from typing import Dict, List, Optional, Tuple from datetime import datetime, timedelta from cachetools import TTLCache from functools import reduce ## I hope you know what these are already from pandas import DataFrame, Series import numpy as np ## Indicator libs import talib.abstract as ta from finta import TA as fta ## FT stuffs from freqtrade.strategy import IStrategy, merge_informative_pair, stoploss_from_open, IntParameter, DecimalParameter, CategoricalParameter import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.exchange import timeframe_to_minutes from freqtrade.persistence import Trade from skopt.space import Dimension class CryptoFrogNFI2(IStrategy): # Sell hyperspace params: sell_params = { "cstp_bail_how": "roc", "cstp_bail_roc": -0.016, "cstp_bail_time": 901, "cstp_threshold": 0.0, "droi_pullback": False, "droi_pullback_amount": 0.008, "droi_pullback_respect_table": False, "droi_trend_type": "rmi", } # ROI table - this strat REALLY benefits from roi and trailing hyperopt: minimal_roi = { "0": 0.191, "35": 0.025, "77": 0.012, "188": 0 } # Stoploss: stoploss = -0.299 # Trailing stop: trailing_stop = True trailing_stop_positive = 0.278 trailing_stop_positive_offset = 0.338 trailing_only_offset_is_reached = True # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 400 use_custom_stoploss = True custom_stop = { # Linear Decay Parameters 'decay-time': 188, # minutes to reach end, I find it works well to match this to the final ROI value - default 1080 'decay-delay': 0, # minutes to wait before decay starts 'decay-start': -0.299, # -0.32118, # -0.07163, # starting value: should be the same or smaller than initial stoploss - default -0.30 'decay-end': -0.02, # ending value - default -0.03 # Profit and TA 'cur-min-diff': 0.03, # diff between current and minimum profit to move stoploss up to min profit point 'cur-threshold': -0.02, # how far negative should current profit be before we consider moving it up based on cur/min or roc 'roc-bail': -0.03, # value for roc to use for dynamic bailout 'rmi-trend': 50, # rmi-slow value to pause stoploss decay 'bail-how': 'immediate', # set the stoploss to the atr offset below current price, or immediate # Positive Trailing 'pos-trail': True, # enable trailing once positive 'pos-threshold': 0.005, # trail after how far positive 'pos-trail-dist': 0.015 # how far behind to place the trail } # Dynamic ROI droi_trend_type = CategoricalParameter(['rmi', 'ssl', 'candle', 'any'], default='any', space='sell', optimize=True) droi_pullback = CategoricalParameter([True, False], default=True, space='sell', optimize=True) droi_pullback_amount = DecimalParameter(0.005, 0.02, default=0.005, space='sell') droi_pullback_respect_table = CategoricalParameter([True, False], default=False, space='sell', optimize=True) # Custom Stoploss cstp_threshold = DecimalParameter(-0.05, 0, default=-0.03, space='sell') cstp_bail_how = CategoricalParameter(['roc', 'time', 'any'], default='roc', space='sell', optimize=True) cstp_bail_roc = DecimalParameter(-0.05, -0.01, default=-0.03, space='sell') cstp_bail_time = IntParameter(720, 1440, default=720, space='sell') stoploss = custom_stop['decay-start'] custom_trade_info = {} custom_current_price_cache: TTLCache = TTLCache(maxsize=100, ttl=300) # 5 minutes # run "populate_indicators" only for new candle process_only_new_candles = True # Experimental settings (configuration will overide these if set) use_sell_signal = True sell_profit_only = False sell_profit_offset = 0.01 ignore_roi_if_buy_signal = False use_dynamic_roi = True timeframe = '5m' informative_timeframe = '1h' # Optional order type mapping order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } plot_config = { 'main_plot': { 'Smooth_HA_H': {'color': 'orange'}, 'Smooth_HA_L': {'color': 'yellow'}, }, 'subplots': { "StochRSI": { 'srsi_k': {'color': 'blue'}, 'srsi_d': {'color': 'red'}, }, "MFI": { 'mfi': {'color': 'green'}, }, "BBEXP": { 'bbw_expansion': {'color': 'orange'}, }, "FAST": { 'fastd': {'color': 'red'}, 'fastk': {'color': 'blue'}, }, "SQZMI": { 'sqzmi': {'color': 'lightgreen'}, }, "VFI": { 'vfi': {'color': 'lightblue'}, }, "DMI": { 'dmi_plus': {'color': 'orange'}, 'dmi_minus': {'color': 'yellow'}, }, "EMACO": { 'emac_1h': {'color': 'red'}, 'emao_1h': {'color': 'blue'}, }, } } ############################################################# buy_params = { ############# # Enable/Disable conditions "buy_condition_1_enable": True, "buy_condition_2_enable": True, "buy_condition_3_enable": True, "buy_condition_4_enable": True, "buy_condition_5_enable": True, "buy_condition_6_enable": True, "buy_condition_7_enable": True, "buy_condition_8_enable": True, "buy_condition_9_enable": True, "buy_condition_10_enable": True, "buy_condition_11_enable": True, "buy_condition_12_enable": True, "buy_condition_13_enable": True, "buy_condition_14_enable": True, "buy_condition_15_enable": True, "buy_condition_16_enable": True, "buy_condition_17_enable": True, } sell_params = { ############# # Enable/Disable conditions "sell_condition_1_enable": True, "sell_condition_2_enable": True, "sell_condition_3_enable": True, "sell_condition_4_enable": True, "sell_condition_5_enable": True, "sell_condition_6_enable": True, "sell_condition_7_enable": True, "sell_condition_8_enable": True, ############# } ############################################################# buy_condition_1_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_2_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_3_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_4_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_5_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_6_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_7_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_8_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_9_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_10_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_11_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_12_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_13_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_14_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_15_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_16_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_17_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) # Normal dips buy_dip_threshold_1 = DecimalParameter(0.001, 0.05, default=0.02, space='buy', decimals=3, optimize=False, load=True) buy_dip_threshold_2 = DecimalParameter(0.01, 0.2, default=0.14, space='buy', decimals=3, optimize=False, load=True) buy_dip_threshold_3 = DecimalParameter(0.05, 0.4, default=0.32, space='buy', decimals=3, optimize=False, load=True) buy_dip_threshold_4 = DecimalParameter(0.2, 0.5, default=0.5, space='buy', decimals=3, optimize=False, load=True) # Strict dips buy_dip_threshold_5 = DecimalParameter(0.001, 0.05, default=0.015, space='buy', decimals=3, optimize=False, load=True) buy_dip_threshold_6 = DecimalParameter(0.01, 0.2, default=0.06, space='buy', decimals=3, optimize=False, load=True) buy_dip_threshold_7 = DecimalParameter(0.05, 0.4, default=0.24, space='buy', decimals=3, optimize=False, load=True) buy_dip_threshold_8 = DecimalParameter(0.2, 0.5, default=0.4, space='buy', decimals=3, optimize=False, load=True) # 12 hours buy_pump_pull_threshold_1 = DecimalParameter(1.5, 3.0, default=1.75, space='buy', decimals=2, optimize=False, load=True) buy_pump_threshold_1 = DecimalParameter(0.4, 1.0, default=0.46, space='buy', decimals=3, optimize=False, load=True) # 36 hours buy_pump_pull_threshold_2 = DecimalParameter(1.5, 3.0, default=1.75, space='buy', decimals=2, optimize=False, load=True) buy_pump_threshold_2 = DecimalParameter(0.4, 1.0, default=0.56, space='buy', decimals=3, optimize=False, load=True) # 48 hours buy_pump_pull_threshold_3 = DecimalParameter(1.5, 3.0, default=1.75, space='buy', decimals=2, optimize=False, load=True) buy_pump_threshold_3 = DecimalParameter(0.4, 1.0, default=0.85, space='buy', decimals=3, optimize=False, load=True) # 12 hours strict buy_pump_pull_threshold_4 = DecimalParameter(1.5, 3.0, default=2.2, space='buy', decimals=2, optimize=False, load=True) buy_pump_threshold_4 = DecimalParameter(0.4, 1.0, default=0.4, space='buy', decimals=3, optimize=False, load=True) # 36 hours strict buy_pump_pull_threshold_5 = DecimalParameter(1.5, 3.0, default=2.0, space='buy', decimals=2, optimize=False, load=True) buy_pump_threshold_5 = DecimalParameter(0.4, 1.0, default=0.56, space='buy', decimals=3, optimize=False, load=True) # 48 hours strict buy_pump_pull_threshold_6 = DecimalParameter(1.5, 3.0, default=2.0, space='buy', decimals=2, optimize=False, load=True) buy_pump_threshold_6 = DecimalParameter(0.4, 1.0, default=0.68, space='buy', decimals=3, optimize=False, load=True) buy_min_inc_1 = DecimalParameter(0.01, 0.05, default=0.022, space='buy', decimals=3, optimize=False, load=True) buy_rsi_1h_min_1 = DecimalParameter(25.0, 40.0, default=30.0, space='buy', decimals=1, optimize=False, load=True) buy_rsi_1h_max_1 = DecimalParameter(70.0, 90.0, default=80.0, space='buy', decimals=1, optimize=False, load=True) buy_rsi_1 = DecimalParameter(20.0, 40.0, default=36.0, space='buy', decimals=1, optimize=False, load=True) buy_mfi_1 = DecimalParameter(20.0, 56.0, default=26.0, space='buy', decimals=1, optimize=False, load=True) buy_volume_2 = DecimalParameter(1.0, 10.0, default=2.0, space='buy', decimals=1, optimize=False, load=True) buy_rsi_1h_min_2 = DecimalParameter(30.0, 40.0, default=36.0, space='buy', decimals=1, optimize=False, load=True) buy_rsi_1h_max_2 = DecimalParameter(70.0, 95.0, default=90.0, space='buy', decimals=1, optimize=False, load=True) buy_rsi_1h_diff_2 = DecimalParameter(30.0, 50.0, default=34.0, space='buy', decimals=1, optimize=False, load=True) buy_mfi_2 = DecimalParameter(30.0, 65.0, default=56.0, space='buy', decimals=1, optimize=False, load=True) buy_bb_offset_2 = DecimalParameter(0.97, 0.99, default=0.983, space='buy', decimals=3, optimize=False, load=True) buy_bb40_bbdelta_close_3 = DecimalParameter(0.005, 0.06, default=0.057, space='buy', optimize=False, load=True) buy_bb40_closedelta_close_3 = DecimalParameter(0.01, 0.03, default=0.023, space='buy', optimize=False, load=True) buy_bb40_tail_bbdelta_3 = DecimalParameter(0.15, 0.45, default=0.418, space='buy', optimize=False, load=True) buy_ema_rel_3 = DecimalParameter(0.97, 0.999, default=0.988, space='buy', decimals=3, optimize=False, load=True) buy_bb20_close_bblowerband_4 = DecimalParameter(0.9, 0.99, default=0.979, space='buy', optimize=False, load=True) buy_bb20_volume_4 = IntParameter(16, 35, default=18, space='buy', optimize=False, load=True) buy_volume_5 = DecimalParameter(1.0, 10.0, default=6.0, space='buy', decimals=1, optimize=False, load=True) buy_ema_open_mult_5 = DecimalParameter(0.016, 0.03, default=0.019, space='buy', decimals=3, optimize=False, load=True) buy_bb_offset_5 = DecimalParameter(0.98, 1.0, default=0.999, space='buy', decimals=3, optimize=False, load=True) buy_ema_rel_5 = DecimalParameter(0.97, 0.999, default=0.988, space='buy', decimals=3, optimize=False, load=True) buy_volume_6 = DecimalParameter(1.0, 10.0, default=1.5, space='buy', decimals=1, optimize=False, load=True) buy_ema_open_mult_6 = DecimalParameter(0.03, 0.04, default=0.025, space='buy', decimals=3, optimize=False, load=True) buy_bb_offset_6 = DecimalParameter(0.98, 0.999, default=0.995, space='buy', decimals=3, optimize=False, load=True) buy_volume_7 = DecimalParameter(1.0, 10.0, default=2.0, space='buy', decimals=1, optimize=False, load=True) buy_ema_open_mult_7 = DecimalParameter(0.02, 0.04, default=0.03, space='buy', decimals=3, optimize=False, load=True) buy_rsi_7 = DecimalParameter(24.0, 50.0, default=36.0, space='buy', decimals=1, optimize=False, load=True) buy_rsi_8 = DecimalParameter(30.0, 50.0, default=46.0, space='buy', decimals=1, optimize=False, load=True) buy_ema_rel_8 = DecimalParameter(0.97, 0.999, default=0.988, space='buy', decimals=3, optimize=False, load=True) buy_volume_9 = DecimalParameter(1.0, 4.0, default=2.0, space='buy', decimals=2, optimize=False, load=True) buy_ma_offset_9 = DecimalParameter(0.94, 0.99, default=0.958, space='buy', decimals=3, optimize=False, load=True) buy_bb_offset_9 = DecimalParameter(0.97, 0.99, default=0.984, space='buy', decimals=3, optimize=False, load=True) buy_rsi_1h_min_9 = DecimalParameter(26.0, 40.0, default=30.0, space='buy', decimals=1, optimize=False, load=True) buy_rsi_1h_max_9 = DecimalParameter(70.0, 90.0, default=80.0, space='buy', decimals=1, optimize=False, load=True) buy_mfi_9 = DecimalParameter(36.0, 65.0, default=56.0, space='buy', decimals=1, optimize=False, load=True) buy_volume_10 = DecimalParameter(1.0, 26.0, default=23.0, space='buy', decimals=1, optimize=False, load=True) buy_ma_offset_10 = DecimalParameter(0.93, 0.97, default=0.94, space='buy', decimals=3, optimize=False, load=True) buy_bb_offset_10 = DecimalParameter(0.97, 0.99, default=0.994, space='buy', decimals=3, optimize=False, load=True) buy_rsi_1h_10 = DecimalParameter(20.0, 40.0, default=39.0, space='buy', decimals=1, optimize=False, load=True) buy_ma_offset_11 = DecimalParameter(0.93, 0.99, default=0.938, space='buy', decimals=3, optimize=False, load=True) buy_min_inc_11 = DecimalParameter(0.005, 0.05, default=0.01, space='buy', decimals=3, optimize=False, load=True) buy_rsi_1h_min_11 = DecimalParameter(40.0, 60.0, default=55.0, space='buy', decimals=1, optimize=False, load=True) buy_rsi_1h_max_11 = DecimalParameter(70.0, 90.0, default=82.0, space='buy', decimals=1, optimize=False, load=True) buy_rsi_11 = DecimalParameter(30.0, 48.0, default=46.0, space='buy', decimals=1, optimize=False, load=True) buy_mfi_11 = DecimalParameter(36.0, 56.0, default=38.0, space='buy', decimals=1, optimize=False, load=True) buy_volume_12 = DecimalParameter(1.0, 10.0, default=2.0, space='buy', decimals=1, optimize=False, load=True) buy_ma_offset_12 = DecimalParameter(0.93, 0.97, default=0.936, space='buy', decimals=3, optimize=False, load=True) buy_rsi_12 = DecimalParameter(26.0, 40.0, default=30.0, space='buy', decimals=1, optimize=False, load=True) buy_ewo_12 = DecimalParameter(2.0, 6.0, default=2.8, space='buy', decimals=1, optimize=False, load=True) buy_ma_offset_13 = DecimalParameter(0.93, 0.98, default=0.952, space='buy', decimals=3, optimize=False, load=True) buy_ewo_13 = DecimalParameter(-14.0, -7.0, default=-7.9, space='buy', decimals=1, optimize=False, load=True) buy_volume_14 = DecimalParameter(1.0, 10.0, default=2.0, space='buy', decimals=1, optimize=False, load=True) buy_ema_open_mult_14 = DecimalParameter(0.01, 0.03, default=0.014, space='buy', decimals=3, optimize=False, load=True) buy_bb_offset_14 = DecimalParameter(0.98, 1.0, default=0.992, space='buy', decimals=3, optimize=False, load=True) buy_ma_offset_14 = DecimalParameter(0.93, 0.99, default=0.998, space='buy', decimals=3, optimize=False, load=True) buy_ema_open_mult_15 = DecimalParameter(0.02, 0.04, default=0.026, space='buy', decimals=3, optimize=False, load=True) buy_ma_offset_15 = DecimalParameter(0.93, 0.99, default=0.985, space='buy', decimals=3, optimize=False, load=True) buy_rsi_15 = DecimalParameter(30.0, 50.0, default=32.0, space='buy', decimals=1, optimize=False, load=True) buy_ema_rel_15 = DecimalParameter(0.97, 0.999, default=0.988, space='buy', decimals=3, optimize=False, load=True) buy_volume_16 = DecimalParameter(1.0, 10.0, default=2.0, space='buy', decimals=1, optimize=False, load=True) buy_ma_offset_16 = DecimalParameter(0.93, 0.97, default=0.95, space='buy', decimals=3, optimize=False, load=True) buy_rsi_16 = DecimalParameter(26.0, 50.0, default=38.0, space='buy', decimals=1, optimize=False, load=True) buy_ewo_16 = DecimalParameter(4.0, 8.0, default=3.6, space='buy', decimals=1, optimize=False, load=True) buy_ma_offset_17 = DecimalParameter(0.93, 0.98, default=0.958, space='buy', decimals=3, optimize=False, load=True) buy_ewo_17 = DecimalParameter(-18.0, -10.0, default=-12.0, space='buy', decimals=1, optimize=False, load=True) # Sell sell_condition_1_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True) sell_condition_2_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True) sell_condition_3_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True) sell_condition_4_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True) sell_condition_5_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True) sell_condition_6_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True) sell_condition_7_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True) sell_condition_8_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True) sell_rsi_bb_1 = DecimalParameter(60.0, 80.0, default=79.5, space='sell', decimals=1, optimize=False, load=True) sell_rsi_bb_2 = DecimalParameter(72.0, 90.0, default=81, space='sell', decimals=1, optimize=False, load=True) sell_rsi_main_3 = DecimalParameter(77.0, 90.0, default=82, space='sell', decimals=1, optimize=False, load=True) sell_dual_rsi_rsi_4 = DecimalParameter(72.0, 84.0, default=73.4, space='sell', decimals=1, optimize=False, load=True) sell_dual_rsi_rsi_1h_4 = DecimalParameter(78.0, 92.0, default=79.6, space='sell', decimals=1, optimize=False, load=True) sell_ema_relative_5 = DecimalParameter(0.005, 0.05, default=0.024, space='sell', optimize=False, load=True) sell_rsi_diff_5 = DecimalParameter(0.0, 20.0, default=4.382, space='sell', optimize=False, load=True) sell_rsi_under_6 = DecimalParameter(72.0, 90.0, default=79.0, space='sell', decimals=1, optimize=False, load=True) sell_rsi_1h_7 = DecimalParameter(80.0, 95.0, default=81.7, space='sell', decimals=1, optimize=False, load=True) sell_bb_relative_8 = DecimalParameter(1.05, 1.3, default=1.1, space='sell', decimals=3, optimize=False, load=True) sell_custom_profit_0 = DecimalParameter(0.01, 0.1, default=0.01, space='sell', decimals=3, optimize=False, load=True) sell_custom_rsi_0 = DecimalParameter(30.0, 40.0, default=30.0, space='sell', decimals=3, optimize=False, load=True) sell_custom_profit_1 = DecimalParameter(0.01, 0.1, default=0.03, space='sell', decimals=3, optimize=False, load=True) sell_custom_rsi_1 = DecimalParameter(30.0, 50.0, default=36.0, space='sell', decimals=2, optimize=False, load=True) sell_custom_profit_2 = DecimalParameter(0.01, 0.1, default=0.05, space='sell', decimals=3, optimize=False, load=True) sell_custom_rsi_2 = DecimalParameter(34.0, 50.0, default=43.0, space='sell', decimals=2, optimize=False, load=True) sell_custom_profit_3 = DecimalParameter(0.06, 0.30, default=0.08, space='sell', decimals=3, optimize=False, load=True) sell_custom_rsi_3 = DecimalParameter(38.0, 55.0, default=48.0, space='sell', decimals=2, optimize=False, load=True) sell_custom_profit_4 = DecimalParameter(0.3, 0.6, default=0.25, space='sell', decimals=3, optimize=False, load=True) sell_custom_rsi_4 = DecimalParameter(40.0, 58.0, default=50.0, space='sell', decimals=2, optimize=False, load=True) sell_custom_under_profit_1 = DecimalParameter(0.01, 0.10, default=0.02, space='sell', decimals=3, optimize=False, load=True) sell_custom_under_profit_2 = DecimalParameter(0.01, 0.10, default=0.035, space='sell', decimals=3, optimize=False, load=True) sell_custom_under_profit_3 = DecimalParameter(0.05, 0.2, default=0.07, space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_min_1 = DecimalParameter(0.1, 0.25, default=0.15, space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_max_1 = DecimalParameter(0.3, 0.5, default=0.46, space='sell', decimals=2, optimize=False, load=True) sell_trail_down_1 = DecimalParameter(0.04, 0.2, default=0.18, space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_min_2 = DecimalParameter(0.01, 0.1, default=0.01, space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_max_2 = DecimalParameter(0.08, 0.25, default=0.12, space='sell', decimals=2, optimize=False, load=True) sell_trail_down_2 = DecimalParameter(0.04, 0.2, default=0.14, space='sell', decimals=3, optimize=False, load=True) ############################################################# 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() max_profit = ((trade.max_rate - trade.open_rate) / trade.open_rate) if (last_candle is not None): if (current_profit > self.sell_custom_profit_11.value) & (last_candle['rsi'] < self.sell_custom_rsi_11.value): return 'signal_profit_11' if (self.sell_custom_profit_11.value > current_profit > self.sell_custom_profit_10.value) & (last_candle['rsi'] < self.sell_custom_rsi_10.value): return 'signal_profit_10' if (self.sell_custom_profit_10.value > current_profit > self.sell_custom_profit_9.value) & (last_candle['rsi'] < self.sell_custom_rsi_9.value): return 'signal_profit_9' if (self.sell_custom_profit_9.value > current_profit > self.sell_custom_profit_8.value) & (last_candle['rsi'] < self.sell_custom_rsi_8.value): return 'signal_profit_8' if (self.sell_custom_profit_8.value > current_profit > self.sell_custom_profit_7.value) & (last_candle['rsi'] < self.sell_custom_rsi_7.value): return 'signal_profit_7' if (self.sell_custom_profit_7.value > current_profit > self.sell_custom_profit_6.value) & (last_candle['rsi'] < self.sell_custom_rsi_6.value): return 'signal_profit_6' if (self.sell_custom_profit_6.value > current_profit > self.sell_custom_profit_5.value) & (last_candle['rsi'] < self.sell_custom_rsi_5.value): return 'signal_profit_5' elif (self.sell_custom_profit_5.value > current_profit > self.sell_custom_profit_4.value) & (last_candle['rsi'] < self.sell_custom_rsi_4.value): return 'signal_profit_4' elif (self.sell_custom_profit_4.value > current_profit > self.sell_custom_profit_3.value) & (last_candle['rsi'] < self.sell_custom_rsi_3.value): return 'signal_profit_3' elif (self.sell_custom_profit_3.value > current_profit > self.sell_custom_profit_2.value) & (last_candle['rsi'] < self.sell_custom_rsi_2.value): return 'signal_profit_2' elif (self.sell_custom_profit_2.value > current_profit > self.sell_custom_profit_1.value) & (last_candle['rsi'] < self.sell_custom_rsi_1.value): return 'signal_profit_1' elif (self.sell_custom_profit_1.value > current_profit > self.sell_custom_profit_0.value) & (last_candle['rsi'] < self.sell_custom_rsi_0.value): return 'signal_profit_0' # check if close is under EMA200 elif (current_profit > self.sell_custom_under_profit_11.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_11.value) & (last_candle['close'] < last_candle['ema_200']): return 'signal_profit_u_11' elif (self.sell_custom_under_profit_11.value > current_profit > self.sell_custom_under_profit_10.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_10.value) & (last_candle['close'] < last_candle['ema_200']): return 'signal_profit_u_10' elif (self.sell_custom_under_profit_10.value > current_profit > self.sell_custom_under_profit_9.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_9.value) & (last_candle['close'] < last_candle['ema_200']): return 'signal_profit_u_9' elif (self.sell_custom_under_profit_9.value > current_profit > self.sell_custom_under_profit_8.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_8.value) & (last_candle['close'] < last_candle['ema_200']): return 'signal_profit_u_8' elif (self.sell_custom_under_profit_8.value > current_profit > self.sell_custom_under_profit_7.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_7.value) & (last_candle['close'] < last_candle['ema_200']): return 'signal_profit_u_7' elif (self.sell_custom_under_profit_7.value > current_profit > self.sell_custom_under_profit_6.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_6.value) & (last_candle['close'] < last_candle['ema_200']): return 'signal_profit_u_6' elif (self.sell_custom_under_profit_6.value > current_profit > self.sell_custom_under_profit_5.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_5.value) & (last_candle['close'] < last_candle['ema_200']): return 'signal_profit_u_5' elif (self.sell_custom_under_profit_5.value > current_profit > self.sell_custom_under_profit_4.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_4.value) & (last_candle['close'] < last_candle['ema_200']): return 'signal_profit_u_4' elif (self.sell_custom_under_profit_4.value > current_profit > self.sell_custom_under_profit_3.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_3.value) & (last_candle['close'] < last_candle['ema_200']): return 'signal_profit_u_3' elif (self.sell_custom_under_profit_3.value > current_profit > self.sell_custom_under_profit_2.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_2.value) & (last_candle['close'] < last_candle['ema_200']): return 'signal_profit_u_2' elif (self.sell_custom_under_profit_2.value > current_profit > self.sell_custom_under_profit_1.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_1.value) & (last_candle['close'] < last_candle['ema_200']): return 'signal_profit_u_1' elif (self.sell_custom_under_profit_1.value > current_profit > self.sell_custom_under_profit_0.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_0.value) & (last_candle['close'] < last_candle['ema_200']): return 'signal_profit_u_0' # check if the pair is "pumped" elif (last_candle['sell_pump_48_1_1h']) & (current_profit > self.sell_custom_pump_profit_1_5.value) & (last_candle['rsi'] < self.sell_custom_pump_rsi_1_5.value): return 'signal_profit_p_1_5' elif (last_candle['sell_pump_48_1_1h']) & (self.sell_custom_pump_profit_1_5.value > current_profit > self.sell_custom_pump_profit_1_4.value) & (last_candle['rsi'] < self.sell_custom_pump_rsi_1_4.value): return 'signal_profit_p_1_4' elif (last_candle['sell_pump_48_1_1h']) & (self.sell_custom_pump_profit_1_4.value > current_profit > self.sell_custom_pump_profit_1_3.value) & (last_candle['rsi'] < self.sell_custom_pump_rsi_1_3.value): return 'signal_profit_p_1_3' elif (last_candle['sell_pump_48_1_1h']) & (self.sell_custom_pump_profit_1_3.value > current_profit > self.sell_custom_pump_profit_1_2.value) & (last_candle['rsi'] < self.sell_custom_pump_rsi_1_2.value): return 'signal_profit_p_1_2' elif (last_candle['sell_pump_48_1_1h']) & (self.sell_custom_pump_profit_1_2.value > current_profit > self.sell_custom_pump_profit_1_1.value) & (last_candle['rsi'] < self.sell_custom_pump_rsi_1_1.value): return 'signal_profit_p_1_1' elif (last_candle['sell_pump_36_1_1h']) & (current_profit > self.sell_custom_pump_profit_2_5.value) & (last_candle['rsi'] < self.sell_custom_pump_rsi_2_5.value): return 'signal_profit_p_2_5' elif (last_candle['sell_pump_36_1_1h']) & (self.sell_custom_pump_profit_2_5.value > current_profit > self.sell_custom_pump_profit_2_4.value) & (last_candle['rsi'] < self.sell_custom_pump_rsi_2_4.value): return 'signal_profit_p_2_4' elif (last_candle['sell_pump_36_1_1h']) & (self.sell_custom_pump_profit_2_4.value > current_profit > self.sell_custom_pump_profit_2_3.value) & (last_candle['rsi'] < self.sell_custom_pump_rsi_2_3.value): return 'signal_profit_p_2_3' elif (last_candle['sell_pump_36_1_1h']) & (self.sell_custom_pump_profit_2_3.value > current_profit > self.sell_custom_pump_profit_2_2.value) & (last_candle['rsi'] < self.sell_custom_pump_rsi_2_2.value): return 'signal_profit_p_2_2' elif (last_candle['sell_pump_36_1_1h']) & (self.sell_custom_pump_profit_2_2.value > current_profit > self.sell_custom_pump_profit_2_1.value) & (last_candle['rsi'] < self.sell_custom_pump_rsi_2_1.value): return 'signal_profit_p_2_1' elif (last_candle['sell_pump_24_1_1h']) & (current_profit > self.sell_custom_pump_profit_3_5.value) & (last_candle['rsi'] < self.sell_custom_pump_rsi_3_5.value): return 'signal_profit_p_3_5' elif (last_candle['sell_pump_24_1_1h']) & (self.sell_custom_pump_profit_3_5.value > current_profit > self.sell_custom_pump_profit_3_4.value) & (last_candle['rsi'] < self.sell_custom_pump_rsi_3_4.value): return 'signal_profit_p_3_4' elif (last_candle['sell_pump_24_1_1h']) & (self.sell_custom_pump_profit_3_4.value > current_profit > self.sell_custom_pump_profit_3_3.value) & (last_candle['rsi'] < self.sell_custom_pump_rsi_3_3.value): return 'signal_profit_p_3_3' elif (last_candle['sell_pump_24_1_1h']) & (self.sell_custom_pump_profit_3_3.value > current_profit > self.sell_custom_pump_profit_3_2.value) & (last_candle['rsi'] < self.sell_custom_pump_rsi_3_2.value): return 'signal_profit_p_3_2' elif (last_candle['sell_pump_24_1_1h']) & (self.sell_custom_pump_profit_3_2.value > current_profit > self.sell_custom_pump_profit_3_1.value) & (last_candle['rsi'] < self.sell_custom_pump_rsi_3_1.value): return 'signal_profit_p_3_1' elif (self.sell_custom_dec_profit_max_1.value > current_profit > self.sell_custom_dec_profit_min_1.value) & (last_candle['sma_200_dec']): return 'signal_profit_d_1' elif (self.sell_custom_dec_profit_max_2.value > current_profit > self.sell_custom_dec_profit_min_2.value) & (last_candle['close'] < last_candle['ema_100']): return 'signal_profit_d_2' # Trailing elif (self.sell_trail_profit_max_1.value > current_profit > self.sell_trail_profit_min_1.value) & (self.sell_trail_rsi_min_1.value < last_candle['rsi'] < self.sell_trail_rsi_max_1.value) & (max_profit > (current_profit + self.sell_trail_down_1.value)): return 'signal_profit_t_1' elif (self.sell_trail_profit_max_2.value > current_profit > self.sell_trail_profit_min_2.value) & (self.sell_trail_rsi_min_2.value < last_candle['rsi'] < self.sell_trail_rsi_max_2.value) & (max_profit > (current_profit + self.sell_trail_down_2.value)): return 'signal_profit_t_2' elif (self.sell_trail_profit_max_3.value > current_profit > self.sell_trail_profit_min_3.value) & (max_profit > (current_profit + self.sell_trail_down_3.value)) & (last_candle['sma_200_dec_1h']): return 'signal_profit_t_3' elif (last_candle['close'] < last_candle['ema_200']) & (current_profit > self.sell_trail_profit_min_3.value) & (current_profit < self.sell_trail_profit_max_3.value) & (max_profit > (current_profit + self.sell_trail_down_3.value)): return 'signal_profit_u_t_1' elif (current_profit > 0.0) & (last_candle['close'] < last_candle['ema_200']) & (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.sell_custom_profit_under_rel_1.value) & (last_candle['rsi'] > last_candle['rsi_1h'] + self.sell_custom_profit_under_rsi_diff_1.value): return 'signal_profit_u_e_1' elif (current_profit < -0.0) & (last_candle['close'] < last_candle['ema_200']) & (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.sell_custom_stoploss_under_rel_1.value) & (last_candle['rsi'] > last_candle['rsi_1h'] + self.sell_custom_stoploss_under_rsi_diff_1.value): return 'signal_stoploss_u_1' elif (self.sell_custom_pump_dec_profit_max_1.value > current_profit > self.sell_custom_pump_dec_profit_min_1.value) & (last_candle['sell_pump_48_1_1h']) & (last_candle['sma_200_dec']) & (last_candle['close'] < last_candle['ema_200']): return 'signal_profit_p_d_1' elif (self.sell_custom_pump_dec_profit_max_2.value > current_profit > self.sell_custom_pump_dec_profit_min_2.value) & (last_candle['sell_pump_48_2_1h']) & (last_candle['sma_200_dec']) & (last_candle['close'] < last_candle['ema_200']): return 'signal_profit_p_d_2' elif (self.sell_custom_pump_dec_profit_max_3.value > current_profit > self.sell_custom_pump_dec_profit_min_3.value) & (last_candle['sell_pump_48_3_1h']) & (last_candle['sma_200_dec']) & (last_candle['close'] < last_candle['ema_200']): return 'signal_profit_p_d_3' elif (self.sell_custom_pump_dec_profit_max_4.value > current_profit > self.sell_custom_pump_dec_profit_min_4.value) & (last_candle['sma_200_dec']) & (last_candle['sell_pump_24_2_1h']): return 'signal_profit_p_d_4' # Pumped 48h 1, under EMA200 elif (self.sell_custom_pump_under_profit_max_1.value > current_profit > self.sell_custom_pump_under_profit_min_1.value) & (last_candle['sell_pump_48_1_1h']) & (last_candle['close'] < last_candle['ema_200']): return 'signal_profit_p_u_1' # Pumped 36h 2, trail 1 elif (last_candle['sell_pump_36_2_1h']) & (self.sell_custom_pump_trail_profit_max_1.value > current_profit > self.sell_custom_pump_trail_profit_min_1.value) & (self.sell_custom_pump_trail_rsi_min_1.value < last_candle['rsi'] < self.sell_custom_pump_trail_rsi_max_1.value) & (max_profit > (current_profit + self.sell_custom_pump_trail_down_1.value)): return 'signal_profit_p_t_1' elif (max_profit < self.sell_custom_stoploss_pump_max_profit_1.value) & (self.sell_custom_stoploss_pump_min_1.value < current_profit < self.sell_custom_stoploss_pump_max_1.value) & (last_candle['sell_pump_48_1_1h']) & (last_candle['sma_200_dec']) & (last_candle['close'] < (last_candle['ema_200'] * self.sell_custom_stoploss_pump_ma_offset_1.value)): return 'signal_stoploss_p_1' elif (max_profit < self.sell_custom_stoploss_pump_max_profit_2.value) & (current_profit < self.sell_custom_stoploss_pump_loss_2.value) & (last_candle['sell_pump_48_1_1h']) & (last_candle['sma_200_dec_1h']) & (last_candle['close'] < (last_candle['ema_200'] * self.sell_custom_stoploss_pump_ma_offset_2.value)): return 'signal_stoploss_p_2' elif (max_profit < self.sell_custom_stoploss_pump_max_profit_3.value) & (current_profit < self.sell_custom_stoploss_pump_loss_3.value) & (last_candle['sell_pump_36_3_1h']) & (last_candle['close'] < (last_candle['ema_200'] * self.sell_custom_stoploss_pump_ma_offset_3.value)): return 'signal_stoploss_p_3' return None def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.informative_timeframe) for pair in pairs] return informative_pairs ## 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 def hansen_HA(self, informative_df, period=6): dataframe = informative_df.copy() dataframe['hhclose']=(dataframe['open'] + dataframe['high'] + dataframe['low'] + dataframe['close']) / 4 dataframe['hhopen']= ((dataframe['open'].shift(2) + dataframe['close'].shift(2))/ 2) #it is not the same as real heikin ashi since I found that this is better. dataframe['hhhigh']=dataframe[['open','close','high']].max(axis=1) dataframe['hhlow']=dataframe[['open','close','low']].min(axis=1) dataframe['emac'] = ta.SMA(dataframe['hhclose'], timeperiod=period) #to smooth out the data and thus less noise. dataframe['emao'] = ta.SMA(dataframe['hhopen'], timeperiod=period) return {'emac': dataframe['emac'], 'emao': dataframe['emao']} ## detect BB width expansion to indicate possible volatility def bbw_expansion(self, bbw_rolling, mult=1.1): bbw = list(bbw_rolling) m = 0.0 for i in range(len(bbw)-1): if bbw[i] > m: m = bbw[i] if (bbw[-1] > (m * mult)): return 1 return 0 ## do_indicator style a la Obelisk strategies def do_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Stoch fast - mainly due to 5m timeframes stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] #StochRSI for double checking things period = 14 smoothD = 3 SmoothK = 3 dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) 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() # Bollinger Bands because obviously bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=1) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] # SAR Parabol - probably don't need this dataframe['sar'] = ta.SAR(dataframe) ## confirm wideboi variance signal with bbw expansion dataframe["bb_width"] = ((dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]) dataframe['bbw_expansion'] = dataframe['bb_width'].rolling(window=4).apply(self.bbw_expansion) # confirm entry and exit on smoothed HA dataframe = self.HA(dataframe, 4) # thanks to Hansen_Khornelius for this idea that I apply to the 1hr informative # https://github.com/hansen1015/freqtrade_strategy hansencalc = self.hansen_HA(dataframe, 6) dataframe['emac'] = hansencalc['emac'] dataframe['emao'] = hansencalc['emao'] # money flow index (MFI) for in/outflow of money, like RSI adjusted for vol dataframe['mfi'] = fta.MFI(dataframe) ## sqzmi to detect quiet periods dataframe['sqzmi'] = fta.SQZMI(dataframe) #, MA=hansencalc['emac']) # Volume Flow Indicator (MFI) for volume based on the direction of price movement dataframe['vfi'] = fta.VFI(dataframe, period=14) dmi = fta.DMI(dataframe, period=14) dataframe['dmi_plus'] = dmi['DI+'] dataframe['dmi_minus'] = dmi['DI-'] dataframe['adx'] = fta.ADX(dataframe, period=14) ## for stoploss - all from Solipsis4 ## simple ATR and ROC for stoploss dataframe['atr'] = ta.ATR(dataframe, timeperiod=14) dataframe['roc'] = ta.ROC(dataframe, timeperiod=9) dataframe['rmi'] = RMI(dataframe, length=24, mom=5) ssldown, sslup = SSLChannels_ATR(dataframe, length=21) dataframe['sroc'] = SROC(dataframe, roclen=21, emalen=13, smooth=21) dataframe['ssl-dir'] = np.where(sslup > ssldown,'up','down') 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['candle-up'] = np.where(dataframe['close'] >= dataframe['close'].shift(),1,0) dataframe['candle-up-trend'] = np.where(dataframe['candle-up'].rolling(5).sum() >= 3,1,0) return dataframe def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, "DataProvider is required for multiple timeframes." # Get the informative pair informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe) # EMA informative_1h['ema_12'] = ta.EMA(informative_1h, timeperiod=12) informative_1h['ema_15'] = ta.EMA(informative_1h, timeperiod=15) informative_1h['ema_20'] = ta.EMA(informative_1h, timeperiod=20) informative_1h['ema_26'] = ta.EMA(informative_1h, timeperiod=26) informative_1h['ema_35'] = ta.EMA(informative_1h, timeperiod=35) informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50) informative_1h['ema_100'] = ta.EMA(informative_1h, timeperiod=100) informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200) # SMA informative_1h['sma_200'] = ta.SMA(informative_1h, timeperiod=200) informative_1h['sma_200_dec'] = informative_1h['sma_200'] < informative_1h['sma_200'].shift(20) # RSI informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14) # BB bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative_1h), window=20, stds=2) informative_1h['bb_lowerband'] = bollinger['lower'] informative_1h['bb_middleband'] = bollinger['mid'] informative_1h['bb_upperband'] = bollinger['upper'] # Chaikin Money Flow informative_1h['cmf'] = chaikin_money_flow(informative_1h, 20) # Pump protections informative_1h['safe_pump_24_normal'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_1.value, self.buy_pump_pull_threshold_1.value) informative_1h['safe_pump_36_normal'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_2.value, self.buy_pump_pull_threshold_2.value) informative_1h['safe_pump_48_normal'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_3.value, self.buy_pump_pull_threshold_3.value) informative_1h['safe_pump_24_strict'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_4.value, self.buy_pump_pull_threshold_4.value) informative_1h['safe_pump_36_strict'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_5.value, self.buy_pump_pull_threshold_5.value) informative_1h['safe_pump_48_strict'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_6.value, self.buy_pump_pull_threshold_6.value) informative_1h['safe_pump_24_loose'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_7.value, self.buy_pump_pull_threshold_7.value) informative_1h['safe_pump_36_loose'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_8.value, self.buy_pump_pull_threshold_8.value) informative_1h['safe_pump_48_loose'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_9.value, self.buy_pump_pull_threshold_9.value) informative_1h['sell_pump_48_1'] = (((informative_1h['high'].rolling(48).max() - informative_1h['low'].rolling(48).min()) / informative_1h['low'].rolling(48).min()) > self.sell_pump_threshold_1.value) informative_1h['sell_pump_48_2'] = (((informative_1h['high'].rolling(48).max() - informative_1h['low'].rolling(48).min()) / informative_1h['low'].rolling(48).min()) > self.sell_pump_threshold_2.value) informative_1h['sell_pump_48_3'] = (((informative_1h['high'].rolling(48).max() - informative_1h['low'].rolling(48).min()) / informative_1h['low'].rolling(48).min()) > self.sell_pump_threshold_3.value) informative_1h['sell_pump_36_1'] = (((informative_1h['high'].rolling(36).max() - informative_1h['low'].rolling(36).min()) / informative_1h['low'].rolling(36).min()) > self.sell_pump_threshold_4.value) informative_1h['sell_pump_36_2'] = (((informative_1h['high'].rolling(36).max() - informative_1h['low'].rolling(36).min()) / informative_1h['low'].rolling(36).min()) > self.sell_pump_threshold_5.value) informative_1h['sell_pump_36_3'] = (((informative_1h['high'].rolling(36).max() - informative_1h['low'].rolling(36).min()) / informative_1h['low'].rolling(36).min()) > self.sell_pump_threshold_6.value) informative_1h['sell_pump_24_1'] = (((informative_1h['high'].rolling(24).max() - informative_1h['low'].rolling(24).min()) / informative_1h['low'].rolling(24).min()) > self.sell_pump_threshold_7.value) informative_1h['sell_pump_24_2'] = (((informative_1h['high'].rolling(24).max() - informative_1h['low'].rolling(24).min()) / informative_1h['low'].rolling(24).min()) > self.sell_pump_threshold_8.value) informative_1h['sell_pump_24_3'] = (((informative_1h['high'].rolling(24).max() - informative_1h['low'].rolling(24).min()) / informative_1h['low'].rolling(24).min()) > self.sell_pump_threshold_9.value) return informative_1h def range_percent_change(self, dataframe: DataFrame, length: int) -> float: """ Rolling Percentage Change Maximum across interval. :param dataframe: DataFrame The original OHLC dataframe :param length: int The length to look back """ df = dataframe.copy() return ((df['open'].rolling(length).max() - df['close'].rolling(length).min()) / df['close'].rolling(length).min()) def range_maxgap(self, dataframe: DataFrame, length: int) -> float: """ Maximum Price Gap across interval. :param dataframe: DataFrame The original OHLC dataframe :param length: int The length to look back """ df = dataframe.copy() return (df['open'].rolling(length).max() - df['close'].rolling(length).min()) def range_maxgap_adjusted(self, dataframe: DataFrame, length: int, adjustment: float) -> float: """ Maximum Price Gap across interval adjusted. :param dataframe: DataFrame The original OHLC dataframe :param length: int The length to look back :param adjustment: int The adjustment to be applied """ return (self.range_maxgap(dataframe,length) / adjustment) def range_height(self, dataframe: DataFrame, length: int) -> float: """ Current close distance to range bottom. :param dataframe: DataFrame The original OHLC dataframe :param length: int The length to look back """ df = dataframe.copy() return (df['close'] - df['close'].rolling(length).min()) def safe_pump(self, dataframe: DataFrame, length: int, thresh: float, pull_thresh: float) -> bool: """ Determine if entry after a pump is safe. :param dataframe: DataFrame The original OHLC dataframe :param length: int The length to look back :param thresh: int Maximum percentage change threshold :param pull_thresh: int Pullback from interval maximum threshold """ df = dataframe.copy() return (self.range_percent_change(df, length) < thresh) | (self.range_maxgap_adjusted(df, length, pull_thresh) > self.range_height(df, length)) def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # BB 40 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() # BB 20 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'] # EMA 200 dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12) dataframe['ema_20'] = ta.EMA(dataframe, timeperiod=20) dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26) dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100) dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) # SMA dataframe['sma_5'] = ta.SMA(dataframe, timeperiod=5) dataframe['sma_30'] = ta.SMA(dataframe, timeperiod=30) dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200) dataframe['sma_200_dec'] = dataframe['sma_200'] < dataframe['sma_200'].shift(20) # MFI dataframe['mfi'] = ta.MFI(dataframe) # EWO dataframe['ewo'] = EWO(dataframe, 50, 200) # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) # Chopiness dataframe['chop']= qtpylib.chopiness(dataframe, 14) # Dip protection dataframe['safe_dips_normal'] = ((((dataframe['open'] - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_1.value) & (((dataframe['open'].rolling(2).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_2.value) & (((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_3.value) & (((dataframe['open'].rolling(144).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_4.value)) dataframe['safe_dips_strict'] = ((((dataframe['open'] - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_5.value) & (((dataframe['open'].rolling(2).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_6.value) & (((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_7.value) & (((dataframe['open'].rolling(144).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_8.value)) dataframe['safe_dips_loose'] = ((((dataframe['open'] - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_9.value) & (((dataframe['open'].rolling(2).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_10.value) & (((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_11.value) & (((dataframe['open'].rolling(144).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_12.value)) # Volume dataframe['volume_mean_4'] = dataframe['volume'].rolling(4).mean().shift(1) dataframe['volume_mean_30'] = dataframe['volume'].rolling(30).mean() return dataframe ## stolen from Obelisk's Ichi strat code and backtest blog post, and Solipsis4 def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # The indicators for the 1h informative timeframe informative_1h = self.informative_1h_indicators(dataframe, metadata) # Populate/update the trade data if there is any, set trades to false if not live/dry self.custom_trade_info[metadata['pair']] = self.populate_trades(metadata['pair']) if self.config['runmode'].value in ('backtest', 'hyperopt'): assert (timeframe_to_minutes(self.timeframe) <= 30), "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) skip_columns = [(s + "_" + self.informative_timeframe) for s in ['date', 'open', 'high', 'low', 'close', 'volume', 'emac', 'emao']] dataframe.rename(columns=lambda s: s.replace("_{}".format(self.informative_timeframe), "") if (not s in skip_columns) else s, inplace=True) # Slam some indicators into the trade_info dict so we can dynamic roi and custom stoploss in backtest if self.dp.runmode.value in ('backtest', 'hyperopt'): self.custom_trade_info[metadata['pair']]['roc'] = dataframe[['date', 'roc']].copy().set_index('date') self.custom_trade_info[metadata['pair']]['atr'] = dataframe[['date', 'atr']].copy().set_index('date') self.custom_trade_info[metadata['pair']]['sroc'] = dataframe[['date', 'sroc']].copy().set_index('date') self.custom_trade_info[metadata['pair']]['ssl-dir'] = dataframe[['date', 'ssl-dir']].copy().set_index('date') self.custom_trade_info[metadata['pair']]['rmi-up-trend'] = dataframe[['date', 'rmi-up-trend']].copy().set_index('date') self.custom_trade_info[metadata['pair']]['candle-up-trend'] = dataframe[['date', 'candle-up-trend']].copy().set_index('date') dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.informative_timeframe, ffill=True) # The indicators for the normal (5m) timeframe dataframe = self.normal_tf_indicators(dataframe, metadata) return dataframe ## cryptofrog signals def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( ( ## close ALWAYS needs to be lower than the heiken low at 5m (dataframe['close'] < dataframe['Smooth_HA_L']) & ## Hansen's HA EMA at informative timeframe (dataframe['emac_1h'] < dataframe['emao_1h']) ) & ( ( ## potential uptick incoming so buy (dataframe['bbw_expansion'] == 1) & (dataframe['sqzmi'] == False) & ( (dataframe['mfi'] < 20) | (dataframe['dmi_minus'] > 30) ) ) | ( # this tries to find extra buys in undersold regions (dataframe['close'] < dataframe['sar']) & ((dataframe['srsi_d'] >= dataframe['srsi_k']) & (dataframe['srsi_d'] < 30)) & ((dataframe['fastd'] > dataframe['fastk']) & (dataframe['fastd'] < 23)) & (dataframe['mfi'] < 30) ) | ( # find smaller temporary dips in sideways ( ((dataframe['dmi_minus'] > 30) & qtpylib.crossed_above(dataframe['dmi_minus'], dataframe['dmi_plus'])) & (dataframe['close'] < dataframe['bb_lowerband']) ) | ( ## if nothing else is making a buy signal ## just throw in any old SQZMI shit based fastd ## this needs work! (dataframe['sqzmi'] == True) & ((dataframe['fastd'] > dataframe['fastk']) & (dataframe['fastd'] < 20)) ) ) ## volume sanity checks & (dataframe['vfi'] < 0.0) & (dataframe['volume'] > 0) ) ), 'buy'] = 1 conditions = [] conditions.append( ( self.buy_condition_1_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['sma_200'] > dataframe['sma_200'].shift(20)) & (dataframe['safe_dips']) & (dataframe['safe_pump_48_1h']) & (((dataframe['close'] - dataframe['open'].rolling(36).min()) / dataframe['open'].rolling(36).min()) > self.buy_min_inc_1.value) & (dataframe['rsi_1h'] > self.buy_rsi_1h_min_1.value) & (dataframe['rsi_1h'] < self.buy_rsi_1h_max_1.value) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe['mfi'] < self.buy_mfi_1.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_2_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['safe_pump_24_strict_1h']) & (dataframe['volume_mean_4'] * self.buy_volume_2.value > dataframe['volume']) & (dataframe['rsi_1h'] > self.buy_rsi_1h_min_2.value) & (dataframe['rsi_1h'] < self.buy_rsi_1h_max_2.value) & (dataframe['rsi'] < dataframe['rsi_1h'] - self.buy_rsi_1h_diff_2.value) & (dataframe['mfi'] < self.buy_mfi_2.value) & (dataframe['close'] < (dataframe['bb_lowerband'] * self.buy_bb_offset_2.value)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_3_enable.value & (dataframe['close'] > (dataframe['ema_200_1h'] * self.buy_ema_rel_3.value)) & (dataframe['ema_100'] > dataframe['ema_200']) & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & (dataframe['ema_100_1h'] > dataframe['ema_200_1h']) & (dataframe['safe_pump_36_1h']) & dataframe['lower'].shift().gt(0) & dataframe['bbdelta'].gt(dataframe['close'] * self.buy_bb40_bbdelta_close_3.value) & dataframe['closedelta'].gt(dataframe['close'] * self.buy_bb40_closedelta_close_3.value) & dataframe['tail'].lt(dataframe['bbdelta'] * self.buy_bb40_tail_bbdelta_3.value) & dataframe['close'].lt(dataframe['lower'].shift()) & dataframe['close'].le(dataframe['close'].shift()) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_4_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['safe_dips_strict']) & (dataframe['safe_pump_24_1h']) & (dataframe['close'] < dataframe['ema_50']) & (dataframe['close'] < self.buy_bb20_close_bblowerband_4.value * dataframe['bb_lowerband']) & (dataframe['volume'] < (dataframe['volume_mean_30'].shift(1) * self.buy_bb20_volume_4.value)) ) ) conditions.append( ( self.buy_condition_5_enable.value & (dataframe['ema_100'] > dataframe['ema_200']) & (dataframe['close'] > (dataframe['ema_200_1h'] * self.buy_ema_rel_5.value)) & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['safe_dips']) & (dataframe['safe_pump_36_strict_1h']) & (dataframe['volume_mean_4'] * self.buy_volume_5.value > dataframe['volume']) & (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_ema_open_mult_5.value)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) & (dataframe['close'] < (dataframe['bb_lowerband'] * self.buy_bb_offset_5.value)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_6_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['safe_dips_strict']) & (dataframe['volume'].rolling(4).mean() * self.buy_volume_6.value > dataframe['volume']) & (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_ema_open_mult_6.value)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) & (dataframe['close'] < (dataframe['bb_lowerband'] * self.buy_bb_offset_6.value)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_7_enable.value & (dataframe['ema_100'] > dataframe['ema_200']) & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['safe_dips']) & (dataframe['volume'].rolling(4).mean() * self.buy_volume_6.value > dataframe['volume']) & (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_ema_open_mult_7.value)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) & (dataframe['rsi'] < self.buy_rsi_7.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_8_enable.value & (dataframe['close'] > (dataframe['ema_200_1h'] * self.buy_ema_rel_8.value)) & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) & (dataframe['close'] > dataframe['open']) & (dataframe['close'] > dataframe['smma_lips']) & (dataframe['smma_lips'] > dataframe['smma_teeth']) & (dataframe['smma_teeth'] > dataframe['smma_jaw']) & (dataframe['smma_lips'].shift(1) > dataframe['smma_teeth'].shift(1)) & (dataframe['smma_teeth'].shift(1) > dataframe['smma_jaw'].shift(1)) & (dataframe['smma_lips'] > dataframe['smma_lips'].shift(1)) & (dataframe['smma_teeth'] > dataframe['smma_teeth'].shift(1)) & (dataframe['smma_jaw'] > dataframe['smma_jaw'].shift(1)) & (dataframe['rsi'] < self.buy_rsi_8.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_9_enable.value & (dataframe['ema_50'] > dataframe['ema_200']) & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & (dataframe['safe_dips_strict']) & (dataframe['volume_mean_4'] * self.buy_volume_9.value > dataframe['volume']) & (dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_9.value) & (dataframe['close'] < dataframe['bb_lowerband'] * self.buy_bb_offset_9.value) & (dataframe['rsi_1h'] > self.buy_rsi_1h_min_9.value) & (dataframe['rsi_1h'] < self.buy_rsi_1h_max_9.value) & (dataframe['mfi'] < self.buy_mfi_9.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_10_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) & (dataframe['safe_dips']) & (dataframe['safe_pump_24_1h']) & ((dataframe['volume_mean_4'] * self.buy_volume_10.value) > dataframe['volume']) & (dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_10.value) & (dataframe['close'] < dataframe['bb_lowerband'] * self.buy_bb_offset_10.value) & (dataframe['rsi_1h'] < self.buy_rsi_1h_10.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_11_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & (dataframe['safe_pump_24_1h']) & (((dataframe['close'] - dataframe['open'].rolling(36).min()) / dataframe['open'].rolling(36).min()) > self.buy_min_inc_11.value) & (dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_11.value) & (dataframe['rsi_1h'] > self.buy_rsi_1h_min_11.value) & (dataframe['rsi_1h'] < self.buy_rsi_1h_max_11.value) & (dataframe['rsi'] < self.buy_rsi_11.value) & (dataframe['mfi'] < self.buy_mfi_11.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_12_enable.value & (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) & (dataframe['safe_dips_strict']) & (dataframe['safe_pump_24_strict_1h']) & ((dataframe['volume_mean_4'] * self.buy_volume_12.value) > dataframe['volume']) & (dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_12.value) & (dataframe['ewo'] > self.buy_ewo_12.value) & (dataframe['rsi'] < self.buy_rsi_12.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_13_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) & (dataframe['safe_dips_strict']) & (dataframe['safe_pump_24_strict_1h']) & (dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_13.value) & (dataframe['ewo'] < self.buy_ewo_13.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_14_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['sma_200'] > dataframe['sma_200'].shift(20)) & (dataframe['safe_dips_strict']) & (dataframe['safe_pump_48_1h']) & (dataframe['volume_mean_4'] * self.buy_volume_14.value > dataframe['volume']) & (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_ema_open_mult_14.value)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) & (dataframe['close'] < (dataframe['bb_lowerband'] * self.buy_bb_offset_14.value)) & (dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_14.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_15_enable.value & (dataframe['close'] > dataframe['ema_200_1h'] * self.buy_ema_rel_15.value) & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['safe_dips_strict']) & (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_ema_open_mult_15.value)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) & (dataframe['rsi'] < self.buy_rsi_15.value) & (dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_15.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_16_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['safe_dips_strict']) & (dataframe['safe_pump_24_strict_1h']) & ((dataframe['volume_mean_4'] * self.buy_volume_16.value) > dataframe['volume']) & (dataframe['close'] < dataframe['ema_20'] * self.buy_ma_offset_16.value) & (dataframe['ewo'] > self.buy_ewo_16.value) & (dataframe['rsi'] < self.buy_rsi_16.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_17_enable.value & (dataframe['safe_dips_strict']) & (dataframe['close'] < dataframe['ema_20'] * self.buy_ma_offset_17.value) & (dataframe['ewo'] < self.buy_ewo_17.value) & (dataframe['volume'] > 0) ) ) if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'buy' ] = 1 return dataframe ## more going on here def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] conditions.append( ( self.sell_condition_1_enable.value & (dataframe['rsi'] > self.sell_rsi_bb_1.value) & (dataframe['close'] > dataframe['bb_upperband']) & (dataframe['close'].shift(1) > dataframe['bb_upperband'].shift(1)) & (dataframe['close'].shift(2) > dataframe['bb_upperband'].shift(2)) & (dataframe['close'].shift(3) > dataframe['bb_upperband'].shift(3)) & (dataframe['close'].shift(4) > dataframe['bb_upperband'].shift(4)) & (dataframe['close'].shift(5) > dataframe['bb_upperband'].shift(5)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.sell_condition_2_enable.value & (dataframe['rsi'] > self.sell_rsi_bb_2.value) & (dataframe['close'] > dataframe['bb_upperband']) & (dataframe['close'].shift(1) > dataframe['bb_upperband'].shift(1)) & (dataframe['close'].shift(2) > dataframe['bb_upperband'].shift(2)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.sell_condition_3_enable.value & (dataframe['rsi'] > self.sell_rsi_main_3.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.sell_condition_4_enable.value & (dataframe['rsi'] > self.sell_dual_rsi_rsi_4.value) & (dataframe['rsi_1h'] > self.sell_dual_rsi_rsi_1h_4.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.sell_condition_6_enable.value & (dataframe['close'] < dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_50']) & (dataframe['rsi'] > self.sell_rsi_under_6.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.sell_condition_7_enable.value & (dataframe['rsi_1h'] > self.sell_rsi_1h_7.value) & qtpylib.crossed_below(dataframe['ema_12'], dataframe['ema_26']) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.sell_condition_8_enable.value & (dataframe['close'] > dataframe['bb_upperband_1h'] * self.sell_bb_relative_8.value) & (dataframe['volume'] > 0) ) ) if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'sell' ] = 1 dataframe.loc[ ( ( ## close ALWAYS needs to be higher than the heiken high at 5m (dataframe['close'] > dataframe['Smooth_HA_H']) & ## Hansen's HA EMA at informative timeframe (dataframe['emac_1h'] > dataframe['emao_1h']) ) & ( ## try to find oversold regions with a corresponding BB expansion ( (dataframe['bbw_expansion'] == 1) & ( (dataframe['mfi'] > 80) | (dataframe['dmi_plus'] > 30) ) ) ## volume sanity checks & (dataframe['vfi'] > 0.0) & (dataframe['volume'] > 0) ) ), 'sell'] = 1 return dataframe """ Everything from here completely stolen from the godly work of @werkkrew Custom Stoploss """ def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60) if self.config['runmode'].value in ('live', 'dry_run'): dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) sroc = dataframe['sroc'].iat[-1] # If in backtest or hyperopt, get the indicator values out of the trades dict (Thanks @JoeSchr!) else: sroc = self.custom_trade_info[trade.pair]['sroc'].loc[current_time]['sroc'] if current_profit < self.cstp_threshold.value: if self.cstp_bail_how.value == 'roc' or self.cstp_bail_how.value == 'any': # Dynamic bailout based on rate of change if (sroc/100) <= self.cstp_bail_roc.value: return 0.001 if self.cstp_bail_how.value == 'time' or self.cstp_bail_how.value == 'any': # Dynamic bailout based on time if trade_dur > self.cstp_bail_time.value: return 0.001 return 1 """ Freqtrade ROI Overload for dynamic ROI functionality """ def min_roi_reached_dynamic(self, trade: Trade, current_profit: float, current_time: datetime, trade_dur: int) -> Tuple[Optional[int], Optional[float]]: minimal_roi = self.minimal_roi _, table_roi = self.min_roi_reached_entry(trade_dur) # see if we have the data we need to do this, otherwise fall back to the standard table if self.custom_trade_info and trade and trade.pair in self.custom_trade_info: if self.config['runmode'].value in ('live', 'dry_run'): dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=trade.pair, timeframe=self.timeframe) rmi_trend = dataframe['rmi-up-trend'].iat[-1] candle_trend = dataframe['candle-up-trend'].iat[-1] ssl_dir = dataframe['ssl-dir'].iat[-1] # If in backtest or hyperopt, get the indicator values out of the trades dict (Thanks @JoeSchr!) else: rmi_trend = self.custom_trade_info[trade.pair]['rmi-up-trend'].loc[current_time]['rmi-up-trend'] candle_trend = self.custom_trade_info[trade.pair]['candle-up-trend'].loc[current_time]['candle-up-trend'] ssl_dir = self.custom_trade_info[trade.pair]['ssl-dir'].loc[current_time]['ssl-dir'] min_roi = table_roi max_profit = trade.calc_profit_ratio(trade.max_rate) pullback_value = (max_profit - self.droi_pullback_amount.value) in_trend = False if self.droi_trend_type.value == 'rmi' or self.droi_trend_type.value == 'any': if rmi_trend == 1: in_trend = True if self.droi_trend_type.value == 'ssl' or self.droi_trend_type.value == 'any': if ssl_dir == 'up': in_trend = True if self.droi_trend_type.value == 'candle' or self.droi_trend_type.value == 'any': if candle_trend == 1: in_trend = True # Force the ROI value high if in trend if (in_trend == True): min_roi = 100 # If pullback is enabled, allow to sell if a pullback from peak has happened regardless of trend if self.droi_pullback.value == True and (current_profit < pullback_value): if self.droi_pullback_respect_table.value == True: min_roi = table_roi else: min_roi = current_profit / 2 else: min_roi = table_roi return trade_dur, min_roi # Change here to allow loading of the dynamic_roi settings def min_roi_reached(self, trade: Trade, current_profit: float, current_time: datetime) -> bool: trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60) if self.use_dynamic_roi: _, roi = self.min_roi_reached_dynamic(trade, current_profit, current_time, trade_dur) else: _, roi = self.min_roi_reached_entry(trade_dur) if roi is None: return False else: return current_profit > roi # Get the current price from the exchange (or local cache) def get_current_price(self, pair: str, refresh: bool) -> float: if not refresh: rate = self.custom_current_price_cache.get(pair) # Check if cache has been invalidated if rate: return rate ask_strategy = self.config.get('ask_strategy', {}) if ask_strategy.get('use_order_book', False): ob = self.dp.orderbook(pair, 1) rate = ob[f"{ask_strategy['price_side']}s"][0][0] else: ticker = self.dp.ticker(pair) rate = ticker['last'] self.custom_current_price_cache[pair] = rate return rate """ Stripped down version from Schism, meant only to update the price data a bit more frequently than the default instead of getting all sorts of trade information """ def populate_trades(self, pair: str) -> dict: # Initialize the trades dict if it doesn't exist, persist it otherwise if not pair in self.custom_trade_info: self.custom_trade_info[pair] = {} # init the temp dicts and set the trade stuff to false trade_data = {} trade_data['active_trade'] = False # active trade stuff only works in live and dry, not backtest if self.config['runmode'].value in ('live', 'dry_run'): # find out if we have an open trade for this pair active_trade = Trade.get_trades([Trade.pair == pair, Trade.is_open.is_(True),]).all() # if so, get some information if active_trade: # get current price and update the min/max rate current_rate = self.get_current_price(pair, True) active_trade[0].adjust_min_max_rates(current_rate) return trade_data # nested hyperopt class class HyperOpt: # defining as dummy, so that no error is thrown about missing # sell indicator space when hyperopting for all spaces @staticmethod def indicator_space() -> List[Dimension]: return [] ## goddamnit 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"] def SSLChannels_ATR(dataframe, length=7): """ SSL Channels with ATR: https://www.tradingview.com/script/SKHqWzql-SSL-ATR-channel/ Credit to @JimmyNixx for python """ df = dataframe.copy() df['ATR'] = ta.ATR(df, timeperiod=14) df['smaHigh'] = df['high'].rolling(length).mean() + df['ATR'] df['smaLow'] = df['low'].rolling(length).mean() - df['ATR'] df['hlv'] = np.where(df['close'] > df['smaHigh'], 1, np.where(df['close'] < df['smaLow'], -1, np.NAN)) df['hlv'] = df['hlv'].ffill() df['sslDown'] = np.where(df['hlv'] < 0, df['smaHigh'], df['smaLow']) df['sslUp'] = np.where(df['hlv'] < 0, df['smaLow'], df['smaHigh']) return df['sslDown'], df['sslUp'] def SROC(dataframe, roclen=21, emalen=13, smooth=21): df = dataframe.copy() roc = ta.ROC(df, timeperiod=roclen) ema = ta.EMA(df, timeperiod=emalen) sroc = ta.ROC(ema, timeperiod=smooth) return sroc # Elliot Wave Oscillator def EWO(dataframe, sma1_length=5, sma2_length=35): df = dataframe.copy() sma1 = ta.EMA(df, timeperiod=sma1_length) sma2 = ta.EMA(df, timeperiod=sma2_length) smadif = (sma1 - sma2) / df['close'] * 100 return smadif # Chaikin Money Flow def chaikin_money_flow(dataframe, n=20, fillna=False): """Chaikin Money Flow (CMF) It measures the amount of Money Flow Volume over a specific period. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_money_flow_cmf Args: dataframe(pandas.Dataframe): dataframe containing ohlcv n(int): n period. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. """ df = dataframe.copy() mfv = ((df['close'] - df['low']) - (df['high'] - df['close'])) / (df['high'] - df['low']) mfv = mfv.fillna(0.0) # float division by zero mfv *= df['volume'] cmf = (mfv.rolling(n, min_periods=0).sum() / df['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')