213 lines
9.2 KiB
Python
213 lines
9.2 KiB
Python
# 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 CryptoFutures1m5mStrategyShortOnly(IStrategy):
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"""
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SOL/USDT 合约策略 - 只做空版本
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基于V5修改：
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- 只做空，禁止做多
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- 优化做空止损和止盈参数
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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 = True
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can_long = False # 禁用做多
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lev = 1.0
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# Trailing设置 - 基于V5
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trailing_stop = True
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trailing_stop_positive = 0.008
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trailing_stop_positive_offset = 0.035
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trailing_only_offset_is_reached = True
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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 [("SOL/USDT:USDT", "5m")]
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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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
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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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informative['ema12_slope'] = (informative['ema12'] - informative['ema12'].shift(3)) / informative['ema12'].shift(3) * 100
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# MACD
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macd, macd_signal, macd_hist = ta.MACD(informative['close'], fastperiod=12, slowperiod=26, signalperiod=9)
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informative['macd_5m'] = macd
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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
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informative['adx_5m'] = ta.ADX(informative['high'], informative['low'], informative['close'], timeperiod=14)
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# RSI
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informative['rsi_5m'] = ta.RSI(informative['close'], timeperiod=14)
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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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informative['atr_pct_ma_5m'] = informative['atr_pct_5m'].rolling(window=100).mean()
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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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informative['ema200_slope'] = (informative['ema200'] - informative['ema200'].shift(20)) / informative['ema200'].shift(20) * 100
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# 做空趋势 - 基于V5优化
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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'] < -0.05) & # V5标准
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(informative['adx_5m'] > 24) & # V5标准
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(informative['adx_5m'] < 51) &
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(informative['close'] < informative['ema12']) &
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(informative['rsi_5m'] < 48) &
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(informative['rsi_5m'] > 29)
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)
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# 大趋势过滤 - 放宽条件，基于V5
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informative['below_ema200'] = informative['ema200_dist_pct'] < -1.0
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# 熊市确认 - 可选，不过度限制
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informative['bear_market'] = (informative['ema200_slope'] < 0) & (informative['ema200_dist_pct'] < 0)
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# 做空条件 - 移除bear_market强制要求，基于V5
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informative['can_short_5m'] = informative['trend_bear_5m'] & informative['below_ema200']
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# ATR过滤 - 基于V5标准
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informative['atr_ok_5m'] = (
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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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# 成交量 - 基于V5标准
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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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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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dataframe['macd_hist'] = hist_1m
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dataframe['ema9'] = ta.EMA(dataframe['close'], timeperiod=9)
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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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dataframe['macd_slope'] = (dataframe['macd'] - dataframe['macd'].shift(3)) / 3
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# 做空信号
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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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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['volume'] > dataframe['vol_ma20'] * 0.8)
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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) &
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(dataframe['rsi'] > 35) &
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(dataframe['volume'] > dataframe['vol_ma20'] * 1.0)
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)
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dataframe['is_bear_candle'] = (dataframe['close'] < dataframe['open']) & ((dataframe['open'] - dataframe['close']) / dataframe['open'] > 0.008)
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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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)
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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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# 类型转换
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bool_cols = ['can_short_5m_5m', 'trend_bear_5m_5m', 'atr_ok_5m_5m', 'below_ema200_5m', 'volume_ok_5m_5m', 'bear_market_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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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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# 只做空 - 基于V5标准
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macd_bear_5m = dataframe['macd_hist_5m_5m'] < 0
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macd_bear_1m = dataframe['macd_hist'] < 0
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dataframe.loc[
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(time_ok) & (atr_ok) & (dataframe['can_short_5m_5m']) &
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(macd_bear_5m) & (macd_bear_1m) & (volume_ok) &
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(dataframe['rsi'] > 30) &
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(dataframe['top_divergence'] | dataframe['ema_cross_down'] | dataframe['bear_pullback']) &
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(dataframe['volume'] > 0),
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'enter_short'
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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_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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trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600
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# 做空时间止损 - 基于V5标准
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if trade_duration > 8 and current_profit < -0.005:
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return 'time_stop_short_8h'
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if trade_duration > 16 and current_profit < 0:
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return 'time_stop_short_16h'
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if trade_duration > 24:
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return 'time_stop_short_24h'
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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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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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