# source: https://raw.githubusercontent.com/rodrigogk87/TradingBot_Freqtrade/0ace4cb52cce97af93198ab3e8654d990512bbf4/user_data/strategies/Obelisk_3EMA_StochRSI_ATR.py # --- Do not remove these libs --- from freqtrade.strategy import IStrategy, merge_informative_pair from pandas import DataFrame import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib # -------------------------------- import pandas as pd import numpy as np import logging from datetime import datetime, timedelta import technical.indicators as ftt from freqtrade.strategy import stoploss_from_open from freqtrade.exchange import timeframe_to_minutes logger = logging.getLogger(__name__) # github_rodrigogk87_TradingBot_Freqtrade__Obelisk_3EMA_StochRSI_ATR__20210420_092749 - 2021-04-10 # # by Obelisk # https://twitter.com/brookmiles # # DO NOT RUN LIVE # # based on Trade Pro "76% Win Rate Highly Profitable Trading Strategy Proven 100 Trades - 3 EMA + Stochastic RSI + ATR" # https://www.youtube.com/watch?v=7NM7bR2mL7U # # correctness/accuracy not guaranteed # # WARNING # # While this strategy is designed to be run at 1h, it should be backtested at 5m (or 1m). # This is done to avoid misleading results produced using trailing stops and roi values at longer timeframes. # # When running at 5m, an informative pair at 1h will be used to generate signals equivalent to running at 1h. # # live / dryrun: use 1h # backtest / hyperopt: use 5m or 1m class github_rodrigogk87_TradingBot_Freqtrade__Obelisk_3EMA_StochRSI_ATR__20210420_092749(IStrategy): # Backtest or hyperopt at this timeframe timeframe = '5m' # Live or Dry-run at this timeframe informative_timeframe = '1h' startup_candle_count = 500 # NOTE: this strat only uses candle information, so processing between # new candles is a waste of resources as nothing will change process_only_new_candles = True minimal_roi = { "0": 1, } stoploss = -0.99 use_custom_stoploss = True custom_info = {} def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.informative_timeframe) for pair in pairs] return informative_pairs def do_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['ema8'] = ta.EMA(dataframe, timeperiod=8) dataframe['ema14'] = ta.EMA(dataframe, timeperiod=14) dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) dataframe['atr'] = ta.ATR(dataframe, timeperiod=14) #RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) #StochRSI period = 14 smoothD = 3 SmoothK = 3 stochrsi = (dataframe['rsi'] - dataframe['rsi'].rolling(period).min()) / (dataframe['rsi'].rolling(period).max() - dataframe['rsi'].rolling(period).min()) dataframe['srsi_k'] = stochrsi.rolling(SmoothK).mean() * 100 dataframe['srsi_d'] = dataframe['srsi_k'].rolling(smoothD).mean() dataframe.loc[ (dataframe['ema8'] > dataframe['ema14']) & (dataframe['ema14'] > dataframe['ema50']) & qtpylib.crossed_above(dataframe['srsi_k'], dataframe['srsi_d']) , 'go_long'] = 1 dataframe['go_long'].fillna(0, inplace=True) dataframe.loc[ qtpylib.crossed_above(dataframe['go_long'], 0), 'take_profit'] = dataframe['close'] + dataframe['atr'] * 2 dataframe['take_profit'].fillna(method='ffill', inplace=True) dataframe.loc[ qtpylib.crossed_above(dataframe['go_long'], 0), 'stop_loss'] = dataframe['close'] - dataframe['atr'] * 3 dataframe['stop_loss'].fillna(method='ffill', inplace=True) dataframe.loc[ qtpylib.crossed_above(dataframe['go_long'], 0), 'stop_pct'] = (dataframe['atr'] * 3) / dataframe['close'] dataframe['stop_pct'].fillna(method='ffill', inplace=True) # add indicator mapped to correct DatetimeIndex to custom_info self.custom_info[metadata['pair']] = dataframe[['date', 'stop_pct', 'take_profit']].copy().set_index('date') return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if self.config['runmode'].value in ('backtest', 'hyperopt'): assert (timeframe_to_minutes(self.timeframe) <= 5), "Backtest this strategy in 5m or 1m timeframe." if self.timeframe == self.informative_timeframe: dataframe = self.do_indicators(dataframe, metadata) else: if not self.dp: return dataframe informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe) informative = self.do_indicators(informative.copy(), metadata) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.informative_timeframe, ffill=True) # don't overwrite the base dataframe's OHLCV information skip_columns = [(s + "_" + self.informative_timeframe) for s in ['date', 'open', 'high', 'low', 'close', 'volume']] dataframe.rename(columns=lambda s: s.replace("_{}".format(self.informative_timeframe), "") if (not s in skip_columns) else s, inplace=True) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ qtpylib.crossed_above(dataframe['go_long'], 0) , 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['sell'] = 0 return dataframe def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: result = 1 custom_info_pair = self.custom_info[pair] if custom_info_pair is not None: # using current_time/open_date directly will only work in backtesting/hyperopt. # in live / dry-run, we have to search for nearest row before tz = custom_info_pair.index.tz open_date = trade.open_date_utc if hasattr(trade, 'open_date_utc') else trade.open_date.replace(tzinfo=custom_info_pair.index.tz) open_date_mask = custom_info_pair.index.unique().get_loc(open_date, method='ffill') open_df = custom_info_pair.iloc[open_date_mask] # trade might be open too long for us to find opening candle if(open_df is None or len(open_df) == 0): logger.debug("No open_df :(") return 1 # oh well # stop out if we have reached our take profit limit take_profit = open_df['take_profit'] if (take_profit is not None): if current_rate > take_profit: logger.debug("take_profit={}, current={}".format( take_profit, current_rate )) return 0.001 # keep trailing stoploss at -stop_pct from the open price stop_pct = open_df['stop_pct'] if (stop_pct is not None): new_stop = stoploss_from_open(-stop_pct, current_profit) logger.debug("open={}, current={}, profit={}, stop_pct={}, stop={}".format( current_rate / (1 + current_profit), current_rate, current_profit, stop_pct, current_rate * (1 - new_stop))) if new_stop > 0: result = new_stop return result plot_config = { 'main_plot': { 'ema50': { 'color': 'orange' }, 'ema14': { 'color': 'blue' }, 'ema8': { 'color': 'purple' }, 'take_profit': { 'color': 'green' }, 'stop_loss': { 'color': 'red' }, }, 'subplots': { "SRSI": { 'srsi_k': {'color': 'blue'}, 'srsi_d': {'color': 'red'}, }, "ATR": { 'atr': {'color': 'blue'}, }, } }