268 lines
10 KiB
Python
268 lines
10 KiB
Python
"""
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股票数据服务
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负责：数据获取、缓存、持仓管理
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"""
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import yfinance as yf
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import pandas as pd
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from sqlalchemy.orm import Session
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from datetime import datetime, timedelta
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import json
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import sys
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import os
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# 添加父目录到路径
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sys.path.append(os.path.dirname(os.path.dirname(__file__)))
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from database import Position, StockData, AnalysisResult, TradeLog
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from models import PositionCreate
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class StockService:
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def __init__(self, db: Session):
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self.db = db
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self.cache_dir = os.path.join(os.path.dirname(__file__), '..', '..', 'data', 'cache')
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os.makedirs(self.cache_dir, exist_ok=True)
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# ═════════════════════════════════════════════════════════════════
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# 持仓管理
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# ═════════════════════════════════════════════════════════════════
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def get_all_positions(self):
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"""获取所有持仓"""
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positions = self.db.query(Position).all()
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# 更新实时价格
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for pos in positions:
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try:
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quote = self.get_realtime_quote(pos.ticker)
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pos.current_price = quote['price']
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pos.market_value = pos.shares * pos.current_price
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pos.pnl = pos.market_value - (pos.shares * pos.cost_price)
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pos.pnl_percent = (pos.pnl / (pos.shares * pos.cost_price)) * 100
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except:
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pass
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self.db.commit()
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return positions
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def create_position(self, position: PositionCreate):
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"""创建持仓"""
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db_position = Position(
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stock_name=position.stock_name,
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ticker=position.ticker,
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shares=position.shares,
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cost_price=position.cost_price,
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strategy=position.strategy,
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notes=position.notes
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)
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self.db.add(db_position)
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self.db.commit()
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self.db.refresh(db_position)
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return db_position
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def update_position(self, position_id: int, position: PositionCreate):
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"""更新持仓"""
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db_position = self.db.query(Position).filter(Position.id == position_id).first()
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if not db_position:
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raise ValueError("持仓不存在")
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db_position.stock_name = position.stock_name
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db_position.ticker = position.ticker
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db_position.shares = position.shares
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db_position.cost_price = position.cost_price
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db_position.strategy = position.strategy
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db_position.notes = position.notes
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self.db.commit()
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self.db.refresh(db_position)
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return db_position
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def delete_position(self, position_id: int):
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"""删除持仓"""
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db_position = self.db.query(Position).filter(Position.id == position_id).first()
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if not db_position:
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raise ValueError("持仓不存在")
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self.db.delete(db_position)
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self.db.commit()
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# ═════════════════════════════════════════════════════════════════
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# 数据获取
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# ═════════════════════════════════════════════════════════════════
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def update_stock_data(self, ticker: str, period: str = "2y"):
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"""更新股票数据"""
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# 从yfinance获取
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df = yf.download(ticker, period=period, auto_adjust=True, progress=False)
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if df.empty:
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raise ValueError(f"无法获取{ticker}的数据")
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if isinstance(df.columns, pd.MultiIndex):
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df.columns = df.columns.droplevel(1)
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# 计算技术指标
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df['MA5'] = df['Close'].rolling(5).mean()
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df['MA20'] = df['Close'].rolling(20).mean()
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df['MA60'] = df['Close'].rolling(60).mean()
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# RSI
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delta = df['Close'].diff()
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gain = delta.clip(lower=0).ewm(alpha=1/14).mean()
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loss = (-delta.clip(upper=0)).ewm(alpha=1/14).mean()
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df['RSI'] = 100 - (100 / (1 + gain / loss))
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# ATR
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high_low = df['High'] - df['Low']
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high_close = (df['High'] - df['Close'].shift(1)).abs()
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low_close = (df['Low'] - df['Close'].shift(1)).abs()
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tr = pd.concat([high_low, high_close, low_close], axis=1).max(axis=1)
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df['ATR'] = tr.rolling(14).mean()
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df = df.dropna()
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# 保存到数据库
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for date, row in df.iterrows():
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date_str = date.strftime('%Y-%m-%d')
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# 检查是否已存在
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existing = self.db.query(StockData).filter(
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StockData.ticker == ticker,
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StockData.date == date_str
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).first()
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if existing:
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existing.open_price = float(row['Open'])
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existing.high_price = float(row['High'])
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existing.low_price = float(row['Low'])
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existing.close_price = float(row['Close'])
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existing.volume = float(row['Volume'])
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existing.ma5 = float(row['MA5'])
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existing.ma20 = float(row['MA20'])
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existing.ma60 = float(row['MA60'])
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existing.rsi = float(row['RSI'])
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existing.atr = float(row['ATR'])
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else:
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new_data = StockData(
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ticker=ticker,
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date=date_str,
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open_price=float(row['Open']),
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high_price=float(row['High']),
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low_price=float(row['Low']),
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close_price=float(row['Close']),
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volume=float(row['Volume']),
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ma5=float(row['MA5']),
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ma20=float(row['MA20']),
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ma60=float(row['MA60']),
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rsi=float(row['RSI']),
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atr=float(row['ATR'])
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)
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self.db.add(new_data)
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self.db.commit()
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return df
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def get_stock_data(self, ticker: str, days: int = 60):
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"""从数据库获取股票数据"""
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data = self.db.query(StockData).filter(
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StockData.ticker == ticker
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).order_by(StockData.date.desc()).limit(days).all()
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if not data:
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return None
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df = pd.DataFrame([{
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'date': d.date,
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'open': d.open_price,
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'high': d.high_price,
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'low': d.low_price,
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'close': d.close_price,
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'volume': d.volume,
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'ma5': d.ma5,
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'ma20': d.ma20,
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'ma60': d.ma60,
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'rsi': d.rsi,
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'atr': d.atr
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} for d in data])
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return df.iloc[::-1] # 正序
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def get_realtime_quote(self, ticker: str):
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"""获取实时行情"""
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stock = yf.Ticker(ticker)
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info = stock.info
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# 尝试获取实时价格
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try:
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hist = stock.history(period="1d")
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if not hist.empty:
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current_price = float(hist['Close'].iloc[-1])
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prev_close = float(hist['Close'].iloc[0]) if len(hist) > 1 else current_price
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change = current_price - prev_close
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change_percent = (change / prev_close) * 100 if prev_close else 0
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else:
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current_price = info.get('currentPrice', 0)
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prev_close = info.get('previousClose', 0)
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change = current_price - prev_close
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change_percent = (change / prev_close) * 100 if prev_close else 0
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except:
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current_price = info.get('currentPrice', 0)
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change = 0
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change_percent = 0
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return {
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'ticker': ticker,
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'name': info.get('longName', ticker),
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'price': current_price,
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'change': change,
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'change_percent': change_percent,
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'volume': info.get('volume', 0),
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'updated_at': datetime.now().isoformat()
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}
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def search_ticker(self, stock_name: str):
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"""搜索股票代码（简化版）"""
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# 港股映射
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hk_mapping = {
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'中芯国际': '0981.HK',
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'平安好医生': '1833.HK',
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'叮当健康': '9886.HK',
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'中原建业': '9982.HK',
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'阅文集团': '0772.HK',
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'泰升集团': '0687.HK'
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}
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if stock_name in hk_mapping:
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return hk_mapping[stock_name]
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# 如果是代码格式，直接返回
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if stock_name.endswith('.HK'):
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return stock_name
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raise ValueError(f"无法识别股票: {stock_name}")
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# ═════════════════════════════════════════════════════════════════
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# 分析结果
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# ═════════════════════════════════════════════════════════════════
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def save_analysis_result(self, ticker: str, result: dict):
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"""保存分析结果"""
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date_str = datetime.now().strftime('%Y-%m-%d')
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analysis = AnalysisResult(
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ticker=ticker,
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date=date_str,
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action=result.get('signal', {}).get('action', 'HOLD'),
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score=result.get('signal', {}).get('score', 0),
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confidence=result.get('signal', {}).get('confidence', 'LOW'),
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full_data=result
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)
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self.db.add(analysis)
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self.db.commit()
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def get_latest_analysis(self, ticker: str):
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"""获取最新分析"""
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result = self.db.query(AnalysisResult).filter(
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AnalysisResult.ticker == ticker
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).order_by(AnalysisResult.created_at.desc()).first()
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if result:
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return result.full_data
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return None
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