import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta from freqtrade.strategy.interface import IStrategy from pandas import DataFrame from datetime import datetime, timedelta from freqtrade.persistence import Trade from freqtrade.data.converter import order_book_to_dataframe import logging ########################################################################################################### ## CombinedBinHAndClucV5 by iterativ ## ## ## ## Fretrade https://github.com/freqtrade/freqtrade ## ## The authors of the original CombinedBinHAndCluc https://github.com/freqtrade/freqtrade-strategies ## ## V5 by iterativ. ## ## ## ########################################################################################################### ## GENERAL RECOMMENDATIONS ## ## ## ## For optimal performance, suggested to use between 4 and 6 open trades, with unlimited stake. ## ## A pairlist with 20 to 40 pairs. Volume pairlist works well. ## ## Prefer stable coin (USDT, BUSDT etc) pairs, instead of BTC or ETH pairs. ## ## Ensure that you don't override any variables in you config.json. Especially ## ## the timeframe (must be 5m) & sell_profit_only (must be true). ## ## ## ########################################################################################################### ## DONATIONS ## ## ## ## Absolutely not required. However, will be accepted as a token of appreciation. ## ## ## ## BTC: bc1qvflsvddkmxh7eqhc4jyu5z5k6xcw3ay8jl49sk ## ## ETH: 0x83D3cFb8001BDC5d2211cBeBB8cB3461E5f7Ec91 ## ## ## ########################################################################################################### class CombinedBinHAndClucV5_with_confirm_trade_entry(IStrategy): INTERFACE_VERSION = 2 minimal_roi = { "0": 0.018 } stoploss = -0.99 # effectively disabled. timeframe = '5m' # Sell signal use_sell_signal = True sell_profit_only = True sell_profit_offset = 0.001 # it doesn't meant anything, just to guarantee there is a minimal profit. ignore_roi_if_buy_signal = True # Trailing stoploss trailing_stop = True trailing_only_offset_is_reached = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.025 # Custom stoploss use_custom_stoploss = True # Run "populate_indicators()" only for new candle. process_only_new_candles = False # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 50 # Optional order type mapping. order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } logger = logging.getLogger(__name__) ob_history={} ob_delta=0.02 ob_ratio=1.4 def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, **kwargs) -> bool: ob = self.dp.orderbook(pair,1000) ob_dp=order_book_to_dataframe(ob['bids'],ob['asks']) mid_price=(ob_dp['bids'][0]+ob_dp['asks'][0])/2 delta = mid_price*self.ob_delta bid_side=ob_dp[ob_dp['bids']>(mid_price-delta)].tail(1)['b_sum'].item() ask_side=ob_dp[ob_dp['asks']<(mid_price+delta)].tail(1)['a_sum'].item() #dp_dir = "depth/"+pair+"/" #try: # os.makedirs(dp_dir) #except OSError: # pass #f=open("log.log", "a+") r=bid_side/ask_side self.ob_history[pair]=0.0*self.ob_history.get(pair,0)+1.0*r if( self.ob_history[pair]> self.ob_ratio): #ob_dp.to_csv(dp_dir+".csv") #f.write("confirm_trade_entry: buying "+pair+ " "+str(self.ob_history[pair])+" "+str(r) + "delta is "+ str(r) + "\n") #f.close() logger.info("confirm_trade_entry: buying "+pair+ " "+str(self.ob_history[pair])+" "+str(r) + "delta is "+ str(r) + "\n") return True #ob_dp.to_csv(dp_dir+"/"+current_time.strftime("not_buy_%m_%d_%Y_%H_%M")+".csv") f.write("confirm_trade_entry: NOT buying "+pair+ " "+str(self.ob_history[pair])+" "+str(r) + "delta is "+ str(r) + "\n") f.close() return False def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: # Manage losing trades and open room for better ones. if (current_profit < 0) & (current_time - timedelta(minutes=300) > trade.open_date_utc): return 0.01 return 0.99 def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # strategy BinHV45 bb_40 = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2) dataframe['lower'] = bb_40['lower'] dataframe['mid'] = bb_40['mid'] dataframe['bbdelta'] = (bb_40['mid'] - dataframe['lower']).abs() dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs() dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs() # strategy ClucMay72018 bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] dataframe['ema_slow'] = ta.EMA(dataframe, timeperiod=50) dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean() return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( # strategy BinHV45 dataframe['lower'].shift().gt(0) & dataframe['bbdelta'].gt(dataframe['close'] * 0.008) & dataframe['closedelta'].gt(dataframe['close'] * 0.0175) & dataframe['tail'].lt(dataframe['bbdelta'] * 0.25) & dataframe['close'].lt(dataframe['lower'].shift()) & dataframe['close'].le(dataframe['close'].shift()) & (dataframe['volume'] > 0) # Make sure Volume is not 0 ) | ( # strategy ClucMay72018 (dataframe['close'] < dataframe['ema_slow']) & (dataframe['close'] < 0.985 * dataframe['bb_lowerband']) & (dataframe['volume'] < (dataframe['volume_mean_slow'].shift(1) * 20)) & (dataframe['volume'] > 0) # Make sure Volume is not 0 ), 'buy' ] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( # Improves the profit slightly. (dataframe['close'] > dataframe['bb_upperband']) & (dataframe['close'].shift(1) > dataframe['bb_upperband'].shift(1)) & (dataframe['volume'] > 0) # Make sure Volume is not 0 ) , 'sell' ] = 1 return dataframe