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https://github.com/silencesdg/mt5_python_ea_suite.git
synced 2026-07-29 03:37:43 +00:00
基本完毕
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songkunling
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import pandas as pd
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from tqdm import tqdm
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from core.risk import RiskController
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from config import SIGNAL_THRESHOLDS, BACKTEST_CONFIG, SPREAD
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from logger import logger
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class BacktestEngine:
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def __init__(self, df, trade_direction="both"):
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self.df = df
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self.trade_direction = trade_direction
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self.spread = BACKTEST_CONFIG.get("spread", SPREAD)
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def run_strategy(self, strategy):
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signals = strategy.run_backtest(self.df)
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buy_count = (signals == 1).sum()
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sell_count = (signals == -1).sum()
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logger.info(f"策略 {strategy.__class__.__module__} 信号统计 - 买入: {buy_count}, 卖出: {sell_count}")
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return signals
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def combine_signals(self, signals_list, weights):
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df_signals = pd.concat(signals_list, axis=1).fillna(0)
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weighted_signals = df_signals * weights
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combined = weighted_signals.sum(axis=1)
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def apply_threshold(score):
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if score > SIGNAL_THRESHOLDS["buy_threshold"]:
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return 1
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elif score < SIGNAL_THRESHOLDS["sell_threshold"]:
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return -1
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else:
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return 0
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combined_signal = combined.apply(apply_threshold)
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buy_signals = (combined_signal == 1).sum()
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sell_signals = (combined_signal == -1).sum()
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logger.info(f"组合信号统计 - 买入: {buy_signals}, 卖出: {sell_signals}")
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return combined_signal
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def run_backtest(self, signals, symbol="XAUUSD"):
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df = self.df.copy()
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df['signal'] = signals.shift(1).fillna(0)
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risk_controller = RiskController(self.trade_direction)
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logger.info(f"回测开始: 交易方向={self.trade_direction}")
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# 使用tqdm创建进度条
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for i in tqdm(range(1, len(df)), desc=f"Backtesting ({len(df)} bars)"):
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current_signal = df['signal'].iloc[i]
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# 计算考虑双向点差的买卖价格
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close_price = df['close'].iloc[i]
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spread_points = self.spread
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spread_half = spread_points * 0.01 / 2 # XAUUSD: 1点 = 0.01，双向点差各一半
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current_price = {
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'bid': close_price - spread_half, # 卖出价格（中间价 - 点差/2）
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'ask': close_price + spread_half, # 买入价格（中间价 + 点差/2）
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'last': close_price # 最后成交价（中间价）
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}
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direction = None
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if current_signal == 1:
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direction = "buy"
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elif current_signal == -1:
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direction = "sell"
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if direction:
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risk_controller.process_trading_signal(
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direction, current_price, abs(current_signal), dry_run=True
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)
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risk_controller.monitor_positions(current_price, dry_run=True)
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risk_controller.position_manager.force_close_all_positions(df['close'].iloc[-1], dry_run=True)
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logger.info("回测完成，生成性能报告...")
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summary = risk_controller.position_manager.get_trade_summary()
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logger.info("=" * 80)
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logger.info("回测性能报告")
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logger.info("=" * 80)
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logger.info(f" 总交易次数: {summary['total_trades']}")
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logger.info(f" 盈利次数: {summary['winning_trades']}")
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logger.info(f" 亏损次数: {summary['losing_trades']}")
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logger.info(f" 胜率: {summary['win_rate']:.2f}%")
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logger.info("-" * 40)
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logger.info(f" 总盈亏: ${summary['total_profit_loss']:.2f}")
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logger.info(f" 平均每笔交易盈亏: ${summary['avg_profit_loss']:.2f}")
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logger.info(f" 最大盈利: ${summary['max_profit']:.2f}")
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logger.info(f" 最大亏损: ${summary['max_loss']:.2f}")
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logger.info("=" * 80)
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risk_controller.position_manager.save_to_csv("backtest_trades.csv")
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risk_controller.position_manager.save_to_json("backtest_trades.json")
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return summary
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from strategies import ma_cross, rsi, bollinger, mean_reversion, momentum_breakout, macd, kdj, turtle, daily_breakout, wave_theory
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from config import DEFAULT_WEIGHTS, SYMBOL, TIMEFRAME
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from logger import logger
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from core.risk.market_state import MarketStateAnalyzer
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class DynamicWeightManager:
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"""
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动态权重管理器
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"""
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def __init__(self, data_provider, market_state_params=None, trend_weights=None, trend_thresholds=None, confidence_thresholds=None):
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self.data_provider = data_provider
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self.market_state_analyzer = MarketStateAnalyzer(
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data_provider,
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market_state_params=market_state_params,
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trend_weights=trend_weights,
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trend_thresholds=trend_thresholds,
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confidence_thresholds=confidence_thresholds
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)
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# 策略类和它们的初始化参数的映射
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self.strategy_blueprints = {
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'ma_cross': (ma_cross.MACrossStrategy, {}),
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'rsi': (rsi.RSIStrategy, {}),
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'bollinger': (bollinger.BollingerStrategy, {}),
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'mean_reversion': (mean_reversion.MeanReversionStrategy, {}),
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'momentum_breakout': (momentum_breakout.MomentumBreakoutStrategy, {}),
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'macd': (macd.MACDStrategy, {}),
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'kdj': (kdj.KDJStrategy, {}),
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'turtle': (turtle.TurtleStrategy, {}),
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'daily_breakout': (daily_breakout.DailyBreakoutStrategy, {}),
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'wave_theory': (wave_theory.WaveTheoryStrategy, {})
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}
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self.strategy_instances = self._create_strategy_instances()
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self.current_weights = None
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self.current_market_state = "none"
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self.current_confidence = 0.0
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def _create_strategy_instances(self):
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instances = {}
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for name, (strategy_class, params) in self.strategy_blueprints.items():
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instances[name] = strategy_class(self.data_provider, SYMBOL, TIMEFRAME, **params)
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return instances
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def get_current_strategies_and_weights(self):
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market_state, confidence = self.market_state_analyzer.get_market_state()
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dynamic_weights = self.market_state_analyzer.get_strategy_weights(market_state, confidence)
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self.current_weights = dynamic_weights
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self.current_market_state = market_state
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self.current_confidence = confidence
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strategies_with_weights = []
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for name, weight in dynamic_weights.items():
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if name in self.strategy_instances:
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strategies_with_weights.append((self.strategy_instances[name], weight))
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return strategies_with_weights
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def get_weight_info(self):
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return {
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'market_state': self.current_market_state,
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'confidence': self.current_confidence,
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'weights': self.current_weights
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}
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@@ -0,0 +1,141 @@
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import time
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import signal
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import sys
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from datetime import datetime
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from logger import logger
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from config import SYMBOL, TIMEFRAME, REALTIME_CONFIG, SIGNAL_THRESHOLDS
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from core.risk import RiskController
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from execution.dynamic_weights import DynamicWeightManager
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class RealtimeTrader:
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"""实时交易器 (已重构为依赖注入)"""
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def __init__(self, data_provider, update_interval=60):
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self.data_provider = data_provider
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self.update_interval = update_interval
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self.running = False
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self.risk_controller = None
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self.weight_manager = None
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def _initialize(self):
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if not self.data_provider.initialize():
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return False
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self.risk_controller = RiskController(self.data_provider)
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self.weight_manager = DynamicWeightManager(self.data_provider)
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self.risk_controller.sync_state()
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signal.signal(signal.SIGINT, self._signal_handler)
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signal.signal(signal.SIGTERM, self._signal_handler)
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logger.info("实时交易系统初始化完成")
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return True
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def _signal_handler(self, signum, frame):
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logger.info(f"接收到信号 {signum}，准备退出...")
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self.stop()
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def _run_cycle(self):
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try:
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self.risk_controller.sync_state()
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current_price = self.data_provider.get_current_price(SYMBOL)
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if not current_price:
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logger.warning("无法获取当前价格，跳过本次循环")
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return
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strategies_with_weights = self.weight_manager.get_current_strategies_and_weights()
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if not strategies_with_weights: return
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signals, weights = [], []
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for strat, weight in strategies_with_weights:
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signals.append(strat.generate_signal())
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weights.append(weight)
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# 打印详细信号日志
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logger.info("--- 信号计算详情 ---")
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for i, (strat, weight) in enumerate(strategies_with_weights):
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signal = signals[i]
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weighted_signal = signal * weight
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strat_name = strat.name
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logger.info(f" 策略: {strat_name:<25} | 信号: {signal:6.2f} | 权重: {weight:6.2f} | 加权信号: {weighted_signal:6.2f}")
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logger.info("--------------------")
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weighted_signal_sum = sum(s * w for s, w in zip(signals, weights))
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buy_threshold = SIGNAL_THRESHOLDS.get('buy_threshold', 1.5)
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sell_threshold = SIGNAL_THRESHOLDS.get('sell_threshold', -1.5)
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logger.info(f"加权信号: {weighted_signal_sum:.2f} (买入阈值: {buy_threshold}, 卖出阈值: {sell_threshold})")
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# logger.info(f"信号比较: {weighted_signal_sum} > {buy_threshold} = {weighted_signal_sum > buy_threshold}")
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# logger.info(f"信号比较: {weighted_signal_sum} < {sell_threshold} = {weighted_signal_sum < sell_threshold}")
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direction = None
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if weighted_signal_sum > buy_threshold:
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direction = "buy"
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elif weighted_signal_sum < sell_threshold:
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direction = "sell"
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if direction:
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logger.info(f"准备执行{direction}交易，信号强度: {weighted_signal_sum:.2f}")
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success = self.risk_controller.process_trading_signal(direction, current_price, weighted_signal_sum)
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if not success:
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logger.warning(f"{direction}交易执行失败")
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else:
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logger.info(f"{direction}交易执行成功")
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self.risk_controller.monitor_positions(current_price)
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# --- 状态汇总日志 ---
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logger.info("--- 财务状况更新 ---")
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open_positions = self.risk_controller.position_manager.positions
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if not open_positions:
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logger.info(" 当前无持仓")
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else:
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logger.info(f" 当前持仓: {len(open_positions)} 个")
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for pos in open_positions:
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pnl_pct = self.risk_controller.position_manager._calculate_pnl_pct(pos, current_price['last'])
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# 计算持仓时间
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holding_time = current_price['time'] - pos['entry_time']
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holding_minutes = holding_time.total_seconds() / 60
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logger.info(f" - Ticket {pos['ticket']}: {pos['position_type']} {pos['symbol']} @ {pos['entry_price']:.2f} | 持仓时间: {holding_minutes:.1f}分钟 | 浮动盈亏: {pnl_pct:.2%}")
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trade_summary = self.risk_controller.position_manager.get_trade_summary()
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if trade_summary and trade_summary['total_trades'] > 0:
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logger.info(" 已平仓交易摘要:")
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logger.info(f" - 总交易: {trade_summary['total_trades']}, 盈利: {trade_summary['winning_trades']}, 亏损: {trade_summary['losing_trades']}, 胜率: {trade_summary['win_rate']:.2f}%")
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logger.info(f" - 总净盈亏: ${trade_summary['total_profit_loss']:.2f}")
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logger.info(f" 总权益: ${self.risk_controller.position_manager.total_equity:.2f}")
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logger.info("----------------------")
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except Exception as e:
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import traceback
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logger.error(f"交易周期执行失败: {e}\n{traceback.format_exc()}")
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def start(self):
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if not self._initialize(): return
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logger.info("=== 启动实时交易系统 ===")
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self.running = True
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while self.running:
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cycle_start = time.time()
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self._run_cycle()
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cycle_time = time.time() - cycle_start
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wait_time = max(0, self.update_interval - cycle_time)
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if wait_time > 0: time.sleep(wait_time)
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def stop(self):
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logger.info("=== 停止实时交易系统 ===")
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self.running = False
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try:
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if self.risk_controller:
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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self.risk_controller.save_trade_history(f"realtime_trades_{timestamp}")
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except Exception as e:
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logger.error(f"保存交易记录失败: {e}")
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finally:
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self.data_provider.shutdown()
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logger.info("实时交易系统已停止")
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sys.exit(0)
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