719 lines
35 KiB
Python
719 lines
35 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
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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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logger = logging.getLogger(__name__)
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class FreqaiPrimer(IStrategy):
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# 策略参数 - 调整以提高单笔盈利潜力
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minimal_roi = {
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"0": 0.05, # 5% ROI (10 分钟内)
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"60": 0.03, # 3% ROI (1 小时)
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"180": 0.01, # 1% ROI (3 小时)
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"360": 0.005 # 0.5% ROI (6 小时)
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}
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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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# 自定义指标参数
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bb_length = 20
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bb_std = 2.0
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rsi_length = 14
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rsi_overbought = 58 # 超买阈值
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rsi_oversold = 42 # 放宽超卖阈值，增加入场信号
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# 剧烈拉升检测参数
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H1_MAX_CANDLES = 200 # 检查最近200根1h K线
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H1_RAPID_RISE_THRESHOLD = 0.05 # 5%的抬升阈值
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H1_MAX_CONSECUTIVE_CANDLES = 3 # 最多连续3根K线内检查
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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 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_3m = ta.bbands(dataframe['close'], length=self.bb_length, std=self.bb_std)
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dataframe['bb_lower_3m'] = bb_3m[f'BBL_{self.bb_length}_{self.bb_std}']
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dataframe['bb_upper_3m'] = bb_3m[f'BBU_{self.bb_length}_{self.bb_std}']
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dataframe['rsi_3m'] = ta.rsi(dataframe['close'], length=self.rsi_length)
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# 新增 StochRSI 指标
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stochrsi_3m = ta.stochrsi(dataframe['close'], length=14, rsi_length=14)
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dataframe['stochrsi_k_3m'] = stochrsi_3m['STOCHRSIk_14_14_3_3']
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dataframe['stochrsi_d_3m'] = stochrsi_3m['STOCHRSId_14_14_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=self.rsi_length)
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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=14, rsi_length=14)
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df_15m['stochrsi_k_15m'] = stochrsi_15m['STOCHRSIk_14_14_3_3']
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df_15m['stochrsi_d_15m'] = stochrsi_15m['STOCHRSId_14_14_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']].fillna(method='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=self.bb_length, std=self.bb_std)
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df_1h['bb_lower_1h'] = bb_1h[f'BBL_{self.bb_length}_{self.bb_std}']
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df_1h['bb_upper_1h'] = bb_1h[f'BBU_{self.bb_length}_{self.bb_std}']
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# 计算 1h RSI 和 EMA
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df_1h['rsi_1h'] = ta.rsi(df_1h['close'], length=self.rsi_length)
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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=14, rsi_length=14)
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df_1h['stochrsi_k_1h'] = stochrsi_1h['STOCHRSIk_14_14_3_3']
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df_1h['stochrsi_d_1h'] = stochrsi_1h['STOCHRSId_14_14_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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# 新增 StochRSI 指标
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stochrsi_1h = ta.stochrsi(df_1h['close'], length=14, rsi_length=14)
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df_1h['stochrsi_k_1h'] = stochrsi_1h['STOCHRSIk_14_14_3_3']
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df_1h['stochrsi_d_1h'] = stochrsi_1h['STOCHRSId_14_14_3_3']
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# 将 1h 数据重新索引到主时间框架 (3m)
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df_1h = df_1h.set_index('date').reindex(dataframe['date']).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').fillna(method='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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# 记录当前的市场状态
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if len(dataframe) > 0:
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current_score = dataframe['market_score'].iloc[-1]
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current_state = dataframe['market_state'].iloc[-1]
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logger.info(f"[{metadata['pair']}] 熊牛得分: {current_score:.1f}, 市场状态: {current_state}")
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logger.info(f"[{metadata['pair']}] 各时间框架趋势: 3m={'牛' if dataframe['trend_3m'].iloc[-1] == 1 else '熊'}, \
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15m={'牛' if dataframe['trend_15m'].iloc[-1] == 1 else '熊'}, \
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1h={'牛' if dataframe['trend_1h_ema'].iloc[-1] == 1 else '熊'}")
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# 调试：打印指标值（最后 5 行），验证时间对齐
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print(f"Pair: {metadata['pair']}, Last 5 rows after reindexing:")
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print(dataframe[['date', 'close', 'bb_lower_3m', 'rsi_3m', 'rsi_15m', 'rsi_1h', 'trend_1h',
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'trend_3m', 'trend_15m', 'trend_1h_ema', 'market_score', 'market_state',
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'bullish_engulfing', 'volume', 'volume_ma']].tail(5))
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# 打印最终数据框的列名以验证
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logger.info(f"[{metadata['pair']}] 最终数据框列名: {list(dataframe.columns)}")
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return dataframe
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# 入场信号主要依据1小时周期
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# 条件1: 价格接近布林带下轨
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close_to_bb_lower_1h = (dataframe['close'] <= dataframe['bb_lower_1h'] * 1.02)
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# 条件2: RSI 不高于阈值
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rsi_condition_1h = dataframe['rsi_1h'] < self.rsi_oversold
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# 条件3: StochRSI 处于超卖区域
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stochrsi_condition_1h = (dataframe['stochrsi_k_1h'] < 20) & (dataframe['stochrsi_d_1h'] < 20)
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# 条件4: MACD 上升趋势
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macd_condition_1h = dataframe['macd_1h'] > dataframe['macd_signal_1h']
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# 辅助条件: 3m 和 15m 趋势确认
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trend_confirmation = (dataframe['trend_3m'] == 1) & (dataframe['trend_15m'] == 1)
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# 合并所有条件
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final_condition = close_to_bb_lower_1h & rsi_condition_1h & stochrsi_condition_1h & macd_condition_1h & trend_confirmation
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# 设置入场信号
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dataframe.loc[final_condition, 'enter_long'] = 1
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# 日志记录
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if dataframe['enter_long'].sum() > 0:
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logger.info(f"[{metadata['pair']}] 发现入场信号数量: {dataframe['enter_long'].sum()}")
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return dataframe
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# 做多条件（放宽以增加入场信号）
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# 1. 价格接近3m布林带下轨（放宽到5%偏差）
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close_to_bb_lower = (dataframe['close'] <= dataframe['bb_lower_3m'] * 1.05)
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# 2. 至少一个时间框架RSI处于超卖区域
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rsi_oversold = (dataframe['rsi_3m'] < self.rsi_oversold) | \
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(dataframe['rsi_15m'] < self.rsi_oversold)
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# 3. 1小时趋势向上或横盘
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trend_1h = dataframe['trend_1h'] | (dataframe['market_state'] == 'neutral')
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# 4. 成交量至少达到平均水平的80%
|
||
volume_condition = dataframe['volume'] > dataframe['volume_ma'] * 0.8
|
||
|
||
# 5. 看涨吞没形态或RSI接近超卖
|
||
special_condition = dataframe['bullish_engulfing'] | (dataframe['rsi_3m'] < self.rsi_oversold + 3)
|
||
|
||
# 检查剧烈拉升情况 - 如果检测到剧烈拉升，则不产生入场信号
|
||
pair = metadata['pair']
|
||
is_unstable_region, _ = self.detect_h1_rapid_rise(pair, dataframe, metadata)
|
||
|
||
# 熊牛得分检查：当得分低于55时禁止入场（放宽门槛）
|
||
market_score_condition = dataframe['market_score'] >= 55
|
||
|
||
# 合并所有条件，并且确保不在不稳固区域且市场状态良好
|
||
final_condition = close_to_bb_lower & rsi_oversold & trend_1h & volume_condition & special_condition & (~is_unstable_region) & market_score_condition
|
||
|
||
# 所有原始条件（不包括剧烈拉升检查和熊牛得分检查）
|
||
original_conditions = close_to_bb_lower & rsi_oversold & trend_1h & volume_condition & special_condition & (~is_unstable_region)
|
||
|
||
dataframe.loc[final_condition, 'enter_long'] = 1
|
||
|
||
# 记录入场信号被阻止的情况
|
||
prevented_entries = dataframe[(~final_condition) & original_conditions]
|
||
|
||
# 统计因熊牛得分不足而被阻止的信号
|
||
low_score_prevented = dataframe[(~market_score_condition) & original_conditions]
|
||
if len(low_score_prevented) > 0:
|
||
logger.info(f"[{pair}] 由于熊牛得分低于60，阻止了 {len(low_score_prevented)} 个潜在的入场信号")
|
||
|
||
if len(prevented_entries) > 0:
|
||
logger.info(f"[{pair}] 总共阻止了 {len(prevented_entries)} 个潜在的入场信号")
|
||
|
||
# 调试：检查每个条件的触发情况（简化输出）
|
||
if dataframe['enter_long'].sum() > 0:
|
||
logger.info(f"[{pair}] 发现入场信号数量: {dataframe['enter_long'].sum()}")
|
||
|
||
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
|
||
breakout_condition = dataframe['close'] >= dataframe['bb_upper_1h']
|
||
|
||
# 条件2: 成交量显著放大
|
||
volume_spike = dataframe['volume'] > dataframe['volume_ma'] * 2
|
||
|
||
# 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
|
||
rsi_overbought = dataframe['rsi_1h'] > self.rsi_overbought
|
||
|
||
# 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
|
||
|
||
# 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:
|
||
# 入场信号主要依据1小时周期
|
||
# 条件1: 价格接近布林带下轨
|
||
close_to_bb_lower_1h = (dataframe['close'] <= dataframe['bb_lower_1h'] * 1.02)
|
||
|
||
# 条件2: RSI 不高于阈值（放宽阈值）
|
||
rsi_condition_1h = dataframe['rsi_1h'] < 50 # 放宽 RSI 阈值
|
||
|
||
# 条件3: StochRSI 处于超卖区域（放宽阈值）
|
||
stochrsi_condition_1h = (dataframe['stochrsi_k_1h'] < 30) & (dataframe['stochrsi_d_1h'] < 30) # 放宽 StochRSI 阈值
|
||
|
||
# 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
|
||
volume_spike = dataframe['volume'] > dataframe['volume_ma'] * 1.5
|
||
|
||
# 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 > 0.02 # 布林带宽度大于2%
|
||
|
||
# 辅助条件: 3m 和 15m 趋势确认
|
||
trend_confirmation = (dataframe['trend_3m'] == 1) & (dataframe['trend_15m'] == 1)
|
||
|
||
# 合并所有条件（增加成交量和布林带宽度过滤）
|
||
final_condition = (
|
||
close_to_bb_lower_1h &
|
||
(rsi_condition_1h | stochrsi_condition_1h) & # 允许 RSI 或 StochRSI 满足其一
|
||
macd_condition_1h &
|
||
volume_spike & # 成交量显著放大
|
||
bb_width_condition & # 布林带宽度过滤
|
||
trend_confirmation
|
||
)
|
||
|
||
# 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" - 趋势确认: {trend_confirmation.sum()} 次")
|
||
logger.info(f" - 最终条件: {final_condition.sum()} 次")
|
||
|
||
# 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
|
||
# 缓存数据框以便在trailing_stop_positive属性中使用
|
||
self._dataframe_cache = dataframe
|
||
|
||
# 基础退出条件
|
||
basic_exit = (
|
||
(dataframe['close'] >= dataframe['bb_upper_3m']) |
|
||
(dataframe['rsi_3m'] > self.rsi_overbought)
|
||
)
|
||
|
||
# 强劲趋势条件：1h趋势向上 + 熊牛得分>70
|
||
strong_trend = (dataframe['trend_1h_ema'] == 1) & (dataframe['market_score'] > 70)
|
||
|
||
# 一般趋势条件：熊牛得分50-70
|
||
normal_trend = (dataframe['market_score'] >= 50) & (dataframe['market_score'] <= 70)
|
||
|
||
# 极端强劲趋势条件：熊牛得分>95
|
||
extreme_strong_trend = (dataframe['market_score'] > 95)
|
||
|
||
# 获取15m数据进行趋势确认
|
||
df_15m = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='15m')
|
||
|
||
# 确保df_15m中包含rsi_15m列，如果没有则计算
|
||
try:
|
||
import pandas_ta as ta
|
||
if 'rsi_15m' not in df_15m.columns:
|
||
# 使用pandas_ta计算14期RSI
|
||
rsi_series = ta.rsi(df_15m['close'], length=14)
|
||
df_15m['rsi_15m'] = rsi_series
|
||
except Exception as e:
|
||
# 如果计算失败，创建一个默认值列
|
||
logger.warning(f"[{metadata['pair']}] 计算15m RSI时出错: {e}")
|
||
df_15m['rsi_15m'] = 50 # 设置默认值为中性
|
||
|
||
# 重命名date列并保留必要的列
|
||
df_15m = df_15m[['date', 'rsi_15m']].rename(columns={'date': 'date_15m'})
|
||
|
||
# 合并数据
|
||
merged_data = dataframe.copy()
|
||
# 创建合并列
|
||
merged_data['rsi_15m'] = 50 # 设置默认值
|
||
|
||
# 尝试合并数据
|
||
try:
|
||
# 使用更简单的方式：直接将df_15m的rsi_15m列映射到dataframe
|
||
# 创建一个日期到rsi的映射字典
|
||
date_rsi_map = df_15m.set_index('date_15m')['rsi_15m'].to_dict()
|
||
# 使用映射填充rsi_15m列
|
||
merged_data['rsi_15m'] = merged_data['date'].map(date_rsi_map).ffill().bfill()
|
||
except Exception as e:
|
||
logger.warning(f"[{metadata['pair']}] 合并15m数据时出错: {e}")
|
||
|
||
# 趋势反转信号：15m RSI超买
|
||
trend_reversal = merged_data['rsi_15m'] > 75
|
||
|
||
# 动态调整退出条件 - 提高ATR倍数以增加单笔盈利潜力
|
||
# 强劲趋势中：大幅提高ATR倍数到5.5倍，充分利用趋势利润
|
||
# 但在极端强劲趋势(market_score>95)下不给出退出信号
|
||
dataframe.loc[strong_trend & ~extreme_strong_trend & ((dataframe['close'] > dataframe['open'] + dataframe['atr'] * 5.5) | (basic_exit & trend_reversal)), 'exit_long'] = 1
|
||
|
||
# 一般趋势中：提高到4倍ATR退出
|
||
# 但在极端强劲趋势(market_score>95)下不给出退出信号
|
||
dataframe.loc[normal_trend & ~extreme_strong_trend & ((dataframe['close'] > dataframe['open'] + dataframe['atr'] * 4) | basic_exit), 'exit_long'] = 1
|
||
|
||
# 非趋势或弱势：保持2倍ATR退出
|
||
# 但在极端强劲趋势(market_score>95)下不给出退出信号
|
||
|
||
# 记录退出决策日志
|
||
if len(dataframe[dataframe['exit_long'] == 1]) > 0:
|
||
last_exit = dataframe[dataframe['exit_long'] == 1].iloc[-1]
|
||
current_state = dataframe['market_state'].iloc[-1]
|
||
logger.info(f"[{metadata['pair']}] 触发退出信号，市场状态: {current_state}, 价格: {last_exit['close']:.2f}")
|
||
|
||
# 记录因极端强劲趋势阻止的退出信号
|
||
extreme_strong_prevented = dataframe[extreme_strong_trend & ((strong_trend & ((dataframe['close'] > dataframe['open'] + dataframe['atr'] * 5.5) | (basic_exit & trend_reversal))) | (normal_trend & ((dataframe['close'] > dataframe['open'] + dataframe['atr'] * 4) | basic_exit)))]
|
||
if len(extreme_strong_prevented) > 0:
|
||
logger.info(f"[{metadata['pair']}] 由于极端强劲趋势(market_score>95)，阻止了 {len(extreme_strong_prevented)} 个潜在的退出信号")
|
||
|
||
return dataframe
|
||
|
||
def detect_h1_rapid_rise(self, pair: str, dataframe: DataFrame, metadata: dict) -> tuple[bool, float]:
|
||
"""
|
||
检测1小时K线图上的剧烈拉升情况
|
||
参数:
|
||
- pair: 交易对
|
||
- dataframe: 数据框
|
||
- metadata: 元数据
|
||
返回:
|
||
- tuple: (是否处于不稳固区域, 当前价格在最近价格范围中的百分比)
|
||
"""
|
||
try:
|
||
# 获取1小时K线数据
|
||
df_1h = self.dp.get_pair_dataframe(pair=pair, timeframe='1h')
|
||
|
||
# 确保有足够的K线数据
|
||
if len(df_1h) < self.H1_MAX_CANDLES:
|
||
logger.warning(f"[{pair}] 1h K线数据不足 {self.H1_MAX_CANDLES} 根，当前只有 {len(df_1h)} 根，无法完整检测剧烈拉升")
|
||
return False, 0.0
|
||
|
||
# 获取最近的K线
|
||
recent_data = df_1h.iloc[-self.H1_MAX_CANDLES:].copy()
|
||
current_price = recent_data['close'].iloc[-1]
|
||
|
||
# 检查连续最多3根K线内的最大涨幅
|
||
rapid_rise_detected = False
|
||
max_rise = 0
|
||
|
||
for i in range(len(recent_data) - self.H1_MAX_CONSECUTIVE_CANDLES + 1):
|
||
window_data = recent_data.iloc[i:i + self.H1_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 >= self.H1_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 {self.H1_MAX_CONSECUTIVE_CANDLES} 根K线内")
|
||
break
|
||
|
||
# 计算当前价格在最近价格范围中的位置百分比
|
||
recent_low = recent_data['low'].min()
|
||
recent_high = recent_data['high'].max()
|
||
|
||
if recent_high > recent_low:
|
||
price_position_pct = (current_price - recent_low) / (recent_high - recent_low) * 100
|
||
else:
|
||
price_position_pct = 50.0 # 默认中间位置
|
||
|
||
# 判断是否处于不稳固区域
|
||
is_unstable_region = rapid_rise_detected
|
||
|
||
logger.info(f"[{pair}] 剧烈拉升检测结果: {'不稳固' if is_unstable_region else '稳固'}")
|
||
logger.info(f"[{pair}] 当前价格位置: {current_price:.2f} ({price_position_pct:.1f}%), 最近最大涨幅: {max_rise:.2%}")
|
||
|
||
return is_unstable_region, price_position_pct
|
||
|
||
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, 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'
|
||
|
||
# 更激进的渐进式止损策略
|
||
if current_profit > 0.04: # 利润超过4%时
|
||
return -2.0 * atr / current_rate # 大幅扩大止损范围，让利润奔跑
|
||
elif current_profit > 0.025: # 利润超过2.5%时
|
||
return -1.7 * atr / current_rate # 中等扩大止损范围
|
||
elif current_profit > 0.01: # 利润超过1%时
|
||
return -1.3 * 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
|
||
|
||
# 动态调整止损范围
|
||
if current_profit > 0.05: # 利润超过5%时
|
||
return -2.5 * 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 -2.0 * atr / current_rate # 中等扩大止损范围
|
||
elif current_profit > 0.01: # 利润超过1%时
|
||
return -1.5 * 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 -1.8 * atr / current_rate
|
||
|
||
if atr > 0:
|
||
return -1.2 * atr / current_rate # 基础1.2倍ATR止损
|
||
return self.stoploss
|
||
|
||
def custom_exit(self, pair: str, trade: 'Trade', current_time, current_rate: float,
|
||
current_profit: float, **kwargs) -> float:
|
||
"""渐进式止盈逻辑"""
|
||
|
||
# 获取当前市场状态
|
||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||
current_state = dataframe['market_state'].iloc[-1] if 'market_state' in dataframe.columns else 'unknown'
|
||
|
||
# 定义更激进的渐进式止盈水平，提高收益上限
|
||
profit_levels = {
|
||
# 状态: [(止盈触发利润, 止盈比例)]
|
||
'strong_bull': [(0.03, 0.2), (0.06, 0.35), (0.09, 0.5), (0.12, 0.65), (0.15, 0.8)], # 强劲牛市的渐进止盈，提高目标
|
||
'weak_bull': [(0.02, 0.25), (0.04, 0.45), (0.07, 0.7), (0.10, 0.9)], # 弱牛市的渐进止盈
|
||
'neutral': [(0.015, 0.35), (0.03, 0.6), (0.05, 0.85)], # 中性市场的渐进止盈
|
||
'bear': [(0.01, 0.6), (0.02, 0.9), (0.03, 1.0)] # 熊市的渐进止盈（更保守）
|
||
}
|
||
|
||
# 默认使用中性市场的止盈设置
|
||
levels = profit_levels.get(current_state, profit_levels['neutral'])
|
||
|
||
# 在强劲牛市中，进一步放宽止盈目标
|
||
if current_state == 'strong_bull':
|
||
levels = [(p + 0.01, r) for p, r in levels] # 将止盈触发利润提高1%
|
||
|
||
# 确定当前应该止盈的比例
|
||
exit_ratio = 0.0
|
||
for profit_target, ratio in levels:
|
||
if current_profit >= profit_target:
|
||
exit_ratio = ratio
|
||
else:
|
||
break
|
||
|
||
# 记录渐进式止盈决策
|
||
if exit_ratio > 0:
|
||
logger.info(f"[{pair}] 渐进式止盈: 当前利润 {current_profit:.2%}, 市场状态 {current_state}, 止盈比例 {exit_ratio:.0%}")
|
||
|
||
# 返回应退出的比例（0.0表示不退出，1.0表示全部退出）
|
||
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0.047（4.7%回调）
|
||
- 加仓额度为: (stake_amount / 2) ^ (加仓次数 - 1)
|
||
"""
|
||
# 检查是否已启用加仓
|
||
if not hasattr(self, 'max_entry_adjustments'):
|
||
self.max_entry_adjustments = 3 # 设置最大加仓次数
|
||
|
||
# 获取当前交易对
|
||
pair = trade.pair
|
||
|
||
# 获取当前交易的加仓次数
|
||
# 初始交易算第1次，加仓次数=total_entry_position - 1
|
||
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:
|
||
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
|
||
price_diff_pct = (current_rate - initial_price) / initial_price
|
||
|
||
# 检查价格回调是否达到加仓间隔（0.047）
|
||
# 价格回调表示价格比初始价格低4.7%或更多
|
||
# 同时考虑市场状态，只在市场不是弱势时加仓
|
||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||
current_state = dataframe['market_state'].iloc[-1] if 'market_state' in dataframe.columns else 'neutral'
|
||
|
||
if price_diff_pct <= -0.047 and current_state not in ['bear', 'weak_bear']:
|
||
# 计算初始入场金额
|
||
initial_stake = trade.orders[0].cost # 第一笔订单的成本
|
||
|
||
# 计算加仓次数（从1开始计数）
|
||
adjustment_count = entry_count - 1 # 已加仓次数
|
||
|
||
# 计算加仓额度: (stake_amount / 2) ^ (加仓次数)
|
||
# 对于第一次加仓，公式为 (initial_stake / 2) ^ 1 = initial_stake / 2
|
||
# 第二次加仓，公式为 (initial_stake / 2) ^ 2
|
||
# 第三次加仓，公式为 (initial_stake / 2) ^ 3
|
||
|
||
# 计算加仓金额
|
||
additional_stake = (initial_stake / 2) ** (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))
|
||
|
||
logger.info(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
|