{"id":1173394,"date":"2025-01-15T17:02:33","date_gmt":"2025-01-15T09:02:33","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1173394.html"},"modified":"2025-01-15T17:02:36","modified_gmt":"2025-01-15T09:02:36","slug":"python%e5%a6%82%e4%bd%95%e5%88%86%e6%9e%90%e5%9c%b0%e4%ba%a7%e8%82%a1","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1173394.html","title":{"rendered":"Python\u5982\u4f55\u5206\u6790\u5730\u4ea7\u80a1"},"content":{"rendered":"

\"Python\u5982\u4f55\u5206\u6790\u5730\u4ea7\u80a1\"<\/p>\n

Python\u53ef\u4ee5\u901a\u8fc7\u6570\u636e\u83b7\u53d6\u3001\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u5206\u6790\u3001\u6570\u636e\u53ef\u89c6\u5316\u7b49\u6b65\u9aa4\u6765\u5206\u6790\u5730\u4ea7\u80a1\u3002<\/strong> \u5176\u4e2d\uff0c\u6570\u636e\u83b7\u53d6\u662f\u57fa\u7840\uff0c\u53ef\u4ee5\u901a\u8fc7API\u6216\u722c\u866b\u6280\u672f\u4ece\u91d1\u878d\u7f51\u7ad9\u83b7\u53d6\u80a1\u7968\u6570\u636e\uff1b\u6570\u636e\u6e05\u6d17\u662f\u5173\u952e\uff0c\u5305\u62ec\u5904\u7406\u7f3a\u5931\u503c\u3001\u5f02\u5e38\u503c\u7b49\uff1b\u6570\u636e\u5206\u6790\u53ef\u4ee5\u4f7f\u7528Pandas\u7b49\u5e93\u8fdb\u884c\u7edf\u8ba1\u5206\u6790\uff0c\u8ba1\u7b97\u80a1\u7968\u7684\u6536\u76ca\u7387\u3001\u6ce2\u52a8\u7387\u7b49\u6307\u6807\uff1b\u6570\u636e\u53ef\u89c6\u5316\u5219\u53ef\u4ee5\u4f7f\u7528Matplotlib\u3001Seaborn\u7b49\u5e93\u6765\u5c55\u793a\u5206\u6790\u7ed3\u679c\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u63cf\u8ff0\u6570\u636e\u83b7\u53d6\u7684\u8fc7\u7a0b\u3002<\/p>\n<\/p>\n

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

\u83b7\u53d6\u5730\u4ea7\u80a1\u7684\u6570\u636e\u662f\u5206\u6790\u7684\u7b2c\u4e00\u6b65\uff0c\u901a\u5e38\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u51e0\u79cd\u65b9\u5f0f\uff1a<\/p>\n<\/p>\n

1\u3001\u4f7f\u7528\u91d1\u878d\u6570\u636eAPI<\/h4>\n<\/p>\n

\u91d1\u878d\u6570\u636eAPI\u662f\u83b7\u53d6\u80a1\u7968\u6570\u636e\u7684\u5e38\u89c1\u65b9\u5f0f\uff0c\u4f8b\u5982Yahoo Finance\u3001Alpha Vantage\u7b49\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u80a1\u7968\u6570\u636e\u63a5\u53e3\u3002\u4f7f\u7528\u8fd9\u4e9bAPI\u9700\u8981\u6ce8\u518c\u5e76\u83b7\u53d6API\u5bc6\u94a5\uff0c\u7136\u540e\u901a\u8fc7HTTP\u8bf7\u6c42\u83b7\u53d6\u6570\u636e\u3002\u4f8b\u5982\uff0c\u4f7f\u7528Alpha Vantage\u7684Python\u5e93\u53ef\u4ee5\u8f7b\u677e\u83b7\u53d6\u80a1\u7968\u6570\u636e\uff1a<\/p>\n<\/p>\n

import requests<\/p>\n
api_key = 'your_api_key'<\/p>\n
symbol = 'AAPL'<\/p>\n
url = f'https:\/\/www.alphavantage.co\/query?function=TIME_SERIES_DAI<\/a>LY&symbol={symbol}&apikey={api_key}'<\/p>\n
response = requests.get(url)<\/p>\n
data = response.json()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528\u722c\u866b\u6280\u672f<\/h4>\n<\/p>\n
\u5982\u679c\u9700\u8981\u83b7\u53d6\u81ea\u5b9a\u4e49\u683c\u5f0f\u7684\u6570\u636e\u6216API\u4e0d\u6ee1\u8db3\u9700\u6c42\uff0c\u53ef\u4ee5\u4f7f\u7528\u722c\u866b\u6280\u672f\u4ece\u91d1\u878d\u7f51\u7ad9\u83b7\u53d6\u6570\u636e\u3002\u5e38\u7528\u7684\u722c\u866b\u5e93\u6709BeautifulSoup\u548cScrapy\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528BeautifulSoup\u4eceYahoo Finance\u83b7\u53d6\u80a1\u7968\u6570\u636e\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import requests<\/p>\n
from bs4 import BeautifulSoup<\/p>\n
url = 'https:\/\/finance.yahoo.com\/quote\/AAPL\/history?p=AAPL'<\/p>\n
response = requests.get(url)<\/p>\n
soup = BeautifulSoup(response.text, 'html.parser')<\/p>\n
\u63d0\u53d6\u6570\u636e<\/strong><\/h2>\n
table = soup.find('table', {'data-test': 'historical-prices'})<\/p>\n
rows = table.find_all('tr')<\/p>\n
for row in rows[1:]:<\/p>\n
    cols = row.find_all('td')<\/p>\n
    print([col.text for col in cols])<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u6570\u636e\u6e05\u6d17<\/h3>\n<\/p>\n
\u83b7\u53d6\u5230\u7684\u6570\u636e\u901a\u5e38\u9700\u8981\u8fdb\u884c\u6e05\u6d17\uff0c\u4ee5\u786e\u4fdd\u6570\u636e\u7684\u8d28\u91cf\u548c\u4e00\u81f4\u6027\u3002\u6570\u636e\u6e05\u6d17\u5305\u62ec\u5904\u7406\u7f3a\u5931\u503c\u3001\u5f02\u5e38\u503c\u4ee5\u53ca\u8f6c\u6362\u6570\u636e\u683c\u5f0f\u7b49\u3002<\/p>\n<\/p>\n
1\u3001\u5904\u7406\u7f3a\u5931\u503c<\/h4>\n<\/p>\n
\u7f3a\u5931\u503c\u662f\u6570\u636e\u5206\u6790\u4e2d\u7684\u5e38\u89c1\u95ee\u9898\uff0c\u53ef\u4ee5\u901a\u8fc7\u5220\u9664\u7f3a\u5931\u503c\u3001\u586b\u5145\u7f3a\u5931\u503c\u7b49\u65b9\u6cd5\u5904\u7406\u3002\u4f8b\u5982\uff0c\u4f7f\u7528Pandas\u5e93\u53ef\u4ee5\u65b9\u4fbf\u5730\u5904\u7406\u7f3a\u5931\u503c\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u8bfb\u53d6\u6570\u636e<\/strong><\/h2>\n
df = pd.read_csv('stock_data.csv')<\/p>\n
\u5220\u9664\u7f3a\u5931\u503c<\/strong><\/h2>\n
df.dropna(inplace=True)<\/p>\n
\u586b\u5145\u7f3a\u5931\u503c<\/strong><\/h2>\n
df.fillna(method='ffill', inplace=True)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5904\u7406\u5f02\u5e38\u503c<\/h4>\n<\/p>\n
\u5f02\u5e38\u503c\u662f\u6307\u504f\u79bb\u6b63\u5e38\u8303\u56f4\u7684\u6570\u636e\u70b9\uff0c\u53ef\u4ee5\u901a\u8fc7\u7edf\u8ba1\u5206\u6790\u65b9\u6cd5\u68c0\u6d4b\u548c\u5904\u7406\u5f02\u5e38\u503c\u3002\u4f8b\u5982\uff0c\u4f7f\u7528Z-score\u65b9\u6cd5\u68c0\u6d4b\u5f02\u5e38\u503c\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u8ba1\u7b97Z-score<\/strong><\/h2>\n
df['z_score'] = (df['Close'] - df['Close'].mean()) \/ df['Close'].std()<\/p>\n
\u8fc7\u6ee4\u5f02\u5e38\u503c<\/strong><\/h2>\n
df = df[np.abs(df['z_score']) < 3]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u6570\u636e\u5206\u6790<\/h3>\n<\/p>\n
\u6570\u636e\u5206\u6790\u662f\u5bf9\u6e05\u6d17\u540e\u7684\u6570\u636e\u8fdb\u884c\u7edf\u8ba1\u5206\u6790\u548c\u5efa\u6a21\uff0c\u4ee5\u63ed\u793a\u6570\u636e\u4e2d\u7684\u89c4\u5f8b\u548c\u8d8b\u52bf\u3002<\/p>\n<\/p>\n
1\u3001\u8ba1\u7b97\u80a1\u7968\u6536\u76ca\u7387<\/h4>\n<\/p>\n
\u80a1\u7968\u6536\u76ca\u7387\u662f\u8861\u91cf\u80a1\u7968\u8868\u73b0\u7684\u91cd\u8981\u6307\u6807\uff0c\u53ef\u4ee5\u901a\u8fc7\u8ba1\u7b97\u6bcf\u65e5\u6536\u76ca\u7387\u548c\u7d2f\u8ba1\u6536\u76ca\u7387\u6765\u5206\u6790\u80a1\u7968\u7684\u8868\u73b0\uff1a<\/p>\n<\/p>\n
# \u8ba1\u7b97\u6bcf\u65e5\u6536\u76ca\u7387<\/p>\n
df['daily_return'] = df['Close'].pct_change()<\/p>\n
\u8ba1\u7b97\u7d2f\u8ba1\u6536\u76ca\u7387<\/strong><\/h2>\n
df['cumulative_return'] = (1 + df['daily_return']).cumprod() - 1<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u8ba1\u7b97\u6ce2\u52a8\u7387<\/h4>\n<\/p>\n
\u6ce2\u52a8\u7387\u662f\u8861\u91cf\u80a1\u7968\u4ef7\u683c\u6ce2\u52a8\u7a0b\u5ea6\u7684\u6307\u6807\uff0c\u53ef\u4ee5\u901a\u8fc7\u8ba1\u7b97\u6807\u51c6\u5dee\u6765\u8861\u91cf\u6ce2\u52a8\u7387\uff1a<\/p>\n<\/p>\n
# \u8ba1\u7b97\u6ce2\u52a8\u7387<\/p>\n
df['volatility'] = df['daily_return'].rolling(window=30).std() * np.sqrt(252)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u6570\u636e\u53ef\u89c6\u5316<\/h3>\n<\/p>\n
\u6570\u636e\u53ef\u89c6\u5316\u662f\u5c55\u793a\u6570\u636e\u5206\u6790\u7ed3\u679c\u7684\u91cd\u8981\u624b\u6bb5\uff0c\u53ef\u4ee5\u901a\u8fc7\u7ed8\u5236\u56fe\u8868\u6765\u76f4\u89c2\u5c55\u793a\u80a1\u7968\u7684\u8868\u73b0\u548c\u8d8b\u52bf\u3002<\/p>\n<\/p>\n
1\u3001\u7ed8\u5236\u80a1\u7968\u4ef7\u683c\u8d70\u52bf<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\u7ed8\u5236\u80a1\u7968\u4ef7\u683c\u8d70\u52bf\u56fe\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
plt.figure(figsize=(10, 6))<\/p>\n
plt.plot(df['Date'], df['Close'], label='Close Price')<\/p>\n
plt.title('Stock Price Trend')<\/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
2\u3001\u7ed8\u5236\u6536\u76ca\u7387\u548c\u6ce2\u52a8\u7387<\/h4>\n<\/p>\n
\u53ef\u4ee5\u5206\u522b\u7ed8\u5236\u80a1\u7968\u7684\u6536\u76ca\u7387\u548c\u6ce2\u52a8\u7387\u56fe\u8868\uff1a<\/p>\n<\/p>\n
# \u7ed8\u5236\u6536\u76ca\u7387<\/p>\n
plt.figure(figsize=(10, 6))<\/p>\n
plt.plot(df['Date'], df['cumulative_return'], label='Cumulative Return')<\/p>\n
plt.title('Cumulative Return')<\/p>\n
plt.xlabel('Date')<\/p>\n
plt.ylabel('Return')<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
\u7ed8\u5236\u6ce2\u52a8\u7387<\/strong><\/h2>\n
plt.figure(figsize=(10, 6))<\/p>\n
plt.plot(df['Date'], df['volatility'], label='Volatility')<\/p>\n
plt.title('Volatility')<\/p>\n
plt.xlabel('Date')<\/p>\n
plt.ylabel('Volatility')<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u6570\u636e\u5efa\u6a21\u4e0e\u9884\u6d4b<\/h3>\n<\/p>\n
\u6570\u636e\u5efa\u6a21\u4e0e\u9884\u6d4b\u662f\u9ad8\u7ea7\u6570\u636e\u5206\u6790\u7684\u5173\u952e\u6b65\u9aa4\uff0c\u53ef\u4ee5\u4f7f\u7528\u673a\u5668\u5b66\u4e60<\/a>\u548c\u7edf\u8ba1\u5efa\u6a21\u65b9\u6cd5\u5bf9\u80a1\u7968\u4ef7\u683c\u8fdb\u884c\u9884\u6d4b\u3002<\/p>\n<\/p>\n
1\u3001\u4f7f\u7528\u65f6\u95f4\u5e8f\u5217\u6a21\u578b<\/h4>\n<\/p>\n
\u65f6\u95f4\u5e8f\u5217\u6a21\u578b\u662f\u80a1\u7968\u4ef7\u683c\u9884\u6d4b\u7684\u5e38\u7528\u65b9\u6cd5\uff0c\u4f8b\u5982ARIMA\u6a21\u578b\uff1a<\/p>\n<\/p>\n
from statsmodels.tsa.arima_model import ARIMA<\/p>\n
\u62c6\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6<\/strong><\/h2>\n
train, test = df['Close'][:int(0.8*len(df))], df['Close'][int(0.8*len(df)):]<\/p>\n
\u8bad\u7ec3ARIMA\u6a21\u578b<\/strong><\/h2>\n
model = ARIMA(train, order=(5, 1, 0))<\/p>\n
model_fit = model.fit(disp=0)<\/p>\n
\u9884\u6d4b<\/strong><\/h2>\n
forecast, stderr, conf_int = model_fit.forecast(steps=len(test))<\/p>\n
\u7ed8\u5236\u9884\u6d4b\u7ed3\u679c<\/strong><\/h2>\n
plt.figure(figsize=(10, 6))<\/p>\n
plt.plot(test.index, test, label='Actual Price')<\/p>\n
plt.plot(test.index, forecast, label='Forecasted Price')<\/p>\n
plt.fill_between(test.index, conf_int[:, 0], conf_int[:, 1], color='k', alpha=0.1)<\/p>\n
plt.title('Stock Price Prediction')<\/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
2\u3001\u4f7f\u7528\u673a\u5668\u5b66\u4e60\u6a21\u578b<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528\u673a\u5668\u5b66\u4e60\u6a21\u578b\u5982\u968f\u673a\u68ee\u6797\u3001\u652f\u6301\u5411\u91cf\u673a\u7b49\u5bf9\u80a1\u7968\u4ef7\u683c\u8fdb\u884c\u9884\u6d4b\uff1a<\/p>\n<\/p>\n
from sklearn.ensemble import RandomForestRegressor<\/p>\n
from sklearn.model_selection import train_test_split<\/p>\n
from sklearn.metrics import mean_squared_error<\/p>\n
\u51c6\u5907\u7279\u5f81\u548c\u76ee\u6807<\/strong><\/h2>\n
X = df[['Open', 'High', 'Low', 'Volume']]<\/p>\n
y = df['Close']<\/p>\n
\u62c6\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 = RandomForestRegressor(n_estimators=100, random_state=42)<\/p>\n
model.fit(X_train, y_train)<\/p>\n
\u9884\u6d4b<\/strong><\/h2>\n
y_pred = model.predict(X_test)<\/p>\n
\u8ba1\u7b97\u8bef\u5dee<\/strong><\/h2>\n
mse = mean_squared_error(y_test, y_pred)<\/p>\n
print(f'Mean Squared Error: {mse}')<\/p>\n
\u7ed8\u5236\u9884\u6d4b\u7ed3\u679c<\/strong><\/h2>\n
plt.figure(figsize=(10, 6))<\/p>\n
plt.plot(y_test.index, y_test, label='Actual Price')<\/p>\n
plt.plot(y_test.index, y_pred, label='Predicted Price')<\/p>\n
plt.title('Stock Price Prediction')<\/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
\u516d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u5206\u6790\u5730\u4ea7\u80a1\u7684\u6570\u636e\uff0c\u5305\u62ec\u83b7\u53d6\u6570\u636e\u3001\u6e05\u6d17\u6570\u636e\u3001\u5206\u6790\u6570\u636e\u3001\u53ef\u89c6\u5316\u6570\u636e\u4ee5\u53ca\u8fdb\u884c\u6570\u636e\u5efa\u6a21\u4e0e\u9884\u6d4b\u3002\u6bcf\u4e2a\u6b65\u9aa4\u90fd\u53ef\u4ee5\u4f7f\u7528\u4e0d\u540c\u7684\u65b9\u6cd5\u548c\u5de5\u5177\uff0c\u6839\u636e\u5b9e\u9645\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u3002\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u53ef\u4ee5\u5168\u9762\u5206\u6790\u5730\u4ea7\u80a1\u7684\u8868\u73b0\u548c\u8d8b\u52bf\uff0c\u4e3a\u6295\u8d44\u51b3\u7b56\u63d0\u4f9b\u79d1\u5b66\u4f9d\u636e\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 1. \u5982\u4f55\u5229\u7528Python\u83b7\u53d6\u5730\u4ea7\u80a1\u7684\u5b9e\u65f6\u6570\u636e\uff1f<\/strong>
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2. \u5728\u5206\u6790\u5730\u4ea7\u80a1\u65f6\uff0cPython\u80fd\u63d0\u4f9b\u54ea\u4e9b\u6570\u636e\u53ef\u89c6\u5316\u5de5\u5177\uff1f<\/strong>
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