import numpy as np import pandas as pd from pandas import DataFrame from datetime import datetime from typing import Optional, Union from datetime import datetime, timedelta from pandas import DataFrame, Series from freqtrade.persistence import Trade from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, RealParameter, IStrategy, merge_informative_pair) import talib.abstract as ta import pandas_ta as pta from technical import qtpylib def williams_r(dataframe: DataFrame, period: int = 14) -> pd.Series: """Williams %R, or just %R, is a technical analysis oscillator showing the current closing price in relation to the high and low of the past N days (for a given N). It was developed by a publisher and promoter of trading materials, Larry Williams. Its purpose is to tell whether a stock or commodity market is trading near the high or the low, or somewhere in between, of its recent trading range. The oscillator is on a negative scale, from −100 (lowest) up to 0 (highest). """ highest_high = dataframe["high"].rolling(center=False, window=period).max() lowest_low = dataframe["low"].rolling(center=False, window=period).min() WR = pd.Series( (highest_high - dataframe["close"]) / (highest_high - lowest_low), name=f"{period} Williams %R", ) return WR * -100 class JT_V04(IStrategy): INTERFACE_VERSION = 3 timeframe = '5m' can_short: bool = False minimal_roi = { "0": 0.15, "5": 0.10, "10": 0.08, "15": 0.06, "30": 0.04, "40": 0.03, "60": 0.02, "120": 0.005 } stoploss = -0.99 use_custom_stoploss = False process_only_new_candles = True use_exit_signal = True startup_candle_count: int = 60 order_time_in_force = { 'entry': 'gtc', 'exit': 'gtc' } plot_config = { 'main_plot': { 'tema': {}, 'sar': {'color': 'white'}, }, 'subplots': { "MACD": { 'macd': {'color': 'blue'}, 'macdsignal': {'color': 'orange'}, }, "RSI": { 'rsi': {'color': 'red'}, } } } buy_params = { "bbdelta_close": 0.00082, "bbdelta_tail": 0.85788, "close_bblower": 0.00128, "closedelta_close": 0.00987, "low_offset": 0.991, "rocr1_1h": 0.9346, "rocr_1h": 0.65666, "base_nb_candles_buy": 12, # value loaded from strategy "buy_bb_delta": 0.025, # value loaded from strategy "buy_bb_factor": 0.995, # value loaded from strategy "buy_bb_width": 0.095, # value loaded from strategy "buy_bb_width_1h": 1.074, # value loaded from strategy "buy_cci": -116, # value loaded from strategy "buy_cci_length": 25, # value loaded from strategy "buy_closedelta": 15.0, # value loaded from strategy "buy_clucha_bbdelta_close": 0.049, # value loaded from strategy "buy_clucha_bbdelta_tail": 1.146, # value loaded from strategy "buy_clucha_close_bblower": 0.018, # value loaded from strategy "buy_clucha_closedelta_close": 0.017, # value loaded from strategy "buy_clucha_rocr_1h": 0.526, # value loaded from strategy "buy_ema_diff": 0.025, # value loaded from strategy "buy_rmi": 49, # value loaded from strategy "buy_rmi_length": 17, # value loaded from strategy "buy_roc_1h": 10, # value loaded from strategy "buy_srsi_fk": 32, # value loaded from strategy } sell_params = { "high_offset": 1.012, "high_offset_2": 1.016, "sell_deadfish_bb_factor": 1.089, "sell_deadfish_bb_width": 0.11, "sell_deadfish_profit": -0.107, "sell_deadfish_volume_factor": 1.761, "base_nb_candles_sell": 22, # value loaded from strategy "pHSL": -0.397, # value loaded from strategy "pPF_1": 0.012, # value loaded from strategy "pPF_2": 0.07, # value loaded from strategy "pSL_1": 0.015, # value loaded from strategy "pSL_2": 0.068, # value loaded from strategy "sell_bbmiddle_close": 1.09092, # value loaded from strategy "sell_fisher": 0.46406, # value loaded from strategy "sell_trail_down_1": 0.03, # value loaded from strategy "sell_trail_down_2": 0.015, # value loaded from strategy "sell_trail_profit_max_1": 0.4, # value loaded from strategy "sell_trail_profit_max_2": 0.11, # value loaded from strategy "sell_trail_profit_min_1": 0.1, # value loaded from strategy "sell_trail_profit_min_2": 0.04, # value loaded from strategy } buy_rsi = IntParameter(10, 40, default=30, space="buy") sell_rsi = IntParameter(60, 90, default=70, space="sell") sell_fisher = RealParameter(0.1, 0.5, default=0.38414, space='sell', optimize=False) sell_bbmiddle_close = RealParameter(0.97, 1.1, default=1.07634, space='sell', optimize=False) is_optimize_deadfish = True sell_deadfish_bb_width = DecimalParameter(0.03, 0.75, default=0.05 , space='sell', optimize = is_optimize_deadfish) sell_deadfish_profit = DecimalParameter(-0.15, -0.05, default=-0.08 , space='sell', optimize = is_optimize_deadfish) sell_deadfish_bb_factor = DecimalParameter(0.90, 1.20, default=1.0 , space='sell', optimize = is_optimize_deadfish) sell_deadfish_volume_factor = DecimalParameter(1, 2.5, default=1.5 ,space='sell', optimize = is_optimize_deadfish) base_nb_candles_buy = IntParameter(8, 20, default=buy_params['base_nb_candles_buy'], space='buy', optimize=False) base_nb_candles_sell = IntParameter(8, 20, default=sell_params['base_nb_candles_sell'], space='sell', optimize=False) low_offset = DecimalParameter(0.985, 0.995, default=buy_params['low_offset'], space='buy', optimize=True) high_offset = DecimalParameter(1.005, 1.015, default=sell_params['high_offset'], space='sell', optimize=True) high_offset_2 = DecimalParameter(1.010, 1.020, default=sell_params['high_offset_2'], space='sell', optimize=True) sell_trail_profit_min_1 = DecimalParameter(0.1, 0.25, default=0.1, space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_max_1 = DecimalParameter(0.3, 0.5, default=0.4, space='sell', decimals=2, optimize=False, load=True) sell_trail_down_1 = DecimalParameter(0.04, 0.1, default=0.03, space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_min_2 = DecimalParameter(0.04, 0.1, default=0.04, space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_max_2 = DecimalParameter(0.08, 0.25, default=0.11, space='sell', decimals=2, optimize=False, load=True) sell_trail_down_2 = DecimalParameter(0.04, 0.2, default=0.015, space='sell', decimals=3, optimize=False, load=True) def informative_pairs(self): """ Define additional, informative pair/interval combinations to be cached from the exchange. These pair/interval combinations are non-tradeable, unless they are part of the whitelist as well. For more information, please consult the documentation :return: List of tuples in the format (pair, interval) Sample: return [("ETH/USDT", "5m"), ("BTC/USDT", "15m"), ] """ return [] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['avg_volume'] = dataframe['volume'].rolling(window=10).mean() heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] upperband, middleband, lowerband = ta.BBANDS(dataframe['ha_close'], timeperiod=40, nbdevup=2.0, nbdevdn=2.0, matype=0) dataframe['lower'] = lowerband dataframe['mid'] = middleband dataframe['bb_upperband'] = upperband dataframe['bb_lowerband'] = dataframe['lower'] dataframe['bb_middleband'] = dataframe['mid'] dataframe['ema_fast'] = ta.EMA(dataframe['ha_close'], timeperiod=3) dataframe['ema_slow'] = ta.EMA(dataframe['ha_close'], timeperiod=50) dataframe['ema_200'] = ta.EMA(dataframe['ha_close'], timeperiod=200) dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean() dataframe['rocr'] = ta.ROCR(dataframe['ha_close'], timeperiod=28) # 能找出低点，但是相对有一些局限性。比较延迟 rsi = ta.RSI(dataframe, timeperiod=14) dataframe["rsi"] = rsi dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20) dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50) dataframe['ma_sell_22'] = ta.EMA(dataframe, timeperiod=22) bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband2'] = bollinger2['lower'] dataframe['bb_middleband2'] = bollinger2['mid'] dataframe['bb_upperband2'] = bollinger2['upper'] dataframe['bb_width'] = ((dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2']) dataframe['volume_mean_12'] = dataframe['volume'].rolling(12).mean().shift(1) dataframe['volume_mean_24'] = dataframe['volume'].rolling(24).mean().shift(1) dataframe['cmf'] = chaikin_money_flow(dataframe, 20) stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] dataframe['vwma'] = pta.vwma(dataframe["ha_close"], dataframe["volume"], 28*0.98) dataframe['close_delta'] = (dataframe['close'] - 0.990 * dataframe['bb_lowerband']) / dataframe['close'] return dataframe def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) current_candle = dataframe.iloc[-1].squeeze() if current_time - timedelta(minutes=60) > trade.open_date_utc: if (current_candle["fastk"] > 75) and (current_profit > -0.01): return -0.001 if current_time - timedelta(days=1) > trade.open_date_utc: if (current_candle["fastk"] > 75) and (current_profit > -0.05): return -0.001 enter_tag = '' if hasattr(trade, 'enter_tag') and trade.enter_tag is not None: enter_tag = trade.enter_tag enter_tags = enter_tag.split() if current_profit >= 0.05: return -0.005 if current_profit > 0: if current_candle["fastk"] > 75: return -0.001 return self.stoploss def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() filled_buys = trade.select_filled_orders('buy') count_of_buys = len(filled_buys) buy_tag = 'empty' if hasattr(trade, 'buy_tag') and trade.buy_tag is not None: buy_tag = trade.buy_tag buy_tags = buy_tag.split() if last_candle is not None and "rocr" in buy_tags: if current_profit >= 0.022: return 'sell dazhuan' if (current_profit > 0.1) & (current_profit < 0.4) & (((trade.max_rate - trade.open_rate) / 100) > (current_profit + 0.03)): return 'trail_target_1' elif (current_profit > 0.04) & (current_profit < 0.11) & (((trade.max_rate - trade.open_rate) / 100) > (current_profit + 0.015)): return 'trail_target_2' elif (current_profit > 0.03) & (last_candle['rsi'] > 85): return 'RSI-85 target' if (current_profit > 0) & (last_candle['close'] > last_candle['hma_50']) & (last_candle['close'] > (last_candle['ma_sell_22'] * 1.012)) & (last_candle['volume'] > 0) & (last_candle['rsi_fast'] > last_candle['rsi_slow']): return 'sell signal2' if ((current_profit < self.sell_deadfish_profit.value) and (last_candle['close'] < last_candle['ema_200']) and (last_candle['bb_width'] < self.sell_deadfish_bb_width.value) and (last_candle['close'] > last_candle['bb_middleband2'] * self.sell_deadfish_bb_factor.value) and (last_candle['volume_mean_12'] < last_candle['volume_mean_24'] * self.sell_deadfish_volume_factor.value) and (last_candle['cmf'] < 0.0) ): return "sell_stoploss_deadfish" if "close_delta" in buy_tags and last_candle is not None: if (current_time - timedelta(minutes=60) > trade.open_date_utc) and last_candle['enter_tag'] == "close_delta": return "sell chuanxi" if current_profit >= 0.0065: return 'sell xiaozhuan' def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['rocr'] < 0.95) & (dataframe['ema_fast'] <= 0.988*dataframe['bb_lowerband']) ), ['enter_long', 'enter_tag'] ] = (1, "rocr") dataframe.loc[ ( (dataframe['rocr'] < 0.965) & # 0.985 (dataframe['ha_high'] < dataframe['vwma']) & (dataframe['close_delta'] >= 0) & (dataframe['close_delta'] <= 0.0035) ), ['enter_long', 'enter_tag'] ] = (1, "close_delta") return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ (dataframe["rocr"] > 1.02) & ((dataframe['ema_fast']) >= dataframe['bb_upperband']) & (dataframe['volume'] > 0), ['exit_long', 'exit_tag'] ] = (1, "tag1") dataframe.loc[ (dataframe["rocr"] > 1.06) & (dataframe["low"] > dataframe['vwma'])& (dataframe['volume'] > 0), ['exit_long', 'exit_tag'] ] = (1, 'tag2') return dataframe def custom_entry_price(self, pair: str, trade: Optional[Trade], current_time: datetime, proposed_rate: float, entry_tag: Optional[str], side: str, **kwargs) -> float: if pair == "BTC/USDT" or pair == "ETH/USDT": return proposed_rate return proposed_rate*0.99 def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float, proposed_stake: float, min_stake: Optional[float], max_stake: float, leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: if entry_tag == "rocr": return self.wallets.get_total_stake_amount() * 0.75 else: return self.wallets.get_total_stake_amount() / self.config['max_open_trades'] def chaikin_money_flow(dataframe, n=20, fillna=False): mfv = ((dataframe['close'] - dataframe['low']) - (dataframe['high'] - dataframe['close'])) / (dataframe['high'] - dataframe['low']) mfv = mfv.fillna(0.0) # float division by zero mfv *= dataframe['volume'] cmf = (mfv.rolling(n, min_periods=0).sum() / dataframe['volume'].rolling(n, min_periods=0).sum()) if fillna: cmf = cmf.replace([np.inf, -np.inf], np.nan).fillna(0) return Series(cmf, name='cmf')