# source: https://raw.githubusercontent.com/fortesenselabs/trade_flow/91d8c549438fbdf81cf5be32905269231b99c6ab/packages/ft_strategies/archive/strategies/zmomz/TrendLines.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # isort: skip_file # --- Do not remove these libs --- import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter) # -------------------------------- # Add your lib to import here from datetime import datetime from typing import Optional import pandas_ta import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib class Github_fortesenselabs_trade_flow__TrendLines__20241128_170354(IStrategy): INTERFACE_VERSION = 3 can_short= True # roi take profit and stop loss points minimal_roi = {"0": 0.99} stoploss = -1 use_custom_stoploss = False trailing_stop = False use_exit_signal = True exit_profit_only = False exit_profit_offset = 0.0 ignore_roi_if_entry_signal = False timeframe = '1h' # Strategy Parameters ATR_Period = 11 LBL = 10 LBR = 10 slope = 17.6 average_price_period = 20 trend_confirmation_period = 50 xlevrage = 10 # Run "populate_indicators()" only for new candle. process_only_new_candles = True # Optional order type mapping. order_types = { 'entry': 'market', 'exit': 'market', 'stoploss': 'market', 'stoploss_on_exchange': False } def checkhl(self, data_back, data_forward, hl): if hl == 'high' or hl == 'High': ref = data_back[len(data_back)-1] for i in range(len(data_back)-1): if ref < data_back[i]: return 0 for i in range(len(data_forward)): if ref <= data_forward[i]: return 0 return 1 if hl == 'low' or hl == 'Low': ref = data_back[len(data_back)-1] for i in range(len(data_back)-1): if ref > data_back[i]: return 0 for i in range(len(data_forward)): if ref >= data_forward[i]: return 0 return 1 def pivot(self, osc, atr, LBL, LBR, highlow): breakouts = pd.Series(dtype='float64') pivpoint = 0 atrpoint = 0 counter=0 pivots=[] atrs=[] count_list=[] left = [] right = [] for i in range(len(osc)): pivots.append(pivpoint) atrs.append(atrpoint) count_list.append(counter) counter +=1 if i < LBL + 1: left.append(osc[i]) if i > LBL: right.append(osc[i]) if i > LBL + LBR: left.append(right[0]) left.pop(0) right.pop(0) if self.checkhl(left, right, highlow): pivots[i - LBR] = osc[i - LBR] pivpoint = osc[i - LBR] atrpoint = atr[i - LBR] counter = 0 slops =list(map(lambda x: x/self.slope,atrs)) if highlow.lower() == 'low': breakouts = pd.Series(pivots) + (pd.Series(count_list)*pd.Series(slops)) elif highlow.lower() == 'high': breakouts = pd.Series(pivots) - (pd.Series(count_list)*pd.Series(slops)) return breakouts, pd.Series(pivots) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # average True range for stoploss extention dataframe['ATR'] = pandas_ta.atr(dataframe['high'],dataframe['low'],dataframe['close'],length= self.ATR_Period) # main indicators for long and short breakout enteries, and exiting targets # all are based on pivot points dataframe['short_breakout'], dataframe['long_target'] = self.pivot(osc=dataframe["high"], atr=dataframe['ATR'], LBL=self.LBL,LBR=self.LBR,highlow="high") dataframe['long_breakout'], dataframe['short_target'] = self.pivot(osc=dataframe["low"], atr=dataframe['ATR'], LBL=self.LBL,LBR=self.LBR,highlow="low") # moving average for trend confirmation dataframe['Trend_confirmation'] = ta.SMA(dataframe, timeperiod=self.trend_confirmation_period) # distance between current (average) price and targets, used for filtering # low profit trades dataframe['average_price'] = ta.SMA(dataframe, timeperiod=self.average_price_period) dataframe['dist_resistance'] = dataframe['long_target']-dataframe['average_price'] dataframe['dist_support'] = dataframe['average_price']-dataframe['short_target'] return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( # long position conditions: # main condition, candle crosses and closes above the long breakout line (qtpylib.crossed_above(dataframe['close'], dataframe['long_breakout'])) # the whole candle body must be above the moving average for confirming up trend & (dataframe['open'] > dataframe['Trend_confirmation']) # the candle must be green & (dataframe['close'] > dataframe['open']) # current price must be closer to the resistance & (dataframe['dist_resistance'] < dataframe['dist_support']) ), 'enter_long'] = 1 dataframe.loc[ ( # short position conditions: # main condition, candle crosses and closes bellow the short breakout line (qtpylib.crossed_below(dataframe['close'], dataframe['short_breakout'])) # the whole candle body must be bellow the moving average for confirming up trend & (dataframe['open'] < dataframe['Trend_confirmation']) # the candle must be red & (dataframe['close'] < dataframe['open']) # current price must be closer to the support & (dataframe['dist_resistance'] > dataframe['dist_support']) ), 'enter_short'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( # exit with profit at long target (dataframe['close'] >= dataframe['long_target']) # dynamic stoploss | (dataframe['close'] <= dataframe['short_target']-dataframe['ATR']) ), 'exit_long'] = 1 dataframe.loc[ ( # exit with profit at short target (dataframe['close'] <= dataframe['short_target']) # dynamic stoploss | (dataframe['close'] >= dataframe['long_target']+dataframe['ATR']) ), 'exit_short'] = 1 return dataframe def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: entry_tag = '' max_leverage = self.xlevrage return max_leverage