# source: https://raw.githubusercontent.com/apolovyi/fbot-config/7a97a7f6959fff025c58f9e4a868be4fec65260a/user_data/strategies/MyStrategy05.py # --- Do not remove these libs --- from freqtrade.strategy import IStrategy from typing import Dict, List from functools import reduce from pandas import DataFrame # -------------------------------- import talib.abstract as ta import numpy as np import freqtrade.vendor.qtpylib.indicators as qtpylib class github_apolovyi_fbot_config__MyStrategy05__20220212_074103(IStrategy): # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi" minimal_roi = { "0": 11.9, # "10": 0.379, # "20": 0.15, # "30": 0 } # Optimal stoploss designed for the strategy # This attribute will be overridden if the config file contains "stoploss" stoploss = -0.05 startup_candle_count: int = 200 # Optimal timeframe for the strategy timeframe = '4h' # signal controls buy_signal_1 = True buy_signal_2 = True buy_signal_3 = True sell_signal_1 = True sell_signal_2 = True sell_signal_3 = True # trailing stoploss # trailing_stop = False # trailing_stop_positive = 0.01 # trailing_stop_positive_offset = 0.02 # run "populate_indicators" only for new candle process_only_new_candles = False # Optional order type mapping order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } plot_config = { 'main_plot': { 'dema_s': {'color': 'purple'}, 'tema_s': {'color': 'blue'}, 'ema_s': {'color': 'orange'}, 'ema_m': {'color': 'red'}, 'ema_l': {'color': 'black'}, }, 'subplots': { "WILLR": { 'willr': {'color': 'yellow'}, 'willr_buy_hline': {'color': 'grey', 'plotly': {'opacity': 0.4}}, 'willr_sell_hline': {'color': 'grey', 'plotly': {'opacity': 0.4}} }, "rsi_6": { 'rsi_6': {'color': 'orange'}, 'rsi_buy_hline': {'color': 'grey', 'plotly': {'opacity': 0.4}}, 'rsi_sell_hline': {'color': 'grey', 'plotly': {'opacity': 0.4}} }, "rsi_15": { 'rsi_15': {'color': 'orange'}, 'rsi_buy_hline': {'color': 'grey', 'plotly': {'opacity': 0.4}}, 'rsi_sell_hline': {'color': 'grey', 'plotly': {'opacity': 0.4}} }, "fisher_rsi_norma": { 'fisher_rsi_norma': {'color': 'orange'}, }, "ADX": { 'adx': {'color': 'orange'}, }, "ema_width": { 'ema_width': {'color': 'blue'}, }, "rmi": { 'rmi': {'color': 'blue'}, }, "rmi_up": { 'rmi_up': {'type': 'bar', 'plotly': {'opacity': 0.9}} }, "rmi_up_trend": { 'rmi_up_trend': {'type': 'bar', 'plotly': {'opacity': 0.9}} }, "rmi_up-2": { 'rmi_up': {} }, } } def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['rsi_6'] = ta.RSI(dataframe, timeperiod=13) dataframe['rsi_15'] = ta.RSI(dataframe, timeperiod=15) dataframe['dema_s'] = ta.DEMA(dataframe, timeperiod=21) dataframe['tema_s'] = ta.TEMA(dataframe, timeperiod=13) dataframe['ema_s'] = ta.EMA(dataframe, timeperiod=5) dataframe['ema_m'] = ta.EMA(dataframe, timeperiod=21) dataframe['ema_l'] = ta.EMA(dataframe, timeperiod=89) dataframe['willr'] = ta.WILLR(dataframe, timeperiod=89) dataframe['willr_buy_hline'] = -85 dataframe['willr_sell_hline'] = -7 dataframe['rsi_buy_hline'] = 15 dataframe['rsi_sell_hline'] = 85 dataframe['adx_trendline'] = 30 dataframe["ema_width"] = ( (dataframe["ema_s"] - dataframe["ema_m"]) ) # dmi = fta.DMI(dataframe, period=14) # dataframe['dmi_plus'] = dmi['DI+'] # dataframe['dmi_minus'] = dmi['DI-'] dataframe['adx'] = ta.ADX(dataframe, period=5) dataframe['rmi'] = RMI(dataframe, length=13, mom=5) dataframe['rmi_up'] = np.where(dataframe['rmi'] >= dataframe['rmi'].shift(), 1, 0) dataframe['rmi_up_trend'] = np.where(dataframe['rmi_up'].rolling(5).sum() >= 3, 1, 0) # dataframe = HA(dataframe, 4) # # Chart type # # ------------------------------------ # # Heikin Ashi Strategy heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] # RSI # dataframe['rsi'] = ta.RSI(dataframe, timeperiod=21) # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy) rsi = 0.1 * (dataframe['rsi_15'] - 50) dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1) # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy) dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if self.buy_signal_1: conditions = [ dataframe["adx"] < dataframe["adx_trendline"], dataframe["close"] < dataframe["ema_m"], dataframe["close"] > dataframe["ema_l"], # qtpylib.crossed_above(dataframe['dema_s'], dataframe['tema_s']), qtpylib.crossed_above(dataframe['ema_s'], dataframe['dema_s']), # dataframe["low"] < dataframe["s1_ema_xxl"], # dataframe["close"] > dataframe["s1_ema_xxl"], # qtpylib.crossed_above(dataframe["s1_ema_sm"], dataframe["s1_ema_md"]), # dataframe["s1_ema_xs"] < dataframe["s1_ema_xl"], dataframe["volume"] > 0, ] dataframe.loc[reduce(lambda x, y: x & y, conditions), ["buy", "buy_tag"]] = (1, "buy_signal_1") if self.buy_signal_2: conditions = [ qtpylib.crossed_above(dataframe["ema_m"], dataframe["ema_l"]), # dataframe["close"] < dataframe["ema_s"], dataframe["volume"] > 0, ] dataframe.loc[reduce(lambda x, y: x & y, conditions), ["buy", "buy_tag"]] = (1, "buy_signal_2") # conditions.append( # (# # qtpylib.crossed_above(dataframe['ema12'], dataframe['ema26']) & # # qtpylib.crossed_above(dataframe['ema_s'], dataframe['tema_s']) # # qtpylib.crossed_below(dataframe['tema_s'], dataframe['dema_s']) # (dataframe['rsi_6'] < dataframe['rsi_buy_hline']) # # (dataframe['willr'] < dataframe['willr_buy_hline']) # # & (dataframe['ema_width'] < -0.01) # # (dataframe['ema200'] > dataframe['ema12'])) # | # (True) # ) # # conditions.append( # # (dataframe['rsi_15'] < dataframe['rsi_buy_hline']) # # # & (dataframe['ema_width'] < -0.01) # # # & (dataframe['ema200'] > dataframe['ema12']) # # ) # if conditions: # dataframe.loc[reduce(lambda x, y: x & y, conditions), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if self.sell_signal_1: conditions = [ dataframe["adx"] < dataframe["adx_trendline"], dataframe["close"] > dataframe["ema_m"], dataframe["close"] > dataframe["close"].shift(1), qtpylib.crossed_below(dataframe['tema_s'], dataframe['dema_s']), # dataframe["low"] < dataframe["s1_ema_xxl"], # dataframe["close"] > dataframe["s1_ema_xxl"], # qtpylib.crossed_above(dataframe["s1_ema_sm"], dataframe["s1_ema_md"]), # dataframe["s1_ema_xs"] < dataframe["s1_ema_xl"], dataframe["volume"] > 0, ] dataframe.loc[reduce(lambda x, y: x & y, conditions), ["sell", "sell_tag"]] = (1, "sell_signal_1") # if self.sell_signal_2: # conditions = [ # # qtpylib.crossed_above(dataframe["s2_fib_lower_band"], dataframe["s2_bb_lower_band"]), # # dataframe["close"] < dataframe["s2_ema"], # qtpylib.crossed_below(dataframe['ema_s'], dataframe['dema_s']), # dataframe["rmi_up_trend"] == 1, # # dataframe["rmi_up"] == 0, # dataframe["volume"] > 0, # ] # dataframe.loc[reduce(lambda x, y: x & y, conditions), ["sell", "sell_tag"]] = (1, "sell_signal_2") # conditions = [] # # conditions.append( # # qtpylib.crossed_below(dataframe['ema12'], dataframe['ema26']) & # # qtpylib.crossed_above(dataframe['tema_s'], dataframe['dema_s']) # (dataframe['rsi_6'] > dataframe['rsi_sell_hline']) # # (dataframe['willr'] > dataframe['willr_sell_hline']) # # & (dataframe['ema_width'] > 0.01) # # & (dataframe['ema200'] < dataframe['ema12']) # ) # conditions.append( # (dataframe['rsi_6'] > dataframe['rsi_sell_hline']) # # & (dataframe['ema_width'] < -0.01) # # & (dataframe['ema200'] > dataframe['ema12']) # ) # if conditions: # dataframe.loc[reduce(lambda x, y: x & y, conditions), 'sell'] = 1 return dataframe def RMI(dataframe, *, length=20, mom=5): """ Source: https://github.com/freqtrade/technical/blob/master/technical/indicators/indicators.py#L912 """ df = dataframe.copy() df['maxup'] = (df['close'] - df['close'].shift(mom)).clip(lower=0) df['maxdown'] = (df['close'].shift(mom) - df['close']).clip(lower=0) df.fillna(0, inplace=True) df["emaInc"] = ta.EMA(df, price='maxup', timeperiod=length) df["emaDec"] = ta.EMA(df, price='maxdown', timeperiod=length) df['RMI'] = np.where(df['emaDec'] == 0, 0, 100 - 100 / (1 + df["emaInc"] / df["emaDec"])) return df["RMI"] ## smoothed Heiken Ashi def HA(self, dataframe, smoothing=None): df = dataframe.copy() df['HA_Close'] = (df['open'] + df['high'] + df['low'] + df['close']) / 4 df.reset_index(inplace=True) ha_open = [(df['open'][0] + df['close'][0]) / 2] [ha_open.append((ha_open[i] + df['HA_Close'].values[i]) / 2) for i in range(0, len(df) - 1)] df['HA_Open'] = ha_open df.set_index('index', inplace=True) df['HA_High'] = df[['HA_Open', 'HA_Close', 'high']].max(axis=1) df['HA_Low'] = df[['HA_Open', 'HA_Close', 'low']].min(axis=1) if smoothing is not None: sml = abs(int(smoothing)) if sml > 0: df['Smooth_HA_O'] = ta.EMA(df['HA_Open'], sml) df['Smooth_HA_C'] = ta.EMA(df['HA_Close'], sml) df['Smooth_HA_H'] = ta.EMA(df['HA_High'], sml) df['Smooth_HA_L'] = ta.EMA(df['HA_Low'], sml) return df