feat: complete trading system with FastAPI backend, web frontend, and auto-analysis
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Stock Buddy Bot
184
backend/services/strategy_service.py
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184
backend/services/strategy_service.py
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"""
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策略服务
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实现v7策略：三维度评分 + 大盘过滤 + 盈利保护
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"""
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import pandas as pd
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import numpy as np
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from sqlalchemy.orm import Session
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class StrategyService:
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def __init__(self, db: Session):
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self.db = db
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self.weights = {'tech': 0.6, 'fund': 0.3, 'sent': 0.1}
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def calculate_signal(self, ticker: str, stock_data: pd.DataFrame, sentiment_score: int):
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"""
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计算交易信号
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"""
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if stock_data is None or len(stock_data) < 20:
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return {
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'action': 'HOLD',
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'score': 0,
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'confidence': 'LOW',
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'stop_loss': 0.08,
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'position_ratio': 0,
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'reasons': ['数据不足']
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}
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latest = stock_data.iloc[-1]
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# ═════════════════════════════════════════════════════════════════
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# 1. 技术面评分
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# ═════════════════════════════════════════════════════════════════
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tech_score = 0
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reasons = []
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# RSI
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rsi = latest.get('rsi', 50)
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if rsi < 30:
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tech_score += 3
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reasons.append(f'RSI超卖({rsi:.1f})')
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elif rsi < 45:
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tech_score += 1
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elif rsi > 70:
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tech_score -= 3
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reasons.append(f'RSI超买({rsi:.1f})')
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elif rsi > 55:
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tech_score -= 1
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# 均线
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close = latest.get('close', 0)
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ma5 = latest.get('ma5', 0)
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ma20 = latest.get('ma20', 0)
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ma60 = latest.get('ma60', 0)
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if close > ma5 > ma20:
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tech_score += 2
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reasons.append('均线多头排列')
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elif close < ma5 < ma20:
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tech_score -= 2
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reasons.append('均线空头排列')
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# 趋势
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if close > ma60:
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tech_score += 1
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else:
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tech_score -= 1
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tech_score = np.clip(tech_score, -10, 10)
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# ═════════════════════════════════════════════════════════════════
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# 2. 基本面（简化，实际应从数据库读取）
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# ═════════════════════════════════════════════════════════════════
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# 这里简化处理，实际应该根据股票获取对应的基本面评分
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fund_score = 0 # 默认为中性
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# ═════════════════════════════════════════════════════════════════
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# 3. 综合评分
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# ═════════════════════════════════════════════════════════════════
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total_score = (
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self.weights['tech'] * tech_score +
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self.weights['fund'] * fund_score +
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self.weights['sent'] * sentiment_score * 2 # sentiment是-5~5，放大
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)
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# ═════════════════════════════════════════════════════════════════
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# 4. 生成信号
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# ═════════════════════════════════════════════════════════════════
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atr = latest.get('atr', close * 0.05)
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atr_percent = atr / close if close > 0 else 0.05
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# 止损设置
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if atr_percent < 0.05:
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stop_loss = 0.08 # 低波动，固定8%
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stop_type = '固定8%'
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elif atr_percent < 0.15:
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stop_loss = min(0.35, max(0.08, atr_percent * 2.5)) # 中波动
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stop_type = f'ATR×2.5 ({stop_loss*100:.1f}%)'
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else:
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stop_loss = min(0.40, max(0.08, atr_percent * 2.0)) # 高波动
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stop_type = f'ATR×2.0 ({stop_loss*100:.1f}%)'
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# 仓位建议
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if total_score >= 5:
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position_ratio = 1.0
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confidence = 'HIGH'
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elif total_score >= 3:
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position_ratio = 0.6
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confidence = 'MEDIUM'
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elif total_score >= 1.5:
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position_ratio = 0.3
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confidence = 'LOW'
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else:
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position_ratio = 0
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confidence = 'LOW'
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# 动作判断
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if total_score >= 1.5:
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action = 'BUY'
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elif total_score <= -1.5:
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action = 'SELL'
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else:
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action = 'HOLD'
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reasons.append(f'舆情{sentiment_score:+d}分')
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reasons.append(f'止损:{stop_type}')
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return {
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'action': action,
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'score': round(total_score, 2),
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'confidence': confidence,
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'stop_loss': round(stop_loss, 4),
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'position_ratio': position_ratio,
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'reasons': reasons,
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'tech_score': round(tech_score, 2),
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'fund_score': round(fund_score, 2),
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'sent_score': sentiment_score
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}
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def get_technical_analysis(self, ticker: str, stock_data: pd.DataFrame):
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"""获取技术分析详情"""
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if stock_data is None or len(stock_data) == 0:
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return None
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latest = stock_data.iloc[-1]
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close = latest.get('close', 0)
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ma5 = latest.get('ma5', 0)
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ma20 = latest.get('ma20', 0)
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ma60 = latest.get('ma60', 0)
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rsi = latest.get('rsi', 50)
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atr = latest.get('atr', 0)
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# 判断趋势
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if close > ma20 > ma60:
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trend = 'UP'
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elif close < ma20 < ma60:
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trend = 'DOWN'
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else:
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trend = 'SIDEWAYS'
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atr_percent = (atr / close * 100) if close > 0 else 0
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return {
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'current_price': round(close, 4),
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'ma5': round(ma5, 4),
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'ma20': round(ma20, 4),
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'ma60': round(ma60, 4),
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'rsi': round(rsi, 2),
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'atr': round(atr, 4),
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'atr_percent': round(atr_percent, 2),
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'trend': trend
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}
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def check_market_filter(self, market_data: pd.DataFrame):
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"""检查大盘过滤条件"""
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if market_data is None or len(market_data) < 20:
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return True # 数据不足，默认允许
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latest = market_data.iloc[-1]
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close = latest.get('close', 0)
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ma20 = latest.get('ma20', 0)
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return close >= ma20
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