{"id":1173937,"date":"2025-01-15T17:10:04","date_gmt":"2025-01-15T09:10:04","guid":{"rendered":""},"modified":"2025-01-15T17:10:09","modified_gmt":"2025-01-15T09:10:09","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e5%81%9a%e5%88%b0%e8%a7%a6%e5%ba%95%e5%8f%8d%e5%bc%b9","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1173937.html","title":{"rendered":"\u5982\u4f55\u7528Python\u505a\u5230\u89e6\u5e95\u53cd\u5f39"},"content":{"rendered":"

\"\u5982\u4f55\u7528Python\u505a\u5230\u89e6\u5e95\u53cd\u5f39\"<\/p>\n

\u8981\u7528Python\u505a\u5230\u89e6\u5e95\u53cd\u5f39\uff0c\u53ef\u4ee5\u4f7f\u7528\u6280\u672f\u5206\u6790\u3001\u6570\u636e\u9884\u5904\u7406\u3001\u673a\u5668\u5b66\u4e60<\/a>\u6a21\u578b\u3001\u81ea\u52a8\u5316\u4ea4\u6613\u7b56\u7565\u7b49\u65b9\u6cd5\u3002<\/strong>\u9996\u5148\uff0c\u5b9a\u4e49\u89e6\u5e95\u53cd\u5f39\u7684\u5224\u5b9a\u6807\u51c6\uff0c\u7136\u540e\u5229\u7528\u6280\u672f\u5206\u6790\u6307\u6807\u6216\u673a\u5668\u5b66\u4e60\u6a21\u578b\u68c0\u6d4b\u89e6\u5e95\u70b9\u548c\u53cd\u5f39\u70b9\uff0c\u6700\u540e\u6784\u5efa\u81ea\u52a8\u5316\u4ea4\u6613\u7b56\u7565\uff0c\u5b9e\u73b0\u89e6\u5e95\u53cd\u5f39\u7684\u81ea\u52a8\u5316\u64cd\u4f5c\u3002<\/p>\n<\/p>\n

\u4e0b\u9762\uff0c\u6211\u5c06\u8be6\u7ec6\u63cf\u8ff0\u5982\u4f55\u4f7f\u7528\u6280\u672f\u5206\u6790\u6765\u5b9e\u73b0\u89e6\u5e95\u53cd\u5f39\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5b9a\u4e49\u89e6\u5e95\u53cd\u5f39\u7684\u5224\u5b9a\u6807\u51c6<\/strong><\/p>\n<\/p>\n

\u89e6\u5e95\u53cd\u5f39\u901a\u5e38\u6307\u4ef7\u683c\u5728\u7ecf\u5386\u4e00\u6bb5\u65f6\u95f4\u7684\u4e0b\u8dcc\u540e\uff0c\u5728\u67d0\u4e2a\u4f4e\u70b9\u4f01\u7a33\u5e76\u5f00\u59cb\u56de\u5347\u3002\u8fd9\u79cd\u6a21\u5f0f\u53ef\u4ee5\u901a\u8fc7\u6280\u672f\u5206\u6790\u6307\u6807\u6765\u8bc6\u522b\uff0c\u5982\u76f8\u5bf9\u5f3a\u5f31\u6307\u6570\uff08RSI\uff09\u3001\u5e03\u6797\u5e26\u3001\u79fb\u52a8\u5e73\u5747\u7ebf\u7b49\u3002<\/p>\n<\/p>\n

RSI\u4f4e\u4e8e30\u901a\u5e38\u88ab\u8ba4\u4e3a\u662f\u8d85\u5356\u72b6\u6001\uff0c\u53ef\u80fd\u89e6\u5e95\uff1b\u5e03\u6797\u5e26\u4e0b\u8f68\u88ab\u7a81\u7834\u540e\u4ef7\u683c\u53cd\u5f39\uff1b\u77ed\u671f\u79fb\u52a8\u5e73\u5747\u7ebf\uff08\u59825\u65e5\u5747\u7ebf\uff09\u4e0e\u957f\u671f\u79fb\u52a8\u5e73\u5747\u7ebf\uff08\u598220\u65e5\u5747\u7ebf\uff09\u4ea4\u53c9\u4e5f\u53ef\u80fd\u662f\u53cd\u5f39\u4fe1\u53f7\u3002<\/p>\n<\/p>\n

\u4e8c\u3001\u6570\u636e\u9884\u5904\u7406<\/strong><\/p>\n<\/p>\n

\u5728\u8fdb\u884c\u6280\u672f\u5206\u6790\u4e4b\u524d\uff0c\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u9884\u5904\u7406\u3002\u5305\u62ec\u83b7\u53d6\u5386\u53f2\u4ef7\u683c\u6570\u636e\u3001\u6570\u636e\u6e05\u6d17\u3001\u8ba1\u7b97\u6280\u672f\u6307\u6807\u7b49\u3002<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
import numpy as np<\/p>\n
import yfinance as yf<\/p>\n
\u83b7\u53d6\u5386\u53f2\u4ef7\u683c\u6570\u636e<\/strong><\/h2>\n
ticker = "AAPL"<\/p>\n
data = yf.download(ticker, start="2020-01-01", end="2023-01-01")<\/p>\n
data['Close'].plot(title=f"{ticker} Close Price")<\/p>\n
\u8ba1\u7b97\u6280\u672f\u6307\u6807<\/strong><\/h2>\n
data['RSI'] = ta.momentum.RSIIndicator(data['Close']).rsi()<\/p>\n
data['MA_5'] = data['Close'].rolling(window=5).mean()<\/p>\n
data['MA_20'] = data['Close'].rolling(window=20).mean()<\/p>\n
data['Upper_BB'], data['Lower_BB'] = ta.volatility.BollingerBands(data['Close']).bollinger_hband(), ta.volatility.BollingerBands(data['Close']).bollinger_lband()<\/p>\n
\u6570\u636e\u6e05\u6d17<\/strong><\/h2>\n
data = data.dropna()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u6280\u672f\u5206\u6790<\/strong><\/p>\n<\/p>\n
\u6839\u636e\u6280\u672f\u6307\u6807\u5224\u65ad\u89e6\u5e95\u53cd\u5f39\u7684\u4fe1\u53f7\u3002<\/p>\n<\/p>\n
# \u89e6\u5e95\u53cd\u5f39\u5224\u5b9a\u6807\u51c6<\/p>\n
def is_rebound(row):<\/p>\n
    if row['RSI'] < 30 and row['Close'] > row['Lower_BB']:<\/p>\n
        return True<\/p>\n
    elif row['MA_5'] > row['MA_20']:<\/p>\n
        return True<\/p>\n
    return False<\/p>\n
data['Rebound'] = data.apply(is_rebound, axis=1)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u6784\u5efa\u81ea\u52a8\u5316\u4ea4\u6613\u7b56\u7565<\/strong><\/p>\n<\/p>\n
\u57fa\u4e8e\u89e6\u5e95\u53cd\u5f39\u7684\u5224\u5b9a\u6807\u51c6\uff0c\u6784\u5efa\u81ea\u52a8\u5316\u4ea4\u6613\u7b56\u7565\u3002\u7b56\u7565\u5305\u62ec\u4e70\u5165\u3001\u5356\u51fa\u4fe1\u53f7\u7684\u786e\u5b9a\u548c\u8d44\u91d1\u7ba1\u7406\u3002<\/p>\n<\/p>\n
# \u521d\u59cb\u5316\u8d44\u91d1\u548c\u6301\u4ed3<\/p>\n
cash = 10000<\/p>\n
position = 0<\/p>\n
initial_cash = cash<\/p>\n
\u4ea4\u6613\u7b56\u7565<\/strong><\/h2>\n
for index, row in data.iterrows():<\/p>\n
    if row['Rebound'] and cash >= row['Close']:<\/p>\n
        # \u4e70\u5165\u4fe1\u53f7<\/p>\n
        position += cash \/\/ row['Close']<\/p>\n
        cash %= row['Close']<\/p>\n
        print(f"Buy: {index.date()}, Price: {row['Close']}, Position: {position}, Cash: {cash}")<\/p>\n
    elif position > 0 and row['Close'] > row['MA_5']:<\/p>\n
        # \u5356\u51fa\u4fe1\u53f7<\/p>\n
        cash += position * row['Close']<\/p>\n
        position = 0<\/p>\n
        print(f"Sell: {index.date()}, Price: {row['Close']}, Position: {position}, Cash: {cash}")<\/p>\n
\u6700\u7ec8\u6536\u76ca<\/strong><\/h2>\n
final_value = cash + position * data.iloc[-1]['Close']<\/p>\n
print(f"Initial Cash: {initial_cash}, Final Value: {final_value}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u603b\u7ed3\u4e0e\u4f18\u5316<\/strong><\/p>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u5b9e\u73b0\u4e00\u4e2a\u7b80\u5355\u7684\u89e6\u5e95\u53cd\u5f39\u4ea4\u6613\u7b56\u7565\u3002\u7136\u800c\uff0c\u8fd9\u53ea\u662f\u4e00\u4e2a\u57fa\u7840\u7248\u672c\u3002\u5b9e\u9645\u5e94\u7528\u4e2d\u9700\u8981\u8fdb\u884c\u66f4\u591a\u7684\u4f18\u5316\u548c\u8c03\u6574\uff0c\u5982\u8003\u8651\u4ea4\u6613\u6210\u672c\u3001\u4f18\u5316\u53c2\u6570\u3001\u7ed3\u5408\u5176\u4ed6\u6307\u6807\u7b49\u3002<\/p>\n<\/p>\n
\u6b64\u5916\uff0c\u53ef\u4ee5\u5229\u7528\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u8fdb\u4e00\u6b65\u63d0\u5347\u7b56\u7565\u7684\u51c6\u786e\u6027\u3002\u4f8b\u5982\uff0c\u4f7f\u7528\u968f\u673a\u68ee\u6797\u3001\u652f\u6301\u5411\u91cf\u673a\u7b49\u6a21\u578b\u8bad\u7ec3\u5206\u7c7b\u5668\uff0c\u9884\u6d4b\u89e6\u5e95\u53cd\u5f39\u7684\u6982\u7387\u3002\u6b64\u7c7b\u65b9\u6cd5\u9700\u8981\u66f4\u591a\u7684\u6570\u636e\u548c\u8ba1\u7b97\u8d44\u6e90\uff0c\u4f46\u53ef\u4ee5\u63d0\u4f9b\u66f4\u9ad8\u7684\u9884\u6d4b\u7cbe\u5ea6\u3002<\/p>\n<\/p>\n
\u516d\u3001\u4f7f\u7528\u673a\u5668\u5b66\u4e60\u6a21\u578b<\/strong><\/p>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528\u673a\u5668\u5b66\u4e60\u6a21\u578b\u6765\u9884\u6d4b\u89e6\u5e95\u53cd\u5f39\u7684\u6982\u7387\uff0c\u4ece\u800c\u63d0\u9ad8\u7b56\u7565\u7684\u51c6\u786e\u6027\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u57fa\u4e8e\u968f\u673a\u68ee\u6797\u7684\u793a\u4f8b\u3002<\/p>\n<\/p>\n
from sklearn.model_selection import trAI<\/a>n_test_split<\/p>\n
from sklearn.ensemble import RandomForestClassifier<\/p>\n
from sklearn.metrics import classification_report<\/p>\n
\u51c6\u5907\u6570\u636e\u96c6<\/strong><\/h2>\n
data['Target'] = data['Close'].shift(-1) > data['Close']<\/p>\n
data = data.dropna()<\/p>\n
features = ['RSI', 'MA_5', 'MA_20', 'Upper_BB', 'Lower_BB']<\/p>\n
X = data[features]<\/p>\n
y = data['Target']<\/p>\n
\u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6<\/strong><\/h2>\n
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)<\/p>\n
\u8bad\u7ec3\u968f\u673a\u68ee\u6797\u6a21\u578b<\/strong><\/h2>\n
model = RandomForestClassifier(n_estimators=100, random_state=42)<\/p>\n
model.fit(X_train, y_train)<\/p>\n
\u9884\u6d4b\u5e76\u8bc4\u4f30\u6a21\u578b<\/strong><\/h2>\n
y_pred = model.predict(X_test)<\/p>\n
print(classification_report(y_test, y_pred))<\/p>\n
\u5e94\u7528\u6a21\u578b\u8fdb\u884c\u4ea4\u6613<\/strong><\/h2>\n
data['Predicted'] = model.predict(X)<\/p>\n
cash = 10000<\/p>\n
position = 0<\/p>\n
for index, row in data.iterrows():<\/p>\n
    if row['Predicted'] and cash >= row['Close']:<\/p>\n
        # \u4e70\u5165\u4fe1\u53f7<\/p>\n
        position += cash \/\/ row['Close']<\/p>\n
        cash %= row['Close']<\/p>\n
        print(f"Buy: {index.date()}, Price: {row['Close']}, Position: {position}, Cash: {cash}")<\/p>\n
    elif position > 0 and row['Close'] > row['MA_5']:<\/p>\n
        # \u5356\u51fa\u4fe1\u53f7<\/p>\n
        cash += position * row['Close']<\/p>\n
        position = 0<\/p>\n
        print(f"Sell: {index.date()}, Price: {row['Close']}, Position: {position}, Cash: {cash}")<\/p>\n
\u6700\u7ec8\u6536\u76ca<\/strong><\/h2>\n
final_value = cash + position * data.iloc[-1]['Close']<\/p>\n
print(f"Initial Cash: {initial_cash}, Final Value: {final_value}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u7ed3\u5408\u6280\u672f\u5206\u6790\u548c\u673a\u5668\u5b66\u4e60\uff0c\u6211\u4eec\u53ef\u4ee5\u6784\u5efa\u4e00\u4e2a\u66f4\u52a0\u667a\u80fd\u7684\u89e6\u5e95\u53cd\u5f39\u4ea4\u6613\u7b56\u7565\u3002\u8fd9\u4e2a\u7b56\u7565\u4e0d\u4ec5\u80fd\u591f\u8bc6\u522b\u51fa\u4ef7\u683c\u89e6\u5e95\u5e76\u53cd\u5f39\u7684\u4fe1\u53f7\uff0c\u8fd8\u80fd\u591f\u901a\u8fc7\u6a21\u578b\u9884\u6d4b\u6765\u63d0\u9ad8\u4ea4\u6613\u7684\u6210\u529f\u7387\u3002\u5f53\u7136\uff0c\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u8fd8\u9700\u8981\u4e0d\u65ad\u4f18\u5316\u548c\u8c03\u6574\u7b56\u7565\uff0c\u4ee5\u9002\u5e94\u5e02\u573a\u7684\u53d8\u5316\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u89e6\u5e95\u53cd\u5f39\u5728\u91d1\u878d\u5e02\u573a\u4e0a\u662f\u4ec0\u4e48\u610f\u601d\uff1f<\/strong>
\n\u89e6\u5e95\u53cd\u5f39\u662f\u6307\u5728\u91d1\u878d\u5e02\u573a\u4e2d\uff0c\u8d44\u4ea7\u4ef7\u683c\u5728\u7ecf\u5386\u4e86\u4e00\u6bb5\u65f6\u95f4\u7684\u4e0b\u8dcc\u540e\uff0c\u8fbe\u5230\u4e00\u4e2a\u4f4e\u70b9\uff0c\u968f\u540e\u5f00\u59cb\u53cd\u5f39\u5e76\u4e0a\u6da8\u3002\u8fd9\u79cd\u73b0\u8c61\u901a\u5e38\u88ab\u89c6\u4e3a\u5e02\u573a\u60c5\u7eea\u7684\u8f6c\u53d8\uff0c\u53ef\u80fd\u53cd\u6620\u51fa\u6295\u8d44\u8005\u5bf9\u8d44\u4ea7\u7684\u91cd\u65b0\u8bc4\u4f30\u3002\u4e86\u89e3\u89e6\u5e95\u53cd\u5f39\u7684\u673a\u5236\u5bf9\u4e8e\u6295\u8d44\u8005\u5236\u5b9a\u4ea4\u6613\u7b56\u7565\u975e\u5e38\u91cd\u8981\u3002<\/p>\n
\u5982\u4f55\u4f7f\u7528Python\u5206\u6790\u89e6\u5e95\u53cd\u5f39\u7684\u8d8b\u52bf\uff1f<\/strong>
\n\u5229\u7528Python\u8fdb\u884c\u89e6\u5e95\u53cd\u5f39\u5206\u6790\uff0c\u53ef\u4ee5\u4f7f\u7528\u6570\u636e\u5206\u6790\u5e93\u5982Pandas\u548c\u6570\u636e\u53ef\u89c6\u5316\u5e93\u5982Matplotlib\u3002\u901a\u8fc7\u83b7\u53d6\u5386\u53f2\u4ef7\u683c\u6570\u636e\uff0c\u53ef\u4ee5\u8ba1\u7b97\u79fb\u52a8\u5e73\u5747\u7ebf\u3001\u76f8\u5bf9\u5f3a\u5f31\u6307\u6570\uff08RSI\uff09\u7b49\u6307\u6807\uff0c\u4ee5\u8bc6\u522b\u6f5c\u5728\u7684\u89e6\u5e95\u53cd\u5f39\u4fe1\u53f7\u3002\u4f8b\u5982\uff0c\u82e5\u4ef7\u683c\u8dcc\u7834\u67d0\u4e00\u91cd\u8981\u652f\u6491\u4f4d\u5e76\u4e14RSI\u8fdb\u5165\u8d85\u5356\u533a\u95f4\uff0c\u53ef\u80fd\u6697\u793a\u89e6\u5e95\u53cd\u5f39\u7684\u673a\u4f1a\u3002<\/p>\n
\u5728\u5b9e\u73b0\u89e6\u5e95\u53cd\u5f39\u7b56\u7565\u65f6\u9700\u8981\u6ce8\u610f\u54ea\u4e9b\u98ce\u9669\uff1f<\/strong>
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