修改了线段中枢逻辑
This commit is contained in:
jackyu66git
@@ -9,68 +9,44 @@ from typing import Optional
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from freqtrade.persistence import Trade
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import warnings
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# 抑制 pandas FutureWarning 关于 fillna 的隐式降级警告
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# 这个警告来自 freqtrade 库的 strategy_helper.py
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warnings.filterwarnings('ignore', category=FutureWarning, message='.*Downcasting object dtype arrays.*')
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# 或者启用未来行为（推荐）
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pd.set_option('future.no_silent_downcasting', True)
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# freqtrade trade -c ./user_data/Chan/config/Local_Test.json --strategy CryptoFutures1m5mStrategy --strategy-path ./user_data/Chan/strategies
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# freqtrade backtesting -c ./user_data/Chan/config/Local_Test.json --strategy CryptoFutures1m5mStrategy --strategy-path ./user_data/Chan/strategies --timerange=20260304-
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# freqtrade download-data -c ./user_data/Chan/config/Local_Test.json -t 1m 5m --data-format-ohlcv json --pairs SOL/USDT:USDT --timerange=20260201-
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class CryptoFutures1m5mStrategy(IStrategy):
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"""
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SOL/USDT 合约策略 - 1分钟+5分钟双时间框架 V12e (Short Only)
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SOL/USDT 合约策略 - 只做多版 (默认策略)
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14个月回测 (2025-01 ~ 2026-03): +107.11%, PF 1.37, DD 23.96%
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每个季度均盈利，市场下跌-54%期间持续获利
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核心设计：
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1. 纯做空策略 - 价格必须低于EMA200至少1%才允许做空
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2. 5分钟趋势确认：EMA12<EMA26<EMA50 + ADX 25-50 + RSI 30-48
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3. ATR自适应波动率过滤：ATR < 长期均值 * 1.5（避免极端波动）
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4. 1分钟精确入场：顶背离 / EMA死叉 / 熊市回调
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5. 双重MACD确认（5分钟+1分钟MACD柱状图均为负）
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6. trailing_stop_positive_offset = 0.030
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7. 时间止损：持仓过久且亏损时提前退出
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基于V5修改：禁用做空，只做多
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"""
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INTERFACE_VERSION = 3
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timeframe = '1m'
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informative_timeframe = '5m'
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can_short = True
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can_short = False # 禁用做空
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can_long = True
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lev = 1.0
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# 止损止盈
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stoploss = -0.025 # 2.5% 硬止损
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stoploss = -0.035
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trailing_stop = True
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trailing_stop_positive = 0.008
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trailing_stop_positive_offset = 0.030
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trailing_stop_positive_offset = 0.035
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trailing_only_offset_is_reached = True
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# 不使用custom_stoploss（会干扰trailing_stop）
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use_custom_stoploss = False
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# 完全禁用 exit_signal
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use_exit_signal = False
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process_only_new_candles = True
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startup_candle_count: int = 1100
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def informative_pairs(self):
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return [
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("SOL/USDT:USDT", "5m"),
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]
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return [("SOL/USDT:USDT", "5m")]
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# ==================== 5分钟指标 ====================
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inf_tf = self.informative_timeframe
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informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
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# EMA趋势
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# EMA
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informative['ema12'] = ta.EMA(informative['close'], timeperiod=12)
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informative['ema26'] = ta.EMA(informative['close'], timeperiod=26)
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informative['ema50'] = ta.EMA(informative['close'], timeperiod=50)
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# EMA12斜率（3根K线变化率，用于确认趋势方向的动量）
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informative['ema12_slope'] = (informative['ema12'] - informative['ema12'].shift(3)) / informative['ema12'].shift(3) * 100
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# MACD
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@@ -79,66 +55,54 @@ class CryptoFutures1m5mStrategy(IStrategy):
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informative['macd_signal_5m'] = macd_signal
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informative['macd_hist_5m'] = macd_hist
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# ADX趋势强度
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# ADX
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informative['adx_5m'] = ta.ADX(informative['high'], informative['low'], informative['close'], timeperiod=14)
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# RSI（5分钟）
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# RSI
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informative['rsi_5m'] = ta.RSI(informative['close'], timeperiod=14)
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# ATR（5分钟）
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# ATR
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informative['atr_5m'] = ta.ATR(informative['high'], informative['low'], informative['close'], timeperiod=14)
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informative['atr_pct_5m'] = informative['atr_5m'] / informative['close'] * 100
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# ATR 长期均值（用于自适应波动率过滤）
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informative['atr_pct_ma_5m'] = informative['atr_pct_5m'].rolling(window=100).mean()
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# ===== EMA200 大趋势过滤 =====
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# EMA200
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informative['ema200'] = ta.EMA(informative['close'], timeperiod=200)
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informative['ema200_dist_pct'] = (informative['close'] - informative['ema200']) / informative['ema200'] * 100
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# EMA200斜率（20根5分钟K线 = 100分钟趋势方向）
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informative['ema200_slope'] = (informative['ema200'] - informative['ema200'].shift(20)) / informative['ema200'].shift(20) * 100
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# ===== 大趋势过滤（Short Only） =====
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# 做空需要价格低于EMA200至少1%
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informative['below_ema200'] = informative['ema200_dist_pct'] < -1.0
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# 牛市暂停：EMA200上升 + 价格在EMA200上方 → 完全停止做空
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informative['bull_pause'] = (
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(informative['ema200_slope'] > 0) &
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(informative['ema200_dist_pct'] > 0)
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# 做多趋势
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informative['trend_bull_5m'] = (
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(informative['ema12'] > informative['ema26']) &
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(informative['ema26'] > informative['ema50']) &
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(informative['ema12_slope'] > 0.05) &
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(informative['adx_5m'] > 24) &
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(informative['adx_5m'] < 51) &
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(informative['close'] > informative['ema12']) &
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(informative['rsi_5m'] > 52) &
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(informative['rsi_5m'] < 72)
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)
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# ===== 5分钟趋势判断（仅Short） =====
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informative['trend_bear_5m'] = (
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(informative['ema12'] < informative['ema26']) &
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(informative['ema26'] < informative['ema50']) &
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(informative['ema12_slope'] < 0) &
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(informative['adx_5m'] > 25) &
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(informative['adx_5m'] < 50) &
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(informative['close'] < informative['ema12']) &
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(informative['rsi_5m'] < 48) &
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(informative['rsi_5m'] > 30)
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)
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# 大趋势过滤
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informative['above_ema200'] = informative['ema200_dist_pct'] > 1.0
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# 做空条件：短期趋势 + EMA200大趋势方向一致 + 非牛市
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informative['can_long_5m'] = False
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informative['can_short_5m'] = (
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informative['trend_bear_5m'] &
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informative['below_ema200'] &
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(~informative['bull_pause'])
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)
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# 做多条件
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informative['can_long_5m'] = informative['trend_bull_5m'] & informative['above_ema200']
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# ATR波动率过滤（自适应）
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# ATR过滤
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informative['atr_ok_5m'] = (
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(informative['atr_pct_5m'] > 0.1) &
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(informative['atr_pct_5m'] < informative['atr_pct_ma_5m'] * 1.5)
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(informative['atr_pct_5m'] > 0.07) &
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(informative['atr_pct_5m'] < informative['atr_pct_ma_5m'] * 2.2)
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)
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# 合并5分钟数据到1分钟
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# 成交量
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informative['volume_ma_5m'] = ta.SMA(informative['volume'], timeperiod=20)
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informative['volume_ok_5m'] = informative['volume'] > informative['volume_ma_5m'] * 0.75
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# 合并
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dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)
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# ==================== 1分钟指标 ====================
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# 1分钟指标
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macd_1m, signal_1m, hist_1m = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
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dataframe['macd'] = macd_1m
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dataframe['macd_signal'] = signal_1m
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@@ -148,116 +112,96 @@ class CryptoFutures1m5mStrategy(IStrategy):
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dataframe['ema21'] = ta.EMA(dataframe['close'], timeperiod=21)
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dataframe['rsi'] = ta.RSI(dataframe['close'], timeperiod=14)
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dataframe['vol_ma20'] = ta.SMA(dataframe['volume'], timeperiod=20)
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# ===== 1分钟MACD斜率 =====
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dataframe['macd_slope'] = (dataframe['macd'] - dataframe['macd'].shift(3)) / 3
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# ===== 1分钟做空入场信号 =====
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dataframe['price_high_5'] = dataframe['high'].rolling(window=5).max()
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dataframe['macd_high_5'] = dataframe['macd'].rolling(window=5).max()
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# 做多信号
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dataframe['price_low_5'] = dataframe['low'].rolling(window=5).min()
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dataframe['macd_low_5'] = dataframe['macd'].rolling(window=5).min()
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dataframe['top_divergence'] = (
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(dataframe['high'] >= dataframe['price_high_5'] * 0.999) &
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(dataframe['macd'] < dataframe['macd_high_5']) &
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(dataframe['macd_slope'] < 0) &
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(dataframe['macd'] < dataframe['macd_signal']) &
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dataframe['bottom_divergence'] = (
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(dataframe['low'] <= dataframe['price_low_5'] * 1.001) &
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(dataframe['macd'] > dataframe['macd_low_5']) &
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(dataframe['macd_slope'] > 0) &
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(dataframe['macd'] > dataframe['macd_signal']) &
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(dataframe['volume'] > dataframe['vol_ma20'] * 0.6)
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)
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dataframe['ema_cross_down'] = (
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(dataframe['ema9'] < dataframe['ema21']) &
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(dataframe['ema9'].shift(1) >= dataframe['ema21'].shift(1)) &
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(dataframe['rsi'] < 55) & (dataframe['rsi'] > 35) &
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dataframe['ema_cross_up'] = (
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(dataframe['ema9'] > dataframe['ema21']) &
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(dataframe['ema9'].shift(1) <= dataframe['ema21'].shift(1)) &
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(dataframe['rsi'] > 45) &
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(dataframe['rsi'] < 70) &
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(dataframe['volume'] > dataframe['vol_ma20'] * 1.0)
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)
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dataframe['is_bear_candle'] = (
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(dataframe['close'] < dataframe['open']) &
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((dataframe['open'] - dataframe['close']) / dataframe['open'] > 0.008)
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)
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dataframe['bear_pullback'] = (
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dataframe['is_bear_candle'].shift(2) &
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(dataframe['close'].shift(1) > dataframe['open'].shift(1)) &
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(dataframe['high'] < dataframe['high'].shift(2)) &
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(dataframe['close'] < dataframe['open']) &
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(dataframe['close'] < dataframe['ema9'])
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dataframe['is_bull_candle'] = (dataframe['close'] > dataframe['open']) & ((dataframe['close'] - dataframe['open']) / dataframe['open'] > 0.008)
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dataframe['bull_pullback'] = (
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dataframe['is_bull_candle'].shift(2) &
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(dataframe['close'].shift(1) < dataframe['open'].shift(1)) &
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(dataframe['low'] > dataframe['low'].shift(2)) &
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(dataframe['close'] > dataframe['open']) &
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(dataframe['close'] > dataframe['ema9'])
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)
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# ==================== 时间过滤 ====================
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# 时间过滤
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dataframe['hour_utc'] = dataframe['date'].dt.hour
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dataframe['is_bad_hour'] = dataframe['hour_utc'].isin([4, 5, 6, 7])
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# 安全转换5分钟布尔列
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bool_cols = [
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'can_long_5m_5m', 'can_short_5m_5m',
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'trend_bear_5m_5m',
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'atr_ok_5m_5m',
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'below_ema200_5m', 'bull_pause_5m',
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]
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# 类型转换
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bool_cols = ['can_long_5m_5m', 'trend_bull_5m_5m', 'atr_ok_5m_5m', 'above_ema200_5m', 'volume_ok_5m_5m']
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for col in bool_cols:
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if col in dataframe.columns:
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dataframe[col] = dataframe[col].astype(bool).fillna(False)
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num_cols = ['atr_pct_5m_5m', 'rsi_5m_5m', 'macd_hist_5m_5m', 'atr_pct_ma_5m_5m',
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'ema200_dist_pct_5m', 'ema200_slope_5m']
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for col in num_cols:
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if col in dataframe.columns:
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dataframe[col] = dataframe[col].astype(float).fillna(0.0)
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return dataframe
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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time_ok = ~dataframe['is_bad_hour']
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atr_ok = dataframe['atr_ok_5m_5m']
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volume_ok = dataframe['volume_ok_5m_5m']
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# 5分钟MACD方向确认
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macd_bear_5m = dataframe['macd_hist_5m_5m'] < 0
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# 只做多
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macd_bull_5m = dataframe['macd_hist_5m_5m'] > 0
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macd_bull_1m = dataframe['macd_hist'] > 0
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# 1分钟MACD方向确认（双重确认）
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macd_bear_1m = dataframe['macd_hist'] < 0
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# ===== 做空入场 =====
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dataframe.loc[
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(time_ok) &
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(atr_ok) &
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(dataframe['can_short_5m_5m']) &
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(macd_bear_5m) &
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(macd_bear_1m) &
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(dataframe['rsi'] > 30) &
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(
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dataframe['top_divergence'] |
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dataframe['ema_cross_down'] |
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dataframe['bear_pullback']
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) &
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(time_ok) & (atr_ok) & (dataframe['can_long_5m_5m']) &
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(macd_bull_5m) & (macd_bull_1m) & (volume_ok) &
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(dataframe['rsi'] < 70) & (dataframe['rsi'] > 40) &
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(dataframe['bottom_divergence'] | dataframe['ema_cross_up'] | dataframe['bull_pullback']) &
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(dataframe['volume'] > 0),
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'enter_short'
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'enter_long'
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] = 1
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return dataframe
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def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe.loc[:, 'exit_long'] = 0
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dataframe.loc[:, 'exit_short'] = 0
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return dataframe
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def custom_exit(self, pair: str, trade: Trade, current_time: datetime,
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current_rate: float, current_profit: float, **kwargs) -> str | bool | None:
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"""时间止损：持仓过久且亏损时提前退出"""
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trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600
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if trade_duration > 8 and current_profit < -0.005:
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return 'time_stop_8h'
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if trade_duration > 16 and current_profit < 0:
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return 'time_stop_16h'
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# 做多时间止损 - 宽松
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if trade_duration > 10 and current_profit < -0.006:
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return 'time_stop_long_10h'
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if trade_duration > 20 and current_profit < 0:
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return 'time_stop_long_20h'
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if trade_duration > 30:
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return 'time_stop_long_30h'
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return None
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def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
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time_in_force: str, current_time: datetime, entry_tag: Optional[str],
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side: str, **kwargs) -> bool:
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"""入场确认 - 时间过滤安全网"""
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hour_utc = current_time.utcnow().hour if current_time.tzinfo is None else current_time.hour
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if hour_utc in {4, 5, 6, 7}:
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return False
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return True
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def leverage(self, pair: str, current_time: datetime, current_rate: float,
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proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
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**kwargs) -> float:
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return self.lev
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@@ -0,0 +1,209 @@
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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
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from freqtrade.strategy import IStrategy, merge_informative_pair
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from pandas import DataFrame
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import pandas as pd
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import talib.abstract as ta
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import numpy as np
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from datetime import datetime
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from typing import Optional
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from freqtrade.persistence import Trade
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import warnings
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warnings.filterwarnings('ignore', category=FutureWarning, message='.*Downcasting object dtype arrays.*')
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pd.set_option('future.no_silent_downcasting', True)
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class CryptoFutures1m5mStrategyLongOnly(IStrategy):
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"""
|
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SOL/USDT 合约策略 - 只做多版本
|
||||
|
||||
基于V5修改：
|
||||
- 只做多，禁止做空
|
||||
- 优化做多止损和止盈参数
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"""
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INTERFACE_VERSION = 3
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timeframe = '1m'
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informative_timeframe = '5m'
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can_short = False # 禁用做空
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can_long = True
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lev = 1.0
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stoploss = -0.035
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trailing_stop = True
|
||||
trailing_stop_positive = 0.008
|
||||
trailing_stop_positive_offset = 0.035
|
||||
trailing_only_offset_is_reached = True
|
||||
|
||||
use_exit_signal = False
|
||||
process_only_new_candles = True
|
||||
startup_candle_count: int = 1100
|
||||
|
||||
def informative_pairs(self):
|
||||
return [("SOL/USDT:USDT", "5m")]
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
inf_tf = self.informative_timeframe
|
||||
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
|
||||
|
||||
# EMA
|
||||
informative['ema12'] = ta.EMA(informative['close'], timeperiod=12)
|
||||
informative['ema26'] = ta.EMA(informative['close'], timeperiod=26)
|
||||
informative['ema50'] = ta.EMA(informative['close'], timeperiod=50)
|
||||
informative['ema12_slope'] = (informative['ema12'] - informative['ema12'].shift(3)) / informative['ema12'].shift(3) * 100
|
||||
|
||||
# MACD
|
||||
macd, macd_signal, macd_hist = ta.MACD(informative['close'], fastperiod=12, slowperiod=26, signalperiod=9)
|
||||
informative['macd_5m'] = macd
|
||||
informative['macd_signal_5m'] = macd_signal
|
||||
informative['macd_hist_5m'] = macd_hist
|
||||
|
||||
# ADX
|
||||
informative['adx_5m'] = ta.ADX(informative['high'], informative['low'], informative['close'], timeperiod=14)
|
||||
|
||||
# RSI
|
||||
informative['rsi_5m'] = ta.RSI(informative['close'], timeperiod=14)
|
||||
|
||||
# ATR
|
||||
informative['atr_5m'] = ta.ATR(informative['high'], informative['low'], informative['close'], timeperiod=14)
|
||||
informative['atr_pct_5m'] = informative['atr_5m'] / informative['close'] * 100
|
||||
informative['atr_pct_ma_5m'] = informative['atr_pct_5m'].rolling(window=100).mean()
|
||||
|
||||
# EMA200
|
||||
informative['ema200'] = ta.EMA(informative['close'], timeperiod=200)
|
||||
informative['ema200_dist_pct'] = (informative['close'] - informative['ema200']) / informative['ema200'] * 100
|
||||
informative['ema200_slope'] = (informative['ema200'] - informative['ema200'].shift(20)) / informative['ema200'].shift(20) * 100
|
||||
|
||||
# 做多趋势
|
||||
informative['trend_bull_5m'] = (
|
||||
(informative['ema12'] > informative['ema26']) &
|
||||
(informative['ema26'] > informative['ema50']) &
|
||||
(informative['ema12_slope'] > 0.05) &
|
||||
(informative['adx_5m'] > 24) &
|
||||
(informative['adx_5m'] < 51) &
|
||||
(informative['close'] > informative['ema12']) &
|
||||
(informative['rsi_5m'] > 52) &
|
||||
(informative['rsi_5m'] < 72)
|
||||
)
|
||||
|
||||
# 大趋势过滤
|
||||
informative['above_ema200'] = informative['ema200_dist_pct'] > 1.0
|
||||
|
||||
# 做多条件
|
||||
informative['can_long_5m'] = informative['trend_bull_5m'] & informative['above_ema200']
|
||||
|
||||
# ATR过滤
|
||||
informative['atr_ok_5m'] = (
|
||||
(informative['atr_pct_5m'] > 0.07) &
|
||||
(informative['atr_pct_5m'] < informative['atr_pct_ma_5m'] * 2.2)
|
||||
)
|
||||
|
||||
# 成交量
|
||||
informative['volume_ma_5m'] = ta.SMA(informative['volume'], timeperiod=20)
|
||||
informative['volume_ok_5m'] = informative['volume'] > informative['volume_ma_5m'] * 0.75
|
||||
|
||||
# 合并
|
||||
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)
|
||||
|
||||
# 1分钟指标
|
||||
macd_1m, signal_1m, hist_1m = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
|
||||
dataframe['macd'] = macd_1m
|
||||
dataframe['macd_signal'] = signal_1m
|
||||
dataframe['macd_hist'] = hist_1m
|
||||
|
||||
dataframe['ema9'] = ta.EMA(dataframe['close'], timeperiod=9)
|
||||
dataframe['ema21'] = ta.EMA(dataframe['close'], timeperiod=21)
|
||||
dataframe['rsi'] = ta.RSI(dataframe['close'], timeperiod=14)
|
||||
dataframe['vol_ma20'] = ta.SMA(dataframe['volume'], timeperiod=20)
|
||||
dataframe['macd_slope'] = (dataframe['macd'] - dataframe['macd'].shift(3)) / 3
|
||||
|
||||
# 做多信号
|
||||
dataframe['price_low_5'] = dataframe['low'].rolling(window=5).min()
|
||||
dataframe['macd_low_5'] = dataframe['macd'].rolling(window=5).min()
|
||||
|
||||
dataframe['bottom_divergence'] = (
|
||||
(dataframe['low'] <= dataframe['price_low_5'] * 1.001) &
|
||||
(dataframe['macd'] > dataframe['macd_low_5']) &
|
||||
(dataframe['macd_slope'] > 0) &
|
||||
(dataframe['macd'] > dataframe['macd_signal']) &
|
||||
(dataframe['volume'] > dataframe['vol_ma20'] * 0.6)
|
||||
)
|
||||
|
||||
dataframe['ema_cross_up'] = (
|
||||
(dataframe['ema9'] > dataframe['ema21']) &
|
||||
(dataframe['ema9'].shift(1) <= dataframe['ema21'].shift(1)) &
|
||||
(dataframe['rsi'] > 45) &
|
||||
(dataframe['rsi'] < 70) &
|
||||
(dataframe['volume'] > dataframe['vol_ma20'] * 1.0)
|
||||
)
|
||||
|
||||
dataframe['is_bull_candle'] = (dataframe['close'] > dataframe['open']) & ((dataframe['close'] - dataframe['open']) / dataframe['open'] > 0.008)
|
||||
dataframe['bull_pullback'] = (
|
||||
dataframe['is_bull_candle'].shift(2) &
|
||||
(dataframe['close'].shift(1) < dataframe['open'].shift(1)) &
|
||||
(dataframe['low'] > dataframe['low'].shift(2)) &
|
||||
(dataframe['close'] > dataframe['open']) &
|
||||
(dataframe['close'] > dataframe['ema9'])
|
||||
)
|
||||
|
||||
# 时间过滤
|
||||
dataframe['hour_utc'] = dataframe['date'].dt.hour
|
||||
dataframe['is_bad_hour'] = dataframe['hour_utc'].isin([4, 5, 6, 7])
|
||||
|
||||
# 类型转换
|
||||
bool_cols = ['can_long_5m_5m', 'trend_bull_5m_5m', 'atr_ok_5m_5m', 'above_ema200_5m', 'volume_ok_5m_5m']
|
||||
for col in bool_cols:
|
||||
if col in dataframe.columns:
|
||||
dataframe[col] = dataframe[col].astype(bool).fillna(False)
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
time_ok = ~dataframe['is_bad_hour']
|
||||
atr_ok = dataframe['atr_ok_5m_5m']
|
||||
volume_ok = dataframe['volume_ok_5m_5m']
|
||||
|
||||
# 只做多
|
||||
macd_bull_5m = dataframe['macd_hist_5m_5m'] > 0
|
||||
macd_bull_1m = dataframe['macd_hist'] > 0
|
||||
|
||||
dataframe.loc[
|
||||
(time_ok) & (atr_ok) & (dataframe['can_long_5m_5m']) &
|
||||
(macd_bull_5m) & (macd_bull_1m) & (volume_ok) &
|
||||
(dataframe['rsi'] < 70) & (dataframe['rsi'] > 40) &
|
||||
(dataframe['bottom_divergence'] | dataframe['ema_cross_up'] | dataframe['bull_pullback']) &
|
||||
(dataframe['volume'] > 0),
|
||||
'enter_long'
|
||||
] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
dataframe.loc[:, 'exit_long'] = 0
|
||||
return dataframe
|
||||
|
||||
def custom_exit(self, pair: str, trade: Trade, current_time: datetime,
|
||||
current_rate: float, current_profit: float, **kwargs) -> str | bool | None:
|
||||
trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600
|
||||
|
||||
# 做多时间止损 - 宽松
|
||||
if trade_duration > 10 and current_profit < -0.006:
|
||||
return 'time_stop_long_10h'
|
||||
if trade_duration > 20 and current_profit < 0:
|
||||
return 'time_stop_long_20h'
|
||||
if trade_duration > 30:
|
||||
return 'time_stop_long_30h'
|
||||
|
||||
return None
|
||||
|
||||
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
|
||||
time_in_force: str, current_time: datetime, entry_tag: Optional[str],
|
||||
side: str, **kwargs) -> bool:
|
||||
hour_utc = current_time.utcnow().hour if current_time.tzinfo is None else current_time.hour
|
||||
if hour_utc in {4, 5, 6, 7}:
|
||||
return False
|
||||
return True
|
||||
|
||||
def leverage(self, pair: str, current_time: datetime, current_rate: float,
|
||||
proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
|
||||
**kwargs) -> float:
|
||||
return self.lev
|
||||
@@ -0,0 +1,212 @@
|
||||
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
|
||||
from freqtrade.strategy import IStrategy, merge_informative_pair
|
||||
from pandas import DataFrame
|
||||
import pandas as pd
|
||||
import talib.abstract as ta
|
||||
import numpy as np
|
||||
from datetime import datetime
|
||||
from typing import Optional
|
||||
from freqtrade.persistence import Trade
|
||||
import warnings
|
||||
|
||||
warnings.filterwarnings('ignore', category=FutureWarning, message='.*Downcasting object dtype arrays.*')
|
||||
pd.set_option('future.no_silent_downcasting', True)
|
||||
|
||||
|
||||
class CryptoFutures1m5mStrategyShortOnly(IStrategy):
|
||||
"""
|
||||
SOL/USDT 合约策略 - 只做空版本
|
||||
|
||||
基于V5修改：
|
||||
- 只做空，禁止做多
|
||||
- 优化做空止损和止盈参数
|
||||
"""
|
||||
INTERFACE_VERSION = 3
|
||||
timeframe = '1m'
|
||||
informative_timeframe = '5m'
|
||||
can_short = True
|
||||
can_long = False # 禁用做多
|
||||
lev = 1.0
|
||||
|
||||
# Trailing设置 - 基于V5
|
||||
trailing_stop = True
|
||||
trailing_stop_positive = 0.008
|
||||
trailing_stop_positive_offset = 0.035
|
||||
trailing_only_offset_is_reached = True
|
||||
|
||||
use_exit_signal = False
|
||||
process_only_new_candles = True
|
||||
startup_candle_count: int = 1100
|
||||
|
||||
def informative_pairs(self):
|
||||
return [("SOL/USDT:USDT", "5m")]
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
inf_tf = self.informative_timeframe
|
||||
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
|
||||
|
||||
# EMA
|
||||
informative['ema12'] = ta.EMA(informative['close'], timeperiod=12)
|
||||
informative['ema26'] = ta.EMA(informative['close'], timeperiod=26)
|
||||
informative['ema50'] = ta.EMA(informative['close'], timeperiod=50)
|
||||
informative['ema12_slope'] = (informative['ema12'] - informative['ema12'].shift(3)) / informative['ema12'].shift(3) * 100
|
||||
|
||||
# MACD
|
||||
macd, macd_signal, macd_hist = ta.MACD(informative['close'], fastperiod=12, slowperiod=26, signalperiod=9)
|
||||
informative['macd_5m'] = macd
|
||||
informative['macd_signal_5m'] = macd_signal
|
||||
informative['macd_hist_5m'] = macd_hist
|
||||
|
||||
# ADX
|
||||
informative['adx_5m'] = ta.ADX(informative['high'], informative['low'], informative['close'], timeperiod=14)
|
||||
|
||||
# RSI
|
||||
informative['rsi_5m'] = ta.RSI(informative['close'], timeperiod=14)
|
||||
|
||||
# ATR
|
||||
informative['atr_5m'] = ta.ATR(informative['high'], informative['low'], informative['close'], timeperiod=14)
|
||||
informative['atr_pct_5m'] = informative['atr_5m'] / informative['close'] * 100
|
||||
informative['atr_pct_ma_5m'] = informative['atr_pct_5m'].rolling(window=100).mean()
|
||||
|
||||
# EMA200
|
||||
informative['ema200'] = ta.EMA(informative['close'], timeperiod=200)
|
||||
informative['ema200_dist_pct'] = (informative['close'] - informative['ema200']) / informative['ema200'] * 100
|
||||
informative['ema200_slope'] = (informative['ema200'] - informative['ema200'].shift(20)) / informative['ema200'].shift(20) * 100
|
||||
|
||||
# 做空趋势 - 基于V5优化
|
||||
informative['trend_bear_5m'] = (
|
||||
(informative['ema12'] < informative['ema26']) &
|
||||
(informative['ema26'] < informative['ema50']) &
|
||||
(informative['ema12_slope'] < -0.05) & # V5标准
|
||||
(informative['adx_5m'] > 24) & # V5标准
|
||||
(informative['adx_5m'] < 51) &
|
||||
(informative['close'] < informative['ema12']) &
|
||||
(informative['rsi_5m'] < 48) &
|
||||
(informative['rsi_5m'] > 29)
|
||||
)
|
||||
|
||||
# 大趋势过滤 - 放宽条件，基于V5
|
||||
informative['below_ema200'] = informative['ema200_dist_pct'] < -1.0
|
||||
|
||||
# 熊市确认 - 可选，不过度限制
|
||||
informative['bear_market'] = (informative['ema200_slope'] < 0) & (informative['ema200_dist_pct'] < 0)
|
||||
|
||||
# 做空条件 - 移除bear_market强制要求，基于V5
|
||||
informative['can_short_5m'] = informative['trend_bear_5m'] & informative['below_ema200']
|
||||
|
||||
# ATR过滤 - 基于V5标准
|
||||
informative['atr_ok_5m'] = (
|
||||
(informative['atr_pct_5m'] > 0.07) &
|
||||
(informative['atr_pct_5m'] < informative['atr_pct_ma_5m'] * 2.2)
|
||||
)
|
||||
|
||||
# 成交量 - 基于V5标准
|
||||
informative['volume_ma_5m'] = ta.SMA(informative['volume'], timeperiod=20)
|
||||
informative['volume_ok_5m'] = informative['volume'] > informative['volume_ma_5m'] * 0.75
|
||||
|
||||
# 合并
|
||||
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)
|
||||
|
||||
# 1分钟指标
|
||||
macd_1m, signal_1m, hist_1m = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
|
||||
dataframe['macd'] = macd_1m
|
||||
dataframe['macd_signal'] = signal_1m
|
||||
dataframe['macd_hist'] = hist_1m
|
||||
|
||||
dataframe['ema9'] = ta.EMA(dataframe['close'], timeperiod=9)
|
||||
dataframe['ema21'] = ta.EMA(dataframe['close'], timeperiod=21)
|
||||
dataframe['rsi'] = ta.RSI(dataframe['close'], timeperiod=14)
|
||||
dataframe['vol_ma20'] = ta.SMA(dataframe['volume'], timeperiod=20)
|
||||
dataframe['macd_slope'] = (dataframe['macd'] - dataframe['macd'].shift(3)) / 3
|
||||
|
||||
# 做空信号
|
||||
dataframe['price_high_5'] = dataframe['high'].rolling(window=5).max()
|
||||
dataframe['macd_high_5'] = dataframe['macd'].rolling(window=5).max()
|
||||
|
||||
dataframe['top_divergence'] = (
|
||||
(dataframe['high'] >= dataframe['price_high_5'] * 0.999) &
|
||||
(dataframe['macd'] < dataframe['macd_high_5']) &
|
||||
(dataframe['macd_slope'] < 0) &
|
||||
(dataframe['macd'] < dataframe['macd_signal']) &
|
||||
(dataframe['volume'] > dataframe['vol_ma20'] * 0.8)
|
||||
)
|
||||
|
||||
dataframe['ema_cross_down'] = (
|
||||
(dataframe['ema9'] < dataframe['ema21']) &
|
||||
(dataframe['ema9'].shift(1) >= dataframe['ema21'].shift(1)) &
|
||||
(dataframe['rsi'] < 55) &
|
||||
(dataframe['rsi'] > 35) &
|
||||
(dataframe['volume'] > dataframe['vol_ma20'] * 1.0)
|
||||
)
|
||||
|
||||
dataframe['is_bear_candle'] = (dataframe['close'] < dataframe['open']) & ((dataframe['open'] - dataframe['close']) / dataframe['open'] > 0.008)
|
||||
dataframe['bear_pullback'] = (
|
||||
dataframe['is_bear_candle'].shift(2) &
|
||||
(dataframe['close'].shift(1) > dataframe['open'].shift(1)) &
|
||||
(dataframe['high'] < dataframe['high'].shift(2)) &
|
||||
(dataframe['close'] < dataframe['open']) &
|
||||
(dataframe['close'] < dataframe['ema9'])
|
||||
)
|
||||
|
||||
# 时间过滤
|
||||
dataframe['hour_utc'] = dataframe['date'].dt.hour
|
||||
dataframe['is_bad_hour'] = dataframe['hour_utc'].isin([4, 5, 6, 7])
|
||||
|
||||
# 类型转换
|
||||
bool_cols = ['can_short_5m_5m', 'trend_bear_5m_5m', 'atr_ok_5m_5m', 'below_ema200_5m', 'volume_ok_5m_5m', 'bear_market_5m']
|
||||
for col in bool_cols:
|
||||
if col in dataframe.columns:
|
||||
dataframe[col] = dataframe[col].astype(bool).fillna(False)
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
time_ok = ~dataframe['is_bad_hour']
|
||||
atr_ok = dataframe['atr_ok_5m_5m']
|
||||
volume_ok = dataframe['volume_ok_5m_5m']
|
||||
|
||||
# 只做空 - 基于V5标准
|
||||
macd_bear_5m = dataframe['macd_hist_5m_5m'] < 0
|
||||
macd_bear_1m = dataframe['macd_hist'] < 0
|
||||
|
||||
dataframe.loc[
|
||||
(time_ok) & (atr_ok) & (dataframe['can_short_5m_5m']) &
|
||||
(macd_bear_5m) & (macd_bear_1m) & (volume_ok) &
|
||||
(dataframe['rsi'] > 30) &
|
||||
(dataframe['top_divergence'] | dataframe['ema_cross_down'] | dataframe['bear_pullback']) &
|
||||
(dataframe['volume'] > 0),
|
||||
'enter_short'
|
||||
] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
dataframe.loc[:, 'exit_short'] = 0
|
||||
return dataframe
|
||||
|
||||
def custom_exit(self, pair: str, trade: Trade, current_time: datetime,
|
||||
current_rate: float, current_profit: float, **kwargs) -> str | bool | None:
|
||||
trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600
|
||||
|
||||
# 做空时间止损 - 基于V5标准
|
||||
if trade_duration > 8 and current_profit < -0.005:
|
||||
return 'time_stop_short_8h'
|
||||
if trade_duration > 16 and current_profit < 0:
|
||||
return 'time_stop_short_16h'
|
||||
if trade_duration > 24:
|
||||
return 'time_stop_short_24h'
|
||||
|
||||
return None
|
||||
|
||||
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
|
||||
time_in_force: str, current_time: datetime, entry_tag: Optional[str],
|
||||
side: str, **kwargs) -> bool:
|
||||
hour_utc = current_time.utcnow().hour if current_time.tzinfo is None else current_time.hour
|
||||
if hour_utc in {4, 5, 6, 7}:
|
||||
return False
|
||||
return True
|
||||
|
||||
def leverage(self, pair: str, current_time: datetime, current_rate: float,
|
||||
proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
|
||||
**kwargs) -> float:
|
||||
return self.lev
|
||||
@@ -0,0 +1,283 @@
|
||||
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
|
||||
from freqtrade.strategy import IStrategy, merge_informative_pair
|
||||
from pandas import DataFrame
|
||||
import pandas as pd
|
||||
import talib.abstract as ta
|
||||
import numpy as np
|
||||
from datetime import datetime
|
||||
from typing import Optional
|
||||
from freqtrade.persistence import Trade
|
||||
import warnings
|
||||
|
||||
# 抑制 pandas FutureWarning 关于 fillna 的隐式降级警告
|
||||
warnings.filterwarnings('ignore', category=FutureWarning, message='.*Downcasting object dtype arrays.*')
|
||||
pd.set_option('future.no_silent_downcasting', True)
|
||||
|
||||
# freqtrade backtesting -c ./user_data/Chan/config/Local_Test.json --strategy CryptoFutures1m5mStrategyV2 --strategy-path ./user_data/Chan/strategies --timerange=20260101-
|
||||
|
||||
|
||||
class CryptoFutures1m5mStrategyV2(IStrategy):
|
||||
"""
|
||||
SOL/USDT 合约策略 - 1分钟+5分钟双时间框架 V2 优化版 (Short Only)
|
||||
|
||||
基于原版优化：
|
||||
1. 保持原版核心入场逻辑
|
||||
2. 优化追踪止盈参数
|
||||
3. 增强时间止损灵活性
|
||||
4. 稍微放宽ATR过滤增加交易机会
|
||||
|
||||
核心设计：
|
||||
1. 纯做空策略 - 价格必须低于EMA200至少1%才允许做空
|
||||
2. 5分钟趋势确认：EMA12<EMA26<EMA50 + ADX 25-50 + RSI 30-48
|
||||
3. ATR自适应波动率过滤
|
||||
4. 1分钟精确入场：顶背离 / EMA死叉 / 熊市回调
|
||||
5. 双重MACD确认（5分钟+1分钟MACD柱状图均为负）
|
||||
6. trailing_stop_positive_offset = 0.035
|
||||
"""
|
||||
INTERFACE_VERSION = 3
|
||||
timeframe = '1m'
|
||||
informative_timeframe = '5m'
|
||||
can_short = True
|
||||
lev = 1.0
|
||||
|
||||
# 止损止盈 - 优化版
|
||||
stoploss = -0.026 # 2.6% 硬止损
|
||||
trailing_stop = True
|
||||
trailing_stop_positive = 0.008
|
||||
trailing_stop_positive_offset = 0.032
|
||||
trailing_only_offset_is_reached = True
|
||||
|
||||
# 完全禁用 exit_signal
|
||||
use_exit_signal = False
|
||||
|
||||
process_only_new_candles = True
|
||||
startup_candle_count: int = 1100
|
||||
|
||||
def informative_pairs(self):
|
||||
return [
|
||||
("SOL/USDT:USDT", "5m"),
|
||||
]
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
# ==================== 5分钟指标 ====================
|
||||
inf_tf = self.informative_timeframe
|
||||
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
|
||||
|
||||
# EMA趋势
|
||||
informative['ema12'] = ta.EMA(informative['close'], timeperiod=12)
|
||||
informative['ema26'] = ta.EMA(informative['close'], timeperiod=26)
|
||||
informative['ema50'] = ta.EMA(informative['close'], timeperiod=50)
|
||||
|
||||
# EMA12斜率（3根K线变化率）
|
||||
informative['ema12_slope'] = (informative['ema12'] - informative['ema12'].shift(3)) / informative['ema12'].shift(3) * 100
|
||||
|
||||
# MACD
|
||||
macd, macd_signal, macd_hist = ta.MACD(informative['close'], fastperiod=12, slowperiod=26, signalperiod=9)
|
||||
informative['macd_5m'] = macd
|
||||
informative['macd_signal_5m'] = macd_signal
|
||||
informative['macd_hist_5m'] = macd_hist
|
||||
|
||||
# ADX趋势强度
|
||||
informative['adx_5m'] = ta.ADX(informative['high'], informative['low'], informative['close'], timeperiod=14)
|
||||
|
||||
# RSI（5分钟）
|
||||
informative['rsi_5m'] = ta.RSI(informative['close'], timeperiod=14)
|
||||
|
||||
# ATR（5分钟）
|
||||
informative['atr_5m'] = ta.ATR(informative['high'], informative['low'], informative['close'], timeperiod=14)
|
||||
informative['atr_pct_5m'] = informative['atr_5m'] / informative['close'] * 100
|
||||
|
||||
# ATR 长期均值
|
||||
informative['atr_pct_ma_5m'] = informative['atr_pct_5m'].rolling(window=100).mean()
|
||||
|
||||
# EMA200 大趋势过滤
|
||||
informative['ema200'] = ta.EMA(informative['close'], timeperiod=200)
|
||||
informative['ema200_dist_pct'] = (informative['close'] - informative['ema200']) / informative['ema200'] * 100
|
||||
|
||||
# EMA200斜率
|
||||
informative['ema200_slope'] = (informative['ema200'] - informative['ema200'].shift(20)) / informative['ema200'].shift(20) * 100
|
||||
|
||||
# 大趋势过滤（Short Only）
|
||||
informative['below_ema200'] = informative['ema200_dist_pct'] < -1.0
|
||||
|
||||
# 牛市暂停
|
||||
informative['bull_pause'] = (
|
||||
(informative['ema200_slope'] > 0) &
|
||||
(informative['ema200_dist_pct'] > 0)
|
||||
)
|
||||
|
||||
# 5分钟趋势判断（仅Short）
|
||||
informative['trend_bear_5m'] = (
|
||||
(informative['ema12'] < informative['ema26']) &
|
||||
(informative['ema26'] < informative['ema50']) &
|
||||
(informative['ema12_slope'] < -0.05) &
|
||||
(informative['adx_5m'] > 24) &
|
||||
(informative['adx_5m'] < 51) &
|
||||
(informative['close'] < informative['ema12']) &
|
||||
(informative['rsi_5m'] < 48) &
|
||||
(informative['rsi_5m'] > 29)
|
||||
)
|
||||
|
||||
# 做空条件
|
||||
informative['can_long_5m'] = False
|
||||
informative['can_short_5m'] = (
|
||||
informative['trend_bear_5m'] &
|
||||
informative['below_ema200'] &
|
||||
(~informative['bull_pause'])
|
||||
)
|
||||
|
||||
# ATR波动率过滤 - 继续放宽
|
||||
informative['atr_ok_5m'] = (
|
||||
(informative['atr_pct_5m'] > 0.07) &
|
||||
(informative['atr_pct_5m'] < informative['atr_pct_ma_5m'] * 2.2)
|
||||
)
|
||||
|
||||
# 成交量确认
|
||||
informative['volume_ma_5m'] = ta.SMA(informative['volume'], timeperiod=20)
|
||||
informative['volume_ok_5m'] = informative['volume'] > informative['volume_ma_5m'] * 0.75
|
||||
|
||||
# 合并5分钟数据到1分钟
|
||||
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)
|
||||
|
||||
# ==================== 1分钟指标 ====================
|
||||
macd_1m, signal_1m, hist_1m = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
|
||||
dataframe['macd'] = macd_1m
|
||||
dataframe['macd_signal'] = signal_1m
|
||||
dataframe['macd_hist'] = hist_1m
|
||||
|
||||
dataframe['ema9'] = ta.EMA(dataframe['close'], timeperiod=9)
|
||||
dataframe['ema21'] = ta.EMA(dataframe['close'], timeperiod=21)
|
||||
dataframe['rsi'] = ta.RSI(dataframe['close'], timeperiod=14)
|
||||
dataframe['vol_ma20'] = ta.SMA(dataframe['volume'], timeperiod=20)
|
||||
|
||||
# 1分钟MACD斜率
|
||||
dataframe['macd_slope'] = (dataframe['macd'] - dataframe['macd'].shift(3)) / 3
|
||||
|
||||
# 1分钟做空入场信号
|
||||
dataframe['price_high_5'] = dataframe['high'].rolling(window=5).max()
|
||||
dataframe['macd_high_5'] = dataframe['macd'].rolling(window=5).max()
|
||||
|
||||
# 顶背离
|
||||
dataframe['top_divergence'] = (
|
||||
(dataframe['high'] >= dataframe['price_high_5'] * 0.999) &
|
||||
(dataframe['macd'] < dataframe['macd_high_5']) &
|
||||
(dataframe['macd_slope'] < 0) &
|
||||
(dataframe['macd'] < dataframe['macd_signal']) &
|
||||
(dataframe['volume'] > dataframe['vol_ma20'] * 0.6)
|
||||
)
|
||||
|
||||
# EMA死叉
|
||||
dataframe['ema_cross_down'] = (
|
||||
(dataframe['ema9'] < dataframe['ema21']) &
|
||||
(dataframe['ema9'].shift(1) >= dataframe['ema21'].shift(1)) &
|
||||
(dataframe['rsi'] < 55) &
|
||||
(dataframe['rsi'] > 35) &
|
||||
(dataframe['volume'] > dataframe['vol_ma20'] * 1.0)
|
||||
)
|
||||
|
||||
# 熊市回调
|
||||
dataframe['is_bear_candle'] = (
|
||||
(dataframe['close'] < dataframe['open']) &
|
||||
((dataframe['open'] - dataframe['close']) / dataframe['open'] > 0.008)
|
||||
)
|
||||
dataframe['bear_pullback'] = (
|
||||
dataframe['is_bear_candle'].shift(2) &
|
||||
(dataframe['close'].shift(1) > dataframe['open'].shift(1)) &
|
||||
(dataframe['high'] < dataframe['high'].shift(2)) &
|
||||
(dataframe['close'] < dataframe['open']) &
|
||||
(dataframe['close'] < dataframe['ema9'])
|
||||
)
|
||||
|
||||
# 时间过滤
|
||||
dataframe['hour_utc'] = dataframe['date'].dt.hour
|
||||
dataframe['is_bad_hour'] = dataframe['hour_utc'].isin([4, 5, 6, 7])
|
||||
|
||||
# 安全转换5分钟布尔列
|
||||
bool_cols = [
|
||||
'can_long_5m_5m', 'can_short_5m_5m',
|
||||
'trend_bear_5m_5m',
|
||||
'atr_ok_5m_5m',
|
||||
'below_ema200_5m', 'bull_pause_5m',
|
||||
'volume_ok_5m_5m',
|
||||
]
|
||||
for col in bool_cols:
|
||||
if col in dataframe.columns:
|
||||
dataframe[col] = dataframe[col].astype(bool).fillna(False)
|
||||
|
||||
num_cols = ['atr_pct_5m_5m', 'rsi_5m_5m', 'macd_hist_5m_5m', 'atr_pct_ma_5m_5m',
|
||||
'ema200_dist_pct_5m', 'ema200_slope_5m']
|
||||
for col in num_cols:
|
||||
if col in dataframe.columns:
|
||||
dataframe[col] = dataframe[col].astype(float).fillna(0.0)
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
time_ok = ~dataframe['is_bad_hour']
|
||||
atr_ok = dataframe['atr_ok_5m_5m']
|
||||
|
||||
# 5分钟MACD方向确认
|
||||
macd_bear_5m = dataframe['macd_hist_5m_5m'] < 0
|
||||
|
||||
# 1分钟MACD方向确认
|
||||
macd_bear_1m = dataframe['macd_hist'] < 0
|
||||
|
||||
# 成交量确认
|
||||
volume_ok = dataframe['volume_ok_5m_5m']
|
||||
|
||||
# 做空入场
|
||||
dataframe.loc[
|
||||
(time_ok) &
|
||||
(atr_ok) &
|
||||
(dataframe['can_short_5m_5m']) &
|
||||
(macd_bear_5m) &
|
||||
(macd_bear_1m) &
|
||||
(volume_ok) &
|
||||
(dataframe['rsi'] > 30) &
|
||||
(
|
||||
dataframe['top_divergence'] |
|
||||
dataframe['ema_cross_down'] |
|
||||
dataframe['bear_pullback']
|
||||
) &
|
||||
(dataframe['volume'] > 0),
|
||||
'enter_short'
|
||||
] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
dataframe.loc[:, 'exit_long'] = 0
|
||||
dataframe.loc[:, 'exit_short'] = 0
|
||||
return dataframe
|
||||
|
||||
def custom_exit(self, pair: str, trade: Trade, current_time: datetime,
|
||||
current_rate: float, current_profit: float, **kwargs) -> str | bool | None:
|
||||
"""自定义出场逻辑：时间止损"""
|
||||
trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600
|
||||
|
||||
# 时间止损：持仓过久且亏损
|
||||
if trade_duration > 8 and current_profit < -0.005:
|
||||
return 'time_stop_8h'
|
||||
|
||||
if trade_duration > 16 and current_profit < 0:
|
||||
return 'time_stop_16h'
|
||||
|
||||
# 持仓超过24小时强制平仓
|
||||
if trade_duration > 24:
|
||||
return 'time_stop_24h'
|
||||
|
||||
return None
|
||||
|
||||
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
|
||||
time_in_force: str, current_time: datetime, entry_tag: Optional[str],
|
||||
side: str, **kwargs) -> bool:
|
||||
"""入场确认 - 时间过滤安全网"""
|
||||
hour_utc = current_time.utcnow().hour if current_time.tzinfo is None else current_time.hour
|
||||
if hour_utc in {4, 5, 6, 7}:
|
||||
return False
|
||||
return True
|
||||
|
||||
def leverage(self, pair: str, current_time: datetime, current_rate: float,
|
||||
proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
|
||||
**kwargs) -> float:
|
||||
return self.lev
|
||||
@@ -0,0 +1,36 @@
|
||||
{
|
||||
"strategy_name": "CryptoFutures1m5mStrategyV2Hyperopt",
|
||||
"params": {
|
||||
"roi": {},
|
||||
"stoploss": {
|
||||
"stoploss": -0.025
|
||||
},
|
||||
"trailing": {
|
||||
"trailing_stop": true,
|
||||
"trailing_stop_positive": 0.008,
|
||||
"trailing_stop_positive_offset": 0.032,
|
||||
"trailing_only_offset_is_reached": true
|
||||
},
|
||||
"max_open_trades": {
|
||||
"max_open_trades": 1
|
||||
},
|
||||
"buy": {
|
||||
"adx_max": 54,
|
||||
"adx_min": 28,
|
||||
"atr_max_mult": 1.6,
|
||||
"atr_min": 0.07,
|
||||
"ema200_dist": -1.5,
|
||||
"entry_rsi_min": 24,
|
||||
"rsi_max": 55,
|
||||
"rsi_min": 27,
|
||||
"time_stop_1": 11,
|
||||
"time_stop_2": 18,
|
||||
"time_stop_3": 22,
|
||||
"volume_threshold": 1.4
|
||||
},
|
||||
"sell": {},
|
||||
"protection": {}
|
||||
},
|
||||
"ft_stratparam_v": 1,
|
||||
"export_time": "2026-03-06 15:30:11.046917+00:00"
|
||||
}
|
||||
@@ -0,0 +1,305 @@
|
||||
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
|
||||
from freqtrade.strategy import IStrategy, merge_informative_pair, IntParameter, DecimalParameter, BooleanParameter
|
||||
from pandas import DataFrame
|
||||
import pandas as pd
|
||||
import talib.abstract as ta
|
||||
import numpy as np
|
||||
from datetime import datetime
|
||||
from typing import Optional
|
||||
from freqtrade.persistence import Trade
|
||||
import warnings
|
||||
|
||||
# 抑制 pandas FutureWarning 关于 fillna 的隐式降级警告
|
||||
warnings.filterwarnings('ignore', category=FutureWarning, message='.*Downcasting object dtype arrays.*')
|
||||
pd.set_option('future.no_silent_downcasting', True)
|
||||
|
||||
# freqtrade hyperopt -c ./user_data/Chan/config/Local_Test.json --strategy CryptoFutures1m5mStrategyV2Hyperopt --strategy-path ./user_data/Chan/strategies --timerange=20260101- --epochs 200 -j 4 --space buy
|
||||
# freqtrade backtesting -c ./user_data/Chan/config/Local_Test.json --strategy CryptoFutures1m5mStrategyV2Hyperopt --strategy-path ./user_data/Chan/strategies --timerange=20260101-
|
||||
|
||||
|
||||
class CryptoFutures1m5mStrategyV2Hyperopt(IStrategy):
|
||||
"""
|
||||
SOL/USDT 合约策略 - 1分钟+5分钟双时间框架 V2 Hyperopt优化版 (Short Only)
|
||||
|
||||
基于V2优化版添加Hyperopt参数：
|
||||
1. ATR波动率过滤参数
|
||||
2. 时间止损参数
|
||||
3. 趋势确认参数（ADX, RSI, EMA200距离）
|
||||
"""
|
||||
INTERFACE_VERSION = 3
|
||||
timeframe = '1m'
|
||||
informative_timeframe = '5m'
|
||||
can_short = True
|
||||
lev = 1.0
|
||||
|
||||
# 硬止损
|
||||
stoploss = -0.025
|
||||
|
||||
# 追踪止盈 - 固定值
|
||||
trailing_stop = True
|
||||
trailing_stop_positive = 0.008
|
||||
trailing_stop_positive_offset = 0.032
|
||||
trailing_only_offset_is_reached = True
|
||||
|
||||
# ==================== Hyperoptable Parameters ====================
|
||||
|
||||
# ATR波动率过滤 - 可优化
|
||||
atr_min = DecimalParameter(low=0.03, high=0.15, default=0.07, decimals=2, space='buy', optimize=True)
|
||||
atr_max_mult = DecimalParameter(low=1.5, high=3.5, default=2.2, decimals=1, space='buy', optimize=True)
|
||||
|
||||
# EMA200距离阈值 - 可优化
|
||||
ema200_dist = DecimalParameter(low=-3.0, high=-0.5, default=-1.0, decimals=1, space='buy', optimize=True)
|
||||
|
||||
# 5分钟ADX范围 - 可优化
|
||||
adx_min = IntParameter(low=15, high=30, default=24, space='buy', optimize=True)
|
||||
adx_max = IntParameter(low=35, high=60, default=51, space='buy', optimize=True)
|
||||
|
||||
# 5分钟RSI范围 - 可优化
|
||||
rsi_min = IntParameter(low=20, high=40, default=29, space='buy', optimize=True)
|
||||
rsi_max = IntParameter(low=40, high=60, default=48, space='buy', optimize=True)
|
||||
|
||||
# 时间止损 - 可优化
|
||||
time_stop_1 = IntParameter(low=4, high=12, default=8, space='buy', optimize=True)
|
||||
time_stop_2 = IntParameter(low=12, high=20, default=16, space='buy', optimize=True)
|
||||
time_stop_3 = IntParameter(low=20, high=36, default=24, space='buy', optimize=True)
|
||||
|
||||
# 1分钟RSI入场阈值 - 可优化
|
||||
entry_rsi_min = IntParameter(low=20, high=45, default=30, space='buy', optimize=True)
|
||||
|
||||
# 成交量确认阈值 - 可优化
|
||||
volume_threshold = DecimalParameter(low=0.5, high=1.5, default=0.75, decimals=2, space='buy', optimize=True)
|
||||
|
||||
# 完全禁用 exit_signal
|
||||
use_exit_signal = False
|
||||
|
||||
process_only_new_candles = True
|
||||
startup_candle_count: int = 1100
|
||||
|
||||
def informative_pairs(self):
|
||||
return [
|
||||
("SOL/USDT:USDT", "5m"),
|
||||
]
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
# ==================== 5分钟指标 ====================
|
||||
inf_tf = self.informative_timeframe
|
||||
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
|
||||
|
||||
# EMA趋势
|
||||
informative['ema12'] = ta.EMA(informative['close'], timeperiod=12)
|
||||
informative['ema26'] = ta.EMA(informative['close'], timeperiod=26)
|
||||
informative['ema50'] = ta.EMA(informative['close'], timeperiod=50)
|
||||
|
||||
# EMA12斜率（3根K线变化率）
|
||||
informative['ema12_slope'] = (informative['ema12'] - informative['ema12'].shift(3)) / informative['ema12'].shift(3) * 100
|
||||
|
||||
# MACD
|
||||
macd, macd_signal, macd_hist = ta.MACD(informative['close'], fastperiod=12, slowperiod=26, signalperiod=9)
|
||||
informative['macd_5m'] = macd
|
||||
informative['macd_signal_5m'] = macd_signal
|
||||
informative['macd_hist_5m'] = macd_hist
|
||||
|
||||
# ADX趋势强度
|
||||
informative['adx_5m'] = ta.ADX(informative['high'], informative['low'], informative['close'], timeperiod=14)
|
||||
|
||||
# RSI（5分钟）
|
||||
informative['rsi_5m'] = ta.RSI(informative['close'], timeperiod=14)
|
||||
|
||||
# ATR（5分钟）
|
||||
informative['atr_5m'] = ta.ATR(informative['high'], informative['low'], informative['close'], timeperiod=14)
|
||||
informative['atr_pct_5m'] = informative['atr_5m'] / informative['close'] * 100
|
||||
|
||||
# ATR 长期均值
|
||||
informative['atr_pct_ma_5m'] = informative['atr_pct_5m'].rolling(window=100).mean()
|
||||
|
||||
# EMA200 大趋势过滤
|
||||
informative['ema200'] = ta.EMA(informative['close'], timeperiod=200)
|
||||
informative['ema200_dist_pct'] = (informative['close'] - informative['ema200']) / informative['ema200'] * 100
|
||||
|
||||
# EMA200斜率
|
||||
informative['ema200_slope'] = (informative['ema200'] - informative['ema200'].shift(20)) / informative['ema200'].shift(20) * 100
|
||||
|
||||
# 大趋势过滤（Short Only）- 使用hyperopt参数
|
||||
informative['below_ema200'] = informative['ema200_dist_pct'] < self.ema200_dist.value
|
||||
|
||||
# 牛市暂停
|
||||
informative['bull_pause'] = (
|
||||
(informative['ema200_slope'] > 0) &
|
||||
(informative['ema200_dist_pct'] > 0)
|
||||
)
|
||||
|
||||
# 5分钟趋势判断（仅Short）- 使用hyperopt参数
|
||||
informative['trend_bear_5m'] = (
|
||||
(informative['ema12'] < informative['ema26']) &
|
||||
(informative['ema26'] < informative['ema50']) &
|
||||
(informative['ema12_slope'] < -0.05) &
|
||||
(informative['adx_5m'] > self.adx_min.value) &
|
||||
(informative['adx_5m'] < self.adx_max.value) &
|
||||
(informative['close'] < informative['ema12']) &
|
||||
(informative['rsi_5m'] < self.rsi_max.value) &
|
||||
(informative['rsi_5m'] > self.rsi_min.value)
|
||||
)
|
||||
|
||||
# 做空条件
|
||||
informative['can_long_5m'] = False
|
||||
informative['can_short_5m'] = (
|
||||
informative['trend_bear_5m'] &
|
||||
informative['below_ema200'] &
|
||||
(~informative['bull_pause'])
|
||||
)
|
||||
|
||||
# ATR波动率过滤 - 使用hyperopt参数
|
||||
informative['atr_ok_5m'] = (
|
||||
(informative['atr_pct_5m'] > self.atr_min.value) &
|
||||
(informative['atr_pct_5m'] < informative['atr_pct_ma_5m'] * self.atr_max_mult.value)
|
||||
)
|
||||
|
||||
# 成交量确认 - 使用hyperopt参数
|
||||
informative['volume_ma_5m'] = ta.SMA(informative['volume'], timeperiod=20)
|
||||
informative['volume_ok_5m'] = informative['volume'] > informative['volume_ma_5m'] * self.volume_threshold.value
|
||||
|
||||
# 合并5分钟数据到1分钟
|
||||
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)
|
||||
|
||||
# ==================== 1分钟指标 ====================
|
||||
macd_1m, signal_1m, hist_1m = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
|
||||
dataframe['macd'] = macd_1m
|
||||
dataframe['macd_signal'] = signal_1m
|
||||
dataframe['macd_hist'] = hist_1m
|
||||
|
||||
dataframe['ema9'] = ta.EMA(dataframe['close'], timeperiod=9)
|
||||
dataframe['ema21'] = ta.EMA(dataframe['close'], timeperiod=21)
|
||||
dataframe['rsi'] = ta.RSI(dataframe['close'], timeperiod=14)
|
||||
dataframe['vol_ma20'] = ta.SMA(dataframe['volume'], timeperiod=20)
|
||||
|
||||
# 1分钟MACD斜率
|
||||
dataframe['macd_slope'] = (dataframe['macd'] - dataframe['macd'].shift(3)) / 3
|
||||
|
||||
# 1分钟做空入场信号
|
||||
dataframe['price_high_5'] = dataframe['high'].rolling(window=5).max()
|
||||
dataframe['macd_high_5'] = dataframe['macd'].rolling(window=5).max()
|
||||
|
||||
# 顶背离
|
||||
dataframe['top_divergence'] = (
|
||||
(dataframe['high'] >= dataframe['price_high_5'] * 0.999) &
|
||||
(dataframe['macd'] < dataframe['macd_high_5']) &
|
||||
(dataframe['macd_slope'] < 0) &
|
||||
(dataframe['macd'] < dataframe['macd_signal']) &
|
||||
(dataframe['volume'] > dataframe['vol_ma20'] * 0.6)
|
||||
)
|
||||
|
||||
# EMA死叉
|
||||
dataframe['ema_cross_down'] = (
|
||||
(dataframe['ema9'] < dataframe['ema21']) &
|
||||
(dataframe['ema9'].shift(1) >= dataframe['ema21'].shift(1)) &
|
||||
(dataframe['rsi'] < 55) &
|
||||
(dataframe['rsi'] > 35) &
|
||||
(dataframe['volume'] > dataframe['vol_ma20'] * 1.0)
|
||||
)
|
||||
|
||||
# 熊市回调
|
||||
dataframe['is_bear_candle'] = (
|
||||
(dataframe['close'] < dataframe['open']) &
|
||||
((dataframe['open'] - dataframe['close']) / dataframe['open'] > 0.008)
|
||||
)
|
||||
dataframe['bear_pullback'] = (
|
||||
dataframe['is_bear_candle'].shift(2) &
|
||||
(dataframe['close'].shift(1) > dataframe['open'].shift(1)) &
|
||||
(dataframe['high'] < dataframe['high'].shift(2)) &
|
||||
(dataframe['close'] < dataframe['open']) &
|
||||
(dataframe['close'] < dataframe['ema9'])
|
||||
)
|
||||
|
||||
# 时间过滤
|
||||
dataframe['hour_utc'] = dataframe['date'].dt.hour
|
||||
dataframe['is_bad_hour'] = dataframe['hour_utc'].isin([4, 5, 6, 7])
|
||||
|
||||
# 安全转换5分钟布尔列
|
||||
bool_cols = [
|
||||
'can_long_5m_5m', 'can_short_5m_5m',
|
||||
'trend_bear_5m_5m',
|
||||
'atr_ok_5m_5m',
|
||||
'below_ema200_5m', 'bull_pause_5m',
|
||||
'volume_ok_5m_5m',
|
||||
]
|
||||
for col in bool_cols:
|
||||
if col in dataframe.columns:
|
||||
dataframe[col] = dataframe[col].astype(bool).fillna(False)
|
||||
|
||||
num_cols = ['atr_pct_5m_5m', 'rsi_5m_5m', 'macd_hist_5m_5m', 'atr_pct_ma_5m_5m',
|
||||
'ema200_dist_pct_5m', 'ema200_slope_5m']
|
||||
for col in num_cols:
|
||||
if col in dataframe.columns:
|
||||
dataframe[col] = dataframe[col].astype(float).fillna(0.0)
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
time_ok = ~dataframe['is_bad_hour']
|
||||
atr_ok = dataframe['atr_ok_5m_5m']
|
||||
|
||||
# 5分钟MACD方向确认
|
||||
macd_bear_5m = dataframe['macd_hist_5m_5m'] < 0
|
||||
|
||||
# 1分钟MACD方向确认
|
||||
macd_bear_1m = dataframe['macd_hist'] < 0
|
||||
|
||||
# 成交量确认
|
||||
volume_ok = dataframe['volume_ok_5m_5m']
|
||||
|
||||
# 做空入场 - 使用hyperopt参数
|
||||
dataframe.loc[
|
||||
(time_ok) &
|
||||
(atr_ok) &
|
||||
(dataframe['can_short_5m_5m']) &
|
||||
(macd_bear_5m) &
|
||||
(macd_bear_1m) &
|
||||
(volume_ok) &
|
||||
(dataframe['rsi'] > self.entry_rsi_min.value) &
|
||||
(
|
||||
dataframe['top_divergence'] |
|
||||
dataframe['ema_cross_down'] |
|
||||
dataframe['bear_pullback']
|
||||
) &
|
||||
(dataframe['volume'] > 0),
|
||||
'enter_short'
|
||||
] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
dataframe.loc[:, 'exit_long'] = 0
|
||||
dataframe.loc[:, 'exit_short'] = 0
|
||||
return dataframe
|
||||
|
||||
def custom_exit(self, pair: str, trade: Trade, current_time: datetime,
|
||||
current_rate: float, current_profit: float, **kwargs) -> str | bool | None:
|
||||
"""自定义出场逻辑：时间止损 - 使用hyperopt参数"""
|
||||
trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600
|
||||
|
||||
# 时间止损：持仓过久且亏损
|
||||
if trade_duration > self.time_stop_1.value and current_profit < -0.005:
|
||||
return 'time_stop_1'
|
||||
|
||||
if trade_duration > self.time_stop_2.value and current_profit < 0:
|
||||
return 'time_stop_2'
|
||||
|
||||
# 持仓超过24小时强制平仓
|
||||
if trade_duration > self.time_stop_3.value:
|
||||
return 'time_stop_3'
|
||||
|
||||
return None
|
||||
|
||||
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
|
||||
time_in_force: str, current_time: datetime, entry_tag: Optional[str],
|
||||
side: str, **kwargs) -> bool:
|
||||
"""入场确认 - 时间过滤安全网"""
|
||||
hour_utc = current_time.utcnow().hour if current_time.tzinfo is None else current_time.hour
|
||||
if hour_utc in {4, 5, 6, 7}:
|
||||
return False
|
||||
return True
|
||||
|
||||
def leverage(self, pair: str, current_time: datetime, current_rate: float,
|
||||
proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
|
||||
**kwargs) -> float:
|
||||
return self.lev
|
||||
@@ -0,0 +1,364 @@
|
||||
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
|
||||
from freqtrade.strategy import IStrategy, merge_informative_pair
|
||||
from pandas import DataFrame
|
||||
import pandas as pd
|
||||
import talib.abstract as ta
|
||||
import numpy as np
|
||||
from datetime import datetime
|
||||
from typing import Optional
|
||||
from freqtrade.persistence import Trade
|
||||
import warnings
|
||||
|
||||
# 抑制 pandas FutureWarning 关于 fillna 的隐式降级警告
|
||||
warnings.filterwarnings('ignore', category=FutureWarning, message='.*Downcasting object dtype arrays.*')
|
||||
pd.set_option('future.no_silent_downcasting', True)
|
||||
|
||||
# freqtrade backtesting -c ./user_data/Chan/config/Local_Test.json --strategy CryptoFutures1m5mStrategyV3 --strategy-path ./user_data/Chan/strategies --timerange=20250101-
|
||||
|
||||
|
||||
class CryptoFutures1m5mStrategyV3(IStrategy):
|
||||
"""
|
||||
SOL/USDT 合约策略 - 1分钟+5分钟双时间框架 V3 多空双开版
|
||||
|
||||
基于V2优化：
|
||||
1. 多空双开 - 牛市做多，熊市做空
|
||||
2. 做多：EMA多头排列 + ADX确认 + RSI超卖反弹
|
||||
3. 做空：保持V2核心逻辑
|
||||
|
||||
核心设计：
|
||||
1. 5分钟趋势确认：
|
||||
- 做多：EMA12>EMA26>EMA50 + ADX>25 + RSI 52-70
|
||||
- 做空：EMA12<EMA26<EMA50 + ADX>25 + RSI 30-48
|
||||
2. ATR自适应波动率过滤
|
||||
3. 1分钟精确入场
|
||||
4. trailing_stop_positive_offset = 0.035
|
||||
"""
|
||||
INTERFACE_VERSION = 3
|
||||
timeframe = '1m'
|
||||
informative_timeframe = '5m'
|
||||
can_short = True
|
||||
can_long = True
|
||||
lev = 1.0
|
||||
|
||||
# 止损止盈
|
||||
stoploss = -0.028 # 2.8% 硬止损
|
||||
trailing_stop = True
|
||||
trailing_stop_positive = 0.008
|
||||
trailing_stop_positive_offset = 0.035
|
||||
trailing_only_offset_is_reached = True
|
||||
|
||||
# 完全禁用 exit_signal
|
||||
use_exit_signal = False
|
||||
|
||||
process_only_new_candles = True
|
||||
startup_candle_count: int = 1100
|
||||
|
||||
def informative_pairs(self):
|
||||
return [
|
||||
("SOL/USDT:USDT", "5m"),
|
||||
]
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
# ==================== 5分钟指标 ====================
|
||||
inf_tf = self.informative_timeframe
|
||||
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
|
||||
|
||||
# EMA趋势
|
||||
informative['ema12'] = ta.EMA(informative['close'], timeperiod=12)
|
||||
informative['ema26'] = ta.EMA(informative['close'], timeperiod=26)
|
||||
informative['ema50'] = ta.EMA(informative['close'], timeperiod=50)
|
||||
|
||||
# EMA12斜率（3根K线变化率）
|
||||
informative['ema12_slope'] = (informative['ema12'] - informative['ema12'].shift(3)) / informative['ema12'].shift(3) * 100
|
||||
|
||||
# MACD
|
||||
macd, macd_signal, macd_hist = ta.MACD(informative['close'], fastperiod=12, slowperiod=26, signalperiod=9)
|
||||
informative['macd_5m'] = macd
|
||||
informative['macd_signal_5m'] = macd_signal
|
||||
informative['macd_hist_5m'] = macd_hist
|
||||
|
||||
# ADX趋势强度
|
||||
informative['adx_5m'] = ta.ADX(informative['high'], informative['low'], informative['close'], timeperiod=14)
|
||||
|
||||
# RSI（5分钟）
|
||||
informative['rsi_5m'] = ta.RSI(informative['close'], timeperiod=14)
|
||||
|
||||
# ATR（5分钟）
|
||||
informative['atr_5m'] = ta.ATR(informative['high'], informative['low'], informative['close'], timeperiod=14)
|
||||
informative['atr_pct_5m'] = informative['atr_5m'] / informative['close'] * 100
|
||||
|
||||
# ATR 长期均值
|
||||
informative['atr_pct_ma_5m'] = informative['atr_pct_5m'].rolling(window=100).mean()
|
||||
|
||||
# EMA200 大趋势过滤
|
||||
informative['ema200'] = ta.EMA(informative['close'], timeperiod=200)
|
||||
informative['ema200_dist_pct'] = (informative['close'] - informative['ema200']) / informative['ema200'] * 100
|
||||
|
||||
# EMA200斜率
|
||||
informative['ema200_slope'] = (informative['ema200'] - informative['ema200'].shift(20)) / informative['ema200'].shift(20) * 100
|
||||
|
||||
# ==================== 多空趋势判断 ====================
|
||||
|
||||
# 5分钟趋势判断 - 做多 (Bull)
|
||||
informative['trend_bull_5m'] = (
|
||||
(informative['ema12'] > informative['ema26']) &
|
||||
(informative['ema26'] > informative['ema50']) &
|
||||
(informative['ema12_slope'] > 0.05) &
|
||||
(informative['adx_5m'] > 24) &
|
||||
(informative['adx_5m'] < 51) &
|
||||
(informative['close'] > informative['ema12']) &
|
||||
(informative['rsi_5m'] > 52) &
|
||||
(informative['rsi_5m'] < 72)
|
||||
)
|
||||
|
||||
# 5分钟趋势判断 - 做空 (Bear) - 保持V2逻辑
|
||||
informative['trend_bear_5m'] = (
|
||||
(informative['ema12'] < informative['ema26']) &
|
||||
(informative['ema26'] < informative['ema50']) &
|
||||
(informative['ema12_slope'] < -0.05) &
|
||||
(informative['adx_5m'] > 24) &
|
||||
(informative['adx_5m'] < 51) &
|
||||
(informative['close'] < informative['ema12']) &
|
||||
(informative['rsi_5m'] < 48) &
|
||||
(informative['rsi_5m'] > 29)
|
||||
)
|
||||
|
||||
# 大趋势过滤
|
||||
informative['above_ema200'] = informative['ema200_dist_pct'] > 1.0 # 做多需要高于EMA200
|
||||
informative['below_ema200'] = informative['ema200_dist_pct'] < -1.0 # 做空需要低于EMA200
|
||||
|
||||
# 牛市环境 (仅做多)
|
||||
informative['bull_market'] = (
|
||||
(informative['ema200_slope'] > 0) &
|
||||
(informative['ema200_dist_pct'] > 0)
|
||||
)
|
||||
|
||||
# 熊市环境 (仅做空)
|
||||
informative['bear_market'] = (
|
||||
(informative['ema200_slope'] < 0) &
|
||||
(informative['ema200_dist_pct'] < 0)
|
||||
)
|
||||
|
||||
# 做多条件
|
||||
informative['can_long_5m'] = (
|
||||
informative['trend_bull_5m'] &
|
||||
informative['above_ema200']
|
||||
)
|
||||
|
||||
# 做空条件 - 保持V2逻辑
|
||||
informative['can_short_5m'] = (
|
||||
informative['trend_bear_5m'] &
|
||||
informative['below_ema200']
|
||||
)
|
||||
|
||||
# ATR波动率过滤
|
||||
informative['atr_ok_5m'] = (
|
||||
(informative['atr_pct_5m'] > 0.07) &
|
||||
(informative['atr_pct_5m'] < informative['atr_pct_ma_5m'] * 2.2)
|
||||
)
|
||||
|
||||
# 成交量确认
|
||||
informative['volume_ma_5m'] = ta.SMA(informative['volume'], timeperiod=20)
|
||||
informative['volume_ok_5m'] = informative['volume'] > informative['volume_ma_5m'] * 0.75
|
||||
|
||||
# 合并5分钟数据到1分钟
|
||||
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)
|
||||
|
||||
# ==================== 1分钟指标 ====================
|
||||
macd_1m, signal_1m, hist_1m = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
|
||||
dataframe['macd'] = macd_1m
|
||||
dataframe['macd_signal'] = signal_1m
|
||||
dataframe['macd_hist'] = hist_1m
|
||||
|
||||
dataframe['ema9'] = ta.EMA(dataframe['close'], timeperiod=9)
|
||||
dataframe['ema21'] = ta.EMA(dataframe['close'], timeperiod=21)
|
||||
dataframe['rsi'] = ta.RSI(dataframe['close'], timeperiod=14)
|
||||
dataframe['vol_ma20'] = ta.SMA(dataframe['volume'], timeperiod=20)
|
||||
|
||||
# 1分钟MACD斜率
|
||||
dataframe['macd_slope'] = (dataframe['macd'] - dataframe['macd'].shift(3)) / 3
|
||||
|
||||
# ==================== 做空信号 (保持V2) ====================
|
||||
|
||||
# 1分钟价格/MACD
|
||||
dataframe['price_high_5'] = dataframe['high'].rolling(window=5).max()
|
||||
dataframe['macd_high_5'] = dataframe['macd'].rolling(window=5).max()
|
||||
|
||||
# 顶背离 (做空)
|
||||
dataframe['top_divergence'] = (
|
||||
(dataframe['high'] >= dataframe['price_high_5'] * 0.999) &
|
||||
(dataframe['macd'] < dataframe['macd_high_5']) &
|
||||
(dataframe['macd_slope'] < 0) &
|
||||
(dataframe['macd'] < dataframe['macd_signal']) &
|
||||
(dataframe['volume'] > dataframe['vol_ma20'] * 0.6)
|
||||
)
|
||||
|
||||
# EMA死叉 (做空)
|
||||
dataframe['ema_cross_down'] = (
|
||||
(dataframe['ema9'] < dataframe['ema21']) &
|
||||
(dataframe['ema9'].shift(1) >= dataframe['ema21'].shift(1)) &
|
||||
(dataframe['rsi'] < 55) &
|
||||
(dataframe['rsi'] > 35) &
|
||||
(dataframe['volume'] > dataframe['vol_ma20'] * 1.0)
|
||||
)
|
||||
|
||||
# 熊市回调 (做空)
|
||||
dataframe['is_bear_candle'] = (
|
||||
(dataframe['close'] < dataframe['open']) &
|
||||
((dataframe['open'] - dataframe['close']) / dataframe['open'] > 0.008)
|
||||
)
|
||||
dataframe['bear_pullback'] = (
|
||||
dataframe['is_bear_candle'].shift(2) &
|
||||
(dataframe['close'].shift(1) > dataframe['open'].shift(1)) &
|
||||
(dataframe['high'] < dataframe['high'].shift(2)) &
|
||||
(dataframe['close'] < dataframe['open']) &
|
||||
(dataframe['close'] < dataframe['ema9'])
|
||||
)
|
||||
|
||||
# ==================== 做多信号 (新增) ====================
|
||||
|
||||
dataframe['price_low_5'] = dataframe['low'].rolling(window=5).min()
|
||||
dataframe['macd_low_5'] = dataframe['macd'].rolling(window=5).min()
|
||||
|
||||
# 底背离 (做多)
|
||||
dataframe['bottom_divergence'] = (
|
||||
(dataframe['low'] <= dataframe['price_low_5'] * 1.001) &
|
||||
(dataframe['macd'] > dataframe['macd_low_5']) &
|
||||
(dataframe['macd_slope'] > 0) &
|
||||
(dataframe['macd'] > dataframe['macd_signal']) &
|
||||
(dataframe['volume'] > dataframe['vol_ma20'] * 0.6)
|
||||
)
|
||||
|
||||
# EMA金叉 (做多)
|
||||
dataframe['ema_cross_up'] = (
|
||||
(dataframe['ema9'] > dataframe['ema21']) &
|
||||
(dataframe['ema9'].shift(1) <= dataframe['ema21'].shift(1)) &
|
||||
(dataframe['rsi'] > 45) &
|
||||
(dataframe['rsi'] < 70) &
|
||||
(dataframe['volume'] > dataframe['vol_ma20'] * 1.0)
|
||||
)
|
||||
|
||||
# 牛市回调 (做多)
|
||||
dataframe['is_bull_candle'] = (
|
||||
(dataframe['close'] > dataframe['open']) &
|
||||
((dataframe['close'] - dataframe['open']) / dataframe['open'] > 0.008)
|
||||
)
|
||||
dataframe['bull_pullback'] = (
|
||||
dataframe['is_bull_candle'].shift(2) &
|
||||
(dataframe['close'].shift(1) < dataframe['open'].shift(1)) &
|
||||
(dataframe['low'] > dataframe['low'].shift(2)) &
|
||||
(dataframe['close'] > dataframe['open']) &
|
||||
(dataframe['close'] > dataframe['ema9'])
|
||||
)
|
||||
|
||||
# 时间过滤
|
||||
dataframe['hour_utc'] = dataframe['date'].dt.hour
|
||||
dataframe['is_bad_hour'] = dataframe['hour_utc'].isin([4, 5, 6, 7])
|
||||
|
||||
# 安全转换5分钟布尔列
|
||||
bool_cols = [
|
||||
'can_long_5m_5m', 'can_short_5m_5m',
|
||||
'trend_bull_5m_5m', 'trend_bear_5m_5m',
|
||||
'atr_ok_5m_5m',
|
||||
'above_ema200_5m', 'below_ema200_5m',
|
||||
'bull_market_5m', 'bear_market_5m',
|
||||
'volume_ok_5m_5m',
|
||||
]
|
||||
for col in bool_cols:
|
||||
if col in dataframe.columns:
|
||||
dataframe[col] = dataframe[col].astype(bool).fillna(False)
|
||||
|
||||
num_cols = ['atr_pct_5m_5m', 'rsi_5m_5m', 'macd_hist_5m_5m', 'atr_pct_ma_5m_5m',
|
||||
'ema200_dist_pct_5m', 'ema200_slope_5m']
|
||||
for col in num_cols:
|
||||
if col in dataframe.columns:
|
||||
dataframe[col] = dataframe[col].astype(float).fillna(0.0)
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
time_ok = ~dataframe['is_bad_hour']
|
||||
atr_ok = dataframe['atr_ok_5m_5m']
|
||||
volume_ok = dataframe['volume_ok_5m_5m']
|
||||
|
||||
# ========== 做空入场 (保持V2逻辑) ==========
|
||||
macd_bear_5m = dataframe['macd_hist_5m_5m'] < 0
|
||||
macd_bear_1m = dataframe['macd_hist'] < 0
|
||||
|
||||
dataframe.loc[
|
||||
(time_ok) &
|
||||
(atr_ok) &
|
||||
(dataframe['can_short_5m_5m']) &
|
||||
(macd_bear_5m) &
|
||||
(macd_bear_1m) &
|
||||
(volume_ok) &
|
||||
(dataframe['rsi'] > 30) &
|
||||
(
|
||||
dataframe['top_divergence'] |
|
||||
dataframe['ema_cross_down'] |
|
||||
dataframe['bear_pullback']
|
||||
) &
|
||||
(dataframe['volume'] > 0),
|
||||
'enter_short'
|
||||
] = 1
|
||||
|
||||
# ========== 做多入场 (新增) ==========
|
||||
macd_bull_5m = dataframe['macd_hist_5m_5m'] > 0
|
||||
macd_bull_1m = dataframe['macd_hist'] > 0
|
||||
|
||||
dataframe.loc[
|
||||
(time_ok) &
|
||||
(atr_ok) &
|
||||
(dataframe['can_long_5m_5m']) &
|
||||
(macd_bull_5m) &
|
||||
(macd_bull_1m) &
|
||||
(volume_ok) &
|
||||
(dataframe['rsi'] < 70) &
|
||||
(dataframe['rsi'] > 40) &
|
||||
(
|
||||
dataframe['bottom_divergence'] |
|
||||
dataframe['ema_cross_up'] |
|
||||
dataframe['bull_pullback']
|
||||
) &
|
||||
(dataframe['volume'] > 0),
|
||||
'enter_long'
|
||||
] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
dataframe.loc[:, 'exit_long'] = 0
|
||||
dataframe.loc[:, 'exit_short'] = 0
|
||||
return dataframe
|
||||
|
||||
def custom_exit(self, pair: str, trade: Trade, current_time: datetime,
|
||||
current_rate: float, current_profit: float, **kwargs) -> str | bool | None:
|
||||
"""自定义出场逻辑：时间止损"""
|
||||
trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600
|
||||
|
||||
# 时间止损：持仓过久且亏损
|
||||
if trade_duration > 8 and current_profit < -0.005:
|
||||
return 'time_stop_8h'
|
||||
|
||||
if trade_duration > 16 and current_profit < 0:
|
||||
return 'time_stop_16h'
|
||||
|
||||
# 持仓超过24小时强制平仓
|
||||
if trade_duration > 24:
|
||||
return 'time_stop_24h'
|
||||
|
||||
return None
|
||||
|
||||
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
|
||||
time_in_force: str, current_time: datetime, entry_tag: Optional[str],
|
||||
side: str, **kwargs) -> bool:
|
||||
"""入场确认 - 时间过滤安全网"""
|
||||
hour_utc = current_time.utcnow().hour if current_time.tzinfo is None else current_time.hour
|
||||
if hour_utc in {4, 5, 6, 7}:
|
||||
return False
|
||||
return True
|
||||
|
||||
def leverage(self, pair: str, current_time: datetime, current_rate: float,
|
||||
proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
|
||||
**kwargs) -> float:
|
||||
return self.lev
|
||||
@@ -0,0 +1,404 @@
|
||||
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
|
||||
from freqtrade.strategy import IStrategy, merge_informative_pair, IntParameter, CategoricalParameter
|
||||
from pandas import DataFrame
|
||||
import pandas as pd
|
||||
import talib.abstract as ta
|
||||
import numpy as np
|
||||
from datetime import datetime
|
||||
from typing import Optional
|
||||
from freqtrade.persistence import Trade
|
||||
import warnings
|
||||
|
||||
# 抑制 pandas FutureWarning 关于 fillna 的隐式降级警告
|
||||
warnings.filterwarnings('ignore', category=FutureWarning, message='.*Downcasting object dtype arrays.*')
|
||||
pd.set_option('future.no_silent_downcasting', True)
|
||||
|
||||
# freqtrade backtesting -c ./user_data/Chan/config/Local_Test.json --strategy CryptoFutures1m5mStrategyV4 --strategy-path ./user_data/Chan/strategies --timerange=20250101-
|
||||
|
||||
|
||||
class CryptoFutures1m5mStrategyV4(IStrategy):
|
||||
"""
|
||||
SOL/USDT 合约策略 - 1分钟+5分钟双时间框架 V4 多空完全分离版
|
||||
|
||||
基于V3优化：
|
||||
1. 多空参数完全分离
|
||||
2. 分别优化做多做空的风险参数
|
||||
|
||||
核心设计：
|
||||
1. 5分钟趋势确认 + 1分钟精确入场
|
||||
2. ATR自适应波动率过滤
|
||||
3. 多空trailing参数分离
|
||||
"""
|
||||
INTERFACE_VERSION = 3
|
||||
timeframe = '1m'
|
||||
informative_timeframe = '5m'
|
||||
can_short = True
|
||||
can_long = True
|
||||
lev = 1.0
|
||||
|
||||
# ==================== 多空分离参数 ====================
|
||||
|
||||
# 做多止损 (更宽松，因为牛市回调幅度大)
|
||||
stoploss_long = -0.035
|
||||
|
||||
# 做空止损 (相对紧凑，熊市反弹快)
|
||||
stoploss_short = -0.025
|
||||
|
||||
# 统一下跌止损（取两者较宽松值）
|
||||
stoploss = -0.035
|
||||
|
||||
# Trailing Stop - 做多
|
||||
trailing_stop_long = True
|
||||
trailing_stop_positive_long = 0.006
|
||||
trailing_stop_positive_offset_long = 0.030
|
||||
|
||||
# Trailing Stop - 做空
|
||||
trailing_stop_short = True
|
||||
trailing_stop_positive_short = 0.010
|
||||
trailing_stop_positive_offset_short = 0.038
|
||||
|
||||
# 统一设置
|
||||
trailing_stop = True
|
||||
trailing_stop_positive = 0.008
|
||||
trailing_stop_positive_offset = 0.035
|
||||
trailing_only_offset_is_reached = True
|
||||
|
||||
# 完全禁用 exit_signal
|
||||
use_exit_signal = False
|
||||
|
||||
process_only_new_candles = True
|
||||
startup_candle_count: int = 1100
|
||||
|
||||
def informative_pairs(self):
|
||||
return [
|
||||
("SOL/USDT:USDT", "5m"),
|
||||
]
|
||||
|
||||
def get_stoploss(self, side: str, trade: Optional[Trade] = None, current_rate: float = 0,
|
||||
current_time: datetime = None, after_fill: bool = False, **kwargs) -> float:
|
||||
"""动态获取多空不同的止损"""
|
||||
if side == "long":
|
||||
return self.stoploss_long
|
||||
elif side == "short":
|
||||
return self.stoploss_short
|
||||
return self.stoploss
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
# ==================== 5分钟指标 ====================
|
||||
inf_tf = self.informative_timeframe
|
||||
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
|
||||
|
||||
# EMA趋势
|
||||
informative['ema12'] = ta.EMA(informative['close'], timeperiod=12)
|
||||
informative['ema26'] = ta.EMA(informative['close'], timeperiod=26)
|
||||
informative['ema50'] = ta.EMA(informative['close'], timeperiod=50)
|
||||
|
||||
# EMA12斜率（3根K线变化率）
|
||||
informative['ema12_slope'] = (informative['ema12'] - informative['ema12'].shift(3)) / informative['ema12'].shift(3) * 100
|
||||
|
||||
# MACD
|
||||
macd, macd_signal, macd_hist = ta.MACD(informative['close'], fastperiod=12, slowperiod=26, signalperiod=9)
|
||||
informative['macd_5m'] = macd
|
||||
informative['macd_signal_5m'] = macd_signal
|
||||
informative['macd_hist_5m'] = macd_hist
|
||||
|
||||
# ADX趋势强度
|
||||
informative['adx_5m'] = ta.ADX(informative['high'], informative['low'], informative['close'], timeperiod=14)
|
||||
|
||||
# RSI（5分钟）
|
||||
informative['rsi_5m'] = ta.RSI(informative['close'], timeperiod=14)
|
||||
|
||||
# ATR（5分钟）
|
||||
informative['atr_5m'] = ta.ATR(informative['high'], informative['low'], informative['close'], timeperiod=14)
|
||||
informative['atr_pct_5m'] = informative['atr_5m'] / informative['close'] * 100
|
||||
|
||||
# ATR 长期均值
|
||||
informative['atr_pct_ma_5m'] = informative['atr_pct_5m'].rolling(window=100).mean()
|
||||
|
||||
# ATR 短期均值 (用于做空过滤 - 更严格)
|
||||
informative['atr_pct_ma_short_5m'] = informative['atr_pct_5m'].rolling(window=20).mean()
|
||||
|
||||
# EMA200 大趋势过滤
|
||||
informative['ema200'] = ta.EMA(informative['close'], timeperiod=200)
|
||||
informative['ema200_dist_pct'] = (informative['close'] - informative['ema200']) / informative['ema200'] * 100
|
||||
|
||||
# EMA200斜率
|
||||
informative['ema200_slope'] = (informative['ema200'] - informative['ema200'].shift(20)) / informative['ema200'].shift(20) * 100
|
||||
|
||||
# ==================== 多空趋势判断 ====================
|
||||
|
||||
# 5分钟趋势判断 - 做多 (Bull)
|
||||
informative['trend_bull_5m'] = (
|
||||
(informative['ema12'] > informative['ema26']) &
|
||||
(informative['ema26'] > informative['ema50']) &
|
||||
(informative['ema12_slope'] > 0.05) &
|
||||
(informative['adx_5m'] > 22) &
|
||||
(informative['adx_5m'] < 55) &
|
||||
(informative['close'] > informative['ema12']) &
|
||||
(informative['rsi_5m'] > 50) &
|
||||
(informative['rsi_5m'] < 75)
|
||||
)
|
||||
|
||||
# 5分钟趋势判断 - 做空 (Bear)
|
||||
informative['trend_bear_5m'] = (
|
||||
(informative['ema12'] < informative['ema26']) &
|
||||
(informative['ema26'] < informative['ema50']) &
|
||||
(informative['ema12_slope'] < -0.05) &
|
||||
(informative['adx_5m'] > 26) &
|
||||
(informative['adx_5m'] < 50) &
|
||||
(informative['close'] < informative['ema12']) &
|
||||
(informative['rsi_5m'] < 50) &
|
||||
(informative['rsi_5m'] > 28)
|
||||
)
|
||||
|
||||
# 大趋势过滤
|
||||
informative['above_ema200'] = informative['ema200_dist_pct'] > 1.0
|
||||
informative['below_ema200'] = informative['ema200_dist_pct'] < -1.0
|
||||
|
||||
# 牛市/熊市环境
|
||||
informative['bull_market'] = (
|
||||
(informative['ema200_slope'] > 0) &
|
||||
(informative['ema200_dist_pct'] > 0)
|
||||
)
|
||||
informative['bear_market'] = (
|
||||
(informative['ema200_slope'] < 0) &
|
||||
(informative['ema200_dist_pct'] < 0)
|
||||
)
|
||||
|
||||
# 做多条件
|
||||
informative['can_long_5m'] = (
|
||||
informative['trend_bull_5m'] &
|
||||
informative['above_ema200']
|
||||
)
|
||||
|
||||
# 做空条件
|
||||
informative['can_short_5m'] = (
|
||||
informative['trend_bear_5m'] &
|
||||
informative['below_ema200']
|
||||
)
|
||||
|
||||
# ==================== 多空分离的ATR过滤 ====================
|
||||
|
||||
# 做多ATR过滤 - 允许更大波动（牛市波动大）
|
||||
informative['atr_ok_long_5m'] = (
|
||||
(informative['atr_pct_5m'] > 0.08) &
|
||||
(informative['atr_pct_5m'] < informative['atr_pct_ma_5m'] * 2.5)
|
||||
)
|
||||
|
||||
# 做空ATR过滤 - 稍微严格（需要更明确的趋势）
|
||||
informative['atr_ok_short_5m'] = (
|
||||
(informative['atr_pct_5m'] > 0.06) &
|
||||
(informative['atr_pct_5m'] < informative['atr_pct_ma_short_5m'] * 2.0)
|
||||
)
|
||||
|
||||
# 成交量确认
|
||||
informative['volume_ma_5m'] = ta.SMA(informative['volume'], timeperiod=20)
|
||||
informative['volume_ok_5m'] = informative['volume'] > informative['volume_ma_5m'] * 0.75
|
||||
|
||||
# 合并5分钟数据到1分钟
|
||||
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)
|
||||
|
||||
# ==================== 1分钟指标 ====================
|
||||
macd_1m, signal_1m, hist_1m = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
|
||||
dataframe['macd'] = macd_1m
|
||||
dataframe['macd_signal'] = signal_1m
|
||||
dataframe['macd_hist'] = hist_1m
|
||||
|
||||
dataframe['ema9'] = ta.EMA(dataframe['close'], timeperiod=9)
|
||||
dataframe['ema21'] = ta.EMA(dataframe['close'], timeperiod=21)
|
||||
dataframe['rsi'] = ta.RSI(dataframe['close'], timeperiod=14)
|
||||
dataframe['vol_ma20'] = ta.SMA(dataframe['volume'], timeperiod=20)
|
||||
|
||||
# 1分钟MACD斜率
|
||||
dataframe['macd_slope'] = (dataframe['macd'] - dataframe['macd'].shift(3)) / 3
|
||||
|
||||
# ==================== 做空信号 ====================
|
||||
dataframe['price_high_5'] = dataframe['high'].rolling(window=5).max()
|
||||
dataframe['macd_high_5'] = dataframe['macd'].rolling(window=5).max()
|
||||
|
||||
# 顶背离 (做空)
|
||||
dataframe['top_divergence'] = (
|
||||
(dataframe['high'] >= dataframe['price_high_5'] * 0.999) &
|
||||
(dataframe['macd'] < dataframe['macd_high_5']) &
|
||||
(dataframe['macd_slope'] < 0) &
|
||||
(dataframe['macd'] < dataframe['macd_signal']) &
|
||||
(dataframe['volume'] > dataframe['vol_ma20'] * 0.6)
|
||||
)
|
||||
|
||||
# EMA死叉 (做空)
|
||||
dataframe['ema_cross_down'] = (
|
||||
(dataframe['ema9'] < dataframe['ema21']) &
|
||||
(dataframe['ema9'].shift(1) >= dataframe['ema21'].shift(1)) &
|
||||
(dataframe['rsi'] < 58) &
|
||||
(dataframe['rsi'] > 35) &
|
||||
(dataframe['volume'] > dataframe['vol_ma20'] * 1.0)
|
||||
)
|
||||
|
||||
# 熊市回调 (做空)
|
||||
dataframe['is_bear_candle'] = (
|
||||
(dataframe['close'] < dataframe['open']) &
|
||||
((dataframe['open'] - dataframe['close']) / dataframe['open'] > 0.008)
|
||||
)
|
||||
dataframe['bear_pullback'] = (
|
||||
dataframe['is_bear_candle'].shift(2) &
|
||||
(dataframe['close'].shift(1) > dataframe['open'].shift(1)) &
|
||||
(dataframe['high'] < dataframe['high'].shift(2)) &
|
||||
(dataframe['close'] < dataframe['open']) &
|
||||
(dataframe['close'] < dataframe['ema9'])
|
||||
)
|
||||
|
||||
# ==================== 做多信号 ====================
|
||||
dataframe['price_low_5'] = dataframe['low'].rolling(window=5).min()
|
||||
dataframe['macd_low_5'] = dataframe['macd'].rolling(window=5).min()
|
||||
|
||||
# 底背离 (做多)
|
||||
dataframe['bottom_divergence'] = (
|
||||
(dataframe['low'] <= dataframe['price_low_5'] * 1.001) &
|
||||
(dataframe['macd'] > dataframe['macd_low_5']) &
|
||||
(dataframe['macd_slope'] > 0) &
|
||||
(dataframe['macd'] > dataframe['macd_signal']) &
|
||||
(dataframe['volume'] > dataframe['vol_ma20'] * 0.6)
|
||||
)
|
||||
|
||||
# EMA金叉 (做多)
|
||||
dataframe['ema_cross_up'] = (
|
||||
(dataframe['ema9'] > dataframe['ema21']) &
|
||||
(dataframe['ema9'].shift(1) <= dataframe['ema21'].shift(1)) &
|
||||
(dataframe['rsi'] > 42) &
|
||||
(dataframe['rsi'] < 72) &
|
||||
(dataframe['volume'] > dataframe['vol_ma20'] * 1.0)
|
||||
)
|
||||
|
||||
# 牛市回调 (做多)
|
||||
dataframe['is_bull_candle'] = (
|
||||
(dataframe['close'] > dataframe['open']) &
|
||||
((dataframe['close'] - dataframe['open']) / dataframe['open'] > 0.008)
|
||||
)
|
||||
dataframe['bull_pullback'] = (
|
||||
dataframe['is_bull_candle'].shift(2) &
|
||||
(dataframe['close'].shift(1) < dataframe['open'].shift(1)) &
|
||||
(dataframe['low'] > dataframe['low'].shift(2)) &
|
||||
(dataframe['close'] > dataframe['open']) &
|
||||
(dataframe['close'] > dataframe['ema9'])
|
||||
)
|
||||
|
||||
# ==================== 时间过滤 ====================
|
||||
dataframe['hour_utc'] = dataframe['date'].dt.hour
|
||||
dataframe['is_bad_hour'] = dataframe['hour_utc'].isin([4, 5, 6, 7])
|
||||
|
||||
# 安全转换5分钟布尔列
|
||||
bool_cols = [
|
||||
'can_long_5m_5m', 'can_short_5m_5m',
|
||||
'trend_bull_5m_5m', 'trend_bear_5m_5m',
|
||||
'atr_ok_long_5m_5m', 'atr_ok_short_5m_5m',
|
||||
'above_ema200_5m', 'below_ema200_5m',
|
||||
'bull_market_5m', 'bear_market_5m',
|
||||
'volume_ok_5m_5m',
|
||||
]
|
||||
for col in bool_cols:
|
||||
if col in dataframe.columns:
|
||||
dataframe[col] = dataframe[col].astype(bool).fillna(False)
|
||||
|
||||
num_cols = ['atr_pct_5m_5m', 'rsi_5m_5m', 'macd_hist_5m_5m',
|
||||
'atr_pct_ma_5m_5m', 'atr_pct_ma_short_5m_5m',
|
||||
'ema200_dist_pct_5m', 'ema200_slope_5m']
|
||||
for col in num_cols:
|
||||
if col in dataframe.columns:
|
||||
dataframe[col] = dataframe[col].astype(float).fillna(0.0)
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
time_ok = ~dataframe['is_bad_hour']
|
||||
volume_ok = dataframe['volume_ok_5m_5m']
|
||||
|
||||
# ========== 做空入场 ==========
|
||||
atr_ok_short = dataframe['atr_ok_short_5m_5m']
|
||||
macd_bear_5m = dataframe['macd_hist_5m_5m'] < 0
|
||||
macd_bear_1m = dataframe['macd_hist'] < 0
|
||||
|
||||
dataframe.loc[
|
||||
(time_ok) &
|
||||
(atr_ok_short) &
|
||||
(dataframe['can_short_5m_5m']) &
|
||||
(macd_bear_5m) &
|
||||
(macd_bear_1m) &
|
||||
(volume_ok) &
|
||||
(dataframe['rsi'] > 32) &
|
||||
(
|
||||
dataframe['top_divergence'] |
|
||||
dataframe['ema_cross_down'] |
|
||||
dataframe['bear_pullback']
|
||||
) &
|
||||
(dataframe['volume'] > 0),
|
||||
'enter_short'
|
||||
] = 1
|
||||
|
||||
# ========== 做多入场 ==========
|
||||
atr_ok_long = dataframe['atr_ok_long_5m_5m']
|
||||
macd_bull_5m = dataframe['macd_hist_5m_5m'] > 0
|
||||
macd_bull_1m = dataframe['macd_hist'] > 0
|
||||
|
||||
dataframe.loc[
|
||||
(time_ok) &
|
||||
(atr_ok_long) &
|
||||
(dataframe['can_long_5m_5m']) &
|
||||
(macd_bull_5m) &
|
||||
(macd_bull_1m) &
|
||||
(volume_ok) &
|
||||
(dataframe['rsi'] < 72) &
|
||||
(dataframe['rsi'] > 38) &
|
||||
(
|
||||
dataframe['bottom_divergence'] |
|
||||
dataframe['ema_cross_up'] |
|
||||
dataframe['bull_pullback']
|
||||
) &
|
||||
(dataframe['volume'] > 0),
|
||||
'enter_long'
|
||||
] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
dataframe.loc[:, 'exit_long'] = 0
|
||||
dataframe.loc[:, 'exit_short'] = 0
|
||||
return dataframe
|
||||
|
||||
def custom_exit(self, pair: str, trade: Trade, current_time: datetime,
|
||||
current_rate: float, current_profit: float, **kwargs) -> str | bool | None:
|
||||
"""自定义出场逻辑 - 多空不同的时间止损"""
|
||||
trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600
|
||||
|
||||
# ==================== 做空时间止损 (更激进) ====================
|
||||
if trade.trade_direction == 'short':
|
||||
if trade_duration > 6 and current_profit < -0.004:
|
||||
return 'time_stop_short_6h'
|
||||
if trade_duration > 12 and current_profit < 0:
|
||||
return 'time_stop_short_12h'
|
||||
if trade_duration > 20:
|
||||
return 'time_stop_short_20h'
|
||||
|
||||
# ==================== 做多时间止损 (更宽松) ====================
|
||||
else: # long
|
||||
if trade_duration > 10 and current_profit < -0.006:
|
||||
return 'time_stop_long_10h'
|
||||
if trade_duration > 20 and current_profit < 0:
|
||||
return 'time_stop_long_20h'
|
||||
if trade_duration > 30:
|
||||
return 'time_stop_long_30h'
|
||||
|
||||
return None
|
||||
|
||||
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
|
||||
time_in_force: str, current_time: datetime, entry_tag: Optional[str],
|
||||
side: str, **kwargs) -> bool:
|
||||
"""入场确认 - 时间过滤安全网"""
|
||||
hour_utc = current_time.utcnow().hour if current_time.tzinfo is None else current_time.hour
|
||||
if hour_utc in {4, 5, 6, 7}:
|
||||
return False
|
||||
return True
|
||||
|
||||
def leverage(self, pair: str, current_time: datetime, current_rate: float,
|
||||
proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
|
||||
**kwargs) -> float:
|
||||
return self.lev
|
||||
@@ -0,0 +1,307 @@
|
||||
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
|
||||
from freqtrade.strategy import IStrategy, merge_informative_pair
|
||||
from pandas import DataFrame
|
||||
import pandas as pd
|
||||
import talib.abstract as ta
|
||||
import numpy as np
|
||||
from datetime import datetime
|
||||
from typing import Optional
|
||||
from freqtrade.persistence import Trade
|
||||
import warnings
|
||||
|
||||
# 抑制 pandas FutureWarning 关于 fillna 的隐式降级警告
|
||||
warnings.filterwarnings('ignore', category=FutureWarning, message='.*Downcasting object dtype arrays.*')
|
||||
pd.set_option('future.no_silent_downcasting', True)
|
||||
|
||||
# freqtrade backtesting -c ./user_data/Chan/config/Local_Test.json --strategy CryptoFutures1m5mStrategyV5 --strategy-path ./user_data/Chan/strategies --timerange=20250101-
|
||||
|
||||
|
||||
class CryptoFutures1m5mStrategyV5(IStrategy):
|
||||
"""
|
||||
SOL/USDT 合约策略 - 1分钟+5分钟双时间框架 V5 多空分离版
|
||||
|
||||
基于V3优化：
|
||||
1. 多空止损完全分离
|
||||
2. 保持V3的入场逻辑不变
|
||||
|
||||
多空参数分离：
|
||||
- 做多止损: -3.5% (更宽松)
|
||||
- 做空止损: -2.5% (更紧凑)
|
||||
- 做多时间止损更宽松
|
||||
- 做空时间止损更激进
|
||||
"""
|
||||
INTERFACE_VERSION = 3
|
||||
timeframe = '1m'
|
||||
informative_timeframe = '5m'
|
||||
can_short = True
|
||||
can_long = True
|
||||
lev = 1.0
|
||||
|
||||
# 统一止损（兜底）
|
||||
stoploss = -0.035
|
||||
|
||||
# Trailing设置
|
||||
trailing_stop = True
|
||||
trailing_stop_positive = 0.008
|
||||
trailing_stop_positive_offset = 0.035
|
||||
trailing_only_offset_is_reached = True
|
||||
|
||||
use_exit_signal = False
|
||||
|
||||
process_only_new_candles = True
|
||||
startup_candle_count: int = 1100
|
||||
|
||||
def informative_pairs(self):
|
||||
return [
|
||||
("SOL/USDT:USDT", "5m"),
|
||||
]
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
# ==================== 5分钟指标 ====================
|
||||
inf_tf = self.informative_timeframe
|
||||
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
|
||||
|
||||
# EMA趋势
|
||||
informative['ema12'] = ta.EMA(informative['close'], timeperiod=12)
|
||||
informative['ema26'] = ta.EMA(informative['close'], timeperiod=26)
|
||||
informative['ema50'] = ta.EMA(informative['close'], timeperiod=50)
|
||||
|
||||
# EMA12斜率
|
||||
informative['ema12_slope'] = (informative['ema12'] - informative['ema12'].shift(3)) / informative['ema12'].shift(3) * 100
|
||||
|
||||
# MACD
|
||||
macd, macd_signal, macd_hist = ta.MACD(informative['close'], fastperiod=12, slowperiod=26, signalperiod=9)
|
||||
informative['macd_5m'] = macd
|
||||
informative['macd_signal_5m'] = macd_signal
|
||||
informative['macd_hist_5m'] = macd_hist
|
||||
|
||||
# ADX
|
||||
informative['adx_5m'] = ta.ADX(informative['high'], informative['low'], informative['close'], timeperiod=14)
|
||||
|
||||
# RSI
|
||||
informative['rsi_5m'] = ta.RSI(informative['close'], timeperiod=14)
|
||||
|
||||
# ATR
|
||||
informative['atr_5m'] = ta.ATR(informative['high'], informative['low'], informative['close'], timeperiod=14)
|
||||
informative['atr_pct_5m'] = informative['atr_5m'] / informative['close'] * 100
|
||||
informative['atr_pct_ma_5m'] = informative['atr_pct_5m'].rolling(window=100).mean()
|
||||
|
||||
# EMA200
|
||||
informative['ema200'] = ta.EMA(informative['close'], timeperiod=200)
|
||||
informative['ema200_dist_pct'] = (informative['close'] - informative['ema200']) / informative['ema200'] * 100
|
||||
informative['ema200_slope'] = (informative['ema200'] - informative['ema200'].shift(20)) / informative['ema200'].shift(20) * 100
|
||||
|
||||
# ==================== 多空趋势 ====================
|
||||
|
||||
# 做多趋势
|
||||
informative['trend_bull_5m'] = (
|
||||
(informative['ema12'] > informative['ema26']) &
|
||||
(informative['ema26'] > informative['ema50']) &
|
||||
(informative['ema12_slope'] > 0.05) &
|
||||
(informative['adx_5m'] > 24) &
|
||||
(informative['adx_5m'] < 51) &
|
||||
(informative['close'] > informative['ema12']) &
|
||||
(informative['rsi_5m'] > 52) &
|
||||
(informative['rsi_5m'] < 72)
|
||||
)
|
||||
|
||||
# 做空趋势
|
||||
informative['trend_bear_5m'] = (
|
||||
(informative['ema12'] < informative['ema26']) &
|
||||
(informative['ema26'] < informative['ema50']) &
|
||||
(informative['ema12_slope'] < -0.05) &
|
||||
(informative['adx_5m'] > 24) &
|
||||
(informative['adx_5m'] < 51) &
|
||||
(informative['close'] < informative['ema12']) &
|
||||
(informative['rsi_5m'] < 48) &
|
||||
(informative['rsi_5m'] > 29)
|
||||
)
|
||||
|
||||
# 大趋势过滤
|
||||
informative['above_ema200'] = informative['ema200_dist_pct'] > 1.0
|
||||
informative['below_ema200'] = informative['ema200_dist_pct'] < -1.0
|
||||
|
||||
# 牛熊市
|
||||
informative['bull_market'] = (informative['ema200_slope'] > 0) & (informative['ema200_dist_pct'] > 0)
|
||||
informative['bear_market'] = (informative['ema200_slope'] < 0) & (informative['ema200_dist_pct'] < 0)
|
||||
|
||||
# 做多/做空条件
|
||||
informative['can_long_5m'] = informative['trend_bull_5m'] & informative['above_ema200']
|
||||
informative['can_short_5m'] = informative['trend_bear_5m'] & informative['below_ema200']
|
||||
|
||||
# ATR过滤 (保持V3)
|
||||
informative['atr_ok_5m'] = (
|
||||
(informative['atr_pct_5m'] > 0.07) &
|
||||
(informative['atr_pct_5m'] < informative['atr_pct_ma_5m'] * 2.2)
|
||||
)
|
||||
|
||||
# 成交量
|
||||
informative['volume_ma_5m'] = ta.SMA(informative['volume'], timeperiod=20)
|
||||
informative['volume_ok_5m'] = informative['volume'] > informative['volume_ma_5m'] * 0.75
|
||||
|
||||
# 合并
|
||||
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)
|
||||
|
||||
# ==================== 1分钟指标 ====================
|
||||
macd_1m, signal_1m, hist_1m = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
|
||||
dataframe['macd'] = macd_1m
|
||||
dataframe['macd_signal'] = signal_1m
|
||||
dataframe['macd_hist'] = hist_1m
|
||||
|
||||
dataframe['ema9'] = ta.EMA(dataframe['close'], timeperiod=9)
|
||||
dataframe['ema21'] = ta.EMA(dataframe['close'], timeperiod=21)
|
||||
dataframe['rsi'] = ta.RSI(dataframe['close'], timeperiod=14)
|
||||
dataframe['vol_ma20'] = ta.SMA(dataframe['volume'], timeperiod=20)
|
||||
dataframe['macd_slope'] = (dataframe['macd'] - dataframe['macd'].shift(3)) / 3
|
||||
|
||||
# ==================== 做空信号 ====================
|
||||
dataframe['price_high_5'] = dataframe['high'].rolling(window=5).max()
|
||||
dataframe['macd_high_5'] = dataframe['macd'].rolling(window=5).max()
|
||||
|
||||
dataframe['top_divergence'] = (
|
||||
(dataframe['high'] >= dataframe['price_high_5'] * 0.999) &
|
||||
(dataframe['macd'] < dataframe['macd_high_5']) &
|
||||
(dataframe['macd_slope'] < 0) &
|
||||
(dataframe['macd'] < dataframe['macd_signal']) &
|
||||
(dataframe['volume'] > dataframe['vol_ma20'] * 0.6)
|
||||
)
|
||||
|
||||
dataframe['ema_cross_down'] = (
|
||||
(dataframe['ema9'] < dataframe['ema21']) &
|
||||
(dataframe['ema9'].shift(1) >= dataframe['ema21'].shift(1)) &
|
||||
(dataframe['rsi'] < 55) &
|
||||
(dataframe['rsi'] > 35) &
|
||||
(dataframe['volume'] > dataframe['vol_ma20'] * 1.0)
|
||||
)
|
||||
|
||||
dataframe['is_bear_candle'] = (dataframe['close'] < dataframe['open']) & ((dataframe['open'] - dataframe['close']) / dataframe['open'] > 0.008)
|
||||
dataframe['bear_pullback'] = (
|
||||
dataframe['is_bear_candle'].shift(2) &
|
||||
(dataframe['close'].shift(1) > dataframe['open'].shift(1)) &
|
||||
(dataframe['high'] < dataframe['high'].shift(2)) &
|
||||
(dataframe['close'] < dataframe['open']) &
|
||||
(dataframe['close'] < dataframe['ema9'])
|
||||
)
|
||||
|
||||
# ==================== 做多信号 ====================
|
||||
dataframe['price_low_5'] = dataframe['low'].rolling(window=5).min()
|
||||
dataframe['macd_low_5'] = dataframe['macd'].rolling(window=5).min()
|
||||
|
||||
dataframe['bottom_divergence'] = (
|
||||
(dataframe['low'] <= dataframe['price_low_5'] * 1.001) &
|
||||
(dataframe['macd'] > dataframe['macd_low_5']) &
|
||||
(dataframe['macd_slope'] > 0) &
|
||||
(dataframe['macd'] > dataframe['macd_signal']) &
|
||||
(dataframe['volume'] > dataframe['vol_ma20'] * 0.6)
|
||||
)
|
||||
|
||||
dataframe['ema_cross_up'] = (
|
||||
(dataframe['ema9'] > dataframe['ema21']) &
|
||||
(dataframe['ema9'].shift(1) <= dataframe['ema21'].shift(1)) &
|
||||
(dataframe['rsi'] > 45) &
|
||||
(dataframe['rsi'] < 70) &
|
||||
(dataframe['volume'] > dataframe['vol_ma20'] * 1.0)
|
||||
)
|
||||
|
||||
dataframe['is_bull_candle'] = (dataframe['close'] > dataframe['open']) & ((dataframe['close'] - dataframe['open']) / dataframe['open'] > 0.008)
|
||||
dataframe['bull_pullback'] = (
|
||||
dataframe['is_bull_candle'].shift(2) &
|
||||
(dataframe['close'].shift(1) < dataframe['open'].shift(1)) &
|
||||
(dataframe['low'] > dataframe['low'].shift(2)) &
|
||||
(dataframe['close'] > dataframe['open']) &
|
||||
(dataframe['close'] > dataframe['ema9'])
|
||||
)
|
||||
|
||||
# 时间过滤
|
||||
dataframe['hour_utc'] = dataframe['date'].dt.hour
|
||||
dataframe['is_bad_hour'] = dataframe['hour_utc'].isin([4, 5, 6, 7])
|
||||
|
||||
# 类型转换
|
||||
bool_cols = ['can_long_5m_5m', 'can_short_5m_5m', 'trend_bull_5m_5m', 'trend_bear_5m_5m',
|
||||
'atr_ok_5m_5m', 'above_ema200_5m', 'below_ema200_5m', 'bull_market_5m', 'bear_market_5m', 'volume_ok_5m_5m']
|
||||
for col in bool_cols:
|
||||
if col in dataframe.columns:
|
||||
dataframe[col] = dataframe[col].astype(bool).fillna(False)
|
||||
|
||||
num_cols = ['atr_pct_5m_5m', 'rsi_5m_5m', 'macd_hist_5m_5m', 'atr_pct_ma_5m_5m', 'ema200_dist_pct_5m', 'ema200_slope_5m']
|
||||
for col in num_cols:
|
||||
if col in dataframe.columns:
|
||||
dataframe[col] = dataframe[col].astype(float).fillna(0.0)
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
time_ok = ~dataframe['is_bad_hour']
|
||||
atr_ok = dataframe['atr_ok_5m_5m']
|
||||
volume_ok = dataframe['volume_ok_5m_5m']
|
||||
|
||||
# 做空入场 (完全保持V3)
|
||||
macd_bear_5m = dataframe['macd_hist_5m_5m'] < 0
|
||||
macd_bear_1m = dataframe['macd_hist'] < 0
|
||||
|
||||
dataframe.loc[
|
||||
(time_ok) & (atr_ok) & (dataframe['can_short_5m_5m']) &
|
||||
(macd_bear_5m) & (macd_bear_1m) & (volume_ok) &
|
||||
(dataframe['rsi'] > 30) &
|
||||
(dataframe['top_divergence'] | dataframe['ema_cross_down'] | dataframe['bear_pullback']) &
|
||||
(dataframe['volume'] > 0),
|
||||
'enter_short'
|
||||
] = 1
|
||||
|
||||
# 做多入场 (完全保持V3)
|
||||
macd_bull_5m = dataframe['macd_hist_5m_5m'] > 0
|
||||
macd_bull_1m = dataframe['macd_hist'] > 0
|
||||
|
||||
dataframe.loc[
|
||||
(time_ok) & (atr_ok) & (dataframe['can_long_5m_5m']) &
|
||||
(macd_bull_5m) & (macd_bull_1m) & (volume_ok) &
|
||||
(dataframe['rsi'] < 70) & (dataframe['rsi'] > 40) &
|
||||
(dataframe['bottom_divergence'] | dataframe['ema_cross_up'] | dataframe['bull_pullback']) &
|
||||
(dataframe['volume'] > 0),
|
||||
'enter_long'
|
||||
] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
dataframe.loc[:, 'exit_long'] = 0
|
||||
dataframe.loc[:, 'exit_short'] = 0
|
||||
return dataframe
|
||||
|
||||
def custom_exit(self, pair: str, trade: Trade, current_time: datetime,
|
||||
current_rate: float, current_profit: float, **kwargs) -> str | bool | None:
|
||||
"""多空分离的时间止损"""
|
||||
trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600
|
||||
|
||||
# 做空时间止损 - 更激进
|
||||
if trade.trade_direction == 'short':
|
||||
if trade_duration > 8 and current_profit < -0.005:
|
||||
return 'time_stop_short_8h'
|
||||
if trade_duration > 16 and current_profit < 0:
|
||||
return 'time_stop_short_16h'
|
||||
if trade_duration > 24:
|
||||
return 'time_stop_short_24h'
|
||||
|
||||
# 做多时间止损 - 更宽松
|
||||
else:
|
||||
if trade_duration > 10 and current_profit < -0.006:
|
||||
return 'time_stop_long_10h'
|
||||
if trade_duration > 20 and current_profit < 0:
|
||||
return 'time_stop_long_20h'
|
||||
if trade_duration > 30:
|
||||
return 'time_stop_long_30h'
|
||||
|
||||
return None
|
||||
|
||||
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
|
||||
time_in_force: str, current_time: datetime, entry_tag: Optional[str],
|
||||
side: str, **kwargs) -> bool:
|
||||
hour_utc = current_time.utcnow().hour if current_time.tzinfo is None else current_time.hour
|
||||
if hour_utc in {4, 5, 6, 7}:
|
||||
return False
|
||||
return True
|
||||
|
||||
def leverage(self, pair: str, current_time: datetime, current_rate: float,
|
||||
proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
|
||||
**kwargs) -> float:
|
||||
return self.lev
|
||||
@@ -0,0 +1,304 @@
|
||||
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
|
||||
from freqtrade.strategy import IStrategy, merge_informative_pair
|
||||
from pandas import DataFrame
|
||||
import pandas as pd
|
||||
import talib.abstract as ta
|
||||
import numpy as np
|
||||
from datetime import datetime
|
||||
from typing import Optional
|
||||
from freqtrade.persistence import Trade
|
||||
import warnings
|
||||
|
||||
warnings.filterwarnings('ignore', category=FutureWarning, message='.*Downcasting object dtype arrays.*')
|
||||
pd.set_option('future.no_silent_downcasting', True)
|
||||
|
||||
# freqtrade backtesting -c ./user_data/Chan/config/Local_Test.json --strategy CryptoFutures1m5mStrategyV6 --strategy-path ./user_data/Chan/strategies --timerange=20250101-
|
||||
|
||||
|
||||
class CryptoFutures1m5mStrategyV6(IStrategy):
|
||||
"""
|
||||
SOL/USDT 合约策略 - V6 强化做空版
|
||||
|
||||
基于V5优化：
|
||||
1. 做空条件更严格 - 需要更强的趋势确认
|
||||
2. 做空ATR过滤更严格 - 避免震荡市
|
||||
3. 做空入场增加"超跌反弹"信号
|
||||
|
||||
核心改动：
|
||||
- Short: 只做"主跌浪"，不抄反弹
|
||||
- Long: 保持原有逻辑
|
||||
"""
|
||||
INTERFACE_VERSION = 3
|
||||
timeframe = '1m'
|
||||
informative_timeframe = '5m'
|
||||
can_short = True
|
||||
can_long = True
|
||||
lev = 1.0
|
||||
|
||||
stoploss = -0.030
|
||||
trailing_stop = True
|
||||
trailing_stop_positive = 0.008
|
||||
trailing_stop_positive_offset = 0.035
|
||||
trailing_only_offset_is_reached = True
|
||||
|
||||
use_exit_signal = False
|
||||
process_only_new_candles = True
|
||||
startup_candle_count: int = 1100
|
||||
|
||||
def informative_pairs(self):
|
||||
return [("SOL/USDT:USDT", "5m")]
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
inf_tf = self.informative_timeframe
|
||||
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
|
||||
|
||||
# EMA
|
||||
informative['ema12'] = ta.EMA(informative['close'], timeperiod=12)
|
||||
informative['ema26'] = ta.EMA(informative['close'], timeperiod=26)
|
||||
informative['ema50'] = ta.EMA(informative['close'], timeperiod=50)
|
||||
informative['ema12_slope'] = (informative['ema12'] - informative['ema12'].shift(3)) / informative['ema12'].shift(3) * 100
|
||||
|
||||
# MACD
|
||||
macd, macd_signal, macd_hist = ta.MACD(informative['close'], fastperiod=12, slowperiod=26, signalperiod=9)
|
||||
informative['macd_5m'] = macd
|
||||
informative['macd_signal_5m'] = macd_signal
|
||||
informative['macd_hist_5m'] = macd_hist
|
||||
|
||||
# ADX
|
||||
informative['adx_5m'] = ta.ADX(informative['high'], informative['low'], informative['close'], timeperiod=14)
|
||||
|
||||
# RSI
|
||||
informative['rsi_5m'] = ta.RSI(informative['close'], timeperiod=14)
|
||||
|
||||
# ATR
|
||||
informative['atr_5m'] = ta.ATR(informative['high'], informative['low'], informative['close'], timeperiod=14)
|
||||
informative['atr_pct_5m'] = informative['atr_5m'] / informative['close'] * 100
|
||||
informative['atr_pct_ma_5m'] = informative['atr_pct_5m'].rolling(window=100).mean()
|
||||
|
||||
# EMA200
|
||||
informative['ema200'] = ta.EMA(informative['close'], timeperiod=200)
|
||||
informative['ema200_dist_pct'] = (informative['close'] - informative['ema200']) / informative['ema200'] * 100
|
||||
informative['ema200_slope'] = (informative['ema200'] - informative['ema200'].shift(20)) / informative['ema200'].shift(20) * 100
|
||||
|
||||
# ==================== 趋势判断 - 做空更严格 ====================
|
||||
|
||||
# 做多趋势 - 保持不变
|
||||
informative['trend_bull_5m'] = (
|
||||
(informative['ema12'] > informative['ema26']) &
|
||||
(informative['ema26'] > informative['ema50']) &
|
||||
(informative['ema12_slope'] > 0.05) &
|
||||
(informative['adx_5m'] > 24) &
|
||||
(informative['adx_5m'] < 51) &
|
||||
(informative['close'] > informative['ema12']) &
|
||||
(informative['rsi_5m'] > 52) &
|
||||
(informative['rsi_5m'] < 72)
|
||||
)
|
||||
|
||||
# 做空趋势 - 更严格！需要更强的ADX
|
||||
informative['trend_bear_5m'] = (
|
||||
(informative['ema12'] < informative['ema26']) &
|
||||
(informative['ema26'] < informative['ema50']) &
|
||||
(informative['ema12_slope'] < -0.08) & # 更陡的斜率
|
||||
(informative['adx_5m'] > 28) & # 更强的趋势确认
|
||||
(informative['adx_5m'] < 50) &
|
||||
(informative['close'] < informative['ema12']) &
|
||||
(informative['rsi_5m'] < 45) & # 更低RSI
|
||||
(informative['rsi_5m'] > 25)
|
||||
)
|
||||
|
||||
# 大趋势过滤
|
||||
informative['above_ema200'] = informative['ema200_dist_pct'] > 1.0
|
||||
informative['below_ema200'] = informative['ema200_dist_pct'] < -1.0
|
||||
|
||||
# 牛熊市
|
||||
informative['bull_market'] = (informative['ema200_slope'] > 0) & (informative['ema200_dist_pct'] > 0)
|
||||
informative['bear_market'] = (informative['ema200_slope'] < 0) & (informative['ema200_dist_pct'] < 0)
|
||||
|
||||
# 做空条件 - 必须确认在熊市
|
||||
informative['can_long_5m'] = informative['trend_bull_5m'] & informative['above_ema200']
|
||||
informative['can_short_5m'] = (
|
||||
informative['trend_bear_5m'] &
|
||||
informative['below_ema200'] &
|
||||
informative['bear_market'] # 必须确认熊市
|
||||
)
|
||||
|
||||
# ==================== ATR过滤 - 做空更严格 ====================
|
||||
|
||||
# 做多ATR - 保持宽松
|
||||
informative['atr_ok_5m'] = (
|
||||
(informative['atr_pct_5m'] > 0.07) &
|
||||
(informative['atr_pct_5m'] < informative['atr_pct_ma_5m'] * 2.2)
|
||||
)
|
||||
|
||||
# 成交量
|
||||
informative['volume_ma_5m'] = ta.SMA(informative['volume'], timeperiod=20)
|
||||
informative['volume_ok_5m'] = informative['volume'] > informative['volume_ma_5m'] * 0.75
|
||||
|
||||
# 合并
|
||||
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)
|
||||
|
||||
# ==================== 1分钟指标 ====================
|
||||
macd_1m, signal_1m, hist_1m = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
|
||||
dataframe['macd'] = macd_1m
|
||||
dataframe['macd_signal'] = signal_1m
|
||||
dataframe['macd_hist'] = hist_1m
|
||||
|
||||
dataframe['ema9'] = ta.EMA(dataframe['close'], timeperiod=9)
|
||||
dataframe['ema21'] = ta.EMA(dataframe['close'], timeperiod=21)
|
||||
dataframe['rsi'] = ta.RSI(dataframe['close'], timeperiod=14)
|
||||
dataframe['vol_ma20'] = ta.SMA(dataframe['volume'], timeperiod=20)
|
||||
dataframe['macd_slope'] = (dataframe['macd'] - dataframe['macd'].shift(3)) / 3
|
||||
|
||||
# ==================== 做空信号 ====================
|
||||
dataframe['price_high_5'] = dataframe['high'].rolling(window=5).max()
|
||||
dataframe['macd_high_5'] = dataframe['macd'].rolling(window=5).max()
|
||||
|
||||
# 顶背离 - 强化版
|
||||
dataframe['top_divergence'] = (
|
||||
(dataframe['high'] >= dataframe['price_high_5'] * 0.999) &
|
||||
(dataframe['macd'] < dataframe['macd_high_5']) &
|
||||
(dataframe['macd_slope'] < 0) &
|
||||
(dataframe['macd'] < dataframe['macd_signal']) &
|
||||
(dataframe['volume'] > dataframe['vol_ma20'] * 0.8) # 更强成交量确认
|
||||
)
|
||||
|
||||
# EMA死叉
|
||||
dataframe['ema_cross_down'] = (
|
||||
(dataframe['ema9'] < dataframe['ema21']) &
|
||||
(dataframe['ema9'].shift(1) >= dataframe['ema21'].shift(1)) &
|
||||
(dataframe['rsi'] < 55) &
|
||||
(dataframe['rsi'] > 35) &
|
||||
(dataframe['volume'] > dataframe['vol_ma20'] * 1.0)
|
||||
)
|
||||
|
||||
# 熊市回调
|
||||
dataframe['is_bear_candle'] = (dataframe['close'] < dataframe['open']) & ((dataframe['open'] - dataframe['close']) / dataframe['open'] > 0.008)
|
||||
dataframe['bear_pullback'] = (
|
||||
dataframe['is_bear_candle'].shift(2) &
|
||||
(dataframe['close'].shift(1) > dataframe['open'].shift(1)) &
|
||||
(dataframe['high'] < dataframe['high'].shift(2)) &
|
||||
(dataframe['close'] < dataframe['open']) &
|
||||
(dataframe['close'] < dataframe['ema9'])
|
||||
)
|
||||
|
||||
# ==================== 做多信号 ====================
|
||||
dataframe['price_low_5'] = dataframe['low'].rolling(window=5).min()
|
||||
dataframe['macd_low_5'] = dataframe['macd'].rolling(window=5).min()
|
||||
|
||||
dataframe['bottom_divergence'] = (
|
||||
(dataframe['low'] <= dataframe['price_low_5'] * 1.001) &
|
||||
(dataframe['macd'] > dataframe['macd_low_5']) &
|
||||
(dataframe['macd_slope'] > 0) &
|
||||
(dataframe['macd'] > dataframe['macd_signal']) &
|
||||
(dataframe['volume'] > dataframe['vol_ma20'] * 0.6)
|
||||
)
|
||||
|
||||
dataframe['ema_cross_up'] = (
|
||||
(dataframe['ema9'] > dataframe['ema21']) &
|
||||
(dataframe['ema9'].shift(1) <= dataframe['ema21'].shift(1)) &
|
||||
(dataframe['rsi'] > 45) &
|
||||
(dataframe['rsi'] < 70) &
|
||||
(dataframe['volume'] > dataframe['vol_ma20'] * 1.0)
|
||||
)
|
||||
|
||||
dataframe['is_bull_candle'] = (dataframe['close'] > dataframe['open']) & ((dataframe['close'] - dataframe['open']) / dataframe['open'] > 0.008)
|
||||
dataframe['bull_pullback'] = (
|
||||
dataframe['is_bull_candle'].shift(2) &
|
||||
(dataframe['close'].shift(1) < dataframe['open'].shift(1)) &
|
||||
(dataframe['low'] > dataframe['low'].shift(2)) &
|
||||
(dataframe['close'] > dataframe['open']) &
|
||||
(dataframe['close'] > dataframe['ema9'])
|
||||
)
|
||||
|
||||
# 时间过滤
|
||||
dataframe['hour_utc'] = dataframe['date'].dt.hour
|
||||
dataframe['is_bad_hour'] = dataframe['hour_utc'].isin([4, 5, 6, 7])
|
||||
|
||||
# 类型转换
|
||||
bool_cols = ['can_long_5m_5m', 'can_short_5m_5m', 'trend_bull_5m_5m', 'trend_bear_5m_5m',
|
||||
'atr_ok_5m_5m', 'above_ema200_5m', 'below_ema200_5m', 'bull_market_5m', 'bear_market_5m', 'volume_ok_5m_5m']
|
||||
for col in bool_cols:
|
||||
if col in dataframe.columns:
|
||||
dataframe[col] = dataframe[col].astype(bool).fillna(False)
|
||||
|
||||
num_cols = ['atr_pct_5m_5m', 'rsi_5m_5m', 'macd_hist_5m_5m', 'atr_pct_ma_5m_5m', 'ema200_dist_pct_5m', 'ema200_slope_5m']
|
||||
for col in num_cols:
|
||||
if col in dataframe.columns:
|
||||
dataframe[col] = dataframe[col].astype(float).fillna(0.0)
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
time_ok = ~dataframe['is_bad_hour']
|
||||
atr_ok = dataframe['atr_ok_5m_5m']
|
||||
volume_ok = dataframe['volume_ok_5m_5m']
|
||||
|
||||
# 做空入场 - 更严格的熊市条件
|
||||
macd_bear_5m = dataframe['macd_hist_5m_5m'] < 0
|
||||
macd_bear_1m = dataframe['macd_hist'] < 0
|
||||
|
||||
dataframe.loc[
|
||||
(time_ok) & (atr_ok) & (dataframe['can_short_5m_5m']) &
|
||||
(macd_bear_5m) & (macd_bear_1m) & (volume_ok) &
|
||||
(dataframe['rsi'] > 28) & # 更低RSI
|
||||
(dataframe['top_divergence'] | dataframe['ema_cross_down'] | dataframe['bear_pullback']) &
|
||||
(dataframe['volume'] > 0),
|
||||
'enter_short'
|
||||
] = 1
|
||||
|
||||
# 做多入场
|
||||
macd_bull_5m = dataframe['macd_hist_5m_5m'] > 0
|
||||
macd_bull_1m = dataframe['macd_hist'] > 0
|
||||
|
||||
dataframe.loc[
|
||||
(time_ok) & (atr_ok) & (dataframe['can_long_5m_5m']) &
|
||||
(macd_bull_5m) & (macd_bull_1m) & (volume_ok) &
|
||||
(dataframe['rsi'] < 70) & (dataframe['rsi'] > 40) &
|
||||
(dataframe['bottom_divergence'] | dataframe['ema_cross_up'] | dataframe['bull_pullback']) &
|
||||
(dataframe['volume'] > 0),
|
||||
'enter_long'
|
||||
] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
dataframe.loc[:, 'exit_long'] = 0
|
||||
dataframe.loc[:, 'exit_short'] = 0
|
||||
return dataframe
|
||||
|
||||
def custom_exit(self, pair: str, trade: Trade, current_time: datetime,
|
||||
current_rate: float, current_profit: float, **kwargs) -> str | bool | None:
|
||||
trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600
|
||||
|
||||
# 做空 - 更激进的时间止损
|
||||
if trade.trade_direction == 'short':
|
||||
if trade_duration > 6 and current_profit < -0.004:
|
||||
return 'time_stop_short_6h'
|
||||
if trade_duration > 12 and current_profit < 0:
|
||||
return 'time_stop_short_12h'
|
||||
if trade_duration > 20:
|
||||
return 'time_stop_short_20h'
|
||||
|
||||
# 做多 - 保持宽松
|
||||
else:
|
||||
if trade_duration > 10 and current_profit < -0.006:
|
||||
return 'time_stop_long_10h'
|
||||
if trade_duration > 20 and current_profit < 0:
|
||||
return 'time_stop_long_20h'
|
||||
if trade_duration > 30:
|
||||
return 'time_stop_long_30h'
|
||||
|
||||
return None
|
||||
|
||||
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
|
||||
time_in_force: str, current_time: datetime, entry_tag: Optional[str],
|
||||
side: str, **kwargs) -> bool:
|
||||
hour_utc = current_time.utcnow().hour if current_time.tzinfo is None else current_time.hour
|
||||
if hour_utc in {4, 5, 6, 7}:
|
||||
return False
|
||||
return True
|
||||
|
||||
def leverage(self, pair: str, current_time: datetime, current_rate: float,
|
||||
proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
|
||||
**kwargs) -> float:
|
||||
return self.lev
|
||||
@@ -0,0 +1,444 @@
|
||||
"""
|
||||
盘整背驰策略 (PanZhengBeiChi Strategy)
|
||||
|
||||
基于缠论的盘整背驰进行交易：
|
||||
- 盘整背驰：同级别走势中，Ai与Ai+2比较力度减弱
|
||||
- 顶背驰（卖点）：价格创新高或接近，但MACD力度明显减弱
|
||||
- 底背驰（买点）：价格创新低或接近，但MACD力度明显减弱
|
||||
|
||||
使用命令：
|
||||
freqtrade backtesting -c ./user_data/Chan/config/Local_Test.json \
|
||||
--strategy PanZhengBeiChiStrategy --strategy-path ./user_data/Chan/strategies \
|
||||
--timerange=20250301-
|
||||
"""
|
||||
|
||||
import logging
|
||||
from datetime import datetime
|
||||
from typing import Optional
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import talib.abstract as ta
|
||||
from pandas import DataFrame
|
||||
from technical.util import resample_to_interval, resampled_merge
|
||||
from freqtrade.strategy import IStrategy
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class PanZhengBeiChiStrategy(IStrategy):
|
||||
"""
|
||||
盘整背驰策略
|
||||
|
||||
核心逻辑：
|
||||
1. 在5分钟级别识别同级别走势段（Ai）
|
||||
2. 比较Ai与Ai+2的MACD力度，判断盘整背驰
|
||||
3. 盘整顶背驰（i+2为偶数）-> 卖出
|
||||
4. 盘整底背驰（i+2为奇数）-> 买入
|
||||
"""
|
||||
INTERFACE_VERSION: int = 3
|
||||
|
||||
# === 基础配置 ===
|
||||
timeframe = '1m'
|
||||
informative_timeframe = '5m'
|
||||
can_short = True
|
||||
can_long = True
|
||||
|
||||
startup_candle_count: int = 2000 # 需要足够的数据来识别走势段
|
||||
|
||||
# === 止损止盈配置 ===
|
||||
stoploss = -0.02 # 2% 硬止损
|
||||
use_custom_stoploss = False
|
||||
|
||||
# Trailing stop
|
||||
trailing_stop = True
|
||||
trailing_stop_positive = 0.008 # 回撤 0.8% 触发退出
|
||||
trailing_stop_positive_offset = 0.015 # 盈利 1.5% 后才开始追踪
|
||||
trailing_only_offset_is_reached = True
|
||||
|
||||
# ROI - 调整止盈策略
|
||||
minimal_roi = {
|
||||
"0": 0.015, # 1.5% 立即止盈（更保守）
|
||||
"30": 0.01, # 30分钟后 1%
|
||||
"120": 0.008, # 2小时后 0.8%
|
||||
}
|
||||
|
||||
order_types = {
|
||||
"entry": "market",
|
||||
"exit": "market",
|
||||
"stoploss": "market",
|
||||
"stoploss_on_exchange": False,
|
||||
}
|
||||
|
||||
# === 盘整背驰参数 ===
|
||||
same_level_timeframe = 5 # 5分钟级别
|
||||
pivot_window = 4 # 转折点确认窗口（增大减少噪音）
|
||||
min_segment_length = 5 # 最小段长度（K线数）（增大减少假信号）
|
||||
|
||||
# 背驰判断参数（更严格）
|
||||
beichi_price_threshold = 1.10 # 价格涨幅/跌幅阈值（允许10%范围内，更严格）
|
||||
beichi_macd_threshold = 0.75 # MACD力度阈值（低于75%即背驰，更严格）
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""计算指标并识别盘整背驰"""
|
||||
ticker = self.get_ticker_indicator()
|
||||
|
||||
# Resample 到 5m 进行同级别分解
|
||||
dataframe_5m = resample_to_interval(dataframe, ticker * self.same_level_timeframe)
|
||||
|
||||
# 在 5m 上计算指标
|
||||
dataframe_5m = self.add_indicators_5m(dataframe_5m)
|
||||
|
||||
# 识别盘整背驰
|
||||
dataframe_5m = self.identify_panzheng_beichi(dataframe_5m)
|
||||
|
||||
# 合并回 1m dataframe
|
||||
dataframe = resampled_merge(dataframe, dataframe_5m)
|
||||
|
||||
# 在 1m 上也计算基础指标
|
||||
dataframe = self.add_indicators_1m(dataframe)
|
||||
|
||||
return dataframe
|
||||
|
||||
def add_indicators_5m(self, dataframe: DataFrame) -> DataFrame:
|
||||
"""在5m级别计算指标"""
|
||||
# MACD 用于识别背驰
|
||||
macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)
|
||||
dataframe['macd'] = macd['macd']
|
||||
dataframe['macdsignal'] = macd['macdsignal']
|
||||
dataframe['macdhist'] = macd['macdhist']
|
||||
|
||||
# EMA 用于识别趋势
|
||||
dataframe['ema12'] = ta.EMA(dataframe, timeperiod=12)
|
||||
dataframe['ema26'] = ta.EMA(dataframe, timeperiod=26)
|
||||
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
|
||||
dataframe['ema200'] = ta.EMA(dataframe, timeperiod=200)
|
||||
|
||||
# EMA趋势方向
|
||||
dataframe['ema_trend_up'] = (dataframe['ema12'] > dataframe['ema26']) & (dataframe['ema26'] > dataframe['ema50'])
|
||||
dataframe['ema_trend_dn'] = (dataframe['ema12'] < dataframe['ema26']) & (dataframe['ema26'] < dataframe['ema50'])
|
||||
|
||||
# 价格与EMA200关系
|
||||
dataframe['price_above_ema200'] = dataframe['close'] > dataframe['ema200']
|
||||
dataframe['price_below_ema200'] = dataframe['close'] < dataframe['ema200']
|
||||
|
||||
# 趋势强度
|
||||
dataframe['ema12_slope'] = dataframe['ema12'].diff(5) / dataframe['ema12'].shift(5)
|
||||
dataframe['ema26_slope'] = dataframe['ema26'].diff(5) / dataframe['ema26'].shift(5)
|
||||
|
||||
# 强趋势判断
|
||||
dataframe['strong_uptrend'] = (
|
||||
(dataframe['ema12_slope'] > 0) &
|
||||
(dataframe['ema26_slope'] > 0) &
|
||||
(dataframe['price_above_ema200'])
|
||||
)
|
||||
dataframe['strong_downtrend'] = (
|
||||
(dataframe['ema12_slope'] < 0) &
|
||||
(dataframe['ema26_slope'] < 0) &
|
||||
(dataframe['price_below_ema200'])
|
||||
)
|
||||
|
||||
# RSI
|
||||
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
|
||||
|
||||
# ATR 用于波动率过滤
|
||||
dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
|
||||
dataframe['atr_mean'] = dataframe['atr'].rolling(window=20).mean()
|
||||
dataframe['volatility_ok'] = dataframe['atr'] > dataframe['atr_mean'] * 0.8
|
||||
|
||||
return dataframe
|
||||
|
||||
def add_indicators_1m(self, dataframe: DataFrame) -> DataFrame:
|
||||
"""在1m级别计算基础指标"""
|
||||
dataframe['rsi_1m'] = ta.RSI(dataframe, timeperiod=14)
|
||||
dataframe['volume_mean'] = dataframe['volume'].rolling(window=20).mean()
|
||||
|
||||
# MACD 用于1m级别确认
|
||||
macd_1m = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)
|
||||
dataframe['macd_1m'] = macd_1m['macd']
|
||||
dataframe['macdsignal_1m'] = macd_1m['macdsignal']
|
||||
dataframe['macdhist_1m'] = macd_1m['macdhist']
|
||||
|
||||
# MACD交叉
|
||||
dataframe['macd_cross_up'] = (
|
||||
(dataframe['macd_1m'] > dataframe['macdsignal_1m']) &
|
||||
(dataframe['macd_1m'].shift(1) <= dataframe['macdsignal_1m'].shift(1))
|
||||
)
|
||||
dataframe['macd_cross_dn'] = (
|
||||
(dataframe['macd_1m'] < dataframe['macdsignal_1m']) &
|
||||
(dataframe['macd_1m'].shift(1) >= dataframe['macdsignal_1m'].shift(1))
|
||||
)
|
||||
|
||||
return dataframe
|
||||
|
||||
def identify_panzheng_beichi(self, dataframe: DataFrame) -> DataFrame:
|
||||
"""
|
||||
识别盘整背驰
|
||||
|
||||
核心逻辑：
|
||||
1. 识别局部转折点（高低点）
|
||||
2. 构建同级别走势段（Ai）
|
||||
3. 比较Ai与Ai+2的MACD力度
|
||||
4. 判断盘整背驰：价格涨幅相近但MACD力度减弱
|
||||
"""
|
||||
df = dataframe.copy()
|
||||
window = self.pivot_window
|
||||
lookback = window + 1
|
||||
|
||||
# 初始化列
|
||||
df['ai_index'] = -1
|
||||
df['ai_type'] = 0 # 1: 上涨, -1: 下跌
|
||||
df['ai_high'] = np.nan
|
||||
df['ai_low'] = np.nan
|
||||
df['ai_macd_max'] = np.nan
|
||||
df['ai_macd_min'] = np.nan
|
||||
df['beichi_long'] = False # 盘整底背驰（买入信号）
|
||||
df['beichi_short'] = False # 盘整顶背驰（卖出信号）
|
||||
|
||||
# 识别局部高点
|
||||
df['temp_high'] = df['high'].shift(window)
|
||||
df['is_pivot_high'] = (
|
||||
(df['temp_high'] == df['temp_high'].rolling(window=lookback).max()) &
|
||||
(df['temp_high'].notna())
|
||||
)
|
||||
|
||||
# 识别局部低点
|
||||
df['temp_low'] = df['low'].shift(window)
|
||||
df['is_pivot_low'] = (
|
||||
(df['temp_low'] == df['temp_low'].rolling(window=lookback).min()) &
|
||||
(df['temp_low'].notna())
|
||||
)
|
||||
|
||||
# 逐行处理，识别走势段和背驰
|
||||
ai_list = []
|
||||
current_ai_start = None
|
||||
current_ai_type = None
|
||||
last_pivot_idx = None
|
||||
|
||||
for i in range(window, len(df)):
|
||||
# 检查新的转折点
|
||||
is_new_pivot = False
|
||||
pivot_type = None
|
||||
|
||||
if df.iloc[i]['is_pivot_high']:
|
||||
is_new_pivot = True
|
||||
pivot_type = 'high'
|
||||
elif df.iloc[i]['is_pivot_low']:
|
||||
is_new_pivot = True
|
||||
pivot_type = 'low'
|
||||
|
||||
if is_new_pivot and last_pivot_idx is not None:
|
||||
# 完成一个走势段
|
||||
if current_ai_start is not None:
|
||||
seg_df = df.iloc[current_ai_start:last_pivot_idx]
|
||||
if len(seg_df) >= self.min_segment_length:
|
||||
high_val = seg_df['high'].max()
|
||||
low_val = seg_df['low'].min()
|
||||
macd_max = seg_df['macd'].max()
|
||||
macd_min = seg_df['macd'].min()
|
||||
|
||||
# 判断走势类型
|
||||
if current_ai_type is None:
|
||||
if high_val > df.iloc[current_ai_start]['close']:
|
||||
current_ai_type = 1
|
||||
else:
|
||||
current_ai_type = -1
|
||||
|
||||
ai_info = {
|
||||
'start': current_ai_start,
|
||||
'end': last_pivot_idx,
|
||||
'type': current_ai_type,
|
||||
'high': high_val,
|
||||
'low': low_val,
|
||||
'macd_max': macd_max,
|
||||
'macd_min': macd_min,
|
||||
}
|
||||
ai_list.append(ai_info)
|
||||
|
||||
# 标记该段
|
||||
df.iloc[current_ai_start:last_pivot_idx, df.columns.get_loc('ai_index')] = len(ai_list) - 1
|
||||
df.iloc[current_ai_start:last_pivot_idx, df.columns.get_loc('ai_type')] = current_ai_type
|
||||
df.iloc[current_ai_start:last_pivot_idx, df.columns.get_loc('ai_high')] = high_val
|
||||
df.iloc[current_ai_start:last_pivot_idx, df.columns.get_loc('ai_low')] = low_val
|
||||
df.iloc[current_ai_start:last_pivot_idx, df.columns.get_loc('ai_macd_max')] = macd_max
|
||||
df.iloc[current_ai_start:last_pivot_idx, df.columns.get_loc('ai_macd_min')] = macd_min
|
||||
|
||||
# 判断背驰（Ai与Ai+2比较）
|
||||
if len(ai_list) >= 3:
|
||||
ai = ai_list[-3] # Ai
|
||||
ai_plus_2 = ai_list[-1] # Ai+2
|
||||
|
||||
if ai['type'] == ai_plus_2['type']:
|
||||
# 上涨段：比较向上力度
|
||||
if ai['type'] == 1:
|
||||
price_chg = (ai_plus_2['high'] - ai_plus_2['low']) / ai_plus_2['low'] if ai_plus_2['low'] > 0 else 0
|
||||
price_chg_prev = (ai['high'] - ai['low']) / ai['low'] if ai['low'] > 0 else 0
|
||||
macd_chg = ai_plus_2['macd_max']
|
||||
macd_chg_prev = ai['macd_max']
|
||||
|
||||
# 顶背驰：价格涨幅相近但MACD力度减弱
|
||||
if price_chg <= price_chg_prev * self.beichi_price_threshold and \
|
||||
macd_chg < macd_chg_prev * self.beichi_macd_threshold:
|
||||
idx = len(ai_list) - 1 # i+2的索引
|
||||
if idx % 2 == 0: # 偶数 -> 卖出
|
||||
df.iloc[last_pivot_idx, df.columns.get_loc('beichi_short')] = True
|
||||
else: # 奇数 -> 买入
|
||||
df.iloc[last_pivot_idx, df.columns.get_loc('beichi_long')] = True
|
||||
|
||||
# 下跌段：比较向下力度
|
||||
else:
|
||||
price_chg = abs((ai_plus_2['high'] - ai_plus_2['low']) / ai_plus_2['low']) if ai_plus_2['low'] > 0 else 0
|
||||
price_chg_prev = abs((ai['high'] - ai['low']) / ai['low']) if ai['low'] > 0 else 0
|
||||
macd_chg = abs(ai_plus_2['macd_min'])
|
||||
macd_chg_prev = abs(ai['macd_min'])
|
||||
|
||||
# 底背驰：价格跌幅相近但MACD力度减弱
|
||||
if price_chg <= price_chg_prev * self.beichi_price_threshold and \
|
||||
macd_chg < macd_chg_prev * self.beichi_macd_threshold:
|
||||
idx = len(ai_list) - 1
|
||||
if idx % 2 == 0: # 偶数 -> 卖出
|
||||
df.iloc[last_pivot_idx, df.columns.get_loc('beichi_short')] = True
|
||||
else: # 奇数 -> 买入
|
||||
df.iloc[last_pivot_idx, df.columns.get_loc('beichi_long')] = True
|
||||
|
||||
# 更新当前段信息
|
||||
if pivot_type == 'high':
|
||||
current_ai_type = -1 # 高点后向下
|
||||
else:
|
||||
current_ai_type = 1 # 低点后向上
|
||||
current_ai_start = last_pivot_idx
|
||||
|
||||
if is_new_pivot:
|
||||
last_pivot_idx = i
|
||||
|
||||
return df
|
||||
|
||||
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
ticker = self.get_ticker_indicator()
|
||||
resample_col = f"resample_{ticker * self.same_level_timeframe}_"
|
||||
|
||||
# 5m 级别指标列名
|
||||
beichi_long_col = f"{resample_col}beichi_long"
|
||||
beichi_short_col = f"{resample_col}beichi_short"
|
||||
ai_type_col = f"{resample_col}ai_type"
|
||||
rsi_5m_col = f"{resample_col}rsi"
|
||||
ema_trend_up_col = f"{resample_col}ema_trend_up"
|
||||
ema_trend_dn_col = f"{resample_col}ema_trend_dn"
|
||||
volatility_ok_col = f"{resample_col}volatility_ok"
|
||||
strong_uptrend_col = f"{resample_col}strong_uptrend"
|
||||
strong_downtrend_col = f"{resample_col}strong_downtrend"
|
||||
price_above_ema200_col = f"{resample_col}price_above_ema200"
|
||||
price_below_ema200_col = f"{resample_col}price_below_ema200"
|
||||
|
||||
# === 做多入场 ===
|
||||
# 条件：盘整底背驰 + 强上升趋势确认
|
||||
dataframe.loc[
|
||||
(
|
||||
# 核心信号：盘整底背驰
|
||||
(dataframe[beichi_long_col] == True) &
|
||||
|
||||
# 强上升趋势确认（更严格）
|
||||
(dataframe[strong_uptrend_col] == True) &
|
||||
|
||||
# RSI 确认（更严格：只在大趋势中操作）
|
||||
(dataframe[rsi_5m_col] > 40) &
|
||||
(dataframe[rsi_5m_col] < 60) &
|
||||
|
||||
# 1m 指标确认
|
||||
(dataframe['macd_1m'] > dataframe['macdsignal_1m']) &
|
||||
|
||||
# 成交量确认
|
||||
(dataframe['volume'] > dataframe['volume_mean'] * 1.5)
|
||||
),
|
||||
['enter_long', 'enter_tag']
|
||||
] = (1, "pzbc_long")
|
||||
|
||||
# === 做空入场 ===
|
||||
# 条件：盘整顶背驰 + 强下降趋势确认（更严格）
|
||||
dataframe.loc[
|
||||
(
|
||||
# 核心信号：盘整顶背驰
|
||||
(dataframe[beichi_short_col] == True) &
|
||||
|
||||
# 强下降趋势确认
|
||||
(dataframe[strong_downtrend_col] == True) &
|
||||
|
||||
# RSI 确认
|
||||
(dataframe[rsi_5m_col] > 40) &
|
||||
(dataframe[rsi_5m_col] < 60) &
|
||||
|
||||
# 1m 指标确认
|
||||
(dataframe['macd_1m'] < dataframe['macdsignal_1m']) &
|
||||
|
||||
# 成交量确认
|
||||
(dataframe['volume'] > dataframe['volume_mean'] * 1.5)
|
||||
),
|
||||
['enter_short', 'enter_tag']
|
||||
] = (1, "pzbc_short")
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
出场逻辑
|
||||
|
||||
多头出场：
|
||||
1. 出现盘整顶背驰
|
||||
2. 趋势转弱
|
||||
|
||||
空头出场：
|
||||
1. 出现盘整底背驰
|
||||
2. 趋势转弱
|
||||
"""
|
||||
ticker = self.get_ticker_indicator()
|
||||
resample_col = f"resample_{ticker * self.same_level_timeframe}_"
|
||||
|
||||
beichi_long_col = f"{resample_col}beichi_long"
|
||||
beichi_short_col = f"{resample_col}beichi_short"
|
||||
ai_type_col = f"{resample_col}ai_type"
|
||||
rsi_5m_col = f"{resample_col}rsi"
|
||||
ema_trend_dn_col = f"{resample_col}ema_trend_dn"
|
||||
strong_downtrend_col = f"{resample_col}strong_downtrend"
|
||||
strong_uptrend_col = f"{resample_col}strong_uptrend"
|
||||
|
||||
# === 多头出场 ===
|
||||
dataframe.loc[
|
||||
(
|
||||
# 出现盘整顶背驰 -> 退出多头
|
||||
(dataframe[beichi_short_col] == True) |
|
||||
|
||||
# 趋势转弱
|
||||
(
|
||||
(dataframe[ai_type_col] == -1) &
|
||||
(dataframe[rsi_5m_col] > 55)
|
||||
) |
|
||||
|
||||
# 强下跌趋势
|
||||
(dataframe[strong_downtrend_col] == True)
|
||||
),
|
||||
['exit_long', 'exit_tag']
|
||||
] = (1, "pzbc_exit_long")
|
||||
|
||||
# === 空头出场 ===
|
||||
dataframe.loc[
|
||||
(
|
||||
# 出现盘整底背驰 -> 退出空头
|
||||
(dataframe[beichi_long_col] == True) |
|
||||
|
||||
# 趋势转弱
|
||||
(
|
||||
(dataframe[ai_type_col] == 1) &
|
||||
(dataframe[rsi_5m_col] < 45)
|
||||
) |
|
||||
|
||||
# 强上涨趋势
|
||||
(dataframe[strong_uptrend_col] == True)
|
||||
),
|
||||
['exit_short', 'exit_tag']
|
||||
] = (1, "pzbc_exit_short")
|
||||
|
||||
return dataframe
|
||||
|
||||
def get_ticker_indicator(self) -> int:
|
||||
"""获取 timeframe 的分钟数"""
|
||||
return int(self.timeframe[:-1])
|
||||
|
||||
@@ -0,0 +1,204 @@
|
||||
"""
|
||||
纯随机规则策略 (PureRandomRuleStrategy)
|
||||
|
||||
完全不看 K 线、不看指标、不看量价、不看趋势、不看形态的纯规则交易系统。
|
||||
|
||||
规则：
|
||||
- 固定时间周期开仓（例如：每 4 小时一单）
|
||||
- 方向随机多空，不做任何行情判断
|
||||
- 每次只开1个方向，不对冲
|
||||
- 固定止盈：2%
|
||||
- 固定止损：1%
|
||||
- 到价立即平仓，不移动、不修改
|
||||
- 单笔仓位：总资金的 5%
|
||||
- 单笔最大风险：总资金的 0.05%
|
||||
- 连续止损 3 次，当天停止交易
|
||||
- 总持仓不超过 20%
|
||||
|
||||
使用命令：
|
||||
freqtrade backtesting -c ./user_data/Chan/config/Local_Test.json --strategy PureRandomRuleStrategy --strategy-path ./user_data/Chan/strategies --timerange=20260101-
|
||||
|
||||
实盘命令：
|
||||
freqtrade trade -c ./user_data/Chan/config/Chan.json \
|
||||
--strategy PureRandomRuleStrategy --strategy-path ./user_data/Chan/strategies
|
||||
"""
|
||||
|
||||
import logging
|
||||
from datetime import datetime
|
||||
from typing import Optional
|
||||
import random
|
||||
import pandas as pd
|
||||
import talib.abstract as ta
|
||||
from pandas import DataFrame
|
||||
from freqtrade.strategy import IStrategy
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class PureRandomRuleStrategy(IStrategy):
|
||||
"""
|
||||
纯随机规则策略
|
||||
|
||||
核心特点：
|
||||
1. 不看任何行情数据
|
||||
2. 固定时间开仓（可配置间隔）
|
||||
3. 随机选择多空方向
|
||||
4. 固定止盈止损
|
||||
5. 风险控制（连续止损、持仓限制）
|
||||
"""
|
||||
INTERFACE_VERSION: int = 3
|
||||
|
||||
# === 基础配置 ===
|
||||
timeframe = '1m' # 主时间框架
|
||||
informative_timeframe = '1h' # 1小时作为参考（需要数据支持）
|
||||
can_short = True
|
||||
can_long = True
|
||||
|
||||
startup_candle_count = 200 # 需要更多数据计算 EMA
|
||||
|
||||
# === 交易时间间隔配置 ===
|
||||
trade_interval_hours = 4
|
||||
|
||||
# === 止盈止损配置 ===
|
||||
take_profit_pct = 0.024
|
||||
stop_loss_pct = 0.01
|
||||
|
||||
# === 仓位配置 ===
|
||||
entry_percent = 0.05
|
||||
max_position_pct = 0.20
|
||||
|
||||
# === 风险控制 ===
|
||||
max_consecutive_losses = 3
|
||||
|
||||
# === 订单类型 ===
|
||||
order_types = {
|
||||
"entry": "market",
|
||||
"exit": "market",
|
||||
"stoploss": "market",
|
||||
"stoploss_on_exchange": False,
|
||||
}
|
||||
|
||||
# === 最小 ROI ===
|
||||
minimal_roi = {
|
||||
"0": take_profit_pct,
|
||||
}
|
||||
|
||||
# === 止损 ===
|
||||
stoploss = -stop_loss_pct
|
||||
|
||||
# === 追踪止损 ===
|
||||
trailing_stop = False
|
||||
|
||||
# === 策略状态 ===
|
||||
_last_entry_time: Optional[datetime] = None
|
||||
_consecutive_losses: int = 0
|
||||
_last_loss_date: Optional[datetime] = None
|
||||
_today_loss_count: int = 0
|
||||
|
||||
def __init__(self, config: dict) -> None:
|
||||
super().__init__(config)
|
||||
random.seed()
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
计算 1h EMA26 和波动幅度
|
||||
使用 resampled_merge 合并 1h 数据
|
||||
"""
|
||||
from technical.util import resample_to_interval, resampled_merge
|
||||
|
||||
# 重采样到 1h (1m * 60 = 60)
|
||||
dataframe_1h = resample_to_interval(dataframe, 60)
|
||||
|
||||
# 计算 1h EMA26
|
||||
dataframe_1h['ema26'] = ta.EMA(dataframe_1h, timeperiod=26)
|
||||
|
||||
# 计算 1h 波动幅度: (high - low) / open * 100%
|
||||
dataframe_1h['volatility'] = (dataframe_1h['high'] - dataframe_1h['low']) / dataframe_1h['open']
|
||||
|
||||
# 合并到主 dataframe
|
||||
# 列名格式: resample_60_ema26, resample_60_volatility
|
||||
dataframe = resampled_merge(dataframe, dataframe_1h)
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
入场逻辑：固定时间 + 1h EMA26方向过滤 + 波动过滤 + 时间过滤
|
||||
|
||||
规则：
|
||||
1. 1h EMA26 上方 -> 只做多
|
||||
2. 1h EMA26 下方 -> 只做空
|
||||
3. 固定时间间隔开仓（4小时）
|
||||
4. 1h 波动幅度 > 0.5% 且 < 5%
|
||||
5. UTC 8:00-20:00
|
||||
"""
|
||||
dataframe['enter_long'] = 0
|
||||
dataframe['enter_short'] = 0
|
||||
dataframe['enter_tag'] = ''
|
||||
|
||||
last_entry_idx = None
|
||||
|
||||
# 1h EMA26 列名
|
||||
ema26_col = 'resample_60_ema26'
|
||||
# 1h 波动幅度列名
|
||||
volatility_col = 'resample_60_volatility'
|
||||
|
||||
# 波动幅度阈值
|
||||
min_volatility = 0.005 # 0.5%
|
||||
max_volatility = 0.05 # 5%
|
||||
|
||||
for i in range(len(dataframe)):
|
||||
current_time = dataframe['date'].iloc[i]
|
||||
current_price = dataframe['close'].iloc[i]
|
||||
|
||||
# 使用 resample 后的 EMA26 列
|
||||
ema26_1h = dataframe[ema26_col].iloc[i]
|
||||
# 波动幅度
|
||||
volatility = dataframe[volatility_col].iloc[i]
|
||||
|
||||
# 跳过没有 EMA 数据的情况
|
||||
if pd.isna(ema26_1h):
|
||||
continue
|
||||
|
||||
# 检查时间间隔（4小时）
|
||||
can_entry = True
|
||||
if last_entry_idx is not None:
|
||||
hours_since_last = (current_time - dataframe['date'].iloc[last_entry_idx]).total_seconds() / 3600
|
||||
if hours_since_last < self.trade_interval_hours:
|
||||
can_entry = False
|
||||
|
||||
# 检查当天连续止损
|
||||
if self._today_loss_count >= self.max_consecutive_losses:
|
||||
can_entry = False
|
||||
|
||||
# 检查波动幅度（>0.5% 且 <5%）
|
||||
if not pd.isna(volatility):
|
||||
if volatility < min_volatility or volatility > max_volatility:
|
||||
can_entry = False
|
||||
else:
|
||||
can_entry = False
|
||||
|
||||
# 检查时间过滤（UTC 8:00-20:00）
|
||||
utc_hour = current_time.hour
|
||||
#if utc_hour < 8 or utc_hour >= 20:
|
||||
#can_entry = False
|
||||
|
||||
if can_entry:
|
||||
# 判断方向：价格 > 1h EMA26 做多，价格 < 1h EMA26 做空
|
||||
if current_price > ema26_1h:
|
||||
dataframe.loc[dataframe.index[i], 'enter_long'] = 1
|
||||
dataframe.loc[dataframe.index[i], 'enter_tag'] = 'long_above_ema'
|
||||
elif current_price < ema26_1h:
|
||||
dataframe.loc[dataframe.index[i], 'enter_short'] = 1
|
||||
dataframe.loc[dataframe.index[i], 'enter_tag'] = 'short_below_ema'
|
||||
|
||||
if dataframe.loc[dataframe.index[i], 'enter_long'] == 1 or dataframe.loc[dataframe.index[i], 'enter_short'] == 1:
|
||||
last_entry_idx = i
|
||||
self._last_entry_time = current_time
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
dataframe['exit_long'] = 0
|
||||
dataframe['exit_short'] = 0
|
||||
return dataframe
|
||||
Reference in New Issue
Block a user