636 lines
33 KiB
Python
636 lines
33 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_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 pandas as pd
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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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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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@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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# 这两个就不优化了, 参数太多了
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h1_max_consecutive_candles = IntParameter(1, 4, default=3, optimize=False, load=True, space='buy')
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h1_max_candles = IntParameter(100, 300, default=200, optimize=False, load=True, space='buy')
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# 第一步 buy 空间优化参数 7个:
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# --- 趋势与信号生成 ---
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bb_length = IntParameter(10, 30, default=14, optimize=False, load=True, space='buy')
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bb_std = DecimalParameter(1.5, 3.0, decimals=1, default=3.0, optimize=False, load=True, space='buy')
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rsi_length = IntParameter(7, 21, default=16, optimize=False, load=True, space='buy')
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# 入场条件阈值
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bb_lower_deviation = DecimalParameter(1.01, 1.05, decimals=2, default=1.05, optimize=False, load=True, space='buy')
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rsi_oversold = IntParameter(30, 50, default=42, optimize=False, load=True, space='buy')
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volume_multiplier = DecimalParameter(1.2, 2.0, decimals=1, default=1.6, optimize=False, load=True, space='buy')
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bb_width_threshold = IntParameter(10, 30, default=12, optimize=False, 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_rapid_rise_threshold = DecimalParameter(0.05, 0.15, decimals=3, default=0.065, optimize=False, load=True, space='buy')
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# 第二步 buy 空间优化参数 7个:
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# --- 资金管理与加仓 ---
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add_position_callback = IntParameter(30, 60, default=28, optimize=True, load=True, space='buy')
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# 合并step_coefficient和stake_divisor为multiplier参数
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adjust_multiplier = DecimalParameter(0.15, 0.6, decimals=2, default=0.52, optimize=True, load=True, space='buy')
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max_entry_adjustments = IntParameter(2, 5, default=2, optimize=True, load=True, space='buy') # 可暂时关闭
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# 市场状态相关（辅助决策）
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min_condition_count = IntParameter(2, 4, default=2, optimize=False, load=True, space='buy')
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stochrsi_neutral_threshold = IntParameter(20, 30, default=29, optimize=True, load=True, space='buy') # 固定为20
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stochrsi_bull_threshold = IntParameter(30, 40, default=36, optimize=True, load=True, space='buy')
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rsi_bull_threshold = IntParameter(45, 55, default=54, optimize=True, load=True, space='buy') # 固定为50
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# 第三步, 优化sell空间
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exit_bb_upper_deviation = DecimalParameter(0.98, 1.02, decimals=2, default=0.99, optimize=True, load=True, space='sell')
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exit_volume_multiplier = DecimalParameter(1.5, 3.0, decimals=1, default=1.7, optimize=True, load=True, space='sell')
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rsi_overbought = IntParameter(57, 59, default=58, optimize=True, load=True, space='sell')
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# -------------------------- 简化后的减仓Hyperopt参数（仅3个，对齐加仓） --------------------------
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reduce_profit_base = DecimalParameter(0.05, 0.12, default=0.075, space='sell', optimize=True) # 减仓基础盈利阈值（触发门槛，默认7.5%）
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reduce_coefficient = DecimalParameter(0.1, 0.6, default=0.25, space='sell', optimize=True) # 减仓金额系数（默认0.25，控制初始金额）
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max_reduce_adjustments = IntParameter(1, 3, default=1, space='sell', optimize=True) # 最大减仓次数（默认1次，避免过度减仓）
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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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def custom_stake_amount(self, pair: str, current_time: pd.Timestamp,
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current_rate: float,
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proposed_stake: float,
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min_stake: float,
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max_stake: float,
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**kwargs) -> float:
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# 获取初始资金（回测中固定为dry_run_wallet的值）
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initial_balance = self.config.get('dry_run_wallet', 10000)
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# 始终以初始资金的3.75%计算
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desired_stake = initial_balance * 0.0375
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desired_stake = math.floor(desired_stake) # 取整，去掉小数点后的数字
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return max(min(desired_stake, max_stake), min_stake)
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# 计算 3m 周期的指标
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bb_length_value = self.bb_length.value
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bb_std_value = self.bb_std.value
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rsi_length_value = self.rsi_length.value
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bb_3m = ta.bbands(dataframe['close'], length=bb_length_value, std=bb_std_value)
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dataframe['bb_lower_3m'] = bb_3m[f'BBL_{bb_length_value}_{bb_std_value}']
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dataframe['bb_upper_3m'] = bb_3m[f'BBU_{bb_length_value}_{bb_std_value}']
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dataframe['rsi_3m'] = ta.rsi(dataframe['close'], length=rsi_length_value)
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# 新增 StochRSI 指标
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stochrsi_3m = ta.stochrsi(dataframe['close'], length=rsi_length_value, rsi_length=rsi_length_value)
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dataframe['stochrsi_k_3m'] = stochrsi_3m[f'STOCHRSIk_{rsi_length_value}_{rsi_length_value}_3_3']
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dataframe['stochrsi_d_3m'] = stochrsi_3m[f'STOCHRSId_{rsi_length_value}_{rsi_length_value}_3_3']
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# 新增 MACD 指标
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macd_3m = ta.macd(dataframe['close'], fast=12, slow=26, signal=9)
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dataframe['macd_3m'] = macd_3m['MACD_12_26_9']
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dataframe['macd_signal_3m'] = macd_3m['MACDs_12_26_9']
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dataframe['macd_hist_3m'] = macd_3m['MACDh_12_26_9']
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# 计算3m时间框架的EMA50和EMA200
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dataframe['ema_50_3m'] = ta.ema(dataframe['close'], length=50)
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dataframe['ema_200_3m'] = ta.ema(dataframe['close'], length=200)
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# 成交量过滤
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dataframe['volume_ma'] = dataframe['volume'].rolling(20).mean()
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# 计算 ATR 用于动态止损和退出
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dataframe['atr'] = ta.atr(dataframe['high'], dataframe['low'], dataframe['close'], length=14)
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# 获取 15m 数据
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df_15m = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='15m')
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df_15m['rsi_15m'] = ta.rsi(df_15m['close'], length=rsi_length_value)
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# 计算15m时间框架的EMA50和EMA200
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df_15m['ema_50_15m'] = ta.ema(df_15m['close'], length=50)
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df_15m['ema_200_15m'] = ta.ema(df_15m['close'], length=200)
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# 新增 StochRSI 指标
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stochrsi_15m = ta.stochrsi(df_15m['close'], length=rsi_length_value, rsi_length=rsi_length_value)
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df_15m['stochrsi_k_15m'] = stochrsi_15m[f'STOCHRSIk_{rsi_length_value}_{rsi_length_value}_3_3']
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df_15m['stochrsi_d_15m'] = stochrsi_15m[f'STOCHRSId_{rsi_length_value}_{rsi_length_value}_3_3']
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# 新增 MACD 指标
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macd_15m = ta.macd(df_15m['close'], fast=12, slow=26, signal=9)
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df_15m['macd_15m'] = macd_15m['MACD_12_26_9']
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df_15m['macd_signal_15m'] = macd_15m['MACDs_12_26_9']
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df_15m['macd_hist_15m'] = macd_15m['MACDh_12_26_9']
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# 将 15m 数据重新索引到主时间框架 (3m)
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df_15m = df_15m.set_index('date').reindex(dataframe['date']).reset_index()
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df_15m = df_15m.rename(columns={'index': 'date'})
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df_15m = df_15m[['date', 'rsi_15m', 'ema_50_15m', 'ema_200_15m']].ffill()
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# 合并 15m 数据
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dataframe = dataframe.merge(df_15m, how='left', on='date')
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# 获取 1h 数据
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df_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1h')
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# 计算 1h 布林带
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bb_1h = ta.bbands(df_1h['close'], length=bb_length_value, std=bb_std_value)
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df_1h['bb_lower_1h'] = bb_1h[f'BBL_{bb_length_value}_{bb_std_value}']
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df_1h['bb_upper_1h'] = bb_1h[f'BBU_{bb_length_value}_{bb_std_value}']
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# 计算 1h RSI 和 EMA
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df_1h['rsi_1h'] = ta.rsi(df_1h['close'], length=rsi_length_value)
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df_1h['ema_50_1h'] = ta.ema(df_1h['close'], length=50) # 1h 50周期EMA
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df_1h['ema_200_1h'] = ta.ema(df_1h['close'], length=200) # 1h 200周期EMA
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df_1h['trend_1h'] = df_1h['close'] > df_1h['ema_50_1h'] # 1h上涨趋势
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# 新增 StochRSI 指标
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stochrsi_1h = ta.stochrsi(df_1h['close'], length=rsi_length_value, rsi_length=rsi_length_value)
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df_1h['stochrsi_k_1h'] = stochrsi_1h[f'STOCHRSIk_{rsi_length_value}_{rsi_length_value}_3_3']
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df_1h['stochrsi_d_1h'] = stochrsi_1h[f'STOCHRSId_{rsi_length_value}_{rsi_length_value}_3_3']
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# 新增 MACD 指标
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macd_1h = ta.macd(df_1h['close'], fast=12, slow=26, signal=9)
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df_1h['macd_1h'] = macd_1h['MACD_12_26_9']
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df_1h['macd_signal_1h'] = macd_1h['MACDs_12_26_9']
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df_1h['macd_hist_1h'] = macd_1h['MACDh_12_26_9']
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# 验证 MACD 列是否正确生成
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#logger.info(f"[{metadata['pair']}] 1小时 MACD 列: {list(macd_1h.columns)}")
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# 确保 StochRSI 指标已正确计算
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# 将 1h 数据重新索引到主时间框架 (3m)，并填充缺失值
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df_1h = df_1h.set_index('date').reindex(dataframe['date']).ffill().bfill().reset_index()
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df_1h = df_1h.rename(columns={'index': 'date'})
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# Include macd_1h and macd_signal_1h in the column selection
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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()
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# Validate that all required columns are present
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required_columns = ['date', 'rsi_1h', 'trend_1h', 'ema_50_1h', 'ema_200_1h',
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'bb_lower_1h', 'bb_upper_1h', 'stochrsi_k_1h', 'stochrsi_d_1h',
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'macd_1h', 'macd_signal_1h']
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missing_columns = [col for col in required_columns if col not in df_1h.columns]
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if missing_columns:
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logger.error(f"[{metadata['pair']}] 缺少以下列: {missing_columns}")
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raise KeyError(f"缺少以下列: {missing_columns}")
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# 确保所有需要的列都被合并
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required_columns = ['date', 'rsi_1h', 'trend_1h', 'ema_50_1h', 'ema_200_1h',
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'bb_lower_1h', 'bb_upper_1h', 'stochrsi_k_1h', 'stochrsi_d_1h',
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'macd_1h', 'macd_signal_1h']
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# 验证所需列是否存在
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missing_columns = [col for col in required_columns if col not in df_1h.columns]
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if missing_columns:
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logger.error(f"[{metadata['pair']}] 缺少以下列: {missing_columns}")
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raise KeyError(f"缺少以下列: {missing_columns}")
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df_1h = df_1h[required_columns] # 确保包含 macd_1h 和 macd_signal_1h
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# 合并 1h 数据
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dataframe = dataframe.merge(df_1h, how='left', on='date').ffill()
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# 验证合并后的列
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#logger.info(f"[{metadata['pair']}] 合并后的数据框列名: {list(dataframe.columns)}")
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# K线形态：看涨吞没
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dataframe['bullish_engulfing'] = (
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(dataframe['close'].shift(1) < dataframe['open'].shift(1)) &
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(dataframe['close'] > dataframe['open']) &
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(dataframe['close'] > dataframe['open'].shift(1)) &
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(dataframe['open'] < dataframe['close'].shift(1))
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)
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# 计算各时间框架的趋势状态（牛/熊）
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# 3m时间框架：ema50下穿ema200为熊，上穿为牛
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dataframe['trend_3m'] = np.where(dataframe['ema_50_3m'] > dataframe['ema_200_3m'], 1, 0)
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# 15m时间框架：ema50下穿ema200为熊，上穿为牛
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dataframe['trend_15m'] = np.where(dataframe['ema_50_15m'] > dataframe['ema_200_15m'], 1, 0)
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# 1h时间框架：ema50下穿ema200为熊，上穿为牛
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dataframe['trend_1h_ema'] = np.where(dataframe['ema_50_1h'] > dataframe['ema_200_1h'], 1, 0)
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# 计算熊牛得分（0-100）
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# 权重：3m熊牛权重10，15m熊牛权重35，1h熊牛权重65
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# 计算加权得分
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dataframe['market_score'] = (
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dataframe['trend_3m'] * 10 +
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dataframe['trend_15m'] * 35 +
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dataframe['trend_1h_ema'] * 65
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)
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# 确保得分在0-100范围内
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dataframe['market_score'] = dataframe['market_score'].clip(lower=0, upper=100)
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# 根据得分分类市场状态
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dataframe['market_state'] = 'neutral'
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dataframe.loc[dataframe['market_score'] > 70, 'market_state'] = 'strong_bull'
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dataframe.loc[(dataframe['market_score'] > 50) & (dataframe['market_score'] <= 70), 'market_state'] = 'weak_bull'
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dataframe.loc[(dataframe['market_score'] >= 30) & (dataframe['market_score'] <= 50), 'market_state'] = 'neutral'
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dataframe.loc[(dataframe['market_score'] > 10) & (dataframe['market_score'] < 30), 'market_state'] = 'weak_bear'
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dataframe.loc[dataframe['market_score'] <= 10, 'market_state'] = 'strong_bear'
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# 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]
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dataframe['prev_market_state'] = dataframe['market_state'].shift(1)
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# 为第一行设置默认值
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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]
|
||
#logger.info(f"[{metadata['pair']}] 熊牛得分: {current_score:.1f}, 市场状态: {current_state}")
|
||
#logger.info(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
|
||
#logger.info(f"[{metadata['pair']}] 最终数据框列名: {list(dataframe.columns)}")
|
||
|
||
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
|
||
|
||
# 增强调试信息
|
||
#logger.info(f"[{metadata['pair']}] 出场条件检查:")
|
||
#logger.info(f" - 价格突破布林带上轨: {breakout_condition.sum()} 次")
|
||
#logger.info(f" - 成交量显著放大: {volume_spike.sum()} 次")
|
||
#logger.info(f" - MACD 下降趋势: {macd_downward.sum()} 次")
|
||
#logger.info(f" - RSI 超买: {rsi_overbought.sum()} 次")
|
||
#logger.info(f" - 最终条件: {final_condition.sum()} 次")
|
||
#logger.info(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:
|
||
logger.info(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 / 1000 # 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
|
||
|
||
# 增强调试信息
|
||
#logger.info(f"[{metadata['pair']}] 入场条件检查:")
|
||
#logger.info(f" - 价格接近布林带下轨: {close_to_bb_lower_1h.sum()} 次")
|
||
#logger.info(f" - RSI 超卖: {rsi_condition_1h.sum()} 次")
|
||
#logger.info(f" - StochRSI 超卖: {stochrsi_condition_1h.sum()} 次")
|
||
#logger.info(f" - MACD 上升趋势: {macd_condition_1h.sum()} 次")
|
||
#logger.info(f" - 成交量或布林带宽度: {(volume_spike | bb_width_condition).sum()} 次")
|
||
#logger.info(f" - 趋势确认: {trend_confirmation.sum()} 次")
|
||
#logger.info(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()
|
||
#logger.info(f"[{metadata['pair']}] 条件平均相关性: {correlation:.2f}")
|
||
# 日志记录
|
||
if dataframe['enter_long'].sum() > 0:
|
||
logger.info(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
|
||
#logger.info(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]
|
||
#logger.info(f"[{pair}] 剧烈拉升检测结果: {'不稳固' if rapid_rise_detected else '稳固'}")
|
||
#logger.info(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逻辑
|
||
"""
|
||
# 默认允许交易
|
||
allow_trade = True
|
||
|
||
# 仅对多头交易进行检查
|
||
if side == 'long':
|
||
# 检查是否处于剧烈拉升的不稳固区域
|
||
is_unstable_region = self.detect_h1_rapid_rise(pair)
|
||
if is_unstable_region:
|
||
#logger.info(f"[{pair}] 由于检测到剧烈拉升，取消入场交易")
|
||
allow_trade = False
|
||
|
||
# 如果没有阻止因素，允许交易
|
||
return allow_trade
|
||
|
||
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'
|
||
|
||
# 更激进的渐进式止损策略
|
||
if current_profit > 0.05: # 利润超过5%时
|
||
return -3.0 * atr / current_rate # 更大幅扩大止损范围，让利润奔跑
|
||
elif current_profit > 0.03: # 利润超过3%时
|
||
return -2.5 * atr / current_rate # 更中等扩大止损范围
|
||
elif current_profit > 0.01: # 利润超过1%时
|
||
return -2.0 * atr / current_rate # 更轻微扩大止损范围
|
||
|
||
# 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 -1.5 * atr / current_rate
|
||
|
||
# 动态调整止损范围
|
||
if current_profit > 0.05: # 利润超过5%时
|
||
return -3.0 * atr / current_rate # 更大幅扩大止损范围，让利润奔跑
|
||
elif current_profit > 0.03: # 利润超过3%时
|
||
return -2.5 * atr / current_rate # 更中等扩大止损范围
|
||
elif current_profit > 0.01: # 利润超过1%时
|
||
return -2.0 * atr / current_rate # 更轻微扩大止损范围
|
||
|
||
# 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 # 基础1.2倍ATR止损
|
||
return self.stoploss
|
||
|
||
|
||
def adjust_trade_position(self, trade: 'Trade', current_time, current_rate: float,
|
||
current_profit: float, min_stake: float, max_stake: float, **kwargs) -> float:
|
||
"""
|
||
简化版：加仓（原有）+ 减仓（1个阈值+公式计算，对齐加仓逻辑）
|
||
- 减仓：盈利≥基础阈值触发，用公式算阶梯金额；每个timeframe+最大次数双重限制
|
||
"""
|
||
pair = trade.pair
|
||
|
||
# -------------------------- 简化减仓逻辑（1个阈值+公式计算） --------------------------
|
||
if current_profit > 0:
|
||
# 1. 基础限制：未达最大减仓次数 + 盈利≥基础阈值（核心触发条件，对齐加仓的跌幅阈值）
|
||
reduce_count = len(trade.select_filled_orders(trade.exit_side)) # 已成功减仓次数（初始0）
|
||
if reduce_count >= self.max_reduce_adjustments.value:
|
||
logger.debug(f"[{pair}] 已达最大减仓次数（{self.max_reduce_adjustments.value}次），停止减仓")
|
||
return 0.0
|
||
if current_profit < self.reduce_profit_base.value:
|
||
logger.debug(f"[{pair}] 盈利{current_profit:.2%}＜减仓基础阈值{self.reduce_profit_base.value:.2%}，不加仓")
|
||
return 0.0
|
||
|
||
# 2. 周期限制（每个timeframe仅1次，保留之前的简化逻辑）
|
||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||
current_kline_time = dataframe.iloc[-1]['date'].strftime('%Y-%m-%d %H:%M:%S')
|
||
last_reduce_kline = trade.get_custom_data("last_reduce_kline")
|
||
if last_reduce_kline == current_kline_time:
|
||
logger.debug(f"[{pair}] 当前{self.timeframe}周期已减仓，本次拒绝")
|
||
return 0.0
|
||
|
||
# 3. 公式计算减仓金额（完全对齐加仓公式逻辑，阶梯递增）
|
||
# 加仓公式：(step_coefficient × 初始金额 / stake_divisor) ^ 加仓次数
|
||
# 减仓公式：(reduce_coefficient × 初始开仓金额) ^ (减仓次数 + 1) → 次数越多，金额越大
|
||
initial_stake = float(trade.orders[0].cost) # 初始开仓金额（与加仓用同一基准）
|
||
reduce_amount = (float(self.reduce_coefficient.value) * initial_stake) ** (reduce_count + 1)
|
||
|
||
# 4. 安全校验（避免减仓超当前持仓/低于最小下单量，与加仓逻辑一致）
|
||
current_stake = float(trade.stake_amount) # 当前剩余持仓金额（减仓后会更新）
|
||
reduce_amount = min(reduce_amount, current_stake * 0.6) # 额外限制：单次减仓不超当前持仓60%（防极端）
|
||
reduce_amount = -reduce_amount # 负号表示减仓（Freqtrade规则）
|
||
reduce_amount = max(-current_stake, min(reduce_amount, -float(min_stake))) # 安全边界
|
||
|
||
# 5. 触发减仓，记录周期
|
||
logger.info(f"[{pair}] 触发减仓: 盈利{current_profit:.2%}≥{self.reduce_profit_base.value:.2%}，第{reduce_count+1}次减仓，金额{abs(reduce_amount):.2f}")
|
||
trade.set_custom_data("last_reduce_kline", current_kline_time)
|
||
return reduce_amount
|
||
|
||
# -------------------------- 原有加仓逻辑（保持不变，确保对齐） --------------------------
|
||
entry_count = len(trade.orders)
|
||
if entry_count > self.max_entry_adjustments.value:
|
||
return 0.0
|
||
initial_price = trade.open_rate
|
||
if initial_price == 0:
|
||
return 0.0
|
||
if current_profit > - self.add_position_callback.value / 100 :
|
||
return 0.0
|
||
|
||
price_diff_pct = (current_rate - initial_price) / initial_price
|
||
|
||
if (price_diff_pct/(entry_count)) <= - self.add_position_callback.value / 100 :
|
||
initial_stake = trade.orders[0].cost
|
||
additional_stake = (self.adjust_multiplier.value * initial_stake) ** (entry_count)
|
||
additional_stake = max(min_stake, min(additional_stake, max_stake - trade.stake_amount))
|
||
current_time_str = current_time.strftime('%Y-%m-%d %H:%M:%S')
|
||
logger.info(f"[{pair}] 触发加仓: 时间={current_time_str}, 第{entry_count}次开仓，初始金额{initial_stake:.2f}，金额{additional_stake:.2f}，跌幅{price_diff_pct:.2%}")
|
||
return additional_stake
|
||
|
||
return 0.0
|
||
|