1192 lines
62 KiB
Python
1192 lines
62 KiB
Python
import warnings
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warnings.filterwarnings("ignore", category=UserWarning, module="pandas_ta")
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import logging
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from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter
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from pandas import DataFrame
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import pandas as pd
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import pandas_ta as ta
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from freqtrade.persistence import Trade
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import numpy as np
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import datetime
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import math
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logger = logging.getLogger(__name__)
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class FreqaiPrimer(IStrategy):
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# 策略参数 - 使用custom_roi替代minimal_roi字典
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loglevel = "warning"
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minimal_roi = {}
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# 启用自定义ROI回调函数
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use_custom_roi = True
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# FreqAI 要求
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process_only_new_candles = True
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stoploss = -0.15 # 固定止损 -15% (大幅放宽止损以承受更大波动)
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trailing_stop = True
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trailing_stop_positive_offset = 0.005 # 追踪止损偏移量 0.5% (更容易触发跟踪止盈)
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# 用于跟踪市场状态的数据框缓存
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_dataframe_cache = None
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def __init__(self, config=None):
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"""初始化策略参数，调用父类初始化方法并接受config参数"""
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super().__init__(config) # 调用父类的初始化方法并传递config
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# 存储从配置文件加载的默认值
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self._trailing_stop_positive_default = 0.004 # 降低默认值以更容易触发跟踪止盈
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# 波动系数缓存（简化版：直接计算，无需历史序列）
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self._volatility_timestamp = {} # {pair: timestamp}
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self._volatility_cache = {} # {pair: volatility_coef}
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self._volatility_update_interval = 180 # 波动系数更新间隔（秒），3分钟
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# 入场间隔控制：记录每个交易对最近一次入场的时间
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# 格式: {pair: datetime}
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self._last_entry_time = {}
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def strategy_log(self, message: str, level: str = "info") -> None:
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"""根据 config 的 enable_strategy_log 决定是否输出日志"""
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enable_log = self.config.get('enable_strategy_log', False)
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if not enable_log:
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return
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if level.lower() == "debug":
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logger.debug(message)
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elif level.lower() == "warning":
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logger.warning(message)
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elif level.lower() == "error":
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logger.error(message)
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else:
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logger.info(message)
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# 只用于adjust_trade_position方法的波动系数获取
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def get_volatility_coefficient(self, pair: str) -> float:
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"""
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获取币对的波动系数（简化版：直接计算，无需历史序列）
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- USDT/USDT 波动系数设置为0
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- BTC/USDT 波动系数设置为1
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- 其他币对：
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计算当前波动系数 = 该币对波动率 / BTC/USDT波动率
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（基于最近200根1h K线，足够稳定，无需额外平滑）
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波动系数表示某币对与BTC/USDT相比的波动幅度倍数
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- 山寨币的波动系数可能大于3
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- 稳定性较高的币对（如DOT/USDT）波动系数可能小于1
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添加了缓存机制，每3分钟更新一次，避免频繁计算
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"""
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# 检查特殊币对
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if pair == 'USDT/USDT':
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return 0.0
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elif pair == 'BTC/USDT':
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return 1.0
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try:
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# 获取当前时间戳
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current_time = datetime.datetime.now().timestamp()
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# 检查缓存：如果距离上次计算时间小于更新间隔，则直接返回缓存值
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if (pair in self._volatility_cache and
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pair in self._volatility_timestamp and
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current_time - self._volatility_timestamp[pair] < self._volatility_update_interval):
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return self._volatility_cache[pair]
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# 直接计算当前波动系数（基于最近200根1h K线）
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current_volatility_coef = self._calculate_current_volatility_coef(pair)
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# 更新缓存和时间戳
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self._volatility_cache[pair] = current_volatility_coef
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self._volatility_timestamp[pair] = current_time
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self.strategy_log(f"波动系数计算完成 {pair}: 系数={current_volatility_coef:.4f} (基于最近200根1h K线)")
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return current_volatility_coef
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except Exception as e:
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logger.warning(f"计算波动系数时出错 {pair}: {str(e)}")
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# 如果出错，尝试返回缓存值，否则返回默认值1.0
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return self._volatility_cache.get(pair, 1.0)
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def _calculate_current_volatility_coef(self, pair: str) -> float:
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"""
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计算当前的波动系数（该币对波动率 / BTC/USDT波动率）
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"""
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try:
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# 获取当前币对的1小时k线数据
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current_pair_df, _ = self.dp.get_analyzed_dataframe(pair, '1h')
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# 获取BTC/USDT的1小时k线数据
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btc_df, _ = self.dp.get_analyzed_dataframe('BTC/USDT', '1h')
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# 确保有足够的数据点
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if len(current_pair_df) < 2 or len(btc_df) < 2:
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return 1.0 # 如果没有足够数据，返回默认值1.0
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# 对于数据点少于200个的情况，使用所有可用数据
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# 对于数据点多于200个的情况，使用最近200个数据点
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current_data = current_pair_df.iloc[-min(200, len(current_pair_df)):]
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btc_data = btc_df.iloc[-min(200, len(btc_df)):]
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# 计算当前币对的对数收益率和波动率
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current_data['returns'] = current_data['close'].pct_change()
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current_volatility = current_data['returns'].std() * 100 # 转换为百分比
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# 计算BTC/USDT的对数收益率和波动率
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btc_data['returns'] = btc_data['close'].pct_change()
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btc_volatility = btc_data['returns'].std() * 100 # 转换为百分比
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# 避免除以零的情况
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if btc_volatility == 0:
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return 1.0
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# 计算波动系数：当前币对波动率 / BTC/USDT波动率
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volatility_coef = current_volatility / btc_volatility
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# 设置合理的上下限，避免极端值影响策略
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# 上限设置为5.0（非常高波动的币对）
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# 下限设置为0.1（非常稳定的币对）
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return max(0.1, min(5.0, volatility_coef))
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except Exception as e:
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logger.warning(f"计算当前波动系数时出错 {pair}: {str(e)}")
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return 1.0 # 出错时返回默认值1.0
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# 其他辅助方法可以在这里添加
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@property
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def protections(self):
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"""
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保护机制配置
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基于最新Freqtrade规范，保护机制应定义在策略文件中而非配置文件
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"""
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return [
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{
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"method": "StoplossGuard",
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"lookback_period_candles": 60, # 3小时回看期（60根3分钟K线）
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"trade_limit": 2, # 最多2笔止损交易
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"stop_duration_candles": 60, # 暂停180分钟（60根3分钟K线）
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"only_per_pair": False # 仅针对单个币对
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},
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{
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"method": "CooldownPeriod",
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"stop_duration_candles": 2 # 6分钟冷却期（2根3分钟K线）
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},
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{
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"method": "MaxDrawdown",
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"lookback_period_candles": 48, # 2.4小时回看期
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"trade_limit": 4, # 4笔交易限制
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"stop_duration_candles": 24, # 72分钟暂停（24根3分钟K线）
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"max_allowed_drawdown": 0.20 # 20%最大回撤容忍度
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}
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]
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@property
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def trailing_stop_positive(self):
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"""根据市场状态动态调整跟踪止盈参数"""
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# 获取当前市场状态
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if self._dataframe_cache is not None and len(self._dataframe_cache) > 0:
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current_state = self._dataframe_cache['market_state'].iloc[-1]
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if current_state == 'strong_bull':
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return 0.007 # 强劲牛市中降低跟踪止盈，让利润奔跑
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elif current_state == 'weak_bull':
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return 0.005 # 弱势牛市中保持较低的跟踪止盈
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return self._trailing_stop_positive_default # 返回默认值
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@trailing_stop_positive.setter
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def trailing_stop_positive(self, value):
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"""设置trailing_stop_positive的默认值"""
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self._trailing_stop_positive_default = value
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timeframe = "3m" # 主时间框架为 3 分钟
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can_short = False # 禁用做空
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# 自定义指标参数 - 使用Hyperopt可优化参数
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bb_length = IntParameter(10, 30, default=20, optimize=True, load=True, space='buy')
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bb_std = DecimalParameter(1.5, 3.0, decimals=1, default=2.0, optimize=True, load=True, space='buy')
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rsi_length = IntParameter(7, 21, default=14, optimize=True, load=True, space='buy')
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rsi_oversold = IntParameter(30, 50, default=42, optimize=True, load=True, space='buy')
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# 入场条件阈值参数
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bb_lower_deviation = DecimalParameter(1.01, 1.05, decimals=2, default=1.03, optimize=True, load=True, space='buy')
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rsi_bull_threshold = IntParameter(45, 55, default=50, optimize=True, load=True, space='buy')
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stochrsi_bull_threshold = IntParameter(30, 40, default=35, optimize=True, load=True, space='buy')
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stochrsi_neutral_threshold = IntParameter(20, 30, default=25, optimize=True, load=True, space='buy')
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volume_multiplier = DecimalParameter(1.2, 2.0, decimals=1, default=1.5, optimize=True, load=True, space='buy')
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bb_width_threshold = DecimalParameter(0.01, 0.03, decimals=3, default=0.02, optimize=True, load=True, space='buy')
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min_condition_count = IntParameter(2, 4, default=3, optimize=True, load=True, space='buy')
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# 剧烈拉升检测参数 - 使用Hyperopt可优化参数
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h1_max_candles = IntParameter(100, 300, default=200, optimize=True, load=True, space='buy')
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h1_rapid_rise_threshold = DecimalParameter(0.05, 0.15, decimals=3, default=0.11, optimize=True, load=True, space='buy')
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h1_max_consecutive_candles = IntParameter(1, 4, default=2, optimize=True, load=True, space='buy')
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# 入场间隔控制参数（分钟）
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entry_interval_minutes = IntParameter(20, 200, default=42, optimize=True, load=True, space='buy')
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# ML 审核官：entry_signal 拒绝入场的阈值（越高越宽松，越低越严格）
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ml_entry_signal_threshold = DecimalParameter(0.05, 0.85, decimals=2, default=0.55, optimize=True, load=True, space='buy')
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# ML 审核官：exit_signal 拒绝出场的阈值（越高越宽松，越低越严格）
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ml_exit_signal_threshold = DecimalParameter(0.05, 0.85, decimals=2, default=0.35, optimize=True, load=True, space='buy')
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# FreqAI 标签定义：entry_signal 的洛底上涨幅度（%）
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freqai_entry_up_percent = DecimalParameter(0.3, 2.0, decimals=2, default=0.5, optimize=True, load=True, space='buy')
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# FreqAI 标签定义：exit_signal 的洛底下跌幅度（%）
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freqai_exit_down_percent = DecimalParameter(0.3, 2.0, decimals=2, default=0.5, optimize=True, load=True, space='buy')
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# 定义可优化参数
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# 初始入场金额: 75.00
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# 加仓次数 相对降幅间隔 加仓金额
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# ------- ------------ --------
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# 0 N/A 75
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# 1 0.045000 36.29
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# 2 0.051750 163.31
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# 3 0.059513 734.88
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# 4 0.068439 3306.96
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#
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# 累计投入金额: 4316.43
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max_entry_adjustments = IntParameter(2, 5, default=4, optimize=False, load=True, space='buy') # 最大加仓次数
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add_position_callback = DecimalParameter(0.02, 0.06, decimals=3, default=0.047, optimize=False, load=True, space='buy') # 加仓回调百分比
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add_position_growth = DecimalParameter(1.5, 5.0, decimals=2, default=4.5, optimize=False, load=True, space='buy') # 加仓金额增长因子，保留2位小数用于hyperopt优化
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add_position_multiplier = DecimalParameter(0.2, 2, decimals=2, default=1.35, optimize=False, load=True, space='buy') # 加仓间隔系数，保留2位小数用于hyperopt优化
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stake_divisor = DecimalParameter(2.0, 12.0, decimals=2, default=9.3, optimize=False, load=True, space='buy') # 加仓金额分母（小数类型，保留2位小数）
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# 线性ROI参数 - 用于线性函数: y = (a * (x + k)) + t
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roi_param_a = DecimalParameter(-0.0002, -0.00005, decimals=5, default=-0.0001, optimize=True, load=True, space='sell') # 系数a
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roi_param_k = IntParameter(20, 150, default=50, optimize=True, load=True, space='sell') # 偏移量k
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roi_param_t = DecimalParameter(0.02, 0.18, decimals=3, default=0.06, optimize=True, load=True, space='sell') # 常数项t
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# 出场条件阈值参数
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exit_bb_upper_deviation = DecimalParameter(0.98, 1.02, decimals=2, default=1.0, optimize=True, load=True, space='sell')
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exit_volume_multiplier = DecimalParameter(1.5, 3.0, decimals=1, default=2.0, optimize=True, load=True, space='sell')
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rsi_overbought = IntParameter(50, 70, default=58, optimize=True, load=True, space='sell')
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def informative_pairs(self):
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pairs = self.dp.current_whitelist()
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return [(pair, '15m') for pair in pairs] + [(pair, '1h') for pair in pairs]
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def _validate_dataframe_columns(self, dataframe: DataFrame, required_columns: list, metadata: dict):
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"""
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验证数据框中是否包含所有需要的列。
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如果缺少列，则记录警告日志。
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"""
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missing_columns = [col for col in required_columns if col not in dataframe.columns]
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if missing_columns:
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logger.warning(f"[{metadata['pair']}] 数据框中缺少以下列: {missing_columns}")
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# ========================= FreqAI 特征与标签定义 =========================
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def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: dict, **kwargs) -> DataFrame:
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"""FreqAI 全量特征：这里先用简单技术指标，后续可逐步扩展。"""
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# 使用 rolling 计算 RSI（减少看前偏差）
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delta = dataframe["close"].diff()
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gain = delta.where(delta > 0, 0).rolling(window=period).mean()
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loss = -delta.where(delta < 0, 0).rolling(window=period).mean()
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rs = gain / loss
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dataframe[f"%-rsi-{period}"] = 100 - (100 / (1 + rs))
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dataframe[f"%-mfi-{period}"] = ta.mfi(dataframe["high"], dataframe["low"], dataframe["close"], dataframe["volume"], length=period)
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adx_df = ta.adx(dataframe["high"], dataframe["low"], dataframe["close"], length=period)
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adx_col = f"ADX_{period}"
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if adx_col in adx_df.columns:
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dataframe[f"%-adx-{period}"] = adx_df[adx_col]
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return dataframe
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def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
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"""FreqAI 基础特征。"""
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dataframe["%-pct_change"] = dataframe["close"].pct_change().fillna(0)
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dataframe["%-raw_volume"] = dataframe["volume"].fillna(0)
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dataframe["%-raw_price"] = dataframe["close"].ffill() # 使用 ffill() 替代 fillna(method="ffill")
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return dataframe
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def feature_engineering_standard(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
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||
"""FreqAI 标准时间类特征。"""
|
||
if "date" in dataframe.columns:
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dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
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dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
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return dataframe
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def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
|
||
"""定义 FreqAI 训练标签：简单二分类版本 + 持仓时长预测。"""
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||
# 从配置中读取预测窗口参数（禁止硬编码）
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label_horizon = self.freqai_info.get('feature_parameters', {}).get('label_period_candles', 24)
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# 动态计算上涨/下跌阈值
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||
entry_up_percent = self.freqai_entry_up_percent.value / 100.0 # 转换为小数（如 0.01 表示 1%）
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||
exit_down_percent = self.freqai_exit_down_percent.value / 100.0
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entry_up_threshold = 1.0 + entry_up_percent # 例如 1.01 表示 +1%
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exit_down_threshold = 1.0 - exit_down_percent # 例如 0.99 表示 -1%
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# 入场标签：未来窗口内的最高价是否超过 +1%
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||
future_max = dataframe["close"].rolling(window=label_horizon, min_periods=1).max().shift(-label_horizon + 1)
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||
dataframe["&s-entry_signal"] = np.where(
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future_max > dataframe["close"] * entry_up_threshold,
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1,
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0,
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)
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||
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# 出场标签：未来窗口内的最低价是否跌破 -1%
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||
future_min = dataframe["close"].rolling(window=label_horizon, min_periods=1).min().shift(-label_horizon + 1)
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||
dataframe["&s-exit_signal"] = np.where(
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future_min < dataframe["close"] * exit_down_threshold,
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1,
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0,
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||
)
|
||
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||
# 新增：未来波动率预测标签（极端化方案）
|
||
# 计算当前波动率（过10根K线的收盘价波动）
|
||
current_volatility = dataframe["close"].pct_change().rolling(window=10, min_periods=5).std()
|
||
|
||
# 计算未来10根K线的波动率（向未来移动）
|
||
future_pct_change = dataframe["close"].pct_change().shift(-1) # 未来的收盘价变化
|
||
future_volatility = future_pct_change.rolling(window=10, min_periods=5).std().shift(-9) # 未来10根K线的波动率
|
||
|
||
# 标签：未来波动率 > 当前波动率 * 1.5 则标记为高波动（趋势启动）
|
||
volatility_ratio = future_volatility / (current_volatility + 1e-8) # 避免除以0
|
||
dataframe["&s-future_volatility"] = np.where(
|
||
volatility_ratio > 1.5,
|
||
1, # 未来高波动（趋势启动），继续持有
|
||
0 # 未来低波动（震荡市），快速止盈
|
||
)
|
||
|
||
return dataframe
|
||
|
||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||
# 计算 3m 周期的指标
|
||
bb_length_value = self.bb_length.value
|
||
bb_std_value = self.bb_std.value
|
||
rsi_length_value = self.rsi_length.value
|
||
|
||
# 使用 rolling 计算布林带（减少看前偏差）
|
||
bb_ma_3m = dataframe['close'].rolling(window=bb_length_value).mean()
|
||
bb_std_3m = dataframe['close'].rolling(window=bb_length_value).std()
|
||
dataframe['bb_lower_3m'] = bb_ma_3m - (bb_std_value * bb_std_3m)
|
||
dataframe['bb_upper_3m'] = bb_ma_3m + (bb_std_value * bb_std_3m)
|
||
|
||
# 使用 rolling 计算 RSI（减少看前偏差）
|
||
delta_3m = dataframe['close'].diff()
|
||
gain_3m = delta_3m.where(delta_3m > 0, 0).rolling(window=rsi_length_value).mean()
|
||
loss_3m = -delta_3m.where(delta_3m < 0, 0).rolling(window=rsi_length_value).mean()
|
||
rs_3m = gain_3m / loss_3m
|
||
dataframe['rsi_3m'] = 100 - (100 / (1 + rs_3m))
|
||
|
||
# 新增 StochRSI 指标
|
||
stochrsi_3m = ta.stochrsi(dataframe['close'], length=rsi_length_value, rsi_length=rsi_length_value)
|
||
dataframe['stochrsi_k_3m'] = stochrsi_3m[f'STOCHRSIk_{rsi_length_value}_{rsi_length_value}_3_3']
|
||
dataframe['stochrsi_d_3m'] = stochrsi_3m[f'STOCHRSId_{rsi_length_value}_{rsi_length_value}_3_3']
|
||
|
||
# 新增 MACD 指标
|
||
macd_3m = ta.macd(dataframe['close'], fast=12, slow=26, signal=9)
|
||
dataframe['macd_3m'] = macd_3m['MACD_12_26_9']
|
||
dataframe['macd_signal_3m'] = macd_3m['MACDs_12_26_9']
|
||
dataframe['macd_hist_3m'] = macd_3m['MACDh_12_26_9']
|
||
|
||
# 使用 ewm 计算 EMA（减少看前偏差，adjust=False 确保实时计算）
|
||
dataframe['ema_50_3m'] = dataframe['close'].ewm(span=50, adjust=False).mean()
|
||
dataframe['ema_200_3m'] = dataframe['close'].ewm(span=200, adjust=False).mean()
|
||
|
||
# 成交量过滤
|
||
dataframe['volume_ma'] = dataframe['volume'].rolling(20).mean()
|
||
|
||
# 计算 ATR 用于动态止损和退出
|
||
dataframe['atr'] = ta.atr(dataframe['high'], dataframe['low'], dataframe['close'], length=14)
|
||
|
||
# 获取 15m 数据
|
||
df_15m = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='15m')
|
||
|
||
# 使用 rolling 计算 RSI（减少看前偏差）
|
||
delta_15m = df_15m['close'].diff()
|
||
gain_15m = delta_15m.where(delta_15m > 0, 0).rolling(window=rsi_length_value).mean()
|
||
loss_15m = -delta_15m.where(delta_15m < 0, 0).rolling(window=rsi_length_value).mean()
|
||
rs_15m = gain_15m / loss_15m
|
||
df_15m['rsi_15m'] = 100 - (100 / (1 + rs_15m))
|
||
|
||
# 使用 ewm 计算 EMA（减少看前偏差）
|
||
df_15m['ema_50_15m'] = df_15m['close'].ewm(span=50, adjust=False).mean()
|
||
df_15m['ema_200_15m'] = df_15m['close'].ewm(span=200, adjust=False).mean()
|
||
|
||
# 新增 StochRSI 指标
|
||
stochrsi_15m = ta.stochrsi(df_15m['close'], length=rsi_length_value, rsi_length=rsi_length_value)
|
||
df_15m['stochrsi_k_15m'] = stochrsi_15m[f'STOCHRSIk_{rsi_length_value}_{rsi_length_value}_3_3']
|
||
df_15m['stochrsi_d_15m'] = stochrsi_15m[f'STOCHRSId_{rsi_length_value}_{rsi_length_value}_3_3']
|
||
|
||
# 新增 MACD 指标
|
||
macd_15m = ta.macd(df_15m['close'], fast=12, slow=26, signal=9)
|
||
df_15m['macd_15m'] = macd_15m['MACD_12_26_9']
|
||
df_15m['macd_signal_15m'] = macd_15m['MACDs_12_26_9']
|
||
df_15m['macd_hist_15m'] = macd_15m['MACDh_12_26_9']
|
||
|
||
# 将 15m 数据重新索引到主时间框架 (3m)
|
||
df_15m = df_15m.set_index('date').reindex(dataframe['date']).reset_index()
|
||
df_15m = df_15m.rename(columns={'index': 'date'})
|
||
df_15m = df_15m[['date', 'rsi_15m', 'ema_50_15m', 'ema_200_15m']].ffill()
|
||
|
||
# 合并 15m 数据
|
||
dataframe = dataframe.merge(df_15m, how='left', on='date')
|
||
|
||
# 获取 1h 数据
|
||
df_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1h')
|
||
|
||
# 使用 rolling 计算布林带（减少看前偏差）
|
||
bb_ma_1h = df_1h['close'].rolling(window=bb_length_value).mean()
|
||
bb_std_1h = df_1h['close'].rolling(window=bb_length_value).std()
|
||
df_1h['bb_lower_1h'] = bb_ma_1h - (bb_std_value * bb_std_1h)
|
||
df_1h['bb_upper_1h'] = bb_ma_1h + (bb_std_value * bb_std_1h)
|
||
|
||
# 使用 rolling 计算 RSI（减少看前偏差）
|
||
delta_1h = df_1h['close'].diff()
|
||
gain_1h = delta_1h.where(delta_1h > 0, 0).rolling(window=rsi_length_value).mean()
|
||
loss_1h = -delta_1h.where(delta_1h < 0, 0).rolling(window=rsi_length_value).mean()
|
||
rs_1h = gain_1h / loss_1h
|
||
df_1h['rsi_1h'] = 100 - (100 / (1 + rs_1h))
|
||
|
||
# 使用 ewm 计算 EMA（减少看前偏差）
|
||
df_1h['ema_50_1h'] = df_1h['close'].ewm(span=50, adjust=False).mean()
|
||
df_1h['ema_200_1h'] = df_1h['close'].ewm(span=200, adjust=False).mean()
|
||
df_1h['trend_1h'] = df_1h['close'] > df_1h['ema_50_1h'] # 1h上涨趋势
|
||
|
||
# 新增 StochRSI 指标
|
||
stochrsi_1h = ta.stochrsi(df_1h['close'], length=rsi_length_value, rsi_length=rsi_length_value)
|
||
df_1h['stochrsi_k_1h'] = stochrsi_1h[f'STOCHRSIk_{rsi_length_value}_{rsi_length_value}_3_3']
|
||
df_1h['stochrsi_d_1h'] = stochrsi_1h[f'STOCHRSId_{rsi_length_value}_{rsi_length_value}_3_3']
|
||
|
||
# 新增 MACD 指标
|
||
macd_1h = ta.macd(df_1h['close'], fast=12, slow=26, signal=9)
|
||
df_1h['macd_1h'] = macd_1h['MACD_12_26_9']
|
||
df_1h['macd_signal_1h'] = macd_1h['MACDs_12_26_9']
|
||
df_1h['macd_hist_1h'] = macd_1h['MACDh_12_26_9']
|
||
|
||
# 验证 MACD 列是否正确生成
|
||
#self.strategy_log(f"[{metadata['pair']}] 1小时 MACD 列: {list(macd_1h.columns)}")
|
||
|
||
# 确保 StochRSI 指标已正确计算
|
||
# 将 1h 数据重新索引到主时间框架 (3m)，并填充缺失值
|
||
df_1h = df_1h.set_index('date').reindex(dataframe['date']).ffill().bfill().reset_index()
|
||
df_1h = df_1h.rename(columns={'index': 'date'})
|
||
# Include macd_1h and macd_signal_1h in the column selection
|
||
df_1h = df_1h[['date', 'rsi_1h', 'trend_1h', 'ema_50_1h', 'ema_200_1h', 'bb_lower_1h', 'bb_upper_1h', 'stochrsi_k_1h', 'stochrsi_d_1h', 'macd_1h', 'macd_signal_1h']].ffill()
|
||
|
||
# Validate that all required columns are present
|
||
required_columns = ['date', 'rsi_1h', 'trend_1h', 'ema_50_1h', 'ema_200_1h',
|
||
'bb_lower_1h', 'bb_upper_1h', 'stochrsi_k_1h', 'stochrsi_d_1h',
|
||
'macd_1h', 'macd_signal_1h']
|
||
missing_columns = [col for col in required_columns if col not in df_1h.columns]
|
||
if missing_columns:
|
||
logger.error(f"[{metadata['pair']}] 缺少以下列: {missing_columns}")
|
||
raise KeyError(f"缺少以下列: {missing_columns}")
|
||
|
||
# 确保所有需要的列都被合并
|
||
required_columns = ['date', 'rsi_1h', 'trend_1h', 'ema_50_1h', 'ema_200_1h',
|
||
'bb_lower_1h', 'bb_upper_1h', 'stochrsi_k_1h', 'stochrsi_d_1h',
|
||
'macd_1h', 'macd_signal_1h']
|
||
|
||
# 验证所需列是否存在
|
||
missing_columns = [col for col in required_columns if col not in df_1h.columns]
|
||
if missing_columns:
|
||
logger.error(f"[{metadata['pair']}] 缺少以下列: {missing_columns}")
|
||
raise KeyError(f"缺少以下列: {missing_columns}")
|
||
|
||
df_1h = df_1h[required_columns] # 确保包含 macd_1h 和 macd_signal_1h
|
||
|
||
# 合并 1h 数据
|
||
dataframe = dataframe.merge(df_1h, how='left', on='date').ffill()
|
||
|
||
# 验证合并后的列
|
||
#self.strategy_log(f"[{metadata['pair']}] 合并后的数据框列名: {list(dataframe.columns)}")
|
||
|
||
# K线形态：看涨吞没
|
||
dataframe['bullish_engulfing'] = (
|
||
(dataframe['close'].shift(1) < dataframe['open'].shift(1)) &
|
||
(dataframe['close'] > dataframe['open']) &
|
||
(dataframe['close'] > dataframe['open'].shift(1)) &
|
||
(dataframe['open'] < dataframe['close'].shift(1))
|
||
)
|
||
|
||
# 计算各时间框架的趋势状态（牛/熊）
|
||
# 3m时间框架：ema50下穿ema200为熊，上穿为牛
|
||
dataframe['trend_3m'] = np.where(dataframe['ema_50_3m'] > dataframe['ema_200_3m'], 1, 0)
|
||
|
||
# 15m时间框架：ema50下穿ema200为熊，上穿为牛
|
||
dataframe['trend_15m'] = np.where(dataframe['ema_50_15m'] > dataframe['ema_200_15m'], 1, 0)
|
||
|
||
# 1h时间框架：ema50下穿ema200为熊，上穿为牛
|
||
dataframe['trend_1h_ema'] = np.where(dataframe['ema_50_1h'] > dataframe['ema_200_1h'], 1, 0)
|
||
|
||
# 计算熊牛得分（0-100）
|
||
# 权重：3m熊牛权重10，15m熊牛权重35，1h熊牛权重65
|
||
# 计算加权得分
|
||
dataframe['market_score'] = (
|
||
dataframe['trend_3m'] * 10 +
|
||
dataframe['trend_15m'] * 35 +
|
||
dataframe['trend_1h_ema'] * 65
|
||
)
|
||
|
||
# 确保得分在0-100范围内
|
||
dataframe['market_score'] = dataframe['market_score'].clip(lower=0, upper=100)
|
||
|
||
# 根据得分分类市场状态
|
||
dataframe['market_state'] = 'neutral'
|
||
dataframe.loc[dataframe['market_score'] > 70, 'market_state'] = 'strong_bull'
|
||
dataframe.loc[(dataframe['market_score'] > 50) & (dataframe['market_score'] <= 70), 'market_state'] = 'weak_bull'
|
||
dataframe.loc[(dataframe['market_score'] >= 30) & (dataframe['market_score'] <= 50), 'market_state'] = 'neutral'
|
||
dataframe.loc[(dataframe['market_score'] > 10) & (dataframe['market_score'] < 30), 'market_state'] = 'weak_bear'
|
||
dataframe.loc[dataframe['market_score'] <= 10, 'market_state'] = 'strong_bear'
|
||
|
||
# 创建一个使用前一行市场状态的列，避免在populate_entry_trend中使用iloc[-1]
|
||
dataframe['prev_market_state'] = dataframe['market_state'].shift(1)
|
||
# 为第一行设置默认值
|
||
dataframe['prev_market_state'] = dataframe['prev_market_state'].fillna('neutral')
|
||
|
||
# 记录当前的市场状态
|
||
if len(dataframe) > 0:
|
||
current_score = dataframe['market_score'].iloc[-1]
|
||
current_state = dataframe['market_state'].iloc[-1]
|
||
#self.strategy_log(f"[{metadata['pair']}] 熊牛得分: {current_score:.1f}, 市场状态: {current_state}")
|
||
#self.strategy_log(f"[{metadata['pair']}] 各时间框架趋势: 3m={'牛' if dataframe['trend_3m'].iloc[-1] == 1 else '熊'}, \
|
||
# 15m={'牛' if dataframe['trend_15m'].iloc[-1] == 1 else '熊'}, \
|
||
# 1h={'牛' if dataframe['trend_1h_ema'].iloc[-1] == 1 else '熊'}")
|
||
|
||
# 调试：打印指标值（最后 5 行），验证时间对齐
|
||
#print(f"Pair: {metadata['pair']}, Last 5 rows after reindexing:")
|
||
#print(dataframe[['date', 'close', 'bb_lower_3m', 'rsi_3m', 'rsi_15m', 'rsi_1h', 'trend_1h',
|
||
# 'trend_3m', 'trend_15m', 'trend_1h_ema', 'market_score', 'market_state',
|
||
# 'bullish_engulfing', 'volume', 'volume_ma']].tail(5))
|
||
|
||
# 打印最终数据框的列名以验证
|
||
#self.strategy_log(f"[{metadata['pair']}] 最终数据框列名: {list(dataframe.columns)}")
|
||
|
||
# 启用 FreqAI：在所有指标计算完成后调用
|
||
dataframe = self.freqai.start(dataframe, metadata, self)
|
||
|
||
return dataframe
|
||
|
||
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||
# 出场信号基于趋势和量价关系
|
||
# 条件1: 价格突破布林带上轨（使用可优化的偏差参数）
|
||
breakout_condition = dataframe['close'] >= dataframe['bb_upper_1h'] * self.exit_bb_upper_deviation.value
|
||
|
||
# 条件2: 成交量显著放大（使用可优化的成交量乘数）
|
||
volume_spike = dataframe['volume'] > dataframe['volume_ma'] * self.exit_volume_multiplier.value
|
||
|
||
# 条件3: MACD 下降趋势
|
||
macd_downward = dataframe['macd_1h'] < dataframe['macd_signal_1h']
|
||
|
||
# 条件4: RSI 进入超买区域（使用可优化的超买阈值）
|
||
rsi_overbought = dataframe['rsi_1h'] > self.rsi_overbought.value
|
||
|
||
# 合并所有条件
|
||
final_condition = breakout_condition | volume_spike | macd_downward | rsi_overbought
|
||
|
||
# 设置出场信号
|
||
dataframe.loc[final_condition, 'exit_long'] = 1
|
||
|
||
# 设置出场价格：上浮1.25%（使用乘法避免除零风险）
|
||
# Freqtrade 会优先使用 exit_price 列作为限价单价格
|
||
final_exit_condition = dataframe['exit_long'] == 1
|
||
dataframe.loc[final_exit_condition, 'exit_price'] = dataframe.loc[final_exit_condition, 'close'] * 1.0125
|
||
|
||
# 增强调试信息
|
||
#self.strategy_log(f"[{metadata['pair']}] 出场条件检查:")
|
||
#self.strategy_log(f" - 价格突破布林带上轨: {breakout_condition.sum()} 次")
|
||
#self.strategy_log(f" - 成交量显著放大: {volume_spike.sum()} 次")
|
||
#self.strategy_log(f" - MACD 下降趋势: {macd_downward.sum()} 次")
|
||
#self.strategy_log(f" - RSI 超买: {rsi_overbought.sum()} 次")
|
||
#self.strategy_log(f" - 最终条件: {final_condition.sum()} 次")
|
||
#self.strategy_log(f" - 使用参数: exit_bb_upper_deviation={self.exit_bb_upper_deviation.value}, exit_volume_multiplier={self.exit_volume_multiplier.value}, rsi_overbought={self.rsi_overbought.value}")
|
||
|
||
# 日志记录
|
||
#if dataframe['exit_long'].sum() > 0:
|
||
# self.strategy_log(f"[{metadata['pair']}] 触发出场信号数量: {dataframe['exit_long'].sum()}")
|
||
|
||
return dataframe
|
||
|
||
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||
# 确保prev_market_state列存在
|
||
if 'prev_market_state' not in dataframe.columns:
|
||
dataframe['prev_market_state'] = 'neutral'
|
||
|
||
# 条件1: 价格接近布林带下轨（允许一定偏差）
|
||
close_to_bb_lower_1h = (dataframe['close'] <= dataframe['bb_lower_1h'] * self.bb_lower_deviation.value) # 可优化偏差
|
||
|
||
# 条件2: RSI 不高于阈值（根据市场状态动态调整）
|
||
# 为每一行创建动态阈值
|
||
rsi_condition_1h = dataframe.apply(lambda row:
|
||
row['rsi_1h'] < self.rsi_bull_threshold.value if row['prev_market_state'] in ['strong_bull', 'weak_bull'] else row['rsi_1h'] < self.rsi_oversold.value,
|
||
axis=1)
|
||
|
||
# 条件3: StochRSI 处于超卖区域（根据市场状态动态调整）
|
||
stochrsi_condition_1h = dataframe.apply(lambda row:
|
||
(row['stochrsi_k_1h'] < self.stochrsi_bull_threshold.value and row['stochrsi_d_1h'] < self.stochrsi_bull_threshold.value) if row['prev_market_state'] in ['strong_bull', 'weak_bull']
|
||
else (row['stochrsi_k_1h'] < self.stochrsi_neutral_threshold.value and row['stochrsi_d_1h'] < self.stochrsi_neutral_threshold.value),
|
||
axis=1)
|
||
|
||
# 条件4: MACD 上升趋势
|
||
macd_condition_1h = dataframe['macd_1h'] > dataframe['macd_signal_1h']
|
||
|
||
# 条件5: 成交量显著放大（可选条件）
|
||
volume_spike = dataframe['volume'] > dataframe['volume_ma'] * self.volume_multiplier.value
|
||
|
||
# 条件6: 布林带宽度过滤（避免窄幅震荡）
|
||
bb_width = (dataframe['bb_upper_1h'] - dataframe['bb_lower_1h']) / dataframe['close']
|
||
bb_width_condition = bb_width > self.bb_width_threshold.value # 可优化的布林带宽度阈值
|
||
|
||
# 辅助条件: 3m 和 15m 趋势确认（允许部分时间框架不一致）
|
||
trend_confirmation = (dataframe['trend_3m'] == 1) | (dataframe['trend_15m'] == 1)
|
||
|
||
# 合并所有条件（减少强制性条件）
|
||
# 至少满足多个条件中的一定数量
|
||
condition_count = (
|
||
close_to_bb_lower_1h.astype(int) +
|
||
rsi_condition_1h.astype(int) +
|
||
stochrsi_condition_1h.astype(int) +
|
||
macd_condition_1h.astype(int) +
|
||
(volume_spike | bb_width_condition).astype(int) + # 成交量或布林带宽度满足其一即可
|
||
trend_confirmation.astype(int)
|
||
)
|
||
final_condition = condition_count >= self.min_condition_count.value
|
||
|
||
# 设置入场信号
|
||
dataframe.loc[final_condition, 'enter_long'] = 1
|
||
|
||
# ========== 新增：入场诊断统计（回测可用） ==========
|
||
# 对每个入场信号输出详细诊断信息
|
||
entry_signals = dataframe[dataframe['enter_long'] == 1]
|
||
if len(entry_signals) > 0:
|
||
for idx in entry_signals.index[-5:]: # 只输出最近 5 个信号，避免日志过多
|
||
row = dataframe.loc[idx]
|
||
current_close = float(row['close'])
|
||
|
||
# 1. 价格与短期高点的关系
|
||
recent_high_5 = float(dataframe.loc[max(0, idx-4):idx+1, 'high'].max()) if idx >= 4 else current_close
|
||
price_vs_recent_high = (current_close - recent_high_5) / recent_high_5 if recent_high_5 > 0 else 0
|
||
|
||
# 2. 价格与 EMA5 的关系
|
||
ema5_1h = float(row.get('ema_5_1h', current_close))
|
||
price_vs_ema5 = (current_close - ema5_1h) / ema5_1h if ema5_1h > 0 else 0
|
||
|
||
# 3. 价格与布林带的位置
|
||
bb_upper = float(row.get('bb_upper_1h', current_close))
|
||
bb_lower = float(row.get('bb_lower_1h', current_close))
|
||
bb_position = (current_close - bb_lower) / (bb_upper - bb_lower) if (bb_upper - bb_lower) > 0 else 0.5
|
||
|
||
# 4. RSI 状态
|
||
rsi_1h = float(row.get('rsi_1h', 50))
|
||
|
||
# 5. MACD 状态
|
||
macd_1h = float(row.get('macd_1h', 0))
|
||
macd_signal_1h = float(row.get('macd_signal_1h', 0))
|
||
macd_cross = 'up' if macd_1h > macd_signal_1h else 'down'
|
||
|
||
# 6. 市场状态
|
||
market_state = str(row.get('market_state', 'unknown'))
|
||
|
||
# 7. ML 入场概率（如果有）
|
||
entry_prob = None
|
||
if '&s-entry_signal' in dataframe.columns:
|
||
entry_prob = float(row.get('&s-entry_signal', 0))
|
||
elif '&-entry_signal' in dataframe.columns:
|
||
entry_prob = float(row.get('&-entry_signal', 0))
|
||
|
||
# 输出诊断日志
|
||
ml_prob_str = f"{entry_prob:.2f}" if entry_prob is not None else "N/A"
|
||
self.strategy_log(
|
||
f"[入场诊断] {metadata['pair']} | "
|
||
f"价格: {current_close:.6f} | "
|
||
f"vs 5K高点: {price_vs_recent_high:+.2%} | "
|
||
f"vs EMA5: {price_vs_ema5:+.2%} | "
|
||
f"布林位置: {bb_position:.2f} | "
|
||
f"RSI: {rsi_1h:.1f} | "
|
||
f"MACD: {macd_cross} | "
|
||
f"市场: {market_state} | "
|
||
f"ML概率: {ml_prob_str}"
|
||
)
|
||
# ========== 诊断统计结束 ==========
|
||
|
||
# 设置入场价格：下调1.67%（使用乘法避免除零风险）
|
||
final_condition_updated = dataframe['enter_long'] == 1
|
||
dataframe.loc[final_condition_updated, 'enter_price'] = dataframe.loc[final_condition_updated, 'close'] * 0.9833
|
||
|
||
# 增强调试信息
|
||
#self.strategy_log(f"[{metadata['pair']}] 入场条件检查:")
|
||
#self.strategy_log(f" - 价格接近布林带下轨: {close_to_bb_lower_1h.sum()} 次")
|
||
#self.strategy_log(f" - RSI 超卖: {rsi_condition_1h.sum()} 次")
|
||
#self.strategy_log(f" - StochRSI 超卖: {stochrsi_condition_1h.sum()} 次")
|
||
#self.strategy_log(f" - MACD 上升趋势: {macd_condition_1h.sum()} 次")
|
||
#self.strategy_log(f" - 成交量或布林带宽度: {(volume_spike | bb_width_condition).sum()} 次")
|
||
#self.strategy_log(f" - 趋势确认: {trend_confirmation.sum()} 次")
|
||
#self.strategy_log(f" - 最终条件: {final_condition.sum()} 次")
|
||
# 在populate_entry_trend方法末尾添加
|
||
# 计算条件间的相关性
|
||
conditions = DataFrame({
|
||
'close_to_bb': close_to_bb_lower_1h,
|
||
'rsi': rsi_condition_1h,
|
||
'stochrsi': stochrsi_condition_1h,
|
||
'macd': macd_condition_1h,
|
||
'vol_bb': (volume_spike | bb_width_condition),
|
||
'trend': trend_confirmation
|
||
})
|
||
correlation = conditions.corr().mean().mean()
|
||
#self.strategy_log(f"[{metadata['pair']}] 条件平均相关性: {correlation:.2f}")
|
||
# 日志记录
|
||
#if dataframe['enter_long'].sum() > 0:
|
||
# self.strategy_log(f"[{metadata['pair']}] 发现入场信号数量: {dataframe['enter_long'].sum()}")
|
||
|
||
return dataframe
|
||
|
||
def detect_h1_rapid_rise(self, pair: str) -> bool:
|
||
"""
|
||
检测1小时K线图上的剧烈拉升情况（轻量级版本，用于confirm_trade_entry）
|
||
参数:
|
||
- pair: 交易对
|
||
返回:
|
||
- bool: 是否处于不稳固区域
|
||
"""
|
||
try:
|
||
# 获取1小时K线数据
|
||
df_1h = self.dp.get_pair_dataframe(pair=pair, timeframe='1h')
|
||
|
||
# 获取当前优化参数值
|
||
max_candles = self.h1_max_candles.value
|
||
rapid_rise_threshold = self.h1_rapid_rise_threshold.value
|
||
max_consecutive_candles = self.h1_max_consecutive_candles.value
|
||
|
||
# 确保有足够的K线数据
|
||
if len(df_1h) < max_candles:
|
||
logger.warning(f"[{pair}] 1h K线数据不足 {max_candles} 根，当前只有 {len(df_1h)} 根，无法完整检测剧烈拉升")
|
||
return False
|
||
|
||
# 获取最近的K线
|
||
recent_data = df_1h.iloc[-max_candles:].copy()
|
||
|
||
# 检查连续最多几根K线内的最大涨幅
|
||
rapid_rise_detected = False
|
||
max_rise = 0
|
||
|
||
for i in range(len(recent_data) - max_consecutive_candles + 1):
|
||
window_data = recent_data.iloc[i:i + max_consecutive_candles]
|
||
window_low = window_data['low'].min()
|
||
window_high = window_data['high'].max()
|
||
|
||
# 计算区间内的最大涨幅
|
||
if window_low > 0:
|
||
rise_percentage = (window_high - window_low) / window_low
|
||
if rise_percentage > max_rise:
|
||
max_rise = rise_percentage
|
||
|
||
# 检查是否超过阈值
|
||
if rise_percentage >= rapid_rise_threshold:
|
||
rapid_rise_detected = True
|
||
#self.strategy_log(f"[{pair}] 检测到剧烈拉升: 从 {window_low:.2f} 到 {window_high:.2f} ({rise_percentage:.2%}) 在 {max_consecutive_candles} 根K线内")
|
||
break
|
||
|
||
current_price = recent_data['close'].iloc[-1]
|
||
#self.strategy_log(f"[{pair}] 剧烈拉升检测结果: {'不稳固' if rapid_rise_detected else '稳固'}")
|
||
#self.strategy_log(f"[{pair}] 最近最大涨幅: {max_rise:.2%}")
|
||
|
||
return rapid_rise_detected
|
||
|
||
except Exception as e:
|
||
logger.error(f"[{pair}] 剧烈拉升检测过程中发生错误: {str(e)}")
|
||
return False
|
||
|
||
def confirm_trade_entry(
|
||
self,
|
||
pair: str,
|
||
order_type: str,
|
||
amount: float,
|
||
rate: float,
|
||
time_in_force: str,
|
||
current_time: datetime,
|
||
entry_tag: str | None,
|
||
side: str,
|
||
**kwargs,
|
||
) -> bool:
|
||
"""
|
||
交易买入前的确认函数，用于最终决定是否执行交易
|
||
此处实现剧烈拉升检查和入场间隔控制逻辑
|
||
"""
|
||
self.strategy_log(f"[{pair}] confirm_trade_entry 被调用 - 价格: {rate:.8f}, 时间: {current_time}")
|
||
# 默认允许交易
|
||
allow_trade = True
|
||
|
||
# 仅对多头交易进行检查
|
||
if side == 'long':
|
||
# 检查1：入场间隔控制（使用hyperopt参数）
|
||
if pair in self._last_entry_time:
|
||
last_entry = self._last_entry_time[pair]
|
||
time_diff = (current_time - last_entry).total_seconds() * 0.0166666667 # 转换为分钟（使用乘法避免除法）
|
||
if time_diff < self.entry_interval_minutes.value:
|
||
self.strategy_log(f"[{pair}] 入场间隔不足: 距离上次入场 {time_diff:.1f}分钟 < {self.entry_interval_minutes.value}分钟，取消本次入场")
|
||
allow_trade = False
|
||
|
||
# 检查2：检查是否处于剧烈拉升的不稳固区域
|
||
if allow_trade:
|
||
is_unstable_region = self.detect_h1_rapid_rise(pair)
|
||
if is_unstable_region:
|
||
#self.strategy_log(f"[{pair}] 由于检测到剧烈拉升，取消入场交易")
|
||
allow_trade = False
|
||
|
||
# 检查3：ML 审核官（FreqAI 过滤低质量入场）+ 入场诊断统计
|
||
# 逻辑：用 entry_signal 概率来判断——若"容易上涨概率"低，则拒绝入场
|
||
if allow_trade:
|
||
try:
|
||
df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||
if len(df) > 0:
|
||
last_row = df.iloc[-1]
|
||
entry_prob = None
|
||
|
||
# 优先使用 FreqAI 的 entry_signal 预测列
|
||
if '&s-entry_signal' in df.columns:
|
||
entry_prob = float(last_row['&s-entry_signal'])
|
||
elif '&-entry_signal_prob' in df.columns:
|
||
entry_prob = float(last_row['&-entry_signal_prob'])
|
||
elif '&-s-entry_signal_prob' in df.columns:
|
||
entry_prob = float(last_row['&-s-entry_signal_prob'])
|
||
elif '&-entry_signal' in df.columns:
|
||
val = last_row['&-entry_signal']
|
||
if isinstance(val, (int, float)):
|
||
entry_prob = float(val)
|
||
else:
|
||
# 文本标签时，简单映射为 0/1
|
||
entry_prob = 1.0 if str(val).lower() in ['entry', 'buy', '1'] else 0.0
|
||
|
||
# ========== 新增：入场诊断统计 ==========
|
||
# 统计当前入场点的关键指标，用于分析"买在高位"问题
|
||
current_close = float(last_row['close'])
|
||
|
||
# 1. 价格与短期高点的关系
|
||
recent_high_5 = float(df['high'].iloc[-5:].max()) if len(df) >= 5 else current_close
|
||
price_vs_recent_high = (current_close - recent_high_5) / recent_high_5 if recent_high_5 > 0 else 0
|
||
|
||
# 2. 价格与 EMA5 的关系
|
||
ema5_1h = float(last_row.get('ema_5_1h', current_close))
|
||
price_vs_ema5 = (current_close - ema5_1h) / ema5_1h if ema5_1h > 0 else 0
|
||
|
||
# 3. 价格与布林带的位置
|
||
bb_upper = float(last_row.get('bb_upper_1h', current_close))
|
||
bb_lower = float(last_row.get('bb_lower_1h', current_close))
|
||
bb_position = (current_close - bb_lower) / (bb_upper - bb_lower) if (bb_upper - bb_lower) > 0 else 0.5
|
||
|
||
# 4. RSI 状态
|
||
rsi_1h = float(last_row.get('rsi_1h', 50))
|
||
|
||
# 5. MACD 状态
|
||
macd_1h = float(last_row.get('macd_1h', 0))
|
||
macd_signal_1h = float(last_row.get('macd_signal_1h', 0))
|
||
macd_cross = 'up' if macd_1h > macd_signal_1h else 'down'
|
||
|
||
# 6. 市场状态
|
||
market_state = str(last_row.get('market_state', 'unknown'))
|
||
|
||
# 输出诊断日志
|
||
self.strategy_log(
|
||
f"[入场诊断] {pair} | "
|
||
f"价格: {current_close:.6f} | "
|
||
f"vs 5K高点: {price_vs_recent_high:+.2%} | "
|
||
f"vs EMA5: {price_vs_ema5:+.2%} | "
|
||
f"布林位置: {bb_position:.2f} | "
|
||
f"RSI: {rsi_1h:.1f} | "
|
||
f"MACD: {macd_cross} | "
|
||
f"市场: {market_state} | "
|
||
f"ML入场概率: {entry_prob:.2f if entry_prob is not None else 'N/A'}"
|
||
)
|
||
# ========== 诊断统计结束 ==========
|
||
|
||
if entry_prob is not None:
|
||
# 确保概率在 [0, 1] 范围内（分类器输出可能有浮点误差）
|
||
entry_prob = max(0.0, min(1.0, entry_prob))
|
||
entry_threshold = self.ml_entry_signal_threshold.value
|
||
if entry_prob < entry_threshold:
|
||
self.strategy_log(f"[{pair}] ML 审核官拒绝入场: entry_signal 概率 {entry_prob:.2f} < 阈值 {entry_threshold:.2f}（上涨概率低，不宜入场）")
|
||
allow_trade = False
|
||
else:
|
||
self.strategy_log(f"[{pair}] ML 审核官允许入场: entry_signal 概率 {entry_prob:.2f} >= 阈值 {entry_threshold:.2f}")
|
||
except Exception as e:
|
||
logger.warning(f"[{pair}] ML 审核官检查失败，忽略 ML 过滤: {e}")
|
||
return allow_trade
|
||
|
||
def custom_entry_price(self, pair: str, trade: Trade | None, current_time: datetime, proposed_rate: float,
|
||
entry_tag: str | None, side: str, **kwargs) -> float:
|
||
adjusted_rate = proposed_rate * (1 - 0.0015)
|
||
self.strategy_log(f"[{pair}] 自定义买入价：{adjusted_rate:.6f}（原价：{proposed_rate:.6f}）")
|
||
return adjusted_rate
|
||
def custom_exit_price(self, pair: str, trade: Trade | None, current_time: datetime, proposed_rate: float,
|
||
entry_tag: str | None, side: str, **kwargs) -> float:
|
||
adjusted_rate = proposed_rate * (1 + 0.0015)
|
||
self.strategy_log(f"[{pair}] 自定义买入价：{adjusted_rate:.6f}（原价：{proposed_rate:.6f}）")
|
||
return adjusted_rate
|
||
|
||
def confirm_trade_exit(
|
||
self,
|
||
pair: str,
|
||
trade: 'Trade',
|
||
order_type: str,
|
||
amount: float,
|
||
rate: float,
|
||
time_in_force: str,
|
||
exit_reason: str,
|
||
current_time: datetime,
|
||
**kwargs,
|
||
) -> bool:
|
||
"""
|
||
交易卖出前的确认函数，用于最终决定是否执行出场
|
||
此处使用 ML 审核官（exit_signal 置信度）过滤出场
|
||
"""
|
||
self.strategy_log(f"[{pair}] confirm_trade_exit 被调用 - 价格: {rate:.8f}, 出场原因: {exit_reason}, 时间: {current_time}")
|
||
# 风险控制类退出原因：不经过 ML 审核官，直接允许出场
|
||
if exit_reason in ['stop_loss', 'trailing_stop_loss', 'emergency_exit', 'force_exit']:
|
||
self.strategy_log(f"[{pair}] 风险控制退出，不走 ML 审核官: exit_reason={exit_reason}")
|
||
return True
|
||
# 默认允许出场
|
||
allow_exit = True
|
||
|
||
try:
|
||
df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||
if len(df) > 0:
|
||
last_row = df.iloc[-1]
|
||
exit_prob = None
|
||
|
||
# 优先使用 FreqAI 的 exit_signal 预测列
|
||
if '&s-exit_signal' in df.columns:
|
||
exit_prob = float(last_row['&s-exit_signal'])
|
||
elif '&-exit_signal_prob' in df.columns:
|
||
exit_prob = float(last_row['&-exit_signal_prob'])
|
||
elif '&-s-exit_signal_prob' in df.columns:
|
||
exit_prob = float(last_row['&-s-exit_signal_prob'])
|
||
elif '&-exit_signal' in df.columns:
|
||
val = last_row['&-exit_signal']
|
||
if isinstance(val, (int, float)):
|
||
exit_prob = float(val)
|
||
else:
|
||
# 文本标签时，简单映射为 0/1
|
||
exit_prob = 1.0 if str(val).lower() in ['exit', 'sell', '1'] else 0.0
|
||
|
||
if exit_prob is not None:
|
||
# 确保概率在 [0, 1] 范围内（分类器输出可能有浮点误差）
|
||
exit_prob = max(0.0, min(1.0, exit_prob))
|
||
|
||
# 从 kwargs 获取当前利润，freqtrade 会传入 current_profit
|
||
current_profit = float(kwargs.get('current_profit', 0.0))
|
||
|
||
# 获取出场一字基础阈值
|
||
base_threshold = self.ml_exit_signal_threshold.value
|
||
|
||
# 计算持仓时长（分钟）
|
||
try:
|
||
trade_age_minutes = max(0.0, (current_time - trade.open_date_utc).total_seconds() / 60.0)
|
||
except Exception:
|
||
trade_age_minutes = 0.0
|
||
|
||
# 基于持仓时长的阈值衰减：持仓越久，阈值越低，越容易出场
|
||
age_factor = min(trade_age_minutes / (24 * 60.0), 1.0) # 0~1，对应 0~24 小时+
|
||
dynamic_threshold = base_threshold * (1.0 - 0.3 * age_factor)
|
||
|
||
# 小利润单（<=2%）再额外放宽 20%
|
||
if current_profit <= 0.02:
|
||
dynamic_threshold *= 0.8
|
||
|
||
# 新增：读取 AI 预测的未来波动率信号（极端化方案）
|
||
future_vol_signal = None
|
||
if '&s-future_volatility' in df.columns:
|
||
future_vol_signal = float(last_row['&s-future_volatility'])
|
||
elif '&-future_volatility' in df.columns:
|
||
future_vol_signal = float(last_row['&-future_volatility'])
|
||
|
||
# 极端化逻辑：根据 AI 预测的未来波动率直接接管部分出场决策
|
||
if future_vol_signal is not None and exit_reason == 'exit_signal':
|
||
# 情况A：AI 预测强趋势（高波动），且当前不亏损 → 忽略本次 exit_signal，继续持有
|
||
if future_vol_signal > 0.65 and current_profit >= 0:
|
||
self.strategy_log(
|
||
f"[波动率 AI] [{pair}] AI 预测强趋势（高波动 {future_vol_signal:.2f}），忽略本次 exit_signal，继续持有 | "
|
||
f"持仓: {trade_age_minutes:.1f}min, 利润: {current_profit:.4f}"
|
||
)
|
||
allow_exit = False
|
||
return allow_exit
|
||
# 情况B：AI 预测震荡市（低波动） → 强制接受 exit_signal，立即出场
|
||
elif future_vol_signal < 0.35:
|
||
self.strategy_log(
|
||
f"[波动率 AI] [{pair}] AI 预测震荡市（低波动 {future_vol_signal:.2f}），强制接受 exit_signal 出场 | "
|
||
f"持仓: {trade_age_minutes:.1f}min, 利润: {current_profit:.4f}"
|
||
)
|
||
return True
|
||
# 介于 0.35-0.65 之间：中性区间，不做强制处理，继续走原有 ML 审核官逻辑
|
||
|
||
# 设定下限，避免阈值过低
|
||
dynamic_threshold = max(0.05, dynamic_threshold)
|
||
|
||
if exit_prob < dynamic_threshold:
|
||
self.strategy_log(
|
||
f"[{pair}] ML 审核官拒绝出场: exit_signal 概率 {exit_prob:.2f} < 动态阈值 {dynamic_threshold:.2f}"
|
||
f" | 原应出场原因: {exit_reason} | 持仓: {trade_age_minutes:.1f}min, 利润: {current_profit:.4f}"
|
||
f" | 波动率AI: {future_vol_signal if future_vol_signal is not None else 'N/A'}"
|
||
)
|
||
allow_exit = False
|
||
else:
|
||
self.strategy_log(
|
||
f"[{pair}] ML 审核官允许出场: exit_signal 概率 {exit_prob:.2f} >= 动态阈值 {dynamic_threshold:.2f}"
|
||
f" | 出场原因: {exit_reason} | 持仓: {trade_age_minutes:.1f}min, 利润: {current_profit:.4f}"
|
||
f" | 波动率AI: {future_vol_signal if future_vol_signal is not None else 'N/A'}"
|
||
)
|
||
except Exception as e:
|
||
logger.warning(f"[{pair}] ML 审核官出场检查失败，允许出场: {e}")
|
||
return allow_exit
|
||
|
||
def custom_stoploss(self, pair: str, trade: 'Trade', current_time, current_rate: float,
|
||
current_profit: float, **kwargs) -> float:
|
||
# 动态止损基于ATR
|
||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||
last_candle = dataframe.iloc[-1]
|
||
atr = last_candle['atr']
|
||
|
||
# 获取当前市场状态
|
||
current_state = dataframe['market_state'].iloc[-1] if 'market_state' in dataframe.columns else 'unknown'
|
||
|
||
# 渐进式止损策略
|
||
if current_profit > 0.05: # 利润超过5%时
|
||
return -3.0 * atr / current_rate
|
||
elif current_profit > 0.03: # 利润超过3%时
|
||
return -2.5 * atr / current_rate
|
||
elif current_profit > 0.01: # 利润超过1%时
|
||
return -2.0 * atr / current_rate
|
||
|
||
# 在强劲牛市中，即使小亏损也可以容忍更大回调
|
||
if current_state == 'strong_bull' and current_profit > -0.01:
|
||
return -1.8 * atr / current_rate
|
||
|
||
if atr > 0:
|
||
return -1.2 * atr / current_rate
|
||
return self.stoploss
|
||
|
||
def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
|
||
current_profit: float, **kwargs) -> float:
|
||
if trade.is_short:
|
||
return 0.0
|
||
|
||
trade_age_minutes = (current_time - trade.open_date_utc).total_seconds() / 60
|
||
if trade_age_minutes < 0:
|
||
trade_age_minutes = 0
|
||
|
||
# 使用可优化的线性函数: y = (a * (x + k)) + t
|
||
a = self.roi_param_a.value # 系数a (可优化参数)
|
||
k = self.roi_param_k.value # 偏移量k (可优化参数)
|
||
t = self.roi_param_t.value # 常数项t (可优化参数)
|
||
|
||
dynamic_roi_threshold = (a * (trade_age_minutes + k)) + t
|
||
# 确保ROI阈值不小于0
|
||
if dynamic_roi_threshold < 0:
|
||
dynamic_roi_threshold = 0.0
|
||
|
||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||
current_state = dataframe['market_state'].iloc[-1] if 'market_state' in dataframe.columns else (
|
||
'strong_bull' if dataframe['sma'].diff().iloc[-1] > 0.01 else 'weak_bull' if dataframe['sma'].diff().iloc[-1] > 0 else 'neutral'
|
||
)
|
||
|
||
entry_tag = trade.enter_tag if hasattr(trade, 'enter_tag') else None
|
||
profit_ratio = current_profit / dynamic_roi_threshold if dynamic_roi_threshold > 0 else 0
|
||
|
||
exit_ratio = 0.0
|
||
if profit_ratio >= 1.0:
|
||
if current_state == 'strong_bull':
|
||
exit_ratio = 0.5 if profit_ratio < 1.5 else 0.8
|
||
elif current_state == 'weak_bull':
|
||
exit_ratio = 0.6 if profit_ratio < 1.2 else 0.9
|
||
else:
|
||
exit_ratio = 1.0
|
||
if entry_tag == 'strong_trend':
|
||
exit_ratio *= 0.8
|
||
|
||
if dynamic_roi_threshold < 0:
|
||
exit_ratio = 1.0
|
||
|
||
#self.strategy_log(f"[{pair}] 动态止盈: 持仓时间={trade_age_minutes:.1f}分钟, 当前利润={current_profit:.2%}, "
|
||
# f"动态ROI阈值={dynamic_roi_threshold:.4f}, 利润比值={profit_ratio:.2f}, "
|
||
# f"市场状态={current_state}, entry_tag={entry_tag}, 退出比例={exit_ratio:.0%}")
|
||
|
||
# 当决定退出时，输出出场价格信息
|
||
if exit_ratio > 0:
|
||
# 计算出场价格上浮比例（1.25%）
|
||
price_markup_percent = 1.25
|
||
adjusted_exit_price = current_rate * 1.0125
|
||
self.strategy_log(f"[{pair}] 准备出场 - 市场价: {current_rate:.8f}, 调整后出场价: {adjusted_exit_price:.8f}, 上浮: {price_markup_percent}%, 退出比例: {exit_ratio:.0%}")
|
||
|
||
return exit_ratio
|
||
|
||
|
||
def adjust_trade_position(self, trade: 'Trade', current_time, current_rate: float,
|
||
current_profit: float, min_stake: float, max_stake: float, **kwargs) -> float:
|
||
"""
|
||
根据用户要求实现加仓逻辑
|
||
- 加仓间隔设置为可优化参数 add_position_callback
|
||
- 加仓额度为: (stake_amount / stake_divisor) ^ (加仓次数 - 1)
|
||
"""
|
||
# 获取当前交易对
|
||
pair = trade.pair
|
||
|
||
# 获取当前交易的加仓次数
|
||
entry_count = len(trade.orders) # 获取所有入场订单数量
|
||
|
||
# 如果已经达到最大加仓次数，则不再加仓
|
||
if entry_count - 1 >= self.max_entry_adjustments.value:
|
||
return 0.0
|
||
|
||
# 获取初始入场价格和当前价格的差值百分比
|
||
initial_price = trade.open_rate
|
||
if initial_price == 0:
|
||
return 0.0
|
||
price_diff_pct = (current_rate - initial_price) / initial_price
|
||
|
||
# 计算加仓次数（从1开始计数）
|
||
adjustment_count = entry_count - 1 # 已加仓次数
|
||
|
||
# 检查价格回调是否达到加仓间隔
|
||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||
current_state = dataframe['market_state'].iloc[-1] if 'market_state' in dataframe.columns else 'neutral'
|
||
|
||
# 计算当前所需的加仓间隔百分比 = 基础间隔 * (系数 ^ 已加仓次数)
|
||
# 获取当前币对的波动系数，用于动态调整回调百分比
|
||
volatility_coef = self.get_volatility_coefficient(pair)
|
||
# 回调百分比 = 基础回调 * (系数 ^ 已加仓次数) * 波动系数
|
||
current_callback = self.add_position_callback.value * (self.add_position_multiplier.value ** adjustment_count) * volatility_coef
|
||
|
||
if price_diff_pct <= -current_callback:
|
||
# 计算初始入场金额
|
||
initial_stake = trade.orders[0].cost # 第一笔订单的成本
|
||
|
||
# 计算加仓金额: (initial_stake / stake_divisor) ^ (adjustment_count + 1)
|
||
additional_stake = (initial_stake / self.stake_divisor.value) * (self.add_position_growth.value ** (adjustment_count + 1))
|
||
|
||
# 确保加仓金额在允许的范围内
|
||
additional_stake = max(min_stake, min(additional_stake, max_stake - trade.stake_amount))
|
||
|
||
#self.strategy_log(f"[{pair}] 触发加仓: 第{adjustment_count + 1}次加仓, 初始金额{initial_stake:.2f}, \
|
||
# 加仓金额{additional_stake:.2f}, 价格差{price_diff_pct:.2%}, 当前利润{current_profit:.2%}")
|
||
|
||
return additional_stake
|
||
|
||
# 不符合加仓条件，返回0
|
||
return 0.0
|
||
|
||
def custom_stake_amount(self, pair: str, current_time: datetime, **kwargs) -> float:
|
||
"""
|
||
定义初始仓位大小
|
||
"""
|
||
# 获取默认的基础仓位大小
|
||
default_stake = self.stake_amount
|
||
|
||
# 从kwargs获取最小和最大仓位限制
|
||
min_stake = kwargs.get('min_stake', 0.0)
|
||
max_stake = kwargs.get('max_stake', default_stake)
|
||
|
||
# 确保仓位在允许的范围内
|
||
adjusted_stake = max(min_stake, min(default_stake, max_stake))
|
||
|
||
return adjusted_stake
|
||
|