{"id":1170601,"date":"2025-01-15T16:24:37","date_gmt":"2025-01-15T08:24:37","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1170601.html"},"modified":"2025-01-15T16:24:40","modified_gmt":"2025-01-15T08:24:40","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e7%94%bb%e6%8c%81%e4%bb%93%e4%bf%a1%e5%8f%b7","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1170601.html","title":{"rendered":"\u5982\u4f55\u7528python\u753b\u6301\u4ed3\u4fe1\u53f7"},"content":{"rendered":"

\"\u5982\u4f55\u7528python\u753b\u6301\u4ed3\u4fe1\u53f7\"<\/p>\n

\u8981\u7528Python\u753b\u6301\u4ed3\u4fe1\u53f7\uff0c\u53ef\u4ee5\u4f7f\u7528\u4e00\u4e9b\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff0c\u4f8b\u5982Matplotlib\u548cPlotly<\/strong>\u3002\u4f60\u9700\u8981\u9996\u5148\u83b7\u53d6\u6240\u9700\u7684\u6570\u636e\u3001\u5904\u7406\u6570\u636e\u3001\u751f\u6210\u6301\u4ed3\u4fe1\u53f7\uff0c\u7136\u540e\u7ed8\u5236\u56fe\u8868<\/strong>\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u4f7f\u7528\u8fd9\u4e9b\u5e93\u7ed8\u5236\u6301\u4ed3\u4fe1\u53f7\u7684\u5177\u4f53\u6b65\u9aa4<\/strong>\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u83b7\u53d6\u548c\u5904\u7406\u6570\u636e<\/h2>\n<\/p>\n

\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u83b7\u53d6\u91d1\u878d\u5e02\u573a\u7684\u6570\u636e\uff0c\u4f8b\u5982\u80a1\u7968\u4ef7\u683c\u6570\u636e\u3002\u53ef\u4ee5\u4f7f\u7528yfinance<\/code>\u5e93\u6765\u83b7\u53d6\u6570\u636e\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n

import yfinance as yf<\/p>\n
\u83b7\u53d6\u80a1\u7968\u6570\u636e<\/strong><\/h2>\n
ticker = 'AAPL'<\/p>\n
data = yf.download(ticker, start='2022-01-01', end='2023-01-01')<\/p>\n
print(data.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u6bb5\u4ee3\u7801\u5c06\u4e0b\u8f7dApple\u516c\u53f8\u57282022\u5e74\u52302023\u5e74\u7684\u80a1\u7968\u6570\u636e\uff0c\u5e76\u663e\u793a\u524d\u51e0\u884c\u6570\u636e\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u751f\u6210\u6301\u4ed3\u4fe1\u53f7<\/h2>\n<\/p>\n
\u5728\u751f\u6210\u6301\u4ed3\u4fe1\u53f7\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u51b3\u5b9a\u4f7f\u7528\u54ea\u79cd\u7b56\u7565\u3002\u4f8b\u5982\uff0c\u8fd9\u91cc\u6211\u4eec\u4f7f\u7528\u7b80\u5355\u79fb\u52a8\u5e73\u5747\u7ebf\u4ea4\u53c9\u7b56\u7565\u6765\u751f\u6210\u4e70\u5356\u4fe1\u53f7\u3002<\/p>\n<\/p>\n
# \u8ba1\u7b97\u77ed\u671f\u548c\u957f\u671f\u79fb\u52a8\u5e73\u5747\u7ebf<\/p>\n
short_window = 40<\/p>\n
long_window = 100<\/p>\n
data['Short_MA'] = data['Close'].rolling(window=short_window).mean()<\/p>\n
data['Long_MA'] = data['Close'].rolling(window=long_window).mean()<\/p>\n
\u751f\u6210\u4e70\u5356\u4fe1\u53f7<\/strong><\/h2>\n
data['Signal'] = 0<\/p>\n
data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)<\/p>\n
data['Position'] = data['Signal'].diff()<\/p>\n
print(data[['Close', 'Short_MA', 'Long_MA', 'Signal', 'Position']].head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u6bb5\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u8ba1\u7b97\u4e86\u77ed\u671f\u548c\u957f\u671f\u7684\u79fb\u52a8\u5e73\u5747\u7ebf\uff0c\u5e76\u6839\u636e\u8fd9\u4e24\u4e2a\u5747\u7ebf\u7684\u4ea4\u53c9\u751f\u6210\u4e70\u5356\u4fe1\u53f7\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528Matplotlib\u7ed8\u56fe<\/h2>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u4f7f\u7528Matplotlib\u5e93\u6765\u7ed8\u5236\u56fe\u8868\u3002\u6211\u4eec\u5c06\u7ed8\u5236\u80a1\u7968\u7684\u6536\u76d8\u4ef7\u683c\u3001\u77ed\u671f\u548c\u957f\u671f\u79fb\u52a8\u5e73\u5747\u7ebf\uff0c\u4ee5\u53ca\u6301\u4ed3\u4fe1\u53f7\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
plt.figure(figsize=(14, 7))<\/p>\n
\u7ed8\u5236\u6536\u76d8\u4ef7\u683c<\/strong><\/h2>\n
plt.plot(data['Close'], label='Close Price')<\/p>\n
\u7ed8\u5236\u77ed\u671f\u548c\u957f\u671f\u79fb\u52a8\u5e73\u5747\u7ebf<\/strong><\/h2>\n
plt.plot(data['Short_MA'], label='40-Day Moving Average')<\/p>\n
plt.plot(data['Long_MA'], label='100-Day Moving Average')<\/p>\n
\u7ed8\u5236\u4e70\u5356\u4fe1\u53f7<\/strong><\/h2>\n
plt.plot(data.loc[data['Position'] == 1].index, <\/p>\n
         data['Short_MA'][data['Position'] == 1], <\/p>\n
         '^', markersize=10, color='g', lw=0, label='Buy Signal')<\/p>\n
plt.plot(data.loc[data['Position'] == -1].index, <\/p>\n
         data['Short_MA'][data['Position'] == -1], <\/p>\n
         'v', markersize=10, color='r', lw=0, label='Sell Signal')<\/p>\n
plt.title(f'{ticker} Price and Trading Signals')<\/p>\n
plt.xlabel('Date')<\/p>\n
plt.ylabel('Price')<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u6bb5\u4ee3\u7801\u4f7f\u7528Matplotlib\u7ed8\u5236\u4e86\u80a1\u7968\u6536\u76d8\u4ef7\u683c\u3001\u77ed\u671f\u548c\u957f\u671f\u79fb\u52a8\u5e73\u5747\u7ebf\uff0c\u4ee5\u53ca\u4e70\u5356\u4fe1\u53f7\u3002\u6211\u4eec\u7528\u7eff\u8272\u5411\u4e0a\u7684\u4e09\u89d2\u5f62\u8868\u793a\u4e70\u5165\u4fe1\u53f7\uff0c\u7528\u7ea2\u8272\u5411\u4e0b\u7684\u4e09\u89d2\u5f62\u8868\u793a\u5356\u51fa\u4fe1\u53f7\u3002<\/p>\n<\/p>\n
\u56db\u3001\u4f7f\u7528Plotly\u7ed8\u56fe<\/h2>\n<\/p>\n
Plotly\u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684\u4ea4\u4e92\u5f0f\u7ed8\u56fe\u5e93\uff0c\u5b83\u4f7f\u5f97\u7ed8\u56fe\u66f4\u52a0\u751f\u52a8\u548c\u4e92\u52a8\u3002\u4e0b\u9762\u662f\u4f7f\u7528Plotly\u7ed8\u5236\u6301\u4ed3\u4fe1\u53f7\u7684\u793a\u4f8b\u4ee3\u7801\u3002<\/p>\n<\/p>\n
import plotly.graph_objects as go<\/p>\n
\u521b\u5efa\u56fe\u8868<\/strong><\/h2>\n
fig = go.Figure()<\/p>\n
\u6dfb\u52a0\u6536\u76d8\u4ef7\u683c<\/strong><\/h2>\n
fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='Close Price'))<\/p>\n
\u6dfb\u52a0\u77ed\u671f\u548c\u957f\u671f\u79fb\u52a8\u5e73\u5747\u7ebf<\/strong><\/h2>\n
fig.add_trace(go.Scatter(x=data.index, y=data['Short_MA'], mode='lines', name='40-Day Moving Average'))<\/p>\n
fig.add_trace(go.Scatter(x=data.index, y=data['Long_MA'], mode='lines', name='100-Day Moving Average'))<\/p>\n
\u6dfb\u52a0\u4e70\u5356\u4fe1\u53f7<\/strong><\/h2>\n
fig.add_trace(go.Scatter(x=data.loc[data['Position'] == 1].index, <\/p>\n
                         y=data['Short_MA'][data['Position'] == 1], <\/p>\n
                         mode='markers', marker=dict(symbol='triangle-up', size=10, color='green'), name='Buy Signal'))<\/p>\n
fig.add_trace(go.Scatter(x=data.loc[data['Position'] == -1].index, <\/p>\n
                         y=data['Short_MA'][data['Position'] == -1], <\/p>\n
                         mode='markers', marker=dict(symbol='triangle-down', size=10, color='red'), name='Sell Signal'))<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
fig.update_layout(title=f'{ticker} Price and Trading Signals',<\/p>\n
                  xaxis_title='Date',<\/p>\n
                  yaxis_title='Price',<\/p>\n
                  legend_title='Legend')<\/p>\n
fig.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u6bb5\u4ee3\u7801\u4f7f\u7528Plotly\u7ed8\u5236\u4e86\u80a1\u7968\u6536\u76d8\u4ef7\u683c\u3001\u77ed\u671f\u548c\u957f\u671f\u79fb\u52a8\u5e73\u5747\u7ebf\uff0c\u4ee5\u53ca\u4e70\u5356\u4fe1\u53f7\u3002Plotly\u7684\u56fe\u8868\u662f\u4ea4\u4e92\u5f0f\u7684\uff0c\u53ef\u4ee5\u653e\u5927\u548c\u7f29\u5c0f\uff0c\u5e76\u63d0\u4f9b\u66f4\u597d\u7684\u7528\u6237\u4f53\u9a8c\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u7ed3\u5408\u5176\u4ed6\u6280\u672f\u6307\u6807<\/h2>\n<\/p>\n
\u9664\u4e86\u7b80\u5355\u79fb\u52a8\u5e73\u5747\u7ebf\uff0c\u6211\u4eec\u8fd8\u53ef\u4ee5\u7ed3\u5408\u5176\u4ed6\u6280\u672f\u6307\u6807\u6765\u751f\u6210\u66f4\u590d\u6742\u7684\u6301\u4ed3\u4fe1\u53f7\u3002\u4f8b\u5982\uff0c\u7ed3\u5408\u76f8\u5bf9\u5f3a\u5f31\u6307\u6570\uff08RSI\uff09\u548c\u79fb\u52a8\u5e73\u5747\u6536\u655b\u6563\u5ea6\uff08MACD\uff09\u6765\u751f\u6210\u4e70\u5356\u4fe1\u53f7\u3002<\/p>\n<\/p>\n
\u8ba1\u7b97RSI<\/h3>\n<\/p>\n
import pandas as pd<\/p>\n
def compute_rsi(data, window=14):<\/p>\n
    delta = data['Close'].diff()<\/p>\n
    gAI<\/a>n = (delta.where(delta > 0, 0)).rolling(window=window).mean()<\/p>\n
    loss = (-delta.where(delta < 0, 0)).rolling(window=window).mean()<\/p>\n
    rs = gain \/ loss<\/p>\n
    rsi = 100 - (100 \/ (1 + rs))<\/p>\n
    return rsi<\/p>\n
data['RSI'] = compute_rsi(data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u6bb5\u4ee3\u7801\u8ba1\u7b97\u4e86\u76f8\u5bf9\u5f3a\u5f31\u6307\u6570\uff08RSI\uff09\uff0c\u8fd9\u662f\u4e00\u4e2a\u8861\u91cf\u4ef7\u683c\u53d8\u52a8\u5f3a\u5ea6\u7684\u6280\u672f\u6307\u6807\u3002<\/p>\n<\/p>\n
\u8ba1\u7b97MACD<\/h3>\n<\/p>\n
def compute_macd(data, short_window=12, long_window=26, signal_window=9):<\/p>\n
    short_ema = data['Close'].ewm(span=short_window, adjust=False).mean()<\/p>\n
    long_ema = data['Close'].ewm(span=long_window, adjust=False).mean()<\/p>\n
    macd = short_ema - long_ema<\/p>\n
    signal = macd.ewm(span=signal_window, adjust=False).mean()<\/p>\n
    return macd, signal<\/p>\n
data['MACD'], data['MACD_Signal'] = compute_macd(data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u6bb5\u4ee3\u7801\u8ba1\u7b97\u4e86\u79fb\u52a8\u5e73\u5747\u6536\u655b\u6563\u5ea6\uff08MACD\uff09\u548c\u4fe1\u53f7\u7ebf\uff0cMACD\u662f\u8861\u91cf\u4ef7\u683c\u53d8\u52a8\u8d8b\u52bf\u7684\u6280\u672f\u6307\u6807\u3002<\/p>\n<\/p>\n
\u7ed3\u5408RSI\u548cMACD\u751f\u6210\u6301\u4ed3\u4fe1\u53f7<\/h3>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u7ed3\u5408RSI\u548cMACD\u6765\u751f\u6210\u66f4\u590d\u6742\u7684\u4e70\u5356\u4fe1\u53f7\u3002\u4f8b\u5982\uff0c\u5f53RSI\u4f4e\u4e8e30\u4e14MACD\u7ebf\u4ece\u4e0b\u65b9\u7a7f\u8fc7\u4fe1\u53f7\u7ebf\u65f6\u751f\u6210\u4e70\u5165\u4fe1\u53f7\uff0c\u5f53RSI\u9ad8\u4e8e70\u4e14MACD\u7ebf\u4ece\u4e0a\u65b9\u7a7f\u8fc7\u4fe1\u53f7\u7ebf\u65f6\u751f\u6210\u5356\u51fa\u4fe1\u53f7\u3002<\/p>\n<\/p>\n
data['Signal'] = 0<\/p>\n
data['Signal'] = np.where((data['RSI'] < 30) & (data['MACD'] > data['MACD_Signal']), 1, data['Signal'])<\/p>\n
data['Signal'] = np.where((data['RSI'] > 70) & (data['MACD'] < data['MACD_Signal']), -1, data['Signal'])<\/p>\n
data['Position'] = data['Signal'].diff()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u6bb5\u4ee3\u7801\u7ed3\u5408\u4e86RSI\u548cMACD\u751f\u6210\u4e70\u5356\u4fe1\u53f7\uff0c\u5e76\u8ba1\u7b97\u4e86\u6301\u4ed3\u72b6\u6001\u3002<\/p>\n<\/p>\n
\u7ed8\u5236\u7ed3\u5408RSI\u548cMACD\u7684\u6301\u4ed3\u4fe1\u53f7<\/h3>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4e4b\u524d\u4ecb\u7ecd\u7684Matplotlib\u548cPlotly\u6765\u7ed8\u5236\u7ed3\u5408RSI\u548cMACD\u751f\u6210\u7684\u6301\u4ed3\u4fe1\u53f7\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528Matplotlib\u7ed8\u5236\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
plt.figure(figsize=(14, 7))<\/p>\n
\u7ed8\u5236\u6536\u76d8\u4ef7\u683c<\/strong><\/h2>\n
plt.plot(data['Close'], label='Close Price')<\/p>\n
\u7ed8\u5236MACD\u548c\u4fe1\u53f7\u7ebf<\/strong><\/h2>\n
plt.plot(data['MACD'], label='MACD')<\/p>\n
plt.plot(data['MACD_Signal'], label='MACD Signal')<\/p>\n
\u7ed8\u5236\u4e70\u5356\u4fe1\u53f7<\/strong><\/h2>\n
plt.plot(data.loc[data['Position'] == 1].index, <\/p>\n
         data['MACD'][data['Position'] == 1], <\/p>\n
         '^', markersize=10, color='g', lw=0, label='Buy Signal')<\/p>\n
plt.plot(data.loc[data['Position'] == -1].index, <\/p>\n
         data['MACD'][data['Position'] == -1], <\/p>\n
         'v', markersize=10, color='r', lw=0, label='Sell Signal')<\/p>\n
plt.title(f'{ticker} Price and Trading Signals')<\/p>\n
plt.xlabel('Date')<\/p>\n
plt.ylabel('Price')<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u6bb5\u4ee3\u7801\u4f7f\u7528Matplotlib\u7ed8\u5236\u4e86\u7ed3\u5408RSI\u548cMACD\u751f\u6210\u7684\u6301\u4ed3\u4fe1\u53f7\u3002<\/p>\n<\/p>\n
\u516d\u3001\u4f18\u5316\u7b56\u7565<\/h2>\n<\/p>\n
\u5728\u5b9e\u9645\u64cd\u4f5c\u4e2d\uff0c\u4ea4\u6613\u7b56\u7565\u9700\u8981\u4e0d\u65ad\u4f18\u5316\u3002\u53ef\u4ee5\u4f7f\u7528\u5386\u53f2\u6570\u636e\u8fdb\u884c\u56de\u6d4b\uff0c\u8bc4\u4f30\u7b56\u7565\u7684\u6536\u76ca\u548c\u98ce\u9669\uff0c\u5e76\u8fdb\u884c\u53c2\u6570\u8c03\u6574\u548c\u4f18\u5316\u3002<\/p>\n<\/p>\n
\u56de\u6d4b\u7b56\u7565<\/h3>\n<\/p>\n
\u4e0b\u9762\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u56de\u6d4b\u7b56\u7565\u793a\u4f8b\uff0c\u6211\u4eec\u8ba1\u7b97\u7b56\u7565\u7684\u7d2f\u8ba1\u6536\u76ca\u3002<\/p>\n<\/p>\n
initial_capital = 10000<\/p>\n
positions = pd.DataFrame(index=data.index).fillna(0)<\/p>\n
positions[ticker] = data['Signal']<\/p>\n
portfolio = positions.multiply(data['Close'], axis=0)<\/p>\n
pos_diff = positions.diff()<\/p>\n
portfolio['holdings'] = (positions.multiply(data['Close'], axis=0)).sum(axis=1)<\/p>\n
portfolio['cash'] = initial_capital - (pos_diff.multiply(data['Close'], axis=0)).sum(axis=1).cumsum()<\/p>\n
portfolio['total'] = portfolio['cash'] + portfolio['holdings']<\/p>\n
portfolio['returns'] = portfolio['total'].pct_change()<\/p>\n
print(portfolio.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u6bb5\u4ee3\u7801\u8ba1\u7b97\u4e86\u7b56\u7565\u7684\u6301\u4ed3\u3001\u73b0\u91d1\u548c\u603b\u8d44\u4ea7\uff0c\u5e76\u8ba1\u7b97\u4e86\u7d2f\u8ba1\u6536\u76ca\u3002<\/p>\n<\/p>\n
\u4f18\u5316\u53c2\u6570<\/h3>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528\u7f51\u683c\u641c\u7d22\u6216\u9057\u4f20\u7b97\u6cd5\u7b49\u65b9\u6cd5\u4f18\u5316\u7b56\u7565\u53c2\u6570\uff0c\u5bfb\u627e\u6700\u4f73\u53c2\u6570\u7ec4\u5408\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u7f51\u683c\u641c\u7d22\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
from sklearn.model_selection import ParameterGrid<\/p>\n
param_grid = {<\/p>\n
    'short_window': [20, 40, 60],<\/p>\n
    'long_window': [100, 150, 200]<\/p>\n
}<\/p>\n
best_params = None<\/p>\n
best_return = -np.inf<\/p>\n
for params in ParameterGrid(param_grid):<\/p>\n
    short_window = params['short_window']<\/p>\n
    long_window = params['long_window']<\/p>\n
    data['Short_MA'] = data['Close'].rolling(window=short_window).mean()<\/p>\n
    data['Long_MA'] = data['Close'].rolling(window=long_window).mean()<\/p>\n
    data['Signal'] = 0<\/p>\n
    data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)<\/p>\n
    data['Position'] = data['Signal'].diff()<\/p>\n
    positions = pd.DataFrame(index=data.index).fillna(0)<\/p>\n
    positions[ticker] = data['Signal']<\/p>\n
    portfolio = positions.multiply(data['Close'], axis=0)<\/p>\n
    pos_diff = positions.diff()<\/p>\n
    portfolio['holdings'] = (positions.multiply(data['Close'], axis=0)).sum(axis=1)<\/p>\n
    portfolio['cash'] = initial_capital - (pos_diff.multiply(data['Close'], axis=0)).sum(axis=1).cumsum()<\/p>\n
    portfolio['total'] = portfolio['cash'] + portfolio['holdings']<\/p>\n
    portfolio['returns'] = portfolio['total'].pct_change()<\/p>\n
    total_return = portfolio['total'][-1]<\/p>\n
    if total_return > best_return:<\/p>\n
        best_return = total_return<\/p>\n
        best_params = params<\/p>\n
print(f'Best parameters: {best_params}')<\/p>\n
print(f'Best return: {best_return}')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u6bb5\u4ee3\u7801\u4f7f\u7528\u7f51\u683c\u641c\u7d22\u4f18\u5316\u4e86\u77ed\u671f\u548c\u957f\u671f\u79fb\u52a8\u5e73\u5747\u7ebf\u7684\u7a97\u53e3\u5927\u5c0f\uff0c\u627e\u5230\u4e86\u6700\u4f73\u53c2\u6570\u7ec4\u5408\u3002<\/p>\n<\/p>\n
\u4e03\u3001\u603b\u7ed3<\/h2>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Python\u7ed8\u5236\u6301\u4ed3\u4fe1\u53f7\uff0c\u5305\u62ec\u6570\u636e\u83b7\u53d6\u3001\u6570\u636e\u5904\u7406\u3001\u751f\u6210\u6301\u4ed3\u4fe1\u53f7\u3001\u7ed8\u5236\u56fe\u8868\u4ee5\u53ca\u4f18\u5316\u7b56\u7565\u7b49\u65b9\u9762\u3002\u901a\u8fc7\u7ed3\u5408\u4e0d\u540c\u7684\u6280\u672f\u6307\u6807\uff0c\u6211\u4eec\u53ef\u4ee5\u751f\u6210\u66f4\u590d\u6742\u7684\u6301\u4ed3\u4fe1\u53f7\uff0c\u5e76\u901a\u8fc7\u56de\u6d4b\u548c\u4f18\u5316\u63d0\u9ad8\u7b56\u7565\u7684\u6536\u76ca\u548c\u7a33\u5b9a\u6027\u3002\u5e0c\u671b\u8fd9\u7bc7\u6587\u7ae0\u80fd\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u5982\u4f55\u7528Python\u7ed8\u5236\u6301\u4ed3\u4fe1\u53f7\uff0c\u5e76\u5e94\u7528\u5230\u5b9e\u9645\u4ea4\u6613\u4e2d\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u7ed8\u5236\u6301\u4ed3\u4fe1\u53f7\u7684\u56fe\u8868\uff1f<\/strong>
\u7ed8\u5236\u6301\u4ed3\u4fe1\u53f7\u7684\u56fe\u8868\u901a\u5e38\u9700\u8981\u4f7f\u7528\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff0c\u4f8b\u5982Matplotlib\u6216Seaborn\u3002\u9996\u5148\uff0c\u60a8\u9700\u8981\u51c6\u5907\u597d\u76f8\u5173\u7684\u6301\u4ed3\u6570\u636e\uff0c\u5305\u62ec\u4e70\u5165\u548c\u5356\u51fa\u4fe1\u53f7\u3002\u5229\u7528\u8fd9\u4e9b\u6570\u636e\uff0c\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a\u65f6\u95f4\u5e8f\u5217\u56fe\uff0c\u5e76\u5728\u56fe\u4e2d\u7528\u4e0d\u540c\u7684\u6807\u8bb0\u8868\u793a\u4e70\u5165\u548c\u5356\u51fa\u4fe1\u53f7\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\u4ee3\u7801\uff1a  <\/p>\n
import matplotlib.pyplot as plt\nimport pandas as pd\n\n# \u5047\u8bbedf\u662f\u5305\u542b\u65e5\u671f\u548c\u6301\u4ed3\u4fe1\u53f7\u7684\u6570\u636e\u6846\n# df['buy_signal'] \u548c df['sell_signal'] \u662f\u5e03\u5c14\u578b\u5217\nplt.figure(figsize=(12,6))\nplt.plot(df['date'], df['price'], label='\u4ef7\u683c', color='blue')\n\n# \u753b\u51fa\u4e70\u5165\u4fe1\u53f7\nplt.scatter(df[df['buy_signal']]['date'], df[df['buy_signal']]['price'], marker='^', color='green', label='\u4e70\u5165\u4fe1\u53f7', s=100)\n\n# \u753b\u51fa\u5356\u51fa\u4fe1\u53f7\nplt.scatter(df[df['sell_signal']]['date'], df[df['sell_signal']]['price'], marker='v', color='red', label='\u5356\u51fa\u4fe1\u53f7', s=100)\n\nplt.title('\u6301\u4ed3\u4fe1\u53f7\u56fe')\nplt.xlabel('\u65e5\u671f')\nplt.ylabel('\u4ef7\u683c')\nplt.legend()\nplt.show()\n<\/code><\/pre>\n
\u8fd9\u6bb5\u4ee3\u7801\u5c06\u5e2e\u52a9\u60a8\u5728\u56fe\u8868\u4e2d\u6e05\u6670\u5730\u6807\u8bc6\u51fa\u4e70\u5165\u548c\u5356\u51fa\u4fe1\u53f7\u3002<\/p>\n
\u5728Python\u4e2d\u5982\u4f55\u5904\u7406\u6301\u4ed3\u4fe1\u53f7\u6570\u636e\uff1f<\/strong>
\u5904\u7406\u6301\u4ed3\u4fe1\u53f7\u6570\u636e\u901a\u5e38\u5305\u62ec\u6570\u636e\u6e05\u7406\u3001\u7279\u5f81\u9009\u62e9\u548c\u4fe1\u53f7\u751f\u6210\u3002\u60a8\u9700\u8981\u786e\u4fdd\u6570\u636e\u7684\u51c6\u786e\u6027\uff0c\u53bb\u9664\u7f3a\u5931\u503c\u6216\u5f02\u5e38\u503c\u3002\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u6765\u64cd\u4f5c\u6570\u636e\u6846\uff0c\u751f\u6210\u4e70\u5165\u548c\u5356\u51fa\u4fe1\u53f7\u3002\u4f8b\u5982\uff0c\u60a8\u53ef\u4ee5\u6839\u636e\u79fb\u52a8\u5e73\u5747\u7ebf\u7684\u4ea4\u53c9\u6765\u786e\u5b9a\u4e70\u5165\u548c\u5356\u51fa\u65f6\u673a\u3002\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u60a8\u53ef\u4ee5\u66f4\u6709\u6548\u5730\u4e3a\u6301\u4ed3\u4fe1\u53f7\u751f\u6210\u505a\u51c6\u5907\u3002<\/p>\n
\u6709\u54ea\u4e9bPython\u5e93\u9002\u5408\u7ed8\u5236\u6301\u4ed3\u4fe1\u53f7\uff1f<\/strong>
\u5728\u7ed8\u5236\u6301\u4ed3\u4fe1\u53f7\u65f6\uff0c\u5e38\u7528\u7684Python\u5e93\u5305\u62ecMatplotlib\u3001Seaborn\u3001Plotly\u548cBokeh\u3002Matplotlib\u662f\u6700\u57fa\u7840\u7684\u7ed8\u56fe\u5e93\uff0c\u9002\u5408\u7ed8\u5236\u7b80\u5355\u7684\u56fe\u8868\u3002Seaborn\u5728Matplotlib\u7684\u57fa\u7840\u4e0a\u8fdb\u884c\u4e86\u6269\u5c55\uff0c\u63d0\u4f9b\u4e86\u66f4\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\u3002Plotly\u548cBokeh\u5219\u652f\u6301\u4ea4\u4e92\u5f0f\u56fe\u8868\uff0c\u9002\u5408\u9700\u8981\u5728\u7f51\u9875\u4e2d\u5c55\u793a\u52a8\u6001\u6570\u636e\u7684\u60c5\u51b5\u3002\u60a8\u53ef\u4ee5\u6839\u636e\u9879\u76ee\u7684\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5e93\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u8981\u7528Python\u753b\u6301\u4ed3\u4fe1\u53f7\uff0c\u53ef\u4ee5\u4f7f\u7528\u4e00\u4e9b\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff0c\u4f8b\u5982Matplotlib\u548cPlotly\u3002\u4f60\u9700\u8981\u9996\u5148\u83b7\u53d6\u6240 […]","protected":false},"author":3,"featured_media":1170608,"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\/1170601"}],"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=1170601"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1170601\/revisions"}],"predecessor-version":[{"id":1170611,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1170601\/revisions\/1170611"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1170608"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1170601"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1170601"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1170601"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}