340 lines
15 KiB
Python
340 lines
15 KiB
Python
# --- Do not remove these libs ---
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from freqtrade.strategy import IStrategy
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from typing import Dict, List
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from functools import reduce
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from pandas import DataFrame, pandas
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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# --------------------------------
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from technical.util import resample_to_interval, resampled_merge
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import talib.abstract as ta
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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from datetime import datetime, timedelta, timezone
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from freqtrade.persistence import Trade, Order
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from typing import Optional
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import logging
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logger = logging.getLogger(__name__)
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### Now you can use logger.info('asfd') to log
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# freqtrade trade -c ./user_data/Chan/config/ChanLun_SOL.json --strategy ChanLun_SOL_2 --strategy-path ./user_data/Chan/strategies
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# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_SOL.json --strategy ChanLun_SOL_2 --strategy-path ./user_data/Chan/strategies --timerange=20250309-
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# freqtrade download-data -c ./user_data/Chan/config/ChanLun_SOL.json -t 1m --pairs SOL/USDT:USDT --timerange=20250501-
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# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi stoploss --strategy ChanLun_SOL_2 --strategy-path ./user_data/strategies -c ./user_data/ChanLun_SOL.json -e 200 --timerange=20250101-20250215
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# sudo docker compose run --rm chan_btc backtesting -c ./user_data/Chan.json --strategy Chan_SOL_2 --strategy-path ./user_data/strategies --timerange=20250101-
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# sudo docker compose run --rm chan_btc download-data -c ./user_data/Chan.json --pairs SOL/USDT:USDT -t 1m --timerange 20240101-
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# sudo docker compose run --rm chan_btc trade -c ./user_data/Chan.json --strategy Chan_SOL_2 --strategy-path ./user_data/strategies
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class ChanLun_SOL_2(IStrategy):
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INTERFACE_VERSION: int = 3
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# 优化的ROI设置 - 更快速获利
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minimal_roi = {
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"0": 0.012, # 立即获利1.2%
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"5": 0.01, # 5分钟后获利1%
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"15": 0.007, # 15分钟后获利0.7%
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"30": 0.005 # 30分钟后获利0.5%
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}
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can_short = True
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stoploss = -0.007 # 降低止损为0.7%
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# 追踪止损设置 - 更积极的追踪止损
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trailing_stop = True
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trailing_stop_positive = 0.003 # 0.3%
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trailing_stop_positive_offset = 0.005 # 0.5%
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trailing_only_offset_is_reached = True
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# 时间周期
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timeframe = '5m'
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informative_timeframe = '1h'
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startup_candle_count = 200
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# 只做空头策略
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only_short = True
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def informative_pairs(self):
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pairs = self.dp.current_whitelist()
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informative_pairs = [(pair, self.informative_timeframe) for pair in pairs]
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return informative_pairs
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# 获取更高时间周期的数据
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informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe)
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# === 高时间周期指标 ===
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# 三均线系统
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informative['ema50'] = ta.EMA(informative, timeperiod=50)
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informative['ema100'] = ta.EMA(informative, timeperiod=100)
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informative['ema200'] = ta.SMA(informative, timeperiod=200) # 使用SMA作为长期趋势
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# 趋势方向
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informative['uptrend'] = (
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(informative['ema50'] > informative['ema100']) &
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(informative['ema100'] > informative['ema200']) &
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(informative['close'] > informative['ema50'])
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).astype(int)
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informative['downtrend'] = (
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(informative['ema50'] < informative['ema100']) &
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(informative['ema100'] < informative['ema200']) &
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(informative['close'] < informative['ema50'])
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).astype(int)
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# 强下降趋势
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informative['strong_downtrend'] = (
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(informative['ema50'] < informative['ema100']) &
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(informative['ema100'] < informative['ema200']) &
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(informative['close'] < informative['ema50']) &
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(informative['ema50'].shift(3) < informative['ema50']) # 确认EMA50下降
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).astype(int)
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# 添加高时间周期的ADX指标
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informative['adx'] = ta.ADX(informative, timeperiod=14)
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# 添加高时间周期的波动率
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informative['atr'] = ta.ATR(informative, timeperiod=14)
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informative['atr_percent'] = (informative['atr'] / informative['close']) * 100
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# 高时间周期RSI
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informative['rsi'] = ta.RSI(informative, timeperiod=14)
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# 将informative数据帧中的列重命名，以便在合并后区分
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for col in informative.columns:
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if col not in ['date', 'open', 'high', 'low', 'close', 'volume']:
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informative[f"{col}_{self.informative_timeframe}"] = informative[col]
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# 删除原始列，只保留重命名后的列和必要的日期、OHLCV列
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for col in list(informative.columns):
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if col not in ['date', 'open', 'high', 'low', 'close', 'volume'] and not col.endswith(f"_{self.informative_timeframe}"):
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del informative[col]
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# 打印列名以便调试
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logger.info(f"Informative columns after renaming: {informative.columns.tolist()}")
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# 合并数据 - 使用正确的参数
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dataframe = resampled_merge(dataframe, informative, self.informative_timeframe)
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# 打印合并后的列名以便调试
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logger.info(f"Dataframe columns after merge: {dataframe.columns.tolist()}")
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# === 主时间周期指标 ===
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# 布林带
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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dataframe['bb_lowerband'] = bollinger['lower']
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dataframe['bb_middleband'] = bollinger['mid']
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dataframe['bb_upperband'] = bollinger['upper']
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dataframe['bb_width'] = ((bollinger['upper'] - bollinger['lower']) / bollinger['mid'])
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# 动量指标
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dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
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dataframe['mfi'] = ta.MFI(dataframe, timeperiod=14)
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# MACD
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macd = ta.MACD(dataframe)
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dataframe['macd'] = macd['macd']
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dataframe['macdsignal'] = macd['macdsignal']
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dataframe['macdhist'] = macd['macdhist']
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# 均线
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dataframe['ema9'] = ta.EMA(dataframe, timeperiod=9)
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dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
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dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
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dataframe['sma200'] = ta.SMA(dataframe, timeperiod=200)
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# 成交量
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dataframe['volume_mean'] = dataframe['volume'].rolling(window=20).mean()
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dataframe['volume_ratio'] = dataframe['volume'] / dataframe['volume_mean']
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# 波动率
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dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
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# ADX - 趋势强度指标
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dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
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# 价格突破
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dataframe['upper_break'] = (
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(dataframe['close'] > dataframe['bb_upperband']) &
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(dataframe['close'].shift() <= dataframe['bb_upperband'].shift())
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).astype(int)
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dataframe['lower_break'] = (
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(dataframe['close'] < dataframe['bb_lowerband']) &
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(dataframe['close'].shift() >= dataframe['bb_lowerband'].shift())
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).astype(int)
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# 均线交叉
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dataframe['ema_cross_up'] = (
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(dataframe['ema9'] > dataframe['ema21']) &
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(dataframe['ema9'].shift() <= dataframe['ema21'].shift())
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).astype(int)
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dataframe['ema_cross_down'] = (
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(dataframe['ema9'] < dataframe['ema21']) &
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(dataframe['ema9'].shift() >= dataframe['ema21'].shift())
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).astype(int)
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# 超买超卖区域
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dataframe['rsi_oversold'] = (dataframe['rsi'] < 30).astype(int)
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dataframe['rsi_overbought'] = (dataframe['rsi'] > 70).astype(int)
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# 价格与均线的关系
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dataframe['price_above_ema50'] = (dataframe['close'] > dataframe['ema50']).astype(int)
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dataframe['price_below_ema50'] = (dataframe['close'] < dataframe['ema50']).astype(int)
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# 趋势强度
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dataframe['strong_trend'] = (dataframe['adx'] > 25).astype(int)
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# 添加蜡烛图形态识别
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dataframe['doji'] = ta.CDLDOJI(dataframe['open'], dataframe['high'], dataframe['low'], dataframe['close'])
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dataframe['engulfing'] = ta.CDLENGULFING(dataframe['open'], dataframe['high'], dataframe['low'], dataframe['close'])
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dataframe['hammer'] = ta.CDLHAMMER(dataframe['open'], dataframe['high'], dataframe['low'], dataframe['close'])
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dataframe['shooting_star'] = ta.CDLSHOOTINGSTAR(dataframe['open'], dataframe['high'], dataframe['low'], dataframe['close'])
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# 价格动量
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dataframe['momentum'] = dataframe['close'] - dataframe['close'].shift(5)
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return dataframe
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# 检查列名是否存在
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downtrend_col = 'resample_60_downtrend_1h'
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strong_downtrend_col = 'resample_60_strong_downtrend_1h'
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adx_col = 'resample_60_adx_1h'
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rsi_col = 'resample_60_rsi_1h'
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# 如果列名不存在，使用替代方案
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for col, default_value in [
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(downtrend_col, 0),
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(strong_downtrend_col, 0),
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(adx_col, 25),
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(rsi_col, 50)
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]:
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if col not in dataframe.columns:
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logger.warning(f"Column {col} not found in dataframe. Creating with default value {default_value}.")
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dataframe[col] = default_value
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# 禁用多头入场
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dataframe['enter_long'] = 0
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# 空头入场条件 - 专注于空头策略
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short_conditions = (
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# 高时间周期处于下降趋势
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(dataframe[downtrend_col] > 0) &
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# 趋势强度确认
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(dataframe[adx_col] > 25) &
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# 条件1: 价格突破上轨后回落 + 成交量确认
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(
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(dataframe['upper_break'].rolling(window=5).sum() > 0) & # 最近5根K线内有突破上轨
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(dataframe['close'] < dataframe['close'].shift(2)) & # 价格开始下跌
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(dataframe['close'] < dataframe['ema9']) & # 价格在短期均线下方
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(dataframe['volume_ratio'] > 1.3) & # 成交量放大
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(dataframe['rsi'] < 70) & # RSI不在极度超买区
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(dataframe['rsi'] > 40) & # RSI不在超卖区
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(dataframe[rsi_col] < 60) # 高时间周期RSI不过高
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) |
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# 条件2: 均线死叉 + RSI超买回落 + 趋势确认
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(
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(dataframe['ema_cross_down'] > 0) & # 均线死叉
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(dataframe['rsi'] > 55) & # RSI相对较高
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(dataframe['rsi'] < dataframe['rsi'].shift(3)) & # RSI下降
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(dataframe['volume_ratio'] > 1.2) & # 成交量放大
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(dataframe['adx'] > 20) & # ADX显示有一定趋势强度
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((dataframe['shooting_star'] > 0) | (dataframe['engulfing'] < 0)) # 流星线或看跌吞没形态
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) |
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# 条件3: 价格在高点回落 + 强趋势
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(
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(dataframe['close'] < dataframe['high'].shift()) &
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(dataframe['high'].shift() > dataframe['high'].shift(2)) &
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(dataframe['close'] < dataframe['ema21']) &
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(dataframe['adx'] > 30) &
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(dataframe['rsi'] < dataframe['rsi'].shift()) &
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(dataframe['rsi'].shift() > 65) &
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(dataframe['volume_ratio'] > 1.0)
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) |
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# 条件4: 强下降趋势确认
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(
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(dataframe[strong_downtrend_col] > 0) &
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(dataframe['close'] < dataframe['ema21']) &
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(dataframe['close'] < dataframe['close'].shift(3)) &
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(dataframe['momentum'] < 0) &
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(dataframe['volume_ratio'] > 1.1) &
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(dataframe['adx'] > 25)
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)
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)
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dataframe.loc[short_conditions, 'enter_short'] = 1
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dataframe.loc[short_conditions, 'enter_tag'] = 'chan_sol_short'
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return dataframe
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def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# 禁用多头出场
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dataframe['exit_long'] = 0
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# 空头出场条件 - 更精确的出场
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short_exit_conditions = (
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# 条件1: 趋势反转信号
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(
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(dataframe['ema_cross_up'] > 0) & # 均线金叉
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(dataframe['volume_ratio'] > 1.0) # 成交量确认
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) |
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# 条件2: 价格突破中期均线
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(
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(dataframe['close'] > dataframe['ema21']) &
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(dataframe['close'].shift() < dataframe['ema21'].shift()) & # 确认是刚刚突破
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(dataframe['volume_ratio'] > 1.2) # 成交量确认
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) |
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# 条件3: 超卖信号
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(
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(dataframe['rsi'] < 30) & # RSI超卖
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(dataframe['close'] < dataframe['bb_lowerband']) # 价格突破下轨
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) |
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# 条件4: 动量减弱
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(
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(dataframe['rsi'] < 35) &
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(dataframe['rsi'] > dataframe['rsi'].shift()) &
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(dataframe['rsi'].shift() > dataframe['rsi'].shift(2)) & # RSI连续两根K线上升
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(dataframe['momentum'] > 0) # 价格动量转为正
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) |
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# 条件5: 锤子线形态 (潜在反转信号)
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(
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(dataframe['hammer'] > 0) &
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(dataframe['volume_ratio'] > 1.3)
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)
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)
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dataframe.loc[short_exit_conditions, 'exit_short'] = 1
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dataframe.loc[short_exit_conditions, 'exit_tag'] = 'chan_sol_short_exit'
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return dataframe
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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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"""
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在进入交易前进行额外的确认
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"""
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# 只做空头交易
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if side == "sell" and entry_tag == "chan_sol_short":
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return True
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return False
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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 1.0
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def get_ticker_indicator(self):
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return int(self.timeframe[:-1]) |