# --- 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(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 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))) # 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 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 buy_tag = 'empty' if hasattr(trade, 'buy_tag') and trade.buy_tag is not None: buy_tag = trade.buy_tag buy_tags = buy_tag.split() if current_profit <= -0.35: return f'stop_loss ({buy_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 # Let unlimited stakes leave funds open for DCA orders 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_buys = trade.select_filled_orders('buy') count_of_buys = len(filled_buys) if 1 <= count_of_buys <= self.max_so_multiplier_orig: # if (1 <= count_of_buys) and (open_trade_value < self.stake_amount * self.overbuy_factor): safety_order_trigger = (abs(self.initial_safety_order_trigger) * count_of_buys) 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_buys - 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_buys - 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_buys[0].cost # Fallback for when the initial stake was not set for whatever reason stake_amount = actual_initial_stake already_bought = sum(filled_buy.cost for filled_buy in filled_buys) 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 buys proposed_initial_stake = self.cust_proposed_initial_stakes[trade.pair] current_actual_stake = already_bought * math.pow(self.safety_order_volume_scale, (count_of_buys - 1)) current_stake_preposition = proposed_initial_stake * math.pow(self.safety_order_volume_scale, (count_of_buys - 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_buys - 1)) amount = stake_amount / current_rate logger.info( f"Initiating safety order buy #{count_of_buys} " f"for {trade.pair} with stake amount of {stake_amount}. " f"which equals {amount}. " f"Previously bought: {already_bought}. " f"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 # 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, } # Sell hyperspace params: sell_params = { "base_nb_candles_sell": 20, "high_offset": 1.010, } INTERFACE_VERSION = 2 # Modified ROI - 20210620 # ROI table: minimal_roi = { "0": 0.028, "10": 0.018, "30": 0.010, "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) # 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' informative_timeframe = '1h' process_only_new_candles = True startup_candle_count: 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 def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.informative_timeframe) for pair in pairs] return informative_pairs def get_informative_indicators(self, metadata: dict): dataframe = self.dp.get_pair_dataframe( pair=metadata['pair'], timeframe=self.informative_timeframe) return dataframe 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 ;) """ for val in self.base_nb_candles_buy.range: dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val) # Calculate all ma_sell values for val in self.base_nb_candles_sell.range: dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val) """ # 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.startup_candle_count, min_periods=self.startup_candle_count).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