# source: https://raw.githubusercontent.com/hangtenz/trading-bot/28f3cdca843c48f895851031c11c76a1079376e6/user_data/strategies/macd.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 #from helper import helper # --- Do not remove these libs --- from email.policy import default from unicodedata import decimal import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame import math import datetime from typing import Optional from freqtrade.persistence import Trade from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, RealParameter, IStrategy, IntParameter, informative) from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal, Real # noqa # -------------------------------- # Add your lib to import here import talib.abstract as ta import pandas_ta as pta import freqtrade.vendor.qtpylib.indicators as qtpylib class github_hangtenz_trading_bot__macd__20230226_111507(IStrategy): INTERFACE_VERSION = 3 timeframe = '1h' big_timeframe = '4h' can_short: bool = True minimal_roi = { "0": 100 } position_adjustment_enable = True distance_price = 0.02 #distance when calculate support,resistance r2r = DecimalParameter(1.5,2.0,decimals=3,default=1.819,space="buy") position_lost_percentage = DecimalParameter(0.5,1,decimals=3,default=0.935,space="buy") risk_level = DecimalParameter(0.02,0.10,decimals=3,default=0.09,space="buy") n1 = 3 #count candle to find support,resistance n2 = 3 stoploss = -1 trailing_stop = False process_only_new_candles = False use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = True startup_candle_count: int = 800 percentage_stop = -1 # record when order, diff between open price and stoploss # Optional order type mapping. order_types = { 'entry': 'market', 'exit': 'market', 'stoploss': 'market', 'stoploss_on_exchange': False } # Optional order time in force. order_time_in_force = { 'entry': 'gtc', 'exit': 'gtc' } plot_config = { 'main_plot': { f'ema200_{big_timeframe}': {'color':'blue'}, f'bb_upper_{big_timeframe}': {'color':'white'}, f'bb_middle_{big_timeframe}': {'color': 'white'}, f'bb_lower_{big_timeframe}': {'color': 'white'} }, 'subplots': { "github_hangtenz_trading_bot__macd__20230226_111507": { 'macd_hist': {'type': 'bar', 'plotly': {'opacity': 0.9}, 'color':'green'} }, } } # This is called when placing the initial order (opening trade) def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float, proposed_stake: float, min_stake: Optional[float], max_stake: float, entry_tag: Optional[str], side: str, **kwargs) -> float: # position size = account size x account risk / invalidation point position_size = (max_stake*self.risk_level.value)/self.position_lost_percentage.value if position_size < min_stake: #print(f"WARNING: enter position {pair} with risk level = {(min_stake*self.position_lost_percentage.value)/max_stake}") return min_stake return position_size @informative(big_timeframe) def populate_indicators_big_timeframe(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['ema200'] = ta.EMA(dataframe['close'], timeperiod = 200) n1 = self.n1 n2 = self.n2 support = [] resistance = [] touch_support = [False] * len(dataframe) touch_resistance = [False] * len(dataframe) is_support = [False] * len(dataframe) is_resistance = [False] * len(dataframe) for i in range(0,len(dataframe)): for j in range(len(support)): if dataframe.iloc[i]['low'] <= support[j] and dataframe.iloc[i]['close'] > support[j]: touch_support[i] = True break for j in range(len(resistance)): if dataframe.iloc[i]['high'] >= resistance[j] and dataframe.iloc[i]['close'] < resistance[j]: touch_resistance[i] = True break if self.is_support(dataframe,i,n1,n2): support.append(dataframe.iloc[i]['low']) is_support[i] = True if self.is_resistance(dataframe,i,n1,n2): resistance.append(dataframe.iloc[i]['high']) is_resistance[i] = True dataframe['is_support'] = is_support dataframe['is_resistance'] = is_resistance dataframe['touch_support'] = touch_support dataframe['touch_resistance'] = touch_resistance bb_upper, bb_middle, bb_lower = ta.BBANDS(dataframe['close'], 20, 2.0, 2.0) dataframe['bb_upper'] = bb_upper dataframe['bb_middle'] = bb_middle dataframe['bb_lower'] = bb_lower #dataframe = self.plot_support_resistance(dataframe) return dataframe def leverage(self, pair: str, current_time: 'datetime', current_rate: float, proposed_leverage: float, max_leverage: float, side: str, **kwargs) -> float: l = max(math.floor(self.position_lost_percentage.value/self.percentage_stop),1) ##print(f"percent_stop = {self.percentage_stop}, leverage={l}") return l def distance_enough(self,p1,p2): p1 = float(p1) p2 = float(p2) return (abs(p1-p2)/min(p1,p2))*100 >= self.distance_price def is_support(self,dataframe:DataFrame,i:int,n1:int,n2:int) -> bool: for j in range(i-n1,i): if j<0 or j+1>=len(dataframe): return False if not self.distance_enough(dataframe.iloc[j]['low'],dataframe.iloc[j+1]['low']): return False if not(dataframe.iloc[j]['low'] > dataframe.iloc[j+1]['low']): return False for j in range(i,i+n2+1): if j<0 or j+1>=len(dataframe): return False if not self.distance_enough(dataframe.iloc[j]['low'],dataframe.iloc[j+1]['low']): return False if not(dataframe.iloc[j]['low'] < dataframe.iloc[j+1]['low']): return False return True def is_resistance(self,dataframe:DataFrame,i:int,n1:int,n2:int) -> bool: for j in range(i-n1,i): if j<0 or j+1>=len(dataframe): return False if not self.distance_enough(dataframe.iloc[j]['high'],dataframe.iloc[j+1]['high']): return False if not(dataframe.iloc[j]['high'] < dataframe.iloc[j+1]['high']): return False for j in range(i,i+n2+1): if j<0 or j+1>=len(dataframe): return False if not self.distance_enough(dataframe.iloc[j]['high'],dataframe.iloc[j+1]['high']): return False if not(dataframe.iloc[j]['high'] > dataframe.iloc[j+1]['high']): return False return True def plot_support_resistance(self,dataframe:DataFrame): main_plot = self.plot_config['main_plot'] for i in range(len(dataframe)): if dataframe.iloc[i]['is_support']: dataframe[f'support_{i}'] = [dataframe.iloc[i]['low']] * len(dataframe) main_plot[f'support_{i}_{self.big_timeframe}'] = {'color':'orange'} if dataframe.iloc[i]['is_resistance']: dataframe[f'resistance_{i}'] = [dataframe.iloc[i]['high']] * len(dataframe) main_plot[f'resistance_{i}_{self.big_timeframe}'] = {'color':'purple'} return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['macd_hist'] = ta.github_hangtenz_trading_bot__macd__20230226_111507(dataframe['close'], timeperiod=14)[2] #helper.save_dataframe(dataframe) return dataframe def touch_support(self,dataframe:DataFrame,i:int,interesting_back_candle:int): for j in range(i-interesting_back_candle,i): if dataframe.iloc[j][f'touch_support_{self.big_timeframe}']: return (True,dataframe.iloc[j][f'low_{self.big_timeframe}']) return (False,None) def touch_resistance(self,dataframe:DataFrame,i:int,interesting_back_candle:int): for j in range(i-interesting_back_candle,i): if dataframe.iloc[j][f'touch_resistance_{self.big_timeframe}']: return (True,dataframe.iloc[j][f'high_{self.big_timeframe}']) return (False,None) def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: distance_stop_loss = 0.02 interesting_back_candle = 10 enter_long = [False] * len(dataframe) enter_short = [False] * len(dataframe) exit_long = [False] * len(dataframe) exit_short = [False] * len(dataframe) entering = None price_stop_loss = -1 exit_price = -1 for i in range(interesting_back_candle,len(dataframe)): touch_support,s = self.touch_support(dataframe,i,interesting_back_candle) current = dataframe.iloc[i] #enter long if entering==None and current['close'] > current[f'ema200_{self.big_timeframe}'] and current['low'] > current[f'ema200_{self.big_timeframe}'] and current['macd_hist']>0 and dataframe.iloc[i-1]['macd_hist']<0 and touch_support: enter_long[i] = True entering = 'long' price_stop_loss = s - (s*distance_stop_loss) self.percentage_stop = (current['close'] - price_stop_loss) / price_stop_loss exit_price = (current['close'] - price_stop_loss)*self.r2r.value + current['close'] ##print(f"enter long position {current['close']} with exit_price={exit_price} stoploss = {price_stop_loss} percentage stop = {self.percentage_stop}") #take profit long if entering=='long' and current['close'] >= exit_price: entering = None exit_long[i] = True #stop loss long if entering=='long' and current['close']<=price_stop_loss: entering = None exit_long[i] = True touch_resistance,r = self.touch_resistance(dataframe,i,interesting_back_candle) #enter short # if touch_resistance: # #print(f"date={current['date']} {entering} {current['close']} {current[f'ema200_{self.big_timeframe}']} {current['high']} {current[f'ema200_{self.big_timeframe}']} {current['macd_hist']} {dataframe.iloc[i-1]['macd_hist']} ") if entering==None and current['close'] < current[f'ema200_{self.big_timeframe}'] and current['high'] < current[f'ema200_{self.big_timeframe}'] and current['macd_hist']<0 and dataframe.iloc[i-1]['macd_hist']>0 and touch_resistance: enter_short[i] = True entering = 'short' price_stop_loss = r + (r*distance_stop_loss) self.percentage_stop = (price_stop_loss - current['close']) / current['close'] exit_price = current['close'] - (price_stop_loss-current['close'])*self.r2r.value #take profit short if entering=='short' and current['close']<=exit_price: entering = None exit_short[i] = True #stop loss short if entering=='short' and current['close']>=price_stop_loss: entering = None exit_short[i] = True dataframe['enter_long'] = enter_long dataframe['enter_short'] = enter_short dataframe['exit_long'] = exit_long dataframe['exit_short'] = exit_short return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: return dataframe