mirror of
https://github.com/silencesdg/mt5_python_ea_suite.git
synced 2026-08-03 06:07:44 +00:00
add ml
This commit is contained in:
songkunling
@@ -77,6 +77,7 @@ class LiveDataProvider(DataProvider):
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return None
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def get_historical_data(self, symbol, timeframe, count, **kwargs):
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# 确保使用MT5标准常量
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return mt5.copy_rates_from_pos(symbol, timeframe, 0, count)
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def get_account_info(self):
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+24
-27
@@ -1,29 +1,39 @@
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import pandas as pd
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import numpy as np
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from logger import logger
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from config import MARKET_STATE_CONFIG, SYMBOL, DEFAULT_WEIGHTS, TREND_INDICATOR_WEIGHTS, TREND_THRESHOLDS, MARKET_STATE_WEIGHTS, CONFIDENCE_THRESHOLDS
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from mt5_constants import TIMEFRAME_H1
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# Removed: from config import MARKET_STATE_CONFIG, SYMBOL, DEFAULT_WEIGHTS, TREND_INDICATOR_WEIGHTS, TREND_THRESHOLDS, MARKET_STATE_WEIGHTS, CONFIDENCE_THRESHOLDS
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class MarketStateAnalyzer:
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"""
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市场状态分析器 (支持参数优化)
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市场状态分析器 (已移除对外部配置的依赖)
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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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def __init__(self, data_provider):
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self.data_provider = data_provider
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self.symbol = SYMBOL
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self.timeframe = 16385 # TIMEFRAME_H1
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self.symbol = "XAUUSD" # Hardcoded SYMBOL, as it's no longer imported from config
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self.timeframe = TIMEFRAME_H1
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self.hourly_data_count = 100
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# 使用传入的参数或默认配置
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market_state_config = market_state_params or MARKET_STATE_CONFIG
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self.trend_period = market_state_config.get("trend_period", 50)
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self.retracement_tolerance = market_state_config.get("retracement_tolerance", 0.30)
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self.volume_period = market_state_config.get("volume_period", 20)
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self.volume_ma_period = market_state_config.get("volume_ma_period", 10)
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# Hardcoded default parameters (from original config.py values)
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self.trend_period = 50 # From MARKET_STATE_CONFIG.get("trend_period", 50)
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self.retracement_tolerance = 0.30 # From MARKET_STATE_CONFIG.get("retracement_tolerance", 0.30)
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self.volume_period = 20 # From MARKET_STATE_CONFIG.get("volume_period", 20)
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self.volume_ma_period = 10 # From MARKET_STATE_CONFIG.get("volume_ma_period", 10)
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self.indicator_weights = trend_weights or TREND_INDICATOR_WEIGHTS
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self.thresholds = trend_thresholds or TREND_THRESHOLDS
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self.confidence_thresholds = confidence_thresholds or CONFIDENCE_THRESHOLDS
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self.indicator_weights = { # From TREND_INDICATOR_WEIGHTS
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"price_breakout": 0.35,
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"volume_confirmation": 0.25,
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"momentum oscillator": 0.20,
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"moving_average": 0.20
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}
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self.thresholds = { # From TREND_THRESHOLDS
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"strong_trend": 0.6,
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"weak_trend": 0.3,
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"volume_spike": 1.5,
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"oversold": 30,
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"overbought": 70
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}
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self.current_trend = "none"
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self.trend_peak = 0.0
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@@ -136,16 +146,3 @@ class MarketStateAnalyzer:
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self._update_trend_state(df, state)
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return state, confidence
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def get_strategy_weights(self, market_state, confidence):
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base_weights = MARKET_STATE_WEIGHTS.get(market_state, DEFAULT_WEIGHTS)
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high_confidence_threshold = self.confidence_thresholds.get("high_confidence", 0.7)
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medium_confidence_threshold = self.confidence_thresholds.get("medium_confidence", 0.4)
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if confidence > high_confidence_threshold:
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return {k: v * confidence for k, v in base_weights.items()}
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elif confidence > medium_confidence_threshold:
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return {k: (v * confidence + DEFAULT_WEIGHTS.get(k, 1.0) * (1 - confidence)) for k, v in base_weights.items()}
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else:
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return DEFAULT_WEIGHTS
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+94
-301
@@ -5,6 +5,13 @@ import os
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from logger import logger
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from config import RISK_CONFIG, SYMBOL, INITIAL_CAPITAL, CAPITAL_ALLOCATION, RISK_CONFIG_CONST
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import pandas as pd
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import numpy as np
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import json
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import os
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from logger import logger
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from config import RISK_CONFIG, SYMBOL, INITIAL_CAPITAL, CAPITAL_ALLOCATION, RISK_CONFIG_CONST, USE_MEMORY_FOR_PEAK_DATA
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class PositionManager:
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"""
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持仓管理器 - 统一管理所有持仓操作和交易记录 (已重构为依赖注入)
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@@ -14,6 +21,7 @@ class PositionManager:
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self.data_provider = data_provider
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self.symbol = SYMBOL
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self.trade_direction = trade_direction
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self.use_memory_storage = USE_MEMORY_FOR_PEAK_DATA
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# 风险管理参数
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self.stop_loss_pct = RISK_CONFIG.get("stop_loss_pct", -0.10)
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@@ -35,395 +43,180 @@ class PositionManager:
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self.closed_trades = []
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self.total_equity = self.initial_capital
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# 持久化文件路径
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self.peak_data_file = "position_peaks.json"
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# 初始化时加载峰值数据
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self._load_peak_data()
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# 根据配置选择存储方式
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if self.use_memory_storage:
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self.peak_data = {}
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logger.info("使用内存变量存储持仓峰值数据")
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else:
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self.peak_data_file = f"position_peaks_{os.getpid()}.json"
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logger.info(f"使用文件 {self.peak_data_file} 存储持仓峰值数据")
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self._load_peak_data() # 初始化时加载一次
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def _calculate_position_size(self, capital_to_allocate, current_price):
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price = current_price['last']
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logger.info(f"当前价格: {price}")
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if not isinstance(price, (int, float)) or price == 0:
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logger.error(f"价格无效: {price}")
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return 0.0
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if not isinstance(price, (int, float)) or price == 0: return 0.0
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symbol_info = self.data_provider.get_symbol_info(self.symbol)
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logger.info(f"合约信息: {symbol_info}")
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if not symbol_info:
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logger.error(f"无法获取 {self.symbol} 的合约信息")
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return 0.0
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if not symbol_info: return 0.0
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is_dict = isinstance(symbol_info, dict)
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contract_size = symbol_info['trade_contract_size'] if is_dict else symbol_info.trade_contract_size
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volume_step = symbol_info['volume_step'] if is_dict else symbol_info.volume_step
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min_volume = symbol_info['volume_min'] if is_dict else symbol_info.volume_min
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max_volume = symbol_info['volume_max'] if is_dict else symbol_info.volume_max
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logger.info(f"合约大小: {contract_size}, 步长: {volume_step}, 最小: {min_volume}, 最大: {max_volume}")
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value_of_one_lot = price * contract_size
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logger.info(f"一手价值: {value_of_one_lot}")
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if value_of_one_lot == 0:
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logger.error("一手价值为0")
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return 0.0
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if value_of_one_lot == 0: return 0.0
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volume = capital_to_allocate / value_of_one_lot
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logger.info(f"原始手数: {volume}")
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volume = round(volume / volume_step) * volume_step
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logger.info(f"调整后手数: {volume}")
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volume = max(min_volume, min(volume, max_volume))
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logger.info(f"最终手数: {volume}")
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return volume
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return max(min_volume, min(volume, max_volume))
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def open_position(self, direction, current_price, signal_strength=0.0, dry_run=False):
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logger.info(f"开始处理{direction}开仓请求")
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position_type_to_open = 'long' if direction == 'buy' else 'short'
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# 根据交易方向选择合适的成交价格
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if dry_run:
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# 在回测/模拟模式中，考虑点差
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if direction == 'buy':
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execution_price = current_price['ask'] # 买入用卖方价（ask）
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else:
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execution_price = current_price['bid'] # 卖出用买方价（bid）
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else:
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# 实盘模式使用最后价格
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execution_price = current_price['last']
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# 检查交易方向限制
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if self.trade_direction == "long" and direction == "sell":
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logger.info("当前配置只允许做多，忽略卖出信号")
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return False
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elif self.trade_direction == "short" and direction == "buy":
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logger.info("当前配置只允许做空，忽略买入信号")
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return False
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# 从配置中获取最大持仓限制
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execution_price = current_price['ask'] if dry_run and direction == 'buy' else current_price['bid'] if dry_run else current_price['last']
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if (self.trade_direction == "long" and direction == "sell") or (self.trade_direction == "short" and direction == "buy"): return False
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from config import REALTIME_CONFIG
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max_positions = REALTIME_CONFIG.get(f'max_{position_type_to_open}_positions', 1)
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logger.info(f"配置文件中的max_{position_type_to_open}_positions: {REALTIME_CONFIG.get(f'max_{position_type_to_open}_positions', 'NOT_FOUND')}")
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logger.info(f"最大{position_type_to_open}持仓数限制: {max_positions}")
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# 检查当前同向持仓数量
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current_positions = [p for p in self.positions if p['position_type'] == position_type_to_open]
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logger.info(f"当前{position_type_to_open}持仓数: {len(current_positions)}")
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# 如果配置为0或负数，表示无限制
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if max_positions <= 0:
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logger.info(f"{position_type_to_open}持仓数无限制")
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elif len(current_positions) >= max_positions:
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logger.info(f"已达到最大{position_type_to_open}持仓数 ({max_positions})，忽略信号")
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return False
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if max_positions > 0 and len(current_positions) >= max_positions: return False
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capital_pct = self.long_capital_pct if direction == 'buy' else self.short_capital_pct
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capital_for_this_trade = self.total_equity * capital_pct
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logger.info(f"分配资金: {capital_for_this_trade:.2f} (总权益: {self.total_equity:.2f}, 比例: {capital_pct:.2%})")
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position_volume = self._calculate_position_size(capital_for_this_trade, {'last': execution_price})
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logger.info(f"计算仓位大小: {position_volume:.2f}")
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if position_volume <= 0:
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logger.info("仓位大小为0，无法开仓")
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return False
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logger.info(f"发送订单: {direction} {position_volume:.2f}手 {self.symbol}")
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if position_volume <= 0: return False
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order_result = self.data_provider.send_order(self.symbol, direction, position_volume)
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logger.info(f"订单结果: {order_result}")
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if order_result is None:
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logger.error("订单返回None，可能是数据提供者问题")
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return False
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if order_result is None: return False
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try:
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order_id = order_result['order'] if isinstance(order_result, dict) else order_result.order
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logger.info(f"订单ID: {order_id}")
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except Exception as e:
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logger.error(f"解析订单ID失败: {e}")
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return False
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except Exception: return False
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if order_result and order_id > 0:
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new_position = {
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'ticket': order_id,
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'symbol': self.symbol,
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'entry_price': execution_price,
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'entry_time': current_price['time'],
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'position_type': position_type_to_open,
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'quantity': position_volume,
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'peak_profit_pct': 0.0,
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'ticket': order_id, 'symbol': self.symbol, 'entry_price': execution_price,
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'entry_time': current_price['time'], 'position_type': position_type_to_open,
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'quantity': position_volume, 'peak_profit_pct': 0.0,
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}
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self.positions.append(new_position)
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logger.info(f"开仓成功: {direction} @ {execution_price:.2f}, 手数: {position_volume:.2f}, Ticket: {order_id}")
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logger.info(f"持仓 {order_id}: 初始化峰值盈利为 0.0%")
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return True
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else:
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logger.error(f"开仓失败: {direction} @ {current_price['last']:.2f}, 订单结果: {order_result}")
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return False
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return False
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def monitor_positions(self, current_price, dry_run=False):
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if not self.positions: return
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# 打印所有持仓的当前状态
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logger.info(f"当前持仓数量: {len(self.positions)}")
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for pos in self.positions:
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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"持仓 {pos['ticket']}: 持仓时间={holding_minutes:.1f}分钟, 当前峰值={pos.get('peak_profit_pct', 0):.6%}")
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positions_to_remove = []
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for position in self.positions:
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pnl_pct = self._calculate_pnl_pct(position, current_price['last'])
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old_peak = position.get('peak_profit_pct', 0)
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# 确保正确更新峰值
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new_peak = max(old_peak, pnl_pct)
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position['peak_profit_pct'] = new_peak
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# 打印详细的追踪止损计算信息
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# logger.info(f"持仓 {position['ticket']}: 当前盈利={pnl_pct:.6%}, 原峰值={old_peak:.6%}, 新峰值={new_peak:.6%}")
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# 如果有新的峰值，打印详细信息并保存到文件
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if new_peak > old_peak:
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logger.info(f"持仓 {position['ticket']}: 新的峰值盈利: {new_peak:.6%}")
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# 确认保存到持仓对象
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logger.info(f"持仓 {position['ticket']}: 确认保存的峰值: {position.get('peak_profit_pct', 0):.6%}")
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# 保存到文件
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self._save_peak_data()
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action, reason = self._check_risk_conditions(
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pnl_pct,
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new_peak,
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position['entry_time'],
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current_price['time'],
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position
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)
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if new_peak > old_peak: self._save_peak_data()
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action, reason = self._check_risk_conditions(pnl_pct, new_peak, position['entry_time'], current_price['time'], position)
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if action == "close":
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logger.info(f"平仓信号触发 (Ticket: {position['ticket']}): {reason}")
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# 在dry_run模式下，计算考虑点差的平仓价格
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close_price = current_price['last']
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if dry_run:
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# 从配置获取点差
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from config import SIMULATION_CONFIG
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spread_points = SIMULATION_CONFIG.get("spread", 16)
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spread_value = spread_points * 0.01 # XAUUSD: 1点 = 0.01
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if position['position_type'] == 'long':
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# 多头平仓用bid价（卖出价）
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close_price = current_price['bid']
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else:
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# 空头平仓用ask价（买入价）
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close_price = current_price['ask']
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else:
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close_price = current_price['last']
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success = self.data_provider.close_position(position['ticket'], position['symbol'], position['quantity'])
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if success:
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close_price = current_price['bid'] if position['position_type'] == 'long' else current_price['ask']
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if self.data_provider.close_position(position['ticket'], position['symbol'], position['quantity']):
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self._record_closed_trade(position, close_price, reason)
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positions_to_remove.append(position)
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else:
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logger.error(f"平仓失败, Ticket: {position['ticket']}")
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if positions_to_remove:
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# 记录平仓的持仓ticket
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closed_tickets = [pos['ticket'] for pos in positions_to_remove]
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logger.info(f"平仓持仓: {closed_tickets}")
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self.positions = [p for p in self.positions if p not in positions_to_remove]
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self.update_equity()
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# 清理已平仓持仓的峰值数据
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self.cleanup_peak_data()
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def _calculate_pnl_pct(self, position, current_price_value):
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entry_price = position['entry_price']
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if position['position_type'] == 'long':
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return (current_price_value - entry_price) / entry_price if entry_price != 0 else 0.0
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else:
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return (entry_price - current_price_value) / entry_price if entry_price != 0 else 0.0
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if entry_price == 0: return 0.0
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return (current_price_value - entry_price) / entry_price if position['position_type'] == 'long' else (entry_price - current_price_value) / entry_price
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def _record_closed_trade(self, position, close_price, close_reason):
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symbol_info = self.data_provider.get_symbol_info(position['symbol'])
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contract_size = (symbol_info['trade_contract_size'] if isinstance(symbol_info, dict)
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else symbol_info.trade_contract_size) if symbol_info else 100
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pnl = 0
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if position['position_type'] == 'long':
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pnl = (close_price - position['entry_price']) * position['quantity'] * contract_size
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else:
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pnl = (position['entry_price'] - close_price) * position['quantity'] * contract_size
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contract_size = (symbol_info['trade_contract_size'] if isinstance(symbol_info, dict) else symbol_info.trade_contract_size) if symbol_info else 100
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pnl = (close_price - position['entry_price']) * position['quantity'] * contract_size if position['position_type'] == 'long' else (position['entry_price'] - close_price) * position['quantity'] * contract_size
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trade_record = position.copy()
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trade_record.update({
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'status': 'closed', 'close_price': close_price, 'close_time': pd.Timestamp.now(),
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'close_reason': close_reason, 'profit_loss': pnl
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})
|
||||
trade_record.update({'status': 'closed', 'close_price': close_price, 'close_time': pd.Timestamp.now(), 'close_reason': close_reason, 'profit_loss': pnl})
|
||||
self.closed_trades.append(trade_record)
|
||||
logger.info(f"平仓记录 #{position['ticket']}: 盈亏: ${pnl:.2f}")
|
||||
|
||||
def _check_risk_conditions(self, current_profit_pct, peak_profit_pct, entry_time, current_time, position=None):
|
||||
# 记录详细的追踪止损计算过程
|
||||
logger.debug(f"追踪止损计算: 当前盈利={current_profit_pct:.4%}, 峰值盈利={peak_profit_pct:.4%}, 阈值={self.min_profit_for_trailing:.4%}")
|
||||
|
||||
if current_profit_pct <= self.stop_loss_pct:
|
||||
logger.debug(f"触发止损: {current_profit_pct:.4%} <= {self.stop_loss_pct:.4%}")
|
||||
return "close", f"止损触发"
|
||||
if current_profit_pct >= self.take_profit_pct:
|
||||
logger.debug(f"触发止盈: {current_profit_pct:.4%} >= {self.take_profit_pct:.4%}")
|
||||
return "close", f"止盈触发"
|
||||
|
||||
# 修正后的追踪止损逻辑
|
||||
if current_profit_pct <= self.stop_loss_pct: return "close", f"止损触发"
|
||||
if current_profit_pct >= self.take_profit_pct: return "close", f"止盈触发"
|
||||
if peak_profit_pct > self.min_profit_for_trailing:
|
||||
stop_level = peak_profit_pct * (1 - self.profit_retracement_pct)
|
||||
retracement_amount = peak_profit_pct - current_profit_pct
|
||||
retracement_pct = (retracement_amount / peak_profit_pct) if peak_profit_pct > 0 else 0
|
||||
|
||||
# 计算实际回撤金额
|
||||
if position:
|
||||
symbol_info = self.data_provider.get_symbol_info(self.symbol)
|
||||
contract_size = (symbol_info['trade_contract_size'] if isinstance(symbol_info, dict)
|
||||
else symbol_info.trade_contract_size) if symbol_info else 100
|
||||
# 使用持仓的entry_price和quantity来计算实际回撤金额
|
||||
position_value = position['entry_price'] * position['quantity'] * contract_size
|
||||
actual_retracement_amount = position_value * retracement_amount
|
||||
else:
|
||||
actual_retracement_amount = 0
|
||||
|
||||
logger.info(f"追踪止损详细计算:")
|
||||
logger.info(f" 峰值盈利: {peak_profit_pct:.4%}")
|
||||
logger.info(f" 当前盈利: {current_profit_pct:.4%}")
|
||||
logger.info(f" 回撤阈值: {self.profit_retracement_pct:.2%}")
|
||||
logger.info(f" 计算止损位: {peak_profit_pct:.4%} × (1 - {self.profit_retracement_pct:.2%}) = {stop_level:.4%}")
|
||||
logger.info(f" 实际回撤: {retracement_pct:.2%} (回撤金额: ${actual_retracement_amount:.2f})")
|
||||
logger.info(f" 触发条件: 当前盈利 {current_profit_pct:.4%} <= 止损位 {stop_level:.4%} = {current_profit_pct <= stop_level}")
|
||||
|
||||
if current_profit_pct <= stop_level:
|
||||
logger.info(f"触发追踪止损: 当前盈利{current_profit_pct:.4%} <= 止损位{stop_level:.4%}")
|
||||
return "close", f"追踪止损触发 (盈利从 {peak_profit_pct:.2%} 回落至 {current_profit_pct:.2%}, 回撤{retracement_pct:.2%})"
|
||||
else:
|
||||
logger.info(f"未触发追踪止损: 当前盈利{current_profit_pct:.4%} > 止损位{stop_level:.4%}")
|
||||
else:
|
||||
pass # 未达到追踪止损条件
|
||||
|
||||
if self.enable_time_based_exit:
|
||||
holding_duration = current_time - entry_time
|
||||
if (holding_duration.total_seconds() / 60) > self.max_holding_minutes:
|
||||
if current_profit_pct < self.min_profit_for_time_exit:
|
||||
return "close", f"超时平仓 (持仓超过{self.max_holding_minutes}分钟且盈利未达标)"
|
||||
|
||||
return "close", f"追踪止损触发"
|
||||
if self.enable_time_based_exit and (current_time - entry_time).total_seconds() / 60 > self.max_holding_minutes and current_profit_pct < self.min_profit_for_time_exit:
|
||||
return "close", f"超时平仓"
|
||||
return "none", ""
|
||||
|
||||
def update_equity(self):
|
||||
# In live mode, always trust the broker's account info
|
||||
if self.data_provider.is_live:
|
||||
account_info = self.data_provider.get_account_info()
|
||||
if account_info:
|
||||
self.total_equity = account_info.equity
|
||||
# In simulation/backtest mode, calculate equity based on trade history
|
||||
if account_info: self.total_equity = account_info.equity
|
||||
else:
|
||||
self.total_equity = self.initial_capital + sum(t['profit_loss'] for t in self.closed_trades)
|
||||
|
||||
def sync_positions(self):
|
||||
# 只在实盘模式下执行同步
|
||||
if not self.data_provider.is_live:
|
||||
return
|
||||
|
||||
if not self.data_provider.is_live: return
|
||||
live_positions = self.data_provider.get_positions(self.symbol)
|
||||
if live_positions is None: return
|
||||
|
||||
# 从文件加载保存的峰值数据
|
||||
saved_peaks = self._load_peak_data()
|
||||
logger.info(f"从文件加载的峰值数据: {saved_peaks}")
|
||||
|
||||
# 保存现有的峰值数据（内存中的）
|
||||
existing_peaks = {pos['ticket']: pos.get('peak_profit_pct', 0.0) for pos in self.positions}
|
||||
|
||||
# 合并数据：优先使用文件中的数据，如果没有则使用内存中的数据
|
||||
merged_peaks = {**existing_peaks, **saved_peaks}
|
||||
# logger.info(f"合并后的峰值数据: {merged_peaks}")
|
||||
|
||||
self.positions.clear()
|
||||
for pos in live_positions:
|
||||
# 获取该持仓之前记录的峰值，优先从文件中获取
|
||||
restored_peak = merged_peaks.get(pos.ticket, 0.0)
|
||||
|
||||
new_position = {
|
||||
restored_peak = saved_peaks.get(str(pos.ticket), 0.0)
|
||||
self.positions.append({
|
||||
'ticket': pos.ticket, 'symbol': pos.symbol, 'entry_price': pos.price_open,
|
||||
'entry_time': pd.to_datetime(pos.time, unit='s'),
|
||||
'position_type': 'long' if pos.type == 0 else 'short',
|
||||
'entry_time': pd.to_datetime(pos.time, unit='s'), 'position_type': 'long' if pos.type == 0 else 'short',
|
||||
'quantity': pos.volume, 'peak_profit_pct': restored_peak
|
||||
}
|
||||
self.positions.append(new_position)
|
||||
|
||||
logger.info(f"同步持仓 {pos.ticket}: 恢复峰值={restored_peak:.6%}")
|
||||
|
||||
logger.info(f"持仓已从MT5同步: {len(self.positions)}个")
|
||||
})
|
||||
|
||||
def get_trade_summary(self):
|
||||
if not self.closed_trades: return {}
|
||||
profits = [t['profit_loss'] for t in self.closed_trades]
|
||||
return {
|
||||
'total_trades': len(self.closed_trades),
|
||||
'winning_trades': len([p for p in profits if p > 0]),
|
||||
'losing_trades': len([p for p in profits if p < 0]),
|
||||
'win_rate': (len([p for p in profits if p > 0]) / len(profits) * 100) if profits else 0,
|
||||
'total_profit_loss': sum(profits),
|
||||
'avg_profit_loss': np.mean(profits) if profits else 0,
|
||||
'max_profit': max(profits) if profits else 0,
|
||||
'max_loss': min(profits) if profits else 0
|
||||
'total_trades': len(self.closed_trades), 'winning_trades': len([p for p in profits if p > 0]),
|
||||
'losing_trades': len([p for p in profits if p < 0]), 'win_rate': (len([p for p in profits if p > 0]) / len(profits) * 100) if profits else 0,
|
||||
'total_profit_loss': sum(profits), 'avg_profit_loss': np.mean(profits) if profits else 0,
|
||||
'max_profit': max(profits) if profits else 0, 'max_loss': min(profits) if profits else 0
|
||||
}
|
||||
|
||||
def save_trade_history(self, base_filename):
|
||||
if not self.closed_trades: return
|
||||
df = pd.DataFrame(self.closed_trades)
|
||||
df.to_csv(f"{base_filename}.csv", index=False, encoding='utf-8-sig')
|
||||
with open(f"{base_filename}.json", 'w', encoding='utf-8') as f:
|
||||
json.dump(self.closed_trades, f, ensure_ascii=False, indent=2, default=str)
|
||||
logger.info(f"交易记录已保存到 {base_filename}.csv/.json")
|
||||
pd.DataFrame(self.closed_trades).to_csv(f"{base_filename}.csv", index=False, encoding='utf-8-sig')
|
||||
|
||||
def _load_peak_data(self):
|
||||
"""从文件加载峰值数据"""
|
||||
try:
|
||||
if os.path.exists(self.peak_data_file):
|
||||
with open(self.peak_data_file, 'r', encoding='utf-8') as f:
|
||||
peak_data = json.load(f)
|
||||
# logger.info(f"从文件加载峰值数据: {peak_data}")
|
||||
return peak_data
|
||||
else:
|
||||
logger.info("峰值数据文件不存在，使用空数据")
|
||||
if self.use_memory_storage:
|
||||
return self.peak_data
|
||||
else:
|
||||
try:
|
||||
if os.path.exists(self.peak_data_file):
|
||||
with open(self.peak_data_file, 'r', encoding='utf-8') as f:
|
||||
return json.load(f)
|
||||
return {}
|
||||
except Exception as e:
|
||||
logger.error(f"从文件加载峰值数据失败: {e}")
|
||||
return {}
|
||||
except Exception as e:
|
||||
logger.error(f"加载峰值数据失败: {e}")
|
||||
return {}
|
||||
|
||||
def _save_peak_data(self):
|
||||
"""保存峰值数据到文件"""
|
||||
try:
|
||||
peak_data = {pos['ticket']: pos.get('peak_profit_pct', 0.0) for pos in self.positions}
|
||||
with open(self.peak_data_file, 'w', encoding='utf-8') as f:
|
||||
json.dump(peak_data, f, ensure_ascii=False, indent=2)
|
||||
#logger.info(f"峰值数据已保存到文件: {peak_data}")
|
||||
except Exception as e:
|
||||
logger.error(f"保存峰值数据失败: {e}")
|
||||
if self.use_memory_storage:
|
||||
try:
|
||||
self.peak_data.update({str(pos['ticket']): pos.get('peak_profit_pct', 0.0) for pos in self.positions})
|
||||
except Exception as e:
|
||||
logger.error(f"更新内存峰值数据失败: {e}")
|
||||
else:
|
||||
try:
|
||||
peak_data = {str(pos['ticket']): pos.get('peak_profit_pct', 0.0) for pos in self.positions}
|
||||
with open(self.peak_data_file, 'w', encoding='utf-8') as f:
|
||||
json.dump(peak_data, f, ensure_ascii=False, indent=2)
|
||||
except Exception as e:
|
||||
logger.error(f"保存峰值数据到文件失败: {e}")
|
||||
|
||||
def cleanup_peak_data(self):
|
||||
"""清理已平仓持仓的峰值数据"""
|
||||
try:
|
||||
if os.path.exists(self.peak_data_file):
|
||||
with open(self.peak_data_file, 'r', encoding='utf-8') as f:
|
||||
peak_data = json.load(f)
|
||||
|
||||
# 获取当前持仓的ticket列表
|
||||
current_tickets = {pos['ticket'] for pos in self.positions}
|
||||
|
||||
# 清理已平仓持仓的数据
|
||||
cleaned_peak_data = {ticket: peak for ticket, peak in peak_data.items()
|
||||
if ticket in current_tickets}
|
||||
|
||||
# 保存清理后的数据
|
||||
with open(self.peak_data_file, 'w', encoding='utf-8') as f:
|
||||
json.dump(cleaned_peak_data, f, ensure_ascii=False, indent=2)
|
||||
|
||||
logger.info(f"清理峰值数据完成，保留 {len(cleaned_peak_data)} 个持仓数据")
|
||||
except Exception as e:
|
||||
logger.error(f"清理峰值数据失败: {e}")
|
||||
current_tickets = {str(pos['ticket']) for pos in self.positions}
|
||||
if self.use_memory_storage:
|
||||
try:
|
||||
self.peak_data = {ticket: peak for ticket, peak in self.peak_data.items() if ticket in current_tickets}
|
||||
except Exception as e:
|
||||
logger.error(f"清理内存峰值数据失败: {e}")
|
||||
else:
|
||||
try:
|
||||
if os.path.exists(self.peak_data_file):
|
||||
with open(self.peak_data_file, 'r', encoding='utf-8') as f:
|
||||
peak_data = json.load(f)
|
||||
cleaned_peak_data = {ticket: peak for ticket, peak in peak_data.items() if ticket in current_tickets}
|
||||
with open(self.peak_data_file, 'w', encoding='utf-8') as f:
|
||||
json.dump(cleaned_peak_data, f, ensure_ascii=False, indent=2)
|
||||
except Exception as e:
|
||||
logger.error(f"清理文件峰值数据失败: {e}")
|
||||
|
||||
Reference in New Issue
Block a user