829 lines
43 KiB
Python
829 lines
43 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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# 启用自定义ROI回调函数
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use_custom_roi = True
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# 写死在策略里，永远不参与 hyperopt
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trailing_stop = True
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trailing_stop_positive = 0.012 # 1.2% 固定回调（震荡市最稳）
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trailing_stop_positive_offset = 0.02 # 1.8% 盈利后才激活（防止过早启动）
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trailing_only_offset_is_reached = True # 必须等盈利超过 offset 才启动 trailing
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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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assert self.h1_max_candles.value <= 50, f"h1_max_candles={self.h1_max_candles.value} 超出安全范围！"
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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": 18 # 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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# [propertiesGrp_List]--------------------------------------------------------------------------------------------------------------------------------------
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# [propertiesGrp step="1" name="第一轮优化" epochs="300" space="buy " description="入场基础条件优化,入场确认条件优化"]
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bb_std = DecimalParameter(2.0, 5.0, decimals=1, default=3.5, optimize=True, load=True, space='buy') # 安全：2.0-5.0
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rsi_length = IntParameter(10, 30, default=14, optimize=True, load=True, space='buy') # 安全：10-30
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bb_lower_deviation = DecimalParameter(0.92, 1.15, decimals=3, default=1.00, optimize=True, load=True, space='buy') # 安全：0.92-1.15
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stochrsi_bull_threshold = IntParameter(20, 50, default=40, optimize=True, load=True, space='buy') # 安全：20-50
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volume_multiplier = DecimalParameter(1.5, 6.0, decimals=1, default=3.5, optimize=True, load=True, space='buy') # 安全：1.5-6.0
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min_condition_count = IntParameter(1, 2, default=1, optimize=True, load=True, space='buy') # 最多只允许2个条件！
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bb_length = IntParameter(20, 60, default=40, optimize=True, load=True, space='buy') # 安全：20-60
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# [/propertiesGrp]
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# [propertiesGrp step="2" name="第二轮优化 - 剧烈拉升检测" epochs="300" space="buy" description="防追高核心参数，绝对不能放宽！"]
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rsi_oversold = IntParameter(20, 50, default=30, optimize=True, load=True, space='buy') # 安全：20-50
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rsi_bull_threshold = IntParameter(40, 68, default=58, optimize=True, load=True, space='buy') # 安全：40-68
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stochrsi_neutral_threshold = IntParameter(15, 40, default=30, optimize=True, load=True, space='buy') # 安全：15-40
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bb_width_threshold = DecimalParameter(0.003, 0.030, decimals=3, default=0.012, optimize=True, load=True, space='buy') # 安全：0.003-0.030
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h1_max_candles = IntParameter(16, 50, default=35, optimize=True, load=True, space='buy') # 黄金区间！绝不能超过50
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h1_rapid_rise_threshold = DecimalParameter(0.08, 0.22, decimals=3, default=0.15, optimize=True, load=True, space='buy') # 0.08-0.22 实盘最稳
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h1_max_consecutive_candles = IntParameter(1, 2, default=1, optimize=True, load=True, space='buy') # 固定为1最稳，2也行
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# [/propertiesGrp]
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# [propertiesGrp step="3" name="第三轮优化 - 加仓策略" epochs="300" space="buy" description="加仓精准度与金额管理，严防爆仓"]
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add_position_callback = DecimalParameter(0.025, 0.070, decimals=3, default=0.045, optimize=True, load=True, space='buy') # 2.5%-7.0% 回调才加
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add_rsi_oversold_threshold = IntParameter(15, 40, default=25, optimize=True, load=True, space='buy') # 不能太低
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add_stochrsi_oversold = IntParameter(10, 35, default=20, optimize=True, load=True, space='buy')
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add_bb_lower_proximity = DecimalParameter(0.85, 1.20, decimals=3, default=1.05, optimize=True, load=True, space='buy') # 不能离下轨太远
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add_position_decrease_ratio= DecimalParameter(0.30, 0.80, decimals=2, default=0.55, optimize=True, load=True, space='buy') # 递减比例别太激进
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max_entry_adjustments = IntParameter(2, 7, default=5, optimize=True, load=True, space='buy') # 最多7次加仓，防爆仓
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adjust_multiplier = DecimalParameter(0.6, 1.6, decimals=2, default=1.0, optimize=True, load=True, space='buy') # 别让加仓金额指数爆炸
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# [/propertiesGrp]
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# [propertiesGrp step="4" name="第四轮优化 - 出场与分级止盈" epochs="360" space="sell" description="出场条件与分级止盈,减仓与风险管理"]
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exit_bb_upper_deviation = DecimalParameter(0.90, 1.15, decimals=3, default=1.00, optimize=True, load=True, space='sell')
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exit_volume_multiplier = DecimalParameter(2.0, 7.0, decimals=1, default=4.5, optimize=True, load=True, space='sell')
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exit_rsi_threshold = IntParameter(55, 72, default=65, optimize=True, load=True, space='sell') # 牛市也能出得了场
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exit_profit_tier1 = DecimalParameter(0.03, 0.12, decimals=3, default=0.06, optimize=True, load=True, space='sell')
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exit_reduce_tier1 = DecimalParameter(0.20, 0.70, decimals=2, default=0.50, optimize=True, load=True, space='sell')
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exit_profit_tier2 = DecimalParameter(0.08, 0.20, decimals=3, default=0.12, optimize=True, load=True, space='sell')
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exit_reduce_tier2 = DecimalParameter(0.15, 0.60, decimals=2, default=0.30, optimize=True, load=True, space='sell')
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reduce_profit_base = DecimalParameter(0.02, 0.12, decimals=3, default=0.05, optimize=True, load=True, space='sell')
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reduce_coefficient = DecimalParameter(0.15, 0.55, decimals=3, default=0.35, optimize=True, load=True, space='sell')
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max_reduce_adjustments = IntParameter(1, 4, default=3, optimize=True, load=True, space='sell') # 最多4次减仓就够了
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# [/propertiesGrp]
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# [propertiesGrp step="5" name="第五轮优化" epochs="80" space="roi stoploss" description="最终ROI与止损微调"]
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# （这里可以放你后续要优化的ROI表、动态止损系数等）
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# [/propertiesGrp]
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# [/propertiesGrp_List]-----------------------------------------------------------------------------------------------------------------------------
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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_entry_price(self, pair: str, current_time: pd.Timestamp, proposed_rate: float,
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entry_tag: str | None, side: str, **kwargs) -> float:
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"""
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自定义入场价格：给入场价格打98折（降低2%）
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添加零值保护，防止除零错误
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"""
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# 零值保护：如果proposed_rate为0或异常小值，直接返回原值
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if proposed_rate <= 0 or proposed_rate < 1e-8:
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logger.warning(f"[{pair}] proposed_rate异常: {proposed_rate}，返回原值")
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return proposed_rate if proposed_rate > 0 else 0.0
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# 入场价格折扣：98折（降低2%）
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discounted_rate = proposed_rate * 0.995
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return discounted_rate
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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
|
||
dataframe = dataframe.merge(df_1h, how='left', on='date').ffill()
|
||
|
||
# 验证合并后的列
|
||
#logger.info(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]
|
||
#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
|
||
# 确保market_state列存在
|
||
if 'market_state' not in dataframe.columns:
|
||
dataframe['market_state'] = 'neutral'
|
||
|
||
# ======================== 出场条件多维度评分 ========================
|
||
# 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（市场自适应）
|
||
# 根据市场状态调整RSI阈值
|
||
def get_exit_rsi_threshold(row):
|
||
market_state = row.get('market_state', 'neutral')
|
||
if market_state == 'strong_bull':
|
||
return self.exit_rsi_threshold.value + 5 # 强牛市提高阈值，让利润奔跑
|
||
elif market_state == 'weak_bull':
|
||
return self.exit_rsi_threshold.value
|
||
else:
|
||
return self.exit_rsi_threshold.value - 5 # 弱市降低阈值，及时止盈
|
||
|
||
rsi_thresholds = dataframe.apply(get_exit_rsi_threshold, axis=1)
|
||
rsi_overbought = dataframe['rsi_1h'] > rsi_thresholds
|
||
|
||
# 评分计算
|
||
condition_score = (
|
||
breakout_condition.astype(int) +
|
||
volume_spike.astype(int) +
|
||
macd_downward.astype(int) +
|
||
rsi_overbought.astype(int)
|
||
)
|
||
|
||
# 触发条件：至少1个条件满足（从≥2降低到≥1，极致放宽）
|
||
final_condition = condition_score >= 1
|
||
|
||
# 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}")
|
||
|
||
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 _check_add_position_conditions(self, pair: str, current_rate: float, current_profit: float,
|
||
entry_count: int, initial_price: float, dataframe) -> dict:
|
||
"""
|
||
检查加仓条件的多维度评分系统
|
||
返回: {'should_add': bool, 'score': float, 'reasons': list}
|
||
"""
|
||
try:
|
||
if dataframe is None or len(dataframe) < 30:
|
||
return {'should_add': False, 'score': 0, 'reasons': ['数据不足']}
|
||
|
||
last_candle = dataframe.iloc[-1]
|
||
reasons = []
|
||
score = 0.0
|
||
max_score = 6.0
|
||
|
||
# 条件1：跌幅确认（基础条件，必须满足）
|
||
price_diff_pct = (current_rate - initial_price) / initial_price
|
||
callback_threshold = -self.add_position_callback.value
|
||
|
||
if price_diff_pct <= callback_threshold:
|
||
score += 1.0
|
||
reasons.append(f"✓ 跌幅{price_diff_pct:.2%}≤{callback_threshold:.2%}")
|
||
else:
|
||
return {'should_add': False, 'score': 0, 'reasons': [f'✗ 跌幅不足: {price_diff_pct:.2%} > {callback_threshold:.2%}']}
|
||
|
||
# 条件2：RSI超卖确认
|
||
rsi_1h = last_candle.get('rsi_1h', 50)
|
||
if rsi_1h < self.add_rsi_oversold_threshold.value:
|
||
score += 1.0
|
||
reasons.append(f"✓ RSI超卖: {rsi_1h:.1f} < {self.add_rsi_oversold_threshold.value}")
|
||
else:
|
||
reasons.append(f"✗ RSI不超卖: {rsi_1h:.1f} ≥ {self.add_rsi_oversold_threshold.value}")
|
||
|
||
# 条件3：StochRSI双线低位确认
|
||
stochrsi_k = last_candle.get('stochrsi_k_1h', 50)
|
||
stochrsi_d = last_candle.get('stochrsi_d_1h', 50)
|
||
if stochrsi_k < self.add_stochrsi_oversold.value and stochrsi_d < self.add_stochrsi_oversold.value:
|
||
score += 1.0
|
||
reasons.append(f"✓ StochRSI双超卖: K={stochrsi_k:.1f}, D={stochrsi_d:.1f}")
|
||
else:
|
||
reasons.append(f"✗ StochRSI未双超卖: K={stochrsi_k:.1f}, D={stochrsi_d:.1f}")
|
||
|
||
# 条件4：MACD上升确认（底部反转信号）
|
||
macd_1h = last_candle.get('macd_1h', 0)
|
||
macd_signal_1h = last_candle.get('macd_signal_1h', 0)
|
||
macd_hist = macd_1h - macd_signal_1h
|
||
|
||
if len(dataframe) >= 2:
|
||
prev_macd_hist = dataframe.iloc[-2].get('macd_1h', 0) - dataframe.iloc[-2].get('macd_signal_1h', 0)
|
||
if macd_hist > prev_macd_hist: # 简化条件，只检查MACD柱值上升
|
||
score += 1.0
|
||
reasons.append(f"✓ MACD底部上升: 柱值={macd_hist:.6f}")
|
||
else:
|
||
reasons.append(f"✗ MACD未确认: 柱值={macd_hist:.6f}")
|
||
|
||
# 条件5：布林带下轨支撑确认
|
||
bb_lower = last_candle.get('bb_lower_1h', current_rate)
|
||
bb_proximity_ratio = current_rate / bb_lower if bb_lower > 0 else 1.0
|
||
|
||
if bb_proximity_ratio <= self.add_bb_lower_proximity.value:
|
||
score += 1.0
|
||
reasons.append(f"✓ 接近BB下轨: 比例={bb_proximity_ratio:.4f}")
|
||
else:
|
||
reasons.append(f"✗ 离BB下轨太远: 比例={bb_proximity_ratio:.4f}")
|
||
|
||
# 条件6：成交量放大确认（简化条件）
|
||
volume = last_candle.get('volume', 0)
|
||
volume_ma = last_candle.get('volume_ma', 1)
|
||
if volume > volume_ma * 1.2: # 固定1.2倍成交量确认
|
||
score += 1.0
|
||
reasons.append(f"✓ 成交量放大: {volume:.0f} > {volume_ma * 1.2:.0f}")
|
||
else:
|
||
reasons.append(f"✗ 成交量不足: {volume:.0f} ≤ {volume_ma * 1.2:.0f}")
|
||
|
||
# 条件7：市场状态过滤（强熊市禁止加仓）
|
||
market_state = last_candle.get('market_state', 'neutral')
|
||
if market_state != 'strong_bear':
|
||
score += 0.5
|
||
reasons.append(f"✓ 市场状态良好: {market_state}")
|
||
else:
|
||
reasons.append(f"✗ 强熊市，避免加仓: {market_state}")
|
||
return {'should_add': False, 'score': score/max_score, 'reasons': reasons}
|
||
|
||
# 综合判断（极致放宽条件）
|
||
condition_met = sum(1 for r in reasons if r.startswith('✓')) >= 2 # 从≥3降低到≥2（极致放宽）
|
||
score_ratio = score / max_score
|
||
|
||
should_add = condition_met and score_ratio >= 0.35 # 从0.5降低到0.35（极致放宽）
|
||
|
||
return {
|
||
'should_add': should_add,
|
||
'score': score_ratio,
|
||
'reasons': reasons,
|
||
'condition_met': condition_met
|
||
}
|
||
|
||
except Exception as e:
|
||
logger.error(f"[{pair}] 加仓条件检查出错: {str(e)}")
|
||
return {'should_add': False, 'score': 0, 'reasons': [f'错误: {str(e)}']}
|
||
|
||
def _calculate_add_position_amount(self, trade: 'Trade', entry_count: int, min_stake: float, max_stake: float) -> float:
|
||
"""
|
||
智能计算加仓金额（支持递减策略）
|
||
- 早期加仓金额较大，后期逐步减小
|
||
- 防止后期加仓金额过大导致爆仓
|
||
"""
|
||
try:
|
||
initial_stake = float(trade.orders[0].cost)
|
||
|
||
# 基础公式：(adjust_multiplier × initial_stake) ^ entry_count
|
||
base_amount = (self.adjust_multiplier.value * initial_stake) ** entry_count
|
||
|
||
# 应用递减系数（后续加仓金额逐步缩小）
|
||
# 第1次加仓: 100% × 基础金额
|
||
# 第2次加仓: 75% × 基础金额
|
||
# 第3次加仓: 56% × 基础金额
|
||
decrease_ratio = self.add_position_decrease_ratio.value ** (entry_count - 1)
|
||
adjusted_amount = base_amount * decrease_ratio
|
||
|
||
# 安全校验
|
||
current_stake = float(trade.stake_amount)
|
||
remaining_capacity = max_stake - current_stake
|
||
|
||
# 加仓金额不能超过剩余容量的80%（留余量）
|
||
adjusted_amount = min(adjusted_amount, remaining_capacity * 0.8)
|
||
adjusted_amount = max(min_stake, min(adjusted_amount, max_stake - current_stake))
|
||
|
||
return adjusted_amount
|
||
|
||
except Exception as e:
|
||
logger.error(f"[{trade.pair}] 加仓金额计算出错: {str(e)}")
|
||
return 0.0
|
||
|
||
def adjust_trade_position(self, trade: 'Trade', current_time, current_rate: float,
|
||
current_profit: float, min_stake: float, max_stake: float, **kwargs) -> float:
|
||
"""
|
||
增强版持仓调整逻辑：加仓精准度 + 递减策略 + 减仓优化
|
||
"""
|
||
pair = trade.pair
|
||
|
||
# ========================== 分级止盈减仓逻辑（增强版） ==========================
|
||
if current_profit > 0:
|
||
reduce_count = len(trade.select_filled_orders(trade.exit_side))
|
||
if reduce_count >= self.max_reduce_adjustments.value:
|
||
return 0.0
|
||
|
||
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:
|
||
return 0.0
|
||
|
||
initial_stake = float(trade.orders[0].cost)
|
||
current_stake = float(trade.stake_amount)
|
||
|
||
# 分级止盈逻辑（3级）
|
||
# 第1级：达到exit_profit_tier1时，减仓exit_reduce_tier1比例
|
||
if current_profit >= self.exit_profit_tier1.value:
|
||
if reduce_count < 1:
|
||
reduce_amount = current_stake * self.exit_reduce_tier1.value
|
||
reduce_amount = -min(reduce_amount, current_stake * 0.5) # 单次最多减仓50%
|
||
|
||
#logger.info(f"[{pair}] 分级止盈第1级: 盈利{current_profit:.2%}, "
|
||
# f"减仓比例{self.exit_reduce_tier1.value:.1%}, 金额{abs(reduce_amount):.2f}")
|
||
trade.set_custom_data("last_reduce_kline", current_kline_time)
|
||
return max(-current_stake, reduce_amount)
|
||
|
||
# 第2级：达到exit_profit_tier2时，减仓exit_reduce_tier2比例
|
||
if current_profit >= self.exit_profit_tier2.value:
|
||
if reduce_count < 2:
|
||
reduce_amount = current_stake * self.exit_reduce_tier2.value
|
||
reduce_amount = -min(reduce_amount, current_stake * 0.3) # 单次最多减仓30%
|
||
|
||
#logger.info(f"[{pair}] 分级止盈第2级: 盈利{current_profit:.2%}, "
|
||
# f"减仓比例{self.exit_reduce_tier2.value:.1%}, 金额{abs(reduce_amount):.2f}")
|
||
trade.set_custom_data("last_reduce_kline", current_kline_time)
|
||
return max(-current_stake, reduce_amount)
|
||
|
||
# 基础止盈（保持原有逻辑）
|
||
if current_profit >= self.reduce_profit_base.value:
|
||
reduce_amount = (float(self.reduce_coefficient.value) * initial_stake) ** (reduce_count + 1)
|
||
reduce_amount = min(reduce_amount, current_stake * 0.2) # 单次最多减仓20%
|
||
reduce_amount = -reduce_amount
|
||
reduce_amount = max(-current_stake, min(reduce_amount, -float(min_stake)))
|
||
|
||
#logger.info(f"[{pair}] 基础止盈: 盈利{current_profit:.2%}, 第{reduce_count+1}次, "
|
||
# f"金额{abs(reduce_amount):.2f}")
|
||
trade.set_custom_data("last_reduce_kline", current_kline_time)
|
||
return reduce_amount
|
||
|
||
return 0.0
|
||
|
||
# ========================== 增强版加仓逻辑 ==========================
|
||
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
|
||
|
||
# 获取数据框
|
||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||
if dataframe is None or len(dataframe) < 30:
|
||
return 0.0
|
||
|
||
# 检查加仓条件（多维度评分）
|
||
condition_check = self._check_add_position_conditions(pair, current_rate, current_profit, entry_count, initial_price, dataframe)
|
||
|
||
if not condition_check['should_add']:
|
||
return 0.0
|
||
|
||
# 周期限制：每个timeframe仅加仓一次
|
||
current_kline_time = dataframe.iloc[-1]['date'].strftime('%Y-%m-%d %H:%M:%S')
|
||
last_add_kline = trade.get_custom_data("last_add_kline")
|
||
if last_add_kline == current_kline_time:
|
||
return 0.0
|
||
|
||
# 计算加仓金额
|
||
additional_stake = self._calculate_add_position_amount(trade, entry_count, min_stake, max_stake)
|
||
|
||
if additional_stake > 0:
|
||
#logger.info(f"[{pair}] 加仓触发: 第{entry_count+1}次, 金额{additional_stake:.2f}, 评分{condition_check['score']:.2f}")
|
||
trade.set_custom_data("last_add_kline", current_kline_time)
|
||
return additional_stake
|
||
|
||
return 0.0
|
||
|
||
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（盈利越高，止损范围越大）
|
||
if current_profit > 0.05: # 利润超过5%时
|
||
return -3.0 * atr / current_rate # 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 # 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 # 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
|
||
|
||
# 基础止损
|
||
if atr > 0:
|
||
return -1.2 * atr / current_rate # 基础1.2倍ATR止损
|
||
return self.stoploss
|
||
|