f7d\u80a1\u7968\u6570\u636e\uff08\u4ee5\u82f9\u679c\u516c\u53f8\u4e3a\u4f8b\uff09<\/strong><\/h2>\n
      stock_data = pd.read_csv('https:\/\/query1.finance.yahoo.com\/v7\/finance\/download\/AAPL?period1=1609459200&period2=1630454400&interval=1d&events=history&includeAdjustedClose=true')<\/p>\n
      stock_data['Date'] = pd.to_datetime(stock_data['Date'])<\/p>\n
      stock_data.set_index('Date', inplace=True)<\/p>\n
      \u8ba1\u7b97\u79fb\u52a8\u5e73\u5747<\/strong><\/h2>\n
      window = 20<\/p>\n
      stock_data['SMA'] = numpy_sma(stock_data['Close'].values, window)<\/p>\n
      stock_data['WMA'] = numpy_wma(stock_data['Close'].values, window)<\/p>\n
      stock_data['EMA'] = pandas_ema(stock_data['Close'].values, window)<\/p>\n
      \u53ef\u89c6\u5316<\/strong><\/h2>\n
      plt.figure(figsize=(14, 7))<\/p>\n
      plt.plot(stock_data['Close'], label='Close Price')<\/p>\n
      plt.plot(stock_data['SMA'], label='Simple Moving Average')<\/p>\n
      plt.plot(stock_data['WMA'], label='Weighted Moving Average')<\/p>\n
      plt.plot(stock_data['EMA'], label='Exponential Moving Average')<\/p>\n
      plt.title('Stock Price and Moving Averages')<\/p>\n
      plt.legend()<\/p>\n
      plt.show()<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u516b\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
      \u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u6211\u4eec\u8be6\u7ec6\u63a2\u8ba8\u4e86Python\u4e2d\u5b9e\u73b0\u79fb\u52a8\u5e73\u5747\u7684\u591a\u79cd\u65b9\u6cd5\uff0c\u5305\u62ec\u7b80\u5355\u79fb\u52a8\u5e73\u5747\u6cd5\uff08SMA\uff09\u3001\u52a0\u6743\u79fb\u52a8\u5e73\u5747\u6cd5\uff08WMA\uff09\u3001\u6307\u6570\u79fb\u52a8\u5e73\u5747\u6cd5\uff08EMA\uff09\u3002\u5e76\u901a\u8fc7\u4ee3\u7801\u793a\u4f8b\u548c\u5b9e\u9645\u5e94\u7528\uff0c\u5c55\u793a\u4e86\u5982\u4f55\u5229\u7528\u8fd9\u4e9b\u65b9\u6cd5\u8fdb\u884c\u6570\u636e\u5206\u6790\u548c\u8d8b\u52bf\u8bc6\u522b\u3002\u5e0c\u671b\u8fd9\u4e9b\u5185\u5bb9\u80fd\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u548c\u5e94\u7528\u79fb\u52a8\u5e73\u5747\u6cd5\u3002<\/p>\n<\/p>\n
      \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
       \u79fb\u52a8\u5e73\u5747\u5728Python\u4e2d\u662f\u5982\u4f55\u8ba1\u7b97\u7684\uff1f<\/strong>
      \u79fb\u52a8\u5e73\u5747\u662f\u4e00\u79cd\u5e38\u7528\u7684\u65f6\u95f4\u5e8f\u5217\u5e73\u6ed1\u6280\u672f\uff0c\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528Python\u7684Pandas\u5e93\u8f7b\u677e\u5b9e\u73b0\u3002\u9996\u5148\uff0c\u786e\u4fdd\u4f60\u5df2\u7ecf\u5b89\u88c5\u4e86Pandas\u5e93\u3002\u63a5\u4e0b\u6765\uff0c\u52a0\u8f7d\u4f60\u7684\u6570\u636e\uff0c\u5e76\u4f7f\u7528rolling()<\/code>\u51fd\u6570\u7ed3\u5408mean()<\/code>\u65b9\u6cd5\u6765\u8ba1\u7b97\u79fb\u52a8\u5e73\u5747\u3002\u4f8b\u5982\uff0cdata['column_name'].rolling(window=3).mean()<\/code>\u5c06\u8ba1\u7b97\u6307\u5b9a\u5217\u76843\u671f\u79fb\u52a8\u5e73\u5747\u3002<\/p>\n
      \u4f7f\u7528Numpy\u5e93\u5b9e\u73b0\u79fb\u52a8\u5e73\u5747\u7684\u6b65\u9aa4\u662f\u4ec0\u4e48\uff1f<\/strong>
      \u5982\u679c\u4f60\u66f4\u503e\u5411\u4e8e\u4f7f\u7528Numpy\u5e93\u6765\u8ba1\u7b97\u79fb\u52a8\u5e73\u5747\uff0c\u53ef\u4ee5\u4f7f\u7528numpy.convolve()<\/code>\u51fd\u6570\u3002\u4f60\u9700\u8981\u5b9a\u4e49\u4e00\u4e2a\u6743\u91cd\u6570\u7ec4\uff0c\u901a\u5e38\u662f\u5747\u5300\u7684\u6570\u7ec4\uff0c\u6bd4\u5982np.ones(window_size)\/window_size<\/code>\uff0c\u7136\u540e\u7528\u8fd9\u4e2a\u6743\u91cd\u6570\u7ec4\u548c\u4f60\u7684\u6570\u636e\u8fdb\u884c\u5377\u79ef\u8fd0\u7b97\u3002\u8fd9\u6837\u53ef\u4ee5\u5feb\u901f\u5b9e\u73b0\u79fb\u52a8\u5e73\u5747\uff0c\u5c24\u5176\u9002\u5408\u5904\u7406\u5927\u578b\u6570\u7ec4\u3002<\/p>\n
      \u5982\u4f55\u9009\u62e9\u79fb\u52a8\u5e73\u5747\u7684\u7a97\u53e3\u5927\u5c0f\uff1f<\/strong>
      \u9009\u62e9\u5408\u9002\u7684\u7a97\u53e3\u5927\u5c0f\u5bf9\u4e8e\u79fb\u52a8\u5e73\u5747\u7684\u6548\u679c\u81f3\u5173\u91cd\u8981\u3002\u7a97\u53e3\u5927\u5c0f\u7684\u9009\u62e9\u901a\u5e38\u53d6\u51b3\u4e8e\u6570\u636e\u7684\u7279\u6027\u548c\u5206\u6790\u76ee\u7684\u3002\u8f83\u5c0f\u7684\u7a97\u53e3\u80fd\u66f4\u654f\u611f\u5730\u53cd\u6620\u6570\u636e\u7684\u53d8\u5316\uff0c\u4f46\u53ef\u80fd\u4f1a\u5f15\u5165\u66f4\u591a\u7684\u566a\u58f0\uff1b\u8f83\u5927\u7684\u7a97\u53e3\u5219\u80fd\u5e73\u6ed1\u6570\u636e\uff0c\u51cf\u5c11\u6ce2\u52a8\uff0c\u4f46\u53ef\u80fd\u4f1a\u5bfc\u81f4\u5ef6\u8fdf\u3002\u56e0\u6b64\uff0c\u5efa\u8bae\u6839\u636e\u6570\u636e\u7684\u6ce2\u52a8\u6027\u548c\u5206\u6790\u9700\u6c42\u8fdb\u884c\u8bd5\u9a8c\uff0c\u5bfb\u627e\u6700\u4f73\u7a97\u53e3\u5927\u5c0f\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u5b9e\u73b0\u79fb\u52a8\u5e73\u5747\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5305\u62ec\u7b80\u5355\u79fb\u52a8\u5e73\u5747\u6cd5\uff08SMA\uff09\u3001\u52a0\u6743\u79fb\u52a8\u5e73\u5747\u6cd5\uff08WMA\uff09\u3001\u6307\u6570\u79fb\u52a8\u5e73\u5747\u6cd5\uff08 […]","protected":false},"author":3,"featured_media":1151172,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1151163"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1151163"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1151163\/revisions"}],"predecessor-version":[{"id":1151173,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1151163\/revisions\/1151173"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1151172"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1151163"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1151163"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1151163"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}