# --- Do not remove these libs --- from freqtrade.strategy.interface import IStrategy from typing import Dict, List from functools import reduce from pandas import DataFrame # -------------------------------- import logging import math from datetime import datetime, timedelta, timezone from freqtrade.persistence import Trade import time from typing import Dict, List, Optional # --- import sys import talib.abstract as ta import numpy as np import freqtrade.vendor.qtpylib.indicators as qtpylib import datetime from technical.util import resample_to_interval, resampled_merge from datetime import datetime, timedelta from freqtrade.persistence import Trade from freqtrade.strategy import stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter import technical.indicators as ftt ######################################## Warning ######################################## # You won't get a lot of benefits by simply changing to this strategy # # with the HyperOpt values changed. # # # # You should test it closely, trying backtesting and dry running, and we recommend # # customizing the terms of sale and purchase as well. # # # # You should always be careful in real trading! # ######################################################################################### def EWO(dataframe, ema_length=5, ema2_length=35): # df = dataframe.copy() ema1 = ta.EMA(dataframe, timeperiod=ema_length) ema2 = ta.EMA(dataframe, timeperiod=ema2_length) emadif = (ema1 - ema2) / dataframe['close'] * 100 return emadif def lerp(a: float, b: float, t: float) -> float: """Linear interpolate on the scale given by a to b, using t as the point on that scale. Examples -------- 50 == lerp(0, 100, 0.5) 4.2 == lerp(1, 5, 0.8) """ return (1 - t) * a + t * b logger = logging.getLogger(__name__) class SMAOffset_Hippocritical_dca_forced_doubling_entry_delay(IStrategy): # Original: SMAOffsetProtectOptV1 by kkeue, shared on the freqtrade discord at 2021-06-19 # Added dca of Stash86, modified and optimized it. # Added jorikito#2815 's partial fill compensation # Use this option with caution! # enables a full 1st slot and base+safety-order for the 2nd slot before you run out of money in your wallet. # (combo from the .ods-file: 4+3, see rows "Overbuy calculation") overbuy_factor = 1.295 position_adjustment_enable = True initial_safety_order_trigger = -0.02 max_so_multiplier_orig = 3 safety_order_step_scale = 2 safety_order_volume_scale = 1.8 # just for initialization, now we calculate it... max_so_multiplier = max_so_multiplier_orig # We will store the size of stake of each trade's first order here cust_proposed_initial_stakes = {} # Amount the strategy should compensate previously partially filled orders for successive safety orders (0.0 - 1.0) partial_fill_compensation_scale = 1 # Modified Buy / Sell params - 20210619 # Buy hyperspace params: buy_params = { 'base_nb_candles_buy': 16, 'ewo_high': 5.672, 'ewo_low': -19.931, 'low_offset': 0.973, 'rsi_buy': 59, 'min_delay_between_safety_orders': 30} # Sell hyperspace params: sell_params = { 'base_nb_candles_sell': 20, 'high_offset': 1.01} INTERFACE_VERSION = 3 # Modified ROI - 20210620 # ROI table: minimal_roi = { '0': 0.028, '10': 0.018, '30': 0.01, '40': 0.005} # SMAOffset base_nb_candles_buy = IntParameter(5, 80, default=buy_params['base_nb_candles_buy'], space='buy', optimize=True) base_nb_candles_sell = IntParameter(5, 80, default=sell_params['base_nb_candles_sell'], space='sell', optimize=True) low_offset = DecimalParameter(0.9, 0.99, default=buy_params['low_offset'], space='buy', optimize=True) high_offset = DecimalParameter(0.99, 1.1, default=sell_params['high_offset'], space='sell', optimize=True) min_delay_between_safety_orders = IntParameter(0, 45, default=buy_params['min_delay_between_safety_orders'], space='buy', optimize=True) # Protection fast_ewo = 50 slow_ewo = 200 ewo_low = DecimalParameter(-20.0, -8.0, default=buy_params['ewo_low'], space='buy', optimize=True) ewo_high = DecimalParameter(2.0, 12.0, default=buy_params['ewo_high'], space='buy', optimize=True) rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=True) # Trailing stop: trailing_stop = False trailing_stop_positive = 0.001 trailing_stop_positive_offset = 0.01 trailing_only_offset_is_reached = True # Sell signal use_exit_signal = True exit_profit_only = False exit_profit_offset = 0.01 ignore_roi_if_entry_signal = False # Optimal timeframe for the strategy timeframe = '5m' process_only_new_candles = True # do not set this below 1k! else this will heavily dip into recursive bias. # link to an explanation (freqtrade discord, link on freqtrade.io) # https://discord.com/channels/700048804539400213/702584639063064586/1148204593969364993 startup_candle_count: int = 1000 no_volume_window_candles: int = 576 plot_config = {'main_plot': {}, 'subplots': {'sub': {'rsi': {'color': '#80dea8', 'type': 'line'}}, 'sub2': {'ma_buy_16': {'color': '#db1ea2', 'type': 'line'}, 'ma_sell_20': {'color': '#645825', 'type': 'line'}, 'EWO': {'color': '#1e5964', 'type': 'line'}, 'missing_data': {'color': '#26b08d', 'type': 'line'}}}} use_custom_stoploss = False # Since stoploss can only go up and can't go down, if you set your stoploss here, your lowest stoploss will always be tied to the first buy rate # So disable the hard stoploss here, and use custom_sell or custom_stoploss to handle the stoploss trigger stoploss = -1 if max_so_multiplier_orig > 0: if safety_order_volume_scale > 1: # #print(safety_order_volume_scale * (math.pow(safety_order_volume_scale,(max_so_multiplier - 1)) - 1)) firstLine = safety_order_volume_scale * (math.pow(safety_order_volume_scale, max_so_multiplier_orig - 1) - 1) divisor = safety_order_volume_scale - 1 max_so_multiplier = 2 + firstLine / divisor # max_so_multiplier = (2 + # (safety_order_volume_scale * # (math.pow(safety_order_volume_scale, (max_so_multiplier - 1)) - 1) / # (safety_order_volume_scale - 1))) elif safety_order_volume_scale < 1: firstLine = safety_order_volume_scale * (1 - math.pow(safety_order_volume_scale, max_so_multiplier_orig - 1)) divisor = 1 - safety_order_volume_scale max_so_multiplier = 2 + firstLine / divisor # max_so_multiplier = (2 + (safety_order_volume_scale * ( # 1 - math.pow(safety_order_volume_scale, (max_so_multiplier - 1))) / ( # 1 - safety_order_volume_scale))) def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): tag = super().custom_sell(pair, trade, current_time, current_rate, current_profit, **kwargs) if tag: return tag enter_tag = '' if hasattr(trade, 'enter_tag') and trade.enter_tag is not None: enter_tag = trade.enter_tag if current_profit <= -0.35: return f'stop_loss ({enter_tag})' return None def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time: datetime, **kwargs) -> bool: # remove pair from custom initial stake dict only if full exit if trade.amount == amount: del self.cust_proposed_initial_stakes[pair] return True def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float, proposed_stake: float, min_stake: float, max_stake: float, **kwargs) -> float: custom_stake = proposed_stake / self.max_so_multiplier * self.overbuy_factor self.cust_proposed_initial_stakes[pair] = custom_stake # Setting of first stake size just before each first order of a trade return custom_stake # set to static 10 to simulate partial fills of 10$, etc def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: float, max_stake: float, **kwargs) -> Optional[float]: if current_profit > self.initial_safety_order_trigger: return None filled_entries = trade.select_filled_orders(trade.entry_side) count_of_entries = len(filled_entries) minutes_since_last_filled: int = int((current_time - trade.date_last_filled_utc).total_seconds() / 60) current_min_delay_between_safety_orders = (count_of_entries - 1) * self.min_delay_between_safety_orders.value if count_of_entries >= 2: if minutes_since_last_filled < current_min_delay_between_safety_orders: logger.info(f're-entry {count_of_entries} denied since delay of {minutes_since_last_filled}m is less than the necessary {current_min_delay_between_safety_orders}m') return None if 1 <= count_of_entries <= self.max_so_multiplier_orig: # if (1 <= count_of_entries) and (open_trade_value < self.stake_amount * self.overbuy_factor): safety_order_trigger = abs(self.initial_safety_order_trigger) * count_of_entries if self.safety_order_step_scale > 1: safety_order_trigger = abs(self.initial_safety_order_trigger) + abs(self.initial_safety_order_trigger) * self.safety_order_step_scale * (math.pow(self.safety_order_step_scale, count_of_entries - 1) - 1) / (self.safety_order_step_scale - 1) elif self.safety_order_step_scale < 1: safety_order_trigger = abs(self.initial_safety_order_trigger) + abs(self.initial_safety_order_trigger) * self.safety_order_step_scale * (1 - math.pow(self.safety_order_step_scale, count_of_entries - 1)) / (1 - self.safety_order_step_scale) if current_profit <= -1 * abs(safety_order_trigger): try: # This returns first order actual stake size actual_initial_stake = filled_entries[0].cost # Fallback for when the initial stake was not set for whatever reason stake_amount = actual_initial_stake already_bought = sum((filled_entry.cost for filled_entry in filled_entries)) if self.cust_proposed_initial_stakes[trade.pair] > 0: # This calculates the amount of stake that will get used for the current safety order, # including compensation for any partial entries proposed_initial_stake = self.cust_proposed_initial_stakes[trade.pair] current_actual_stake = already_bought * math.pow(self.safety_order_volume_scale, count_of_entries - 1) current_stake_preposition = proposed_initial_stake * math.pow(self.safety_order_volume_scale, count_of_entries - 1) current_stake_preposition_compensation = current_stake_preposition + abs(current_stake_preposition - current_actual_stake) total_so_stake = lerp(current_actual_stake, current_stake_preposition_compensation, self.partial_fill_compensation_scale) # Set the calculated stake amount stake_amount = total_so_stake else: # Fallback stake amount calculation stake_amount = stake_amount * math.pow(self.safety_order_volume_scale, count_of_entries - 1) amount = stake_amount / current_rate logger.info(f'Initiating safety order entry #{count_of_entries} for {trade.pair} with stake amount of {stake_amount}. which equals {amount}. Previously bought: {already_bought}. Now overall:{already_bought + stake_amount}. ') return stake_amount except Exception as exception: logger.info(f'Error occured while trying to get stake amount for {trade.pair}: {str(exception)}') # #print(f'Error occured while trying to get stake amount for {trade.pair}: {str(exception)}') return None return None def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Calculate all ma_buy values # moved into entry / exit functions to not have redundant calculations being done. # backtesting will be slower but who cares about a few seconds during backtests right? # All that counts is dry / live performance, not backtesting speed imo ... # If you want to put in the work with conditions be my guest ;) # Elliot dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo) # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) # Check for 0 volume candles in the last day dataframe['missing_data'] = (dataframe['volume'] <= 0).rolling( window=self.no_volume_window_candles, min_periods=self.no_volume_window_candles).sum() return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Speed optimization for dry / live runs, not looping through for ... values with it, nothing else. dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] = ta.EMA(dataframe, timeperiod=self.base_nb_candles_buy.value) conditions = [] conditions.append((dataframe['close'] < dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_buy.value) & (dataframe['missing_data'] < 1)) conditions.append((dataframe['close'] < dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value) & (dataframe['EWO'] < self.ewo_low.value) & (dataframe['missing_data'] < 1)) if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), 'enter_long'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Speed optimization for dry / live runs, not looping through for ... values with it, nothing else. dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] = ta.EMA(dataframe, timeperiod=self.base_nb_candles_sell.value) conditions = [] conditions.append((dataframe['close'] > dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value) & (dataframe['volume'] > 0)) if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), 'exit_long'] = 1 return dataframe class SMAOffset_Hippocritical_dca_forced_doubling_entry_delay_5m(SMAOffset_Hippocritical_dca_forced_doubling_entry_delay): buy_params = {'base_nb_candles_buy': 16, 'ewo_high': 5.672, 'ewo_low': -19.931, 'low_offset': 0.973, 'rsi_buy': 59, 'min_delay_between_safety_orders': 5} class SMAOffset_Hippocritical_dca_forced_doubling_entry_delay_10m(SMAOffset_Hippocritical_dca_forced_doubling_entry_delay): buy_params = {'base_nb_candles_buy': 16, 'ewo_high': 5.672, 'ewo_low': -19.931, 'low_offset': 0.973, 'rsi_buy': 59, 'min_delay_between_safety_orders': 10} class SMAOffset_Hippocritical_dca_forced_doubling_entry_delay_15m(SMAOffset_Hippocritical_dca_forced_doubling_entry_delay): buy_params = {'base_nb_candles_buy': 16, 'ewo_high': 5.672, 'ewo_low': -19.931, 'low_offset': 0.973, 'rsi_buy': 59, 'min_delay_between_safety_orders': 15} class SMAOffset_Hippocritical_dca_forced_doubling_entry_delay_20m(SMAOffset_Hippocritical_dca_forced_doubling_entry_delay): buy_params = {'base_nb_candles_buy': 16, 'ewo_high': 5.672, 'ewo_low': -19.931, 'low_offset': 0.973, 'rsi_buy': 59, 'min_delay_between_safety_orders': 20} class SMAOffset_Hippocritical_dca_forced_doubling_entry_delay_25m(SMAOffset_Hippocritical_dca_forced_doubling_entry_delay): buy_params = {'base_nb_candles_buy': 16, 'ewo_high': 5.672, 'ewo_low': -19.931, 'low_offset': 0.973, 'rsi_buy': 59, 'min_delay_between_safety_orders': 25} class SMAOffset_Hippocritical_dca_forced_doubling_entry_delay_30m(SMAOffset_Hippocritical_dca_forced_doubling_entry_delay): buy_params = {'base_nb_candles_buy': 16, 'ewo_high': 5.672, 'ewo_low': -19.931, 'low_offset': 0.973, 'rsi_buy': 59, 'min_delay_between_safety_orders': 30} class SMAOffset_Hippocritical_dca_forced_doubling_entry_delay_35m(SMAOffset_Hippocritical_dca_forced_doubling_entry_delay): buy_params = {'base_nb_candles_buy': 16, 'ewo_high': 5.672, 'ewo_low': -19.931, 'low_offset': 0.973, 'rsi_buy': 59, 'min_delay_between_safety_orders': 35} class SMAOffset_Hippocritical_dca_forced_doubling_entry_delay_40m(SMAOffset_Hippocritical_dca_forced_doubling_entry_delay): buy_params = {'base_nb_candles_buy': 16, 'ewo_high': 5.672, 'ewo_low': -19.931, 'low_offset': 0.973, 'rsi_buy': 59, 'min_delay_between_safety_orders': 40} class SMAOffset_Hippocritical_dca_forced_doubling_entry_delay_45m(SMAOffset_Hippocritical_dca_forced_doubling_entry_delay): buy_params = {'base_nb_candles_buy': 16, 'ewo_high': 5.672, 'ewo_low': -19.931, 'low_offset': 0.973, 'rsi_buy': 59, 'min_delay_between_safety_orders': 45} class SMAOffset_Hippocritical_dca_forced_doubling_entry_delay_50m(SMAOffset_Hippocritical_dca_forced_doubling_entry_delay): buy_params = {'base_nb_candles_buy': 16, 'ewo_high': 5.672, 'ewo_low': -19.931, 'low_offset': 0.973, 'rsi_buy': 59, 'min_delay_between_safety_orders': 50} class SMAOffset_Hippocritical_dca_forced_doubling_entry_delay_55m(SMAOffset_Hippocritical_dca_forced_doubling_entry_delay): buy_params = {'base_nb_candles_buy': 16, 'ewo_high': 5.672, 'ewo_low': -19.931, 'low_offset': 0.973, 'rsi_buy': 59, 'min_delay_between_safety_orders': 55} class SMAOffset_Hippocritical_dca_forced_doubling_entry_delay_60m(SMAOffset_Hippocritical_dca_forced_doubling_entry_delay): buy_params = {'base_nb_candles_buy': 16, 'ewo_high': 5.672, 'ewo_low': -19.931, 'low_offset': 0.973, 'rsi_buy': 59, 'min_delay_between_safety_orders': 60} class SMAOffset_Hippocritical_dca_forced_doubling_entry_delay_90m(SMAOffset_Hippocritical_dca_forced_doubling_entry_delay): buy_params = {'base_nb_candles_buy': 16, 'ewo_high': 5.672, 'ewo_low': -19.931, 'low_offset': 0.973, 'rsi_buy': 59, 'min_delay_between_safety_orders': 90} class SMAOffset_Hippocritical_dca_forced_doubling_entry_delay_120m(SMAOffset_Hippocritical_dca_forced_doubling_entry_delay): buy_params = {'base_nb_candles_buy': 16, 'ewo_high': 5.672, 'ewo_low': -19.931, 'low_offset': 0.973, 'rsi_buy': 59, 'min_delay_between_safety_orders': 120}