208 lines
8.8 KiB
Python
208 lines
8.8 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 CryptoFutures1m5mStrategy(IStrategy):
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"""
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SOL/USDT 合约策略 - 只做多版 (默认策略)
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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 = 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
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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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# 大趋势过滤
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informative['above_ema200'] = informative['ema200_dist_pct'] > 1.0
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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过滤
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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
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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['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_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_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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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_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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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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dataframe.loc[
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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_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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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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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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