{"id":1025201,"date":"2024-12-30T14:20:34","date_gmt":"2024-12-30T06:20:34","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1025201.html"},"modified":"2024-12-30T14:20:36","modified_gmt":"2024-12-30T06:20:36","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python-%e8%82%a1%e7%a5%a8%e7%ad%9b%e9%80%89-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1025201.html","title":{"rendered":"\u5982\u4f55\u7528python \u80a1\u7968\u7b5b\u9009"},"content":{"rendered":"

\"\u5982\u4f55\u7528python<\/p>\n

\u5982\u4f55\u7528Python\u8fdb\u884c\u80a1\u7968\u7b5b\u9009<\/strong><\/p>\n<\/p>\n

\u4f7f\u7528Python\u8fdb\u884c\u80a1\u7968\u7b5b\u9009\u662f\u4e00\u79cd\u9ad8\u6548\u3001\u7075\u6d3b\u7684\u65b9\u6cd5\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6295\u8d44\u8005\u4ece\u6210\u5343\u4e0a\u4e07\u7684\u80a1\u7968\u4e2d\u627e\u5230\u7b26\u5408\u7279\u5b9a\u6761\u4ef6\u7684\u80a1\u7968\u3002\u9996\u5148\uff0c\u83b7\u53d6\u80a1\u7968\u6570\u636e\u3001\u5176\u6b21\uff0c\u5b9a\u4e49\u7b5b\u9009\u6761\u4ef6\u3001\u63a5\u7740\u4f7f\u7528Python\u5e93\u8fdb\u884c\u7b5b\u9009\u3001\u6700\u540e\u53ef\u89c6\u5316\u548c\u5206\u6790\u7b5b\u9009\u7ed3\u679c<\/strong>\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u5b9e\u73b0\u8fd9\u4e9b\u6b65\u9aa4\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u83b7\u53d6\u80a1\u7968\u6570\u636e<\/p>\n<\/p>\n

\u8981\u8fdb\u884c\u80a1\u7968\u7b5b\u9009\uff0c\u9996\u5148\u9700\u8981\u83b7\u53d6\u80a1\u7968\u6570\u636e\u3002\u53ef\u4ee5\u4f7f\u7528\u8bf8\u5982Yahoo Finance\u3001Alpha Vantage\u548cQuandl\u7b49API\u6765\u83b7\u53d6\u80a1\u7968\u7684\u5386\u53f2\u6570\u636e\u548c\u5b9e\u65f6\u6570\u636e\u3002<\/p>\n<\/p>\n

1. \u4f7f\u7528Yahoo Finance\u83b7\u53d6\u6570\u636e<\/h3>\n<\/p>\n

Yahoo Finance\u662f\u4e00\u4e2a\u975e\u5e38\u53d7\u6b22\u8fce\u7684\u91d1\u878d\u6570\u636e\u6e90\uff0cPython\u4e2d\u53ef\u4ee5\u4f7f\u7528yfinance<\/code>\u5e93\u6765\u8bbf\u95eeYahoo Finance\u7684\u6570\u636e\u3002<\/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="2020-01-01", end="2021-01-01")<\/p>\n
print(data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u4f7f\u7528Alpha Vantage\u83b7\u53d6\u6570\u636e<\/h3>\n<\/p>\n
Alpha Vantage\u63d0\u4f9b\u4e86\u4e00\u7cfb\u5217\u514d\u8d39\u7684API\uff0c\u53ef\u4ee5\u83b7\u53d6\u80a1\u7968\u7684\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u3002\u9700\u8981\u5148\u6ce8\u518c\u4e00\u4e2aAPI\u5bc6\u94a5\u3002<\/p>\n<\/p>\n
from alpha_vantage.timeseries import TimeSeries<\/p>\n
\u4f7f\u7528Alpha Vantage\u83b7\u53d6\u6570\u636e<\/strong><\/h2>\n
api_key = 'your_api_key'<\/p>\n
ts = TimeSeries(key=api_key, output_format='pandas')<\/p>\n
data, meta_data = ts.get_dAI<\/a>ly(symbol='AAPL', outputsize='full')<\/p>\n
print(data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u5b9a\u4e49\u7b5b\u9009\u6761\u4ef6<\/p>\n<\/p>\n
\u7b5b\u9009\u6761\u4ef6\u53ef\u4ee5\u6839\u636e\u6280\u672f\u6307\u6807\u3001\u57fa\u672c\u9762\u5206\u6790\u3001\u6216\u5176\u4ed6\u81ea\u5b9a\u4e49\u6761\u4ef6\u6765\u5b9a\u4e49\u3002\u5e38\u7528\u7684\u7b5b\u9009\u6761\u4ef6\u5305\u62ec\u5e02\u76c8\u7387\uff08P\/E\uff09\u3001\u5e02\u51c0\u7387\uff08P\/B\uff09\u3001\u80a1\u606f\u7387\u3001\u52a8\u91cf\u6307\u6807\u3001\u79fb\u52a8\u5e73\u5747\u7ebf\u7b49\u3002<\/p>\n<\/p>\n
1. \u6280\u672f\u6307\u6807\u7b5b\u9009<\/h3>\n<\/p>\n
\u4f7f\u7528\u6280\u672f\u6307\u6807\u5982\u79fb\u52a8\u5e73\u5747\u7ebf\u3001\u76f8\u5bf9\u5f3a\u5f31\u6307\u6570\uff08RSI\uff09\u7b49\u8fdb\u884c\u7b5b\u9009\u3002<\/p>\n<\/p>\n
import talib<\/p>\n
\u8ba1\u7b97\u79fb\u52a8\u5e73\u5747\u7ebf<\/strong><\/h2>\n
data['SMA_50'] = talib.SMA(data['Close'], timeperiod=50)<\/p>\n
data['SMA_200'] = talib.SMA(data['Close'], timeperiod=200)<\/p>\n
\u7b5b\u9009\u6761\u4ef6\uff1a\u80a1\u4ef7\u9ad8\u4e8e50\u65e5\u79fb\u52a8\u5e73\u5747\u7ebf<\/strong><\/h2>\n
condition = data['Close'] > data['SMA_50']<\/p>\n
filtered_data = data[condition]<\/p>\n
print(filtered_data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u57fa\u672c\u9762\u5206\u6790\u7b5b\u9009<\/h3>\n<\/p>\n
\u4f7f\u7528\u5e02\u76c8\u7387\uff08P\/E\uff09\u3001\u5e02\u51c0\u7387\uff08P\/B\uff09\u7b49\u57fa\u672c\u9762\u6307\u6807\u8fdb\u884c\u7b5b\u9009\u3002<\/p>\n<\/p>\n
import requests<\/p>\n
\u4f7f\u7528Yahoo Finance API\u83b7\u53d6\u57fa\u672c\u9762\u6570\u636e<\/strong><\/h2>\n
url = "https:\/\/query1.finance.yahoo.com\/v10\/finance\/quoteSummary\/AAPL?modules=summaryDetail"<\/p>\n
response = requests.get(url)<\/p>\n
data = response.json()<\/p>\n
pe_ratio = data['quoteSummary']['result'][0]['summaryDetail']['trailingPE']['fmt']<\/p>\n
pb_ratio = data['quoteSummary']['result'][0]['summaryDetail']['priceToBook']['fmt']<\/p>\n
\u7b5b\u9009\u6761\u4ef6\uff1a\u5e02\u76c8\u7387\u4f4e\u4e8e20<\/strong><\/h2>\n
if float(pe_ratio) < 20:<\/p>\n
    print("AAPL is a good candidate based on P\/E ratio.")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528Python\u5e93\u8fdb\u884c\u7b5b\u9009<\/p>\n<\/p>\n
\u6709\u591a\u4e2aPython\u5e93\u53ef\u4ee5\u5e2e\u52a9\u8fdb\u884c\u80a1\u7968\u7b5b\u9009\uff0c\u5982Pandas\u3001NumPy\u3001TA-Lib\u7b49\u3002\u8fd9\u4e9b\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u529f\u80fd\u3002<\/p>\n<\/p>\n
1. Pandas\u8fdb\u884c\u6570\u636e\u5904\u7406<\/h3>\n<\/p>\n
Pandas\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u5e93\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u7b5b\u9009\u548c\u5206\u6790\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u4f7f\u7528Pandas\u8fdb\u884c\u6570\u636e\u7b5b\u9009<\/strong><\/h2>\n
data = pd.read_csv("stock_data.csv")<\/p>\n
filtered_data = data[(data['P\/E'] < 20) & (data['Dividend Yield'] > 2)]<\/p>\n
print(filtered_data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. NumPy\u8fdb\u884c\u6570\u503c\u8ba1\u7b97<\/h3>\n<\/p>\n
NumPy\u662f\u4e00\u4e2a\u9ad8\u6027\u80fd\u7684\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u53ef\u4ee5\u8fdb\u884c\u9ad8\u6548\u7684\u6570\u503c\u8ba1\u7b97\u548c\u77e9\u9635\u8fd0\u7b97\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u4f7f\u7528NumPy\u8fdb\u884c\u6570\u636e\u7b5b\u9009<\/strong><\/h2>\n
data = np.genfromtxt("stock_data.csv", delimiter=',', names=True)<\/p>\n
pe_ratio = data['PE']<\/p>\n
dividend_yield = data['Dividend_Yield']<\/p>\n
\u7b5b\u9009\u6761\u4ef6<\/strong><\/h2>\n
condition = (pe_ratio < 20) & (dividend_yield > 2)<\/p>\n
filtered_data = data[condition]<\/p>\n
print(filtered_data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u53ef\u89c6\u5316\u548c\u5206\u6790\u7b5b\u9009\u7ed3\u679c<\/p>\n<\/p>\n
\u7b5b\u9009\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u3001Seaborn\u7b49\u5e93\u5bf9\u7ed3\u679c\u8fdb\u884c\u53ef\u89c6\u5316\u548c\u8fdb\u4e00\u6b65\u5206\u6790\u3002<\/p>\n<\/p>\n
1. \u4f7f\u7528Matplotlib\u8fdb\u884c\u53ef\u89c6\u5316<\/h3>\n<\/p>\n
Matplotlib\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u7ed8\u56fe\u5e93\uff0c\u53ef\u4ee5\u521b\u5efa\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u7ed8\u5236\u7b5b\u9009\u7ed3\u679c\u7684\u6536\u76d8\u4ef7\u66f2\u7ebf<\/strong><\/h2>\n
plt.plot(filtered_data['Date'], filtered_data['Close'])<\/p>\n
plt.xlabel('Date')<\/p>\n
plt.ylabel('Close Price')<\/p>\n
plt.title('Filtered Stocks Close Price')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u4f7f\u7528Seaborn\u8fdb\u884c\u9ad8\u7ea7\u53ef\u89c6\u5316<\/h3>\n<\/p>\n
Seaborn\u662f\u5728Matplotlib\u57fa\u7840\u4e0a\u6784\u5efa\u7684\u9ad8\u7ea7\u53ef\u89c6\u5316\u5e93\uff0c\u53ef\u4ee5\u521b\u5efa\u66f4\u7f8e\u89c2\u7684\u56fe\u8868\u3002<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
\u7ed8\u5236\u7b5b\u9009\u7ed3\u679c\u7684\u5206\u5e03\u56fe<\/strong><\/h2>\n
sns.histplot(filtered_data['Close'])<\/p>\n
plt.xlabel('Close Price')<\/p>\n
plt.title('Distribution of Filtered Stocks Close Price')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u7efc\u5408\u8fd0\u7528\u591a\u79cd\u65b9\u6cd5\u8fdb\u884c\u80a1\u7968\u7b5b\u9009<\/p>\n<\/p>\n
\u4e3a\u4e86\u63d0\u9ad8\u7b5b\u9009\u7684\u51c6\u786e\u6027\uff0c\u53ef\u4ee5\u7efc\u5408\u8fd0\u7528\u591a\u79cd\u65b9\u6cd5\u8fdb\u884c\u80a1\u7968\u7b5b\u9009\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u7ed3\u5408\u6280\u672f\u5206\u6790\u548c\u57fa\u672c\u9762\u5206\u6790\uff0c\u6216\u8005\u4f7f\u7528\u673a\u5668\u5b66\u4e60<\/a>\u7b97\u6cd5\u8fdb\u884c\u667a\u80fd\u7b5b\u9009\u3002<\/p>\n<\/p>\n
1. \u7ed3\u5408\u6280\u672f\u5206\u6790\u548c\u57fa\u672c\u9762\u5206\u6790<\/h3>\n<\/p>\n
\u53ef\u4ee5\u5148\u4f7f\u7528\u6280\u672f\u5206\u6790\u65b9\u6cd5\u7b5b\u9009\u51fa\u6f5c\u529b\u80a1\u7968\uff0c\u7136\u540e\u518d\u8fdb\u884c\u57fa\u672c\u9762\u5206\u6790\u3002<\/p>\n<\/p>\n
# \u6280\u672f\u5206\u6790\u7b5b\u9009<\/p>\n
condition_tech = data['Close'] > data['SMA_50']<\/p>\n
filtered_data_tech = data[condition_tech]<\/p>\n
\u57fa\u672c\u9762\u5206\u6790\u7b5b\u9009<\/strong><\/h2>\n
condition_fund = filtered_data_tech['P\/E'] < 20<\/p>\n
filtered_data_combined = filtered_data_tech[condition_fund]<\/p>\n
print(filtered_data_combined)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u4f7f\u7528\u673a\u5668\u5b66\u4e60\u8fdb\u884c\u667a\u80fd\u7b5b\u9009<\/h3>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u5bf9\u80a1\u7968\u8fdb\u884c\u5206\u7c7b\u548c\u9884\u6d4b\uff0c\u4ece\u800c\u5b9e\u73b0\u667a\u80fd\u7b5b\u9009\u3002<\/p>\n<\/p>\n
from sklearn.ensemble import RandomForestClassifier<\/p>\n
from sklearn.model_selection import train_test_split<\/p>\n
from sklearn.metrics import accuracy_score<\/p>\n
\u51c6\u5907\u6570\u636e<\/strong><\/h2>\n
X = data[['P\/E', 'P\/B', 'Dividend Yield', 'SMA_50', 'SMA_200']]<\/p>\n
y = data['Target']  # \u76ee\u6807\u53d8\u91cf\uff0c\u4f8b\u5982\u662f\u5426\u4e0a\u6da8<\/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\u6a21\u578b<\/strong><\/h2>\n
model = RandomForestClassifier()<\/p>\n
model.fit(X_train, y_train)<\/p>\n
\u9884\u6d4b<\/strong><\/h2>\n
y_pred = model.predict(X_test)<\/p>\n
accuracy = accuracy_score(y_test, y_pred)<\/p>\n
print(f"Accuracy: {accuracy}")<\/p>\n
\u7b5b\u9009\u7b26\u5408\u6761\u4ef6\u7684\u80a1\u7968<\/strong><\/h2>\n
filtered_data_ml = data[model.predict(X) == 1]<\/p>\n
print(filtered_data_ml)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u7ed3\u8bba<\/p>\n<\/p>\n
\u901a\u8fc7\u4f7f\u7528Python\u8fdb\u884c\u80a1\u7968\u7b5b\u9009\uff0c\u53ef\u4ee5\u5927\u5927\u63d0\u9ad8\u7b5b\u9009\u6548\u7387\u548c\u51c6\u786e\u6027\u3002\u83b7\u53d6\u80a1\u7968\u6570\u636e\u3001\u5b9a\u4e49\u7b5b\u9009\u6761\u4ef6\u3001\u4f7f\u7528Python\u5e93\u8fdb\u884c\u7b5b\u9009\u3001\u53ef\u89c6\u5316\u548c\u5206\u6790\u7b5b\u9009\u7ed3\u679c<\/strong>\uff0c\u662f\u5b9e\u73b0\u8fd9\u4e00\u76ee\u6807\u7684\u5173\u952e\u6b65\u9aa4\u3002\u7efc\u5408\u8fd0\u7528\u6280\u672f\u5206\u6790\u3001\u57fa\u672c\u9762\u5206\u6790\u548c\u673a\u5668\u5b66\u4e60\u65b9\u6cd5\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6295\u8d44\u8005\u66f4\u597d\u5730\u53d1\u73b0\u6f5c\u529b\u80a1\u7968\uff0c\u5b9e\u73b0\u6295\u8d44\u76ee\u6807\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u8fdb\u884c\u80a1\u7968\u7b5b\u9009\u7684\u57fa\u672c\u6b65\u9aa4\u662f\u4ec0\u4e48\uff1f<\/strong>
\u5728\u4f7f\u7528Python\u8fdb\u884c\u80a1\u7968\u7b5b\u9009\u65f6\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5\u76f8\u5173\u7684\u5e93\uff0c\u5982pandas\u7528\u4e8e\u6570\u636e\u5904\u7406\uff0cnumpy\u7528\u4e8e\u6570\u503c\u8ba1\u7b97\uff0c\u4ee5\u53camatplotlib\u6216seaborn\u7528\u4e8e\u53ef\u89c6\u5316\u3002\u63a5\u4e0b\u6765\uff0c\u53ef\u4ee5\u901a\u8fc7API\u83b7\u53d6\u80a1\u7968\u6570\u636e\uff0c\u4f8b\u5982\u4f7f\u7528Yahoo Finance\u6216Alpha Vantage\u7b49\u670d\u52a1\u3002\u83b7\u53d6\u6570\u636e\u540e\uff0c\u53ef\u4ee5\u6839\u636e\u7528\u6237\u7684\u7b5b\u9009\u6761\u4ef6\uff08\u5982\u5e02\u76c8\u7387\u3001\u80a1\u606f\u6536\u76ca\u7387\u3001\u6210\u957f\u6027\u7b49\uff09\u8fdb\u884c\u6570\u636e\u6e05\u6d17\u548c\u5904\u7406\uff0c\u6700\u540e\u8f93\u51fa\u7b26\u5408\u6761\u4ef6\u7684\u80a1\u7968\u5217\u8868\u3002<\/p>\n
\u6709\u54ea\u4e9b\u5e38\u7528\u7684Python\u5e93\u53ef\u4ee5\u5e2e\u52a9\u8fdb\u884c\u80a1\u7968\u7b5b\u9009\uff1f<\/strong>
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