115 lines
5.4 KiB
Python
115 lines
5.4 KiB
Python
# --- Do not remove these libs ---
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from statistics import median
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from freqtrade.strategy import IStrategy
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from technical.util import resample_to_interval, resampled_merge
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from pandas import DataFrame
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import talib.abstract as ta
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from technical import qtpylib
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### Now you can use logger.info('asfd') to log
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# freqtrade plot-dataframe --strategy EMA_Pattern --datadir user_data/data/binance -c ./user_data/Chan/EMA_Pattern.json --timerange=20250309-
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# freqtrade backtesting -c ./user_data/Chan/config/EMA_Pattern.json --strategy EMA_Pattern --strategy-path ./user_data/Chan/strategies --timerange=20251030-
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# freqtrade download-data -c ./user_data/Chan/config/EMA_Pattern.json -t 1m 3m 5m 15m 30m 1h --pairs BTC/USDT:USDT --timerange=20250405-
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# freqtrade download-data -c ./user_data/Chan/config/EMA_Pattern.json -t 1m 1h 1d 1M --pairs BTC/USDT --timerange=20170101-
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# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi --strategy EMA_Pattern --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/EMA_Pattern.json -e 200 --timerange=20250201-20250901
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# freqtrade edge -c ./user_data/Chan/config/EMA_Pattern.json --strategy EMA_Pattern --strategy-path ./user_data/Chan/strategies --timerange 20250721-20250901
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# freqtrade plot-dataframe -c ./user_data/Chan/config/EMA_Pattern.json --strategy EMA_Pattern --strategy-path ./user_data/Chan/strategies --timerange 20250721-20250901
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class EMA_Pattern(IStrategy):
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time1h = 1440
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can_short: bool = True
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timeframe: str = "1m"
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process_only_new_candles: bool = False
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# ROI 与止损可根据需要在配置中覆盖
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minimal_roi = {
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"60": 0.005,
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"30": 0.01,
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"0": 0.02,
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}
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stoploss: float = -0.30
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# 需要的历史K线数量（包含EMA等指标预热）
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startup_candle_count: int = 200
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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if dataframe is None or dataframe.empty:
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return dataframe
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dataframe = self.add_indicators(dataframe)
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return dataframe
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def add_indicators(self, dataframe: DataFrame) -> DataFrame:
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macd = ta.MACD(dataframe, timeperiod=12, fastperiod=12, slowperiod=26, signalperiod=9)
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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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dataframe['ema6'] = ta.EMA(dataframe, timeperiod=6)
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dataframe['ema12'] = ta.EMA(dataframe, timeperiod=12)
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dataframe['ema24'] = ta.EMA(dataframe, timeperiod=24)
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dataframe['ema52'] = ta.EMA(dataframe, timeperiod=52)
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dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
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dataframe['strong_trend'] = dataframe['adx'] > 25
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dataframe['UP_Pattern'] = (dataframe['ema6'] > dataframe['ema12']) & (dataframe['ema12'] > dataframe['ema24']) & (dataframe['ema24'] > dataframe['ema52'])
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dataframe['DOWN_Pattern'] = (dataframe['ema6'] < dataframe['ema12']) & (dataframe['ema12'] < dataframe['ema24']) & (dataframe['ema24'] < dataframe['ema52'])
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dataframe['UP_Confirm'] = (dataframe['ema6'] > dataframe['ema6'].shift(1)) & (dataframe['ema12'] > dataframe['ema12'].shift(1)) & (dataframe['ema24'] > dataframe['ema24'].shift(1)) & (dataframe['ema52'] > dataframe['ema52'].shift(1))
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dataframe['DOWN_Confirm'] = (dataframe['ema6'] < dataframe['ema6'].shift(1)) & (dataframe['ema12'] < dataframe['ema12'].shift(1)) & (dataframe['ema24'] < dataframe['ema24'].shift(1)) & (dataframe['ema52'] < dataframe['ema52'].shift(1))
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dataframe['EMA52_Cross_EMA24_UP'] = (dataframe['ema52'] < dataframe['ema24']) & (dataframe['ema52'].shift(1) > dataframe['ema24'].shift(1))
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dataframe['EMA52_Cross_EMA24_DOWN'] = (dataframe['ema52'] > dataframe['ema24']) & (dataframe['ema52'].shift(1) < dataframe['ema24'].shift(1))
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dataframe['Price_Above_EMA52'] = (dataframe['close'] > dataframe['ema52'])
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dataframe['Price_Below_EMA52'] = (dataframe['close'] < dataframe['ema52'])
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dataframe['MACD_Above_Zero'] = (dataframe['macd'] > 0) & (dataframe['macdsignal'] > 0)
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dataframe['MACD_Below_Zero'] = (dataframe['macd'] < 0) & (dataframe['macdsignal'] < 0)
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dataframe['BUY_END'] = dataframe['close'] < dataframe['ema52']
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dataframe['SELL_END'] = dataframe['close'] > dataframe['ema52']
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return dataframe
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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if dataframe is None or dataframe.empty:
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return dataframe
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dataframe.loc[
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(
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(dataframe['UP_Pattern']) &
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(dataframe['UP_Confirm']) &
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(dataframe['Price_Above_EMA52']) &
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(dataframe['MACD_Above_Zero']) &
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(dataframe['EMA52_Cross_EMA24_UP']) &
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(dataframe['strong_trend'])
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),
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["enter_long", "enter_tag"],
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] = (1, "ema_up_trend")
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dataframe.loc[
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(
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(dataframe['DOWN_Pattern']) &
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(dataframe['DOWN_Confirm']) &
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(dataframe['Price_Below_EMA52']) &
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(dataframe['MACD_Below_Zero']) &
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(dataframe['EMA52_Cross_EMA24_DOWN']) &
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(dataframe['strong_trend'])
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),
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["enter_short", "enter_tag"],
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] = (1, "ema_down_trend")
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return dataframe
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def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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if dataframe is None or dataframe.empty:
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return dataframe
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dataframe.loc[
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(
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(dataframe['BUY_END']) |
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(dataframe['DOWN_Pattern']) |
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(dataframe['EMA52_Cross_EMA24_DOWN'])
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),
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["exit_long", "exit_tag"],
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] = (1, "ema_long_exit")
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dataframe.loc[
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(
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(dataframe['SELL_END']) |
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(dataframe['UP_Pattern']) |
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(dataframe['EMA52_Cross_EMA24_UP'])
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),
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["exit_short", "exit_tag"],
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] = (1, "ema_short_exit")
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return dataframe
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def get_ticker_indicator(self):
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return int(self.timeframe[:-1]) |