345 lines
12 KiB
Python
345 lines
12 KiB
Python
import pandas as pd
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import numpy as np
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from datetime import datetime, timedelta
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import os
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import requests
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from typing import Dict, List, Optional, Tuple
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from dotenv import load_dotenv
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class StockAnalyzer:
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def __init__(self, initial_cash=1000000, custom_api_url=None, custom_api_key=None, custom_api_model=None):
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# 加载环境变量
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load_dotenv()
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# 设置 API 配置，优先使用自定义配置，否则使用环境变量
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self.API_URL = custom_api_url or os.getenv('API_URL')
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self.API_KEY = custom_api_key or os.getenv('API_KEY')
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self.API_MODEL = custom_api_model or os.getenv('API_MODEL', 'gpt-3.5-turbo')
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# 配置参数
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self.params = {
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'ma_periods': {'short': 5, 'medium': 20, 'long': 60},
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'rsi_period': 14,
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'bollinger_period': 20,
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'bollinger_std': 2,
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'volume_ma_period': 20,
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'atr_period': 14
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}
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def get_stock_data(self, stock_code, market_type='A', start_date=None, end_date=None, ):
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"""获取股票数据"""
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import akshare as ak
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if start_date is None:
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start_date = (datetime.now() - timedelta(days=365)).strftime('%Y%m%d')
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if end_date is None:
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end_date = datetime.now().strftime('%Y%m%d')
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try:
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# 根据市场类型获取数据
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if market_type == 'A':
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df = ak.stock_zh_a_hist(
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symbol=stock_code,
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start_date=start_date,
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end_date=end_date,
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adjust="qfq"
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)
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# A股数据列名映射
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elif market_type == 'HK':
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df = ak.stock_hk_daily(
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symbol=stock_code,
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adjust="qfq"
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)
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elif market_type == 'US':
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df = ak.stock_us_hist(
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symbol=stock_code,
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start_date=start_date,
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end_date=end_date,
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adjust="qfq"
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)
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# elif market_type == 'CRYPTO':
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# df = ak.crypto_js_spot(
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# symbol=stock_code
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# )
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else:
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raise ValueError(f"不支持的市场类型: {market_type}")
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# 重命名列名以匹配分析需求
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df = df.rename(columns={
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"日期": "date",
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"开盘": "open",
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"收盘": "close",
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"最高": "high",
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"最低": "low",
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"成交量": "volume"
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})
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# 确保日期格式正确
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df['date'] = pd.to_datetime(df['date'])
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# 数据类型转换
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numeric_columns = ['open', 'close', 'high', 'low', 'volume']
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df[numeric_columns] = df[numeric_columns].apply(pd.to_numeric, errors='coerce')
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# 删除空值
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df = df.dropna()
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return df.sort_values('date')
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except Exception as e:
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raise Exception(f"获取股票数据失败: {str(e)}")
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def calculate_ema(self, series, period):
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"""计算指数移动平均线"""
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return series.ewm(span=period, adjust=False).mean()
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def calculate_rsi(self, series, period):
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"""计算RSI指标"""
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delta = series.diff()
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gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
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loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
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rs = gain / loss
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return 100 - (100 / (1 + rs))
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def calculate_macd(self, series):
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"""计算MACD指标"""
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exp1 = series.ewm(span=12, adjust=False).mean()
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exp2 = series.ewm(span=26, adjust=False).mean()
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macd = exp1 - exp2
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signal = macd.ewm(span=9, adjust=False).mean()
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hist = macd - signal
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return macd, signal, hist
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def calculate_bollinger_bands(self, series, period, std_dev):
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"""计算布林带"""
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middle = series.rolling(window=period).mean()
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std = series.rolling(window=period).std()
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upper = middle + (std * std_dev)
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lower = middle - (std * std_dev)
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return upper, middle, lower
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def calculate_atr(self, df, period):
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"""计算ATR指标"""
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high = df['high']
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low = df['low']
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close = df['close'].shift(1)
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tr1 = high - low
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tr2 = abs(high - close)
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tr3 = abs(low - close)
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tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
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return tr.rolling(window=period).mean()
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def calculate_indicators(self, df):
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"""计算技术指标"""
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try:
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# 计算移动平均线
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df['MA5'] = self.calculate_ema(df['close'], self.params['ma_periods']['short'])
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df['MA20'] = self.calculate_ema(df['close'], self.params['ma_periods']['medium'])
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df['MA60'] = self.calculate_ema(df['close'], self.params['ma_periods']['long'])
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# 计算RSI
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df['RSI'] = self.calculate_rsi(df['close'], self.params['rsi_period'])
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# 计算MACD
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df['MACD'], df['Signal'], df['MACD_hist'] = self.calculate_macd(df['close'])
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# 计算布林带
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df['BB_upper'], df['BB_middle'], df['BB_lower'] = self.calculate_bollinger_bands(
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df['close'],
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self.params['bollinger_period'],
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self.params['bollinger_std']
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)
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# 成交量分析
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df['Volume_MA'] = df['volume'].rolling(window=self.params['volume_ma_period']).mean()
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df['Volume_Ratio'] = df['volume'] / df['Volume_MA']
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# 计算ATR和波动率
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df['ATR'] = self.calculate_atr(df, self.params['atr_period'])
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df['Volatility'] = df['ATR'] / df['close'] * 100
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# 动量指标
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df['ROC'] = df['close'].pct_change(periods=10) * 100
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return df
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except Exception as e:
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print(f"计算技术指标时出错: {str(e)}")
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raise
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def calculate_score(self, df):
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"""计算股票评分"""
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try:
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score = 0
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latest = df.iloc[-1]
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# 趋势得分 (30分)
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if latest['MA5'] > latest['MA20']:
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score += 15
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if latest['MA20'] > latest['MA60']:
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score += 15
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# RSI得分 (20分)
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if 30 <= latest['RSI'] <= 70:
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score += 20
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elif latest['RSI'] < 30: # 超卖
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score += 15
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# MACD得分 (20分)
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if latest['MACD'] > latest['Signal']:
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score += 20
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# 成交量得分 (30分)
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if latest['Volume_Ratio'] > 1.5:
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score += 30
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elif latest['Volume_Ratio'] > 1:
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score += 15
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return score
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except Exception as e:
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print(f"计算评分时出错: {str(e)}")
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raise
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def get_ai_analysis(self, df, stock_code):
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"""使用 OpenAI 进行 AI 分析"""
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try:
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recent_data = df.tail(14).to_dict('records')
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technical_summary = {
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'trend': 'upward' if df.iloc[-1]['MA5'] > df.iloc[-1]['MA20'] else 'downward',
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'volatility': f"{df.iloc[-1]['Volatility']:.2f}%",
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'volume_trend': 'increasing' if df.iloc[-1]['Volume_Ratio'] > 1 else 'decreasing',
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'rsi_level': df.iloc[-1]['RSI']
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}
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prompt = f"""
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分析股票 {stock_code}：
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技术指标概要：
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{technical_summary}
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近14日交易数据：
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{recent_data}
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请提供：
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1. 趋势分析（包含支撑位和压力位）
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2. 成交量分析及其含义
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3. 风险评估（包含波动率分析）
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4. 短期和中期目标价位
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5. 关键技术位分析
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6. 具体交易建议（包含止损位）
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请基于技术指标和市场动态进行分析，给出具体数据支持。
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"""
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# OpenAI API 调用
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api_urls = [
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f"{self.API_URL}/chat/completions",
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f"{self.API_URL}/v1/chat/completions"
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]
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last_error = None
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for api_url in api_urls:
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try:
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response = requests.post(
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api_url,
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headers={
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"Authorization": f"Bearer {self.API_KEY}",
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"Content-Type": "application/json"
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},
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json={
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"model": self.API_MODEL,
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"messages": [{"role": "user", "content": prompt}]
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},
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timeout=30
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)
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if response.status_code == 200:
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return response.json()['choices'][0]['message']['content']
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else:
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last_error = f"API 错误: {response.status_code} - {response.text}"
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continue
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except Exception as e:
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last_error = str(e)
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continue
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print(f"AI 分析暂时无法使用: {last_error}")
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return f"AI 分析暂时无法使用: {last_error}"
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except Exception as e:
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print(f"AI 分析发生错误: {str(e)}")
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return f"AI 分析过程中发生错误: {str(e)}"
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def get_recommendation(self, score):
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"""根据得分给出建议"""
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if score >= 80:
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return '强烈推荐买入'
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elif score >= 60:
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return '建议买入'
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elif score >= 40:
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return '观望'
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elif score >= 20:
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return '建议卖出'
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else:
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return '强烈建议卖出'
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def analyze_stock(self, stock_code, market_type='A'):
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"""分析单个股票"""
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try:
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# 获取股票数据
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df = self.get_stock_data(stock_code, market_type)
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# 计算技术指标
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df = self.calculate_indicators(df)
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# 评分系统
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score = self.calculate_score(df)
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# 获取最新数据
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latest = df.iloc[-1]
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prev = df.iloc[-2]
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# 生成报告（保持原有格式）
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report = {
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'stock_code': stock_code,
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'analysis_date': datetime.now().strftime('%Y-%m-%d'),
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'score': score,
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'price': latest['close'],
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'price_change': (latest['close'] - prev['close']) / prev['close'] * 100,
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'ma_trend': 'UP' if latest['MA5'] > latest['MA20'] else 'DOWN',
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'rsi': latest['RSI'],
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'macd_signal': 'BUY' if latest['MACD'] > latest['Signal'] else 'SELL',
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'volume_status': 'HIGH' if latest['Volume_Ratio'] > 1.5 else 'NORMAL',
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'recommendation': self.get_recommendation(score),
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'ai_analysis': self.get_ai_analysis(df, stock_code)
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}
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return report
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except Exception as e:
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print(f"分析股票时出错: {str(e)}")
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raise
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def scan_market(self, stock_list, min_score=60, market_type='A'):
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"""扫描市场，寻找符合条件的股票"""
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recommendations = []
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for stock_code in stock_list:
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try:
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report = self.analyze_stock(stock_code, market_type)
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if report['score'] >= min_score:
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recommendations.append(report)
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except Exception as e:
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print(f"分析股票 {stock_code} 时出错: {str(e)}")
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continue
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# 按得分排序
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recommendations.sort(key=lambda x: x['score'], reverse=True)
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return recommendations
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