# source: https://raw.githubusercontent.com/hamidreza07/freqtrade/2d378aa9ceb8f44e420e507903706b75ac1afb32/freqtrade/templates/PivotPoint.py import logging import talib.abstract as ta from datetime import datetime, timedelta, timezone from freqtrade.persistence import Trade import numpy as np # noqa import pandas as pd import freqtrade.vendor.qtpylib.indicators as qtpylib from functools import reduce from pandas import DataFrame # import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.strategy import IStrategy, CategoricalParameter, merge_informative_pair, DecimalParameter, IntParameter logger = logging.getLogger(__name__) from typing import Optional import warnings warnings.filterwarnings('ignore') class Github_hamidreza07_freqtrade__PivotPoint__20231127_092233(IStrategy): timeframe = '5m' # ROI table: minimal_roi = { "0": 0.02 ,"69": 0.01, } # Stoploss: stoploss = -0.05 plot_config = { "main_plot": { "Pivot_1d": {"Pivot_1d": {"color": "navy"},}, "Support1_1d": {"Support1_1d": {"color": "brown"},}, "Support2_1d": {"Support2_1d": {"color": "yellow"},}, "Support3_1d": {"Support3_1d": {"color": "black"},}, "Resistance1_1d": {"Resistance1_1d": {"color": "olive"},}, "Resistance2_1d": {"Resistance2_1d": {"color": "blue"},}, "Resistance3_1d": {"Resistance3_1d": {"color": "teal"},}, }, } can_short = True Support1_parameter = DecimalParameter(0.00, 1, default=.382, space="buy", optimize=True, load=True) Support2_parameter = DecimalParameter(0.00, 1, default=.618, space="buy", optimize=True, load=True) Resistance1_parameter = DecimalParameter(0.00, 1, default=.382, space="buy", optimize=True, load=True) Resistance2_parameter = DecimalParameter(0.00, 1, default=.618, space="buy", optimize=True, load=True) Support1_Woodie_parameter = IntParameter(low=1, high=20, default=2, space='buy', optimize=True, load=True) Support3_Woodie_parameter = IntParameter(low=1, high=20, default=2, space='buy', optimize=True, load=True) Resistance1_Woodie_parameter = IntParameter(low=1, high=20, default=2, space='buy', optimize=True, load=True) Resistance3_Woodie_parameter = IntParameter(low=1, high=20, default=2, space='buy', optimize=True, load=True) time_trigger = CategoricalParameter(["1w", "1d"],default="1d", space='buy', optimize=True) trade_trigger = CategoricalParameter(["can_short", "can_long","can_both"],default="can_both", space='buy', optimize=True) calculation_trigger = CategoricalParameter(["Woodie", "Fibonacci"],default="Fibonacci", space='buy', optimize=True) def informative_pairs(self): # get access to all pairs available in whitelist. pairs = self.dp.current_whitelist() # Assign tf to each pair so they can be downloaded and cached for strategy. informative_pairs = [(pair, '1d') for pair in pairs] informative_pairs +=[(pair, '1w') for pair in pairs] return informative_pairs def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if self.time_trigger.value=="1w": self.inf_tf = '1w' if self.time_trigger.value=="1d": self.inf_tf = '1d' # Get the informative pair informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_tf) if self.calculation_trigger.value=="Woodie": informative['Pivot'] = (informative['high'] + informative['low'] + informative['close']) / 3 informative['Support1'] = (self.Support1_Woodie_parameter.value * informative['Pivot']) - informative['high'] informative['Support2'] = informative['Pivot'] - (informative['high'] - informative['low']) informative['Support3'] = informative['low'] - self.Support3_Woodie_parameter.value * (informative['high'] - informative['Pivot']) informative['Resistance1'] = (self.Resistance1_Woodie_parameter.value * informative['Pivot']) - informative['low'] informative['Resistance2'] = informative['Pivot'] + (informative['high'] - informative['low']) informative['Resistance3'] = informative['high'] + self.Resistance3_Woodie_parameter.value * (informative['Pivot'] - informative['low']) if self.calculation_trigger.value=="Fibonacci": informative['Pivot'] = (informative['high'] + informative['low'] + informative['close']) / 3 informative['Support1'] = informative['Pivot'] - self.Support1_parameter.value * (informative['high'] - informative['low']) informative['Support2'] = informative['Pivot'] - self.Support2_parameter.value * (informative['high'] - informative['low']) informative['Support3'] = informative['Pivot'] - (informative['high'] - informative['low']) informative['Resistance1'] = informative['Pivot'] + self.Resistance1_parameter.value * (informative['high'] - informative['low']) informative['Resistance2'] = informative['Pivot'] + self.Resistance2_parameter.value * (informative['high'] - informative['low']) informative['Resistance3'] = informative['Pivot'] + (informative['high'] - informative['low']) informative['date'] = pd.to_datetime(informative['date']) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.inf_tf, ffill=True) return dataframe def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: df['condition_entry_long_1'] = qtpylib.crossed_above(df['close'],df[f'Support2_{self.inf_tf}']) df['condition_entry_long_2'] = qtpylib.crossed_above (df['close'] , df[f'Support1_{self.inf_tf}']) df['condition_entry_long_3'] = qtpylib.crossed_above (df['close'] , df[f'Pivot_{self.inf_tf}']) df['condition_entry_long_4'] = qtpylib.crossed_above(df['close'] , df[f'Resistance1_{self.inf_tf}']) df['condition_entry_long_5'] = qtpylib.crossed_above (df['close'] , df[f'Resistance2_{self.inf_tf}']) df['condition_entry_short_1'] = qtpylib.crossed_below (df['close'] , df[f'Resistance2_{self.inf_tf}']) df['condition_entry_short_2'] = qtpylib.crossed_below (df['close'] , df[f'Resistance1_{self.inf_tf}']) df['condition_entry_short_3'] = qtpylib.crossed_below (df['close'] , df[f'Pivot_{self.inf_tf}']) df['condition_entry_short_4'] = qtpylib.crossed_below (df['close'] , df[f'Support1_{self.inf_tf}']) df['condition_entry_short_5'] = qtpylib.crossed_below (df['close'] , df[f'Support2_{self.inf_tf}']) if self.trade_trigger.value=="can_both": df = self.trading_both(df) if self.trade_trigger.value == "can_long": df = self.trading_long(df) if self.trade_trigger.value == "can_short": df = self.trading_short(df) return df def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) dataframe['condition_exit_long_1'] = (qtpylib.crossed_above (dataframe['close'] , dataframe[f'Support1_{self.inf_tf}']))|(qtpylib.crossed_below(dataframe['close'],dataframe[f'Support3_{self.inf_tf}'])) dataframe['condition_exit_long_2'] = (qtpylib.crossed_above (dataframe['close'] , dataframe[f'Pivot_{self.inf_tf}']))| (qtpylib.crossed_below(dataframe['close'],dataframe[f'Support2_{self.inf_tf}'])) dataframe['condition_exit_long_3'] = (qtpylib.crossed_above (dataframe['close'] , dataframe[f'Resistance1_{self.inf_tf}']))|(qtpylib.crossed_below(dataframe['close'],dataframe[f'Support1_{self.inf_tf}'])) dataframe['condition_exit_long_4'] = (qtpylib.crossed_above (dataframe['close'] , dataframe[f'Resistance2_{self.inf_tf}']))|(qtpylib.crossed_below(dataframe['close'],dataframe[f'Pivot_{self.inf_tf}'])) dataframe['condition_exit_long_5'] = (qtpylib.crossed_above (dataframe['close'] , dataframe[f'Resistance3_{self.inf_tf}']))|(qtpylib.crossed_below(dataframe['close'],dataframe[f'Resistance1_{self.inf_tf}'])) dataframe['condition_exit_short_1'] = (qtpylib.crossed_below (dataframe['close'] , dataframe[f'Resistance1_{self.inf_tf}']))|(qtpylib.crossed_above(dataframe['close'],dataframe[f'Resistance3_{self.inf_tf}'])) dataframe['condition_exit_short_2'] = (qtpylib.crossed_below (dataframe['close'] , dataframe[f'Pivot_{self.inf_tf}']))|( qtpylib.crossed_above(dataframe['close'],dataframe[f'Resistance2_{self.inf_tf}'])) dataframe['condition_exit_short_3'] = (qtpylib.crossed_below (dataframe['close'] , dataframe[f'Support1_{self.inf_tf}']))|(qtpylib.crossed_above(dataframe['close'],dataframe[f'Resistance1_{self.inf_tf}'])) dataframe['condition_exit_short_4'] = (qtpylib.crossed_below (dataframe['close'] , dataframe[f'Support2_{self.inf_tf}']))|(qtpylib.crossed_above(dataframe['close'],dataframe[f'Resistance1_{self.inf_tf}'])) dataframe['condition_exit_short_5'] = (qtpylib.crossed_below (dataframe['close'] , dataframe[f'Support3_{self.inf_tf}']))|(qtpylib.crossed_above(dataframe['close'],dataframe[f'Support1_{self.inf_tf}'])) last_candle = dataframe.iloc[-1].squeeze() last_candle['date_stamp'] = pd.to_datetime(last_candle['date']) H = last_candle['date_stamp'].hour M = last_candle['date_stamp'].minute W = last_candle['date_stamp'].dayofweek if self.time_trigger.value=="1w": time_condition = (W==0) if self.time_trigger.value=="1d": time_condition =(H==0) & (M==5) if trade.enter_tag =="long_signal_1" : if time_condition: return f'exit_{trade.enter_tag}' elif last_candle['condition_exit_long_1']: return f'exit_{trade.enter_tag}' if trade.enter_tag =="long_signal_2" : if time_condition: return f'exit_{trade.enter_tag}' elif last_candle['condition_exit_long_2']: return f'exit_{trade.enter_tag}' if trade.enter_tag =="long_signal_3" : if time_condition: return f'exit_{trade.enter_tag}' elif last_candle['condition_exit_long_3']: return f'exit_{trade.enter_tag}' if trade.enter_tag =="long_signal_4" : if time_condition: return f'exit_{trade.enter_tag}' elif last_candle['condition_exit_long_4']: return f'exit_{trade.enter_tag}' if trade.enter_tag =="long_signal_5" : if time_condition: return f'exit_{trade.enter_tag}' elif last_candle['condition_exit_long_5']: return f'exit_{trade.enter_tag}' if trade.enter_tag =="short_signal_1" : if time_condition: return f'exit_{trade.enter_tag}' elif last_candle['condition_exit_short_1']: return f'exit_{trade.enter_tag}' if trade.enter_tag =="short_signal_2" : if time_condition: return f'exit_{trade.enter_tag}' elif last_candle['condition_exit_short_2']: return f'exit_{trade.enter_tag}' if trade.enter_tag =="short_signal_3" : if time_condition: return f'exit_{trade.enter_tag}' elif last_candle['condition_exit_short_3']: return f'exit_{trade.enter_tag}' if trade.enter_tag =="short_signal_4" : if time_condition: return f'exit_{trade.enter_tag}' elif last_candle['condition_exit_short_4']: return f'exit_{trade.enter_tag}' if trade.enter_tag =="short_signal_5" : if time_condition: return f'exit_{trade.enter_tag}' elif last_candle['condition_exit_short_5']: return f'exit_{trade.enter_tag}' return None def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame: return df def trading_long(self,df:DataFrame): df.loc[df['condition_entry_long_1'], ["enter_long", "enter_tag"]] = (1, "long_signal_1") df.loc[df['condition_entry_long_2'], ["enter_long", "enter_tag"]] = (1, "long_signal_2") df.loc[df['condition_entry_long_3'], ["enter_long", "enter_tag"]] = (1, "long_signal_3") df.loc[df['condition_entry_long_4'], ["enter_long", "enter_tag"]] = (1, "long_signal_4") df.loc[ df['condition_entry_long_5'], ["enter_long", "enter_tag"]] = (1, "long_signal_5") return df def trading_short(self,df:DataFrame): df.loc[df['condition_entry_short_1'], ["enter_short", "enter_tag"]] = (1, "short_signal_1") df.loc[df['condition_entry_short_2'], ["enter_short", "enter_tag"]] = (1, "short_signal_2") df.loc[df['condition_entry_short_3'], ["enter_short", "enter_tag"]] = (1, "short_signal_3") df.loc[df['condition_entry_short_4'], ["enter_short", "enter_tag"]] = (1, "short_signal_4") df.loc[ df['condition_entry_short_5'], ["enter_short", "enter_tag"]] = (1, "short_signal_5") return df def trading_both(self,df:DataFrame): df = self.trading_long(df) df = self.trading_short(df) return df