#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ 低估值量化交易策略 实现基于持股周期和RSI超买的买卖择时策略 """ import pandas as pd import akshare as ak from datetime import datetime, timedelta from typing import Dict, List, Optional import logging class ValueStockStrategy: """低估值量化交易策略""" def __init__(self, initial_capital: float = 1000000.0): """ 初始化策略 Args: initial_capital: 初始资金（默认100万） """ self.logger = logging.getLogger(__name__) # 策略参数 self.initial_capital = initial_capital self.available_cash = initial_capital self.max_stocks = 4 # 账户最大持股数 self.max_position_per_stock = 0.3 # 个股最大仓位30% self.max_daily_buy = 2 # 单日最大买入数 self.holding_period = 30 # 持股周期（天） self.rsi_period = 14 # RSI计算周期 self.rsi_overbought = 70 # RSI超买阈值 # 持仓信息 self.positions: Dict[str, Dict] = {} # {股票代码: {买入价, 数量, 买入日期, 持有天数}} self.trade_history: List[Dict] = [] # 当日交易计数 self.daily_buy_count = 0 self.current_date = None def reset_daily_counter(self, date): """重置当日计数器""" if self.current_date != date: self.current_date = date self.daily_buy_count = 0 def can_buy(self, stock_code: str) -> tuple: """ 检查是否可以买入 Returns: (是否可买, 原因) """ if stock_code in self.positions: return False, "已持有该股票" if len(self.positions) >= self.max_stocks: return False, f"已达最大持股数限制({self.max_stocks}只)" if self.daily_buy_count >= self.max_daily_buy: return False, f"今日已达最大买入数限制({self.max_daily_buy}只)" if self.available_cash <= 0: return False, "可用资金不足" return True, "可以买入" def calculate_buy_amount(self, stock_price: float) -> tuple: """ 计算买入数量 Args: stock_price: 股票价格 Returns: (买入股数, 买入金额) """ max_amount = self.available_cash max_per_stock = self.initial_capital * self.max_position_per_stock target_amount = min(max_amount, max_per_stock) # A股100股为1手 shares = int(target_amount / stock_price / 100) * 100 if shares < 100: return 0, 0 actual_amount = shares * stock_price return shares, actual_amount def buy(self, stock_code: str, stock_name: str, price: float, date: str) -> tuple: """ 执行买入操作 Returns: (是否成功, 消息, 交易详情) """ can, reason = self.can_buy(stock_code) if not can: return False, reason, None shares, amount = self.calculate_buy_amount(price) if shares == 0: return False, "资金不足以买入1手", None # 更新持仓 self.positions[stock_code] = { 'name': stock_name, 'buy_price': price, 'shares': shares, 'amount': amount, 'buy_date': date, 'holding_days': 0 } self.available_cash -= amount self.daily_buy_count += 1 trade = { 'action': '买入', 'code': stock_code, 'name': stock_name, 'price': price, 'shares': shares, 'amount': amount, 'date': date, 'reason': '开盘买入信号' } self.trade_history.append(trade) msg = f"买入 {stock_code} {stock_name} {shares}股 @ {price}元, 金额: {amount:.2f}元" return True, msg, trade def calculate_rsi(self, stock_code: str) -> Optional[float]: """ 计算股票的RSI指标 Args: stock_code: 股票代码 Returns: RSI值 或 None """ try: # 获取近60天日线数据 df = ak.stock_zh_a_hist( symbol=stock_code, period="daily", start_date=(datetime.now() - timedelta(days=90)).strftime("%Y%m%d"), end_date=datetime.now().strftime("%Y%m%d"), adjust="qfq" ) if df is None or len(df) < self.rsi_period + 1: return None # 计算RSI close = df['收盘'].astype(float) delta = close.diff() gain = delta.where(delta > 0, 0) loss = (-delta).where(delta < 0, 0) avg_gain = gain.rolling(window=self.rsi_period).mean() avg_loss = loss.rolling(window=self.rsi_period).mean() rs = avg_gain / avg_loss rsi = 100 - (100 / (1 + rs)) latest_rsi = rsi.iloc[-1] return round(float(latest_rsi), 2) if pd.notna(latest_rsi) else None except Exception as e: self.logger.warning(f"RSI计算失败 {stock_code}: {e}") return None def should_sell(self, stock_code: str, current_date: str = None) -> tuple: """ 判断是否应该卖出 策略： 1. 持股满30天强制卖出 2. RSI超买（>70）卖出 Returns: (是否卖出, 原因, RSI值) """ if stock_code not in self.positions: return False, "未持有该股票", None position = self.positions[stock_code] position['holding_days'] += 1 # 条件1：持股满30天 if position['holding_days'] >= self.holding_period: return True, f"持股满{self.holding_period}天，到期卖出", None # 条件2：RSI超买 rsi = self.calculate_rsi(stock_code) if rsi is not None and rsi > self.rsi_overbought: return True, f"RSI={rsi} 超买（>{self.rsi_overbought}），卖出离场", rsi return False, f"继续持有 (已持{position['holding_days']}天, RSI={rsi})", rsi def sell(self, stock_code: str, price: float, date: str, reason: str = "") -> tuple: """ 执行卖出操作 Returns: (是否成功, 消息, 交易详情) """ if stock_code not in self.positions: return False, "未持有该股票", None position = self.positions[stock_code] amount = position['shares'] * price profit = amount - position['amount'] profit_pct = (price - position['buy_price']) / position['buy_price'] * 100 trade = { 'action': '卖出', 'code': stock_code, 'name': position['name'], 'price': price, 'shares': position['shares'], 'amount': amount, 'date': date, 'buy_price': position['buy_price'], 'profit': profit, 'profit_pct': round(profit_pct, 2), 'holding_days': position['holding_days'], 'reason': reason } self.trade_history.append(trade) self.available_cash += amount del self.positions[stock_code] emoji = "🟢" if profit >= 0 else "🔴" msg = f"{emoji} 卖出 {stock_code} {position['name']} {position['shares']}股 @ {price}元, 盈亏: {profit:.2f}元 ({profit_pct:+.2f}%), 原因: {reason}" return True, msg, trade def get_portfolio_summary(self) -> Dict: """获取投资组合摘要""" total_position_value = sum( pos['shares'] * pos['buy_price'] for pos in self.positions.values() ) total_assets = self.available_cash + total_position_value # 统计交易 sells = [t for t in self.trade_history if t['action'] == '卖出'] total_profit = sum(t.get('profit', 0) for t in sells) win_trades = sum(1 for t in sells if t.get('profit', 0) > 0) total_trades = len(sells) win_rate = (win_trades / total_trades * 100) if total_trades > 0 else 0 return { 'initial_capital': self.initial_capital, 'available_cash': round(self.available_cash, 2), 'position_value': round(total_position_value, 2), 'total_assets': round(total_assets, 2), 'total_return': round((total_assets - self.initial_capital) / self.initial_capital * 100, 2), 'total_profit': round(total_profit, 2), 'holding_count': len(self.positions), 'max_stocks': self.max_stocks, 'total_trades': total_trades, 'win_trades': win_trades, 'win_rate': round(win_rate, 2) } def get_positions(self) -> List[Dict]: """获取当前持仓列表""" positions = [] for code, pos in self.positions.items(): positions.append({ 'code': code, 'name': pos['name'], 'buy_price': pos['buy_price'], 'shares': pos['shares'], 'amount': pos['amount'], 'buy_date': pos['buy_date'], 'holding_days': pos['holding_days'] }) return positions def get_trade_history(self) -> List[Dict]: """获取交易历史""" return self.trade_history