# source: https://raw.githubusercontent.com/Bldnr94/FreqTrade/d56ef86cf87c86b79643693dbf2686a2fcfc6c54/ft_userdata/user_data/strategies/LSV1.py from freqtrade.strategy.interface import IStrategy from pandas import DataFrame import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib import pandas as pd pd.options.mode.chained_assignment = None import technical.indicators as ftt from functools import reduce from datetime import datetime, timedelta from freqtrade.strategy import merge_informative_pair import numpy as np from freqtrade.strategy import stoploss_from_open class Github_Bldnr94_FreqTrade__LSV1__20240620_104620(IStrategy): can_short = True # Enter Long hyperspace params: enter_long_params = { "buy_trend_above_senkou_level": 1, "buy_trend_bullish_level": 6, "buy_fan_magnitude_shift_value": 3, "buy_min_fan_magnitude_gain": 1.002 } # Exit Long hyperspace params: exit_long_params = { "exit_long_indicator": "trend_close_2h", } # Enter Short hyperspace params: enter_short_params = { "short_trend_below_senkou_level": 1, "short_trend_bearish_level": 6, "short_fan_magnitude_shift_value": 3, "short_min_fan_magnitude_gain": 0.998 } # Exit Short hyperspace params: exit_short_params = { "exit_short_indicator": "trend_close_2h", } # ROI table: minimal_roi = { "0": 0.14, "20": 0.08, "50": 0.04, "90": 0.015, "140": 0 } # Stoploss: stoploss = -0.275 # Optimal timeframe for the strategy timeframe = '15m' startup_candle_count = 96 process_only_new_candles = False trailing_stop = False use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False plot_config = { 'main_plot': { 'senkou_a': { 'color': 'green', 'fill_to': 'senkou_b', 'fill_label': 'Ichimoku Cloud', 'fill_color': 'rgba(255,76,46,0.2)', }, 'senkou_b': {}, 'trend_close_5m': {'color': '#FF5733'}, 'trend_close_15m': {'color': '#FF8333'}, 'trend_close_30m': {'color': '#FFB533'}, 'trend_close_1h': {'color': '#FFE633'}, 'trend_close_2h': {'color': '#E3FF33'}, 'trend_close_4h': {'color': '#C4FF33'}, 'trend_close_6h': {'color': '#61FF33'}, 'trend_close_8h': {'color': '#33FF7D'} }, 'subplots': { 'fan_magnitude': { 'fan_magnitude': {} }, 'fan_magnitude_gain': { 'fan_magnitude_gain': {} } } } def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: heikinashi = qtpylib.heikinashi(dataframe) dataframe['open'] = heikinashi['open'] dataframe['high'] = heikinashi['high'] dataframe['low'] = heikinashi['low'] dataframe['trend_close_5m'] = dataframe['close'] dataframe['trend_close_15m'] = ta.EMA(dataframe['close'], timeperiod=3) dataframe['trend_close_30m'] = ta.EMA(dataframe['close'], timeperiod=6) dataframe['trend_close_1h'] = ta.EMA(dataframe['close'], timeperiod=12) dataframe['trend_close_2h'] = ta.EMA(dataframe['close'], timeperiod=24) dataframe['trend_close_4h'] = ta.EMA(dataframe['close'], timeperiod=48) dataframe['trend_close_6h'] = ta.EMA(dataframe['close'], timeperiod=72) dataframe['trend_close_8h'] = ta.EMA(dataframe['close'], timeperiod=96) dataframe['trend_open_5m'] = dataframe['open'] dataframe['trend_open_15m'] = ta.EMA(dataframe['open'], timeperiod=3) dataframe['trend_open_30m'] = ta.EMA(dataframe['open'], timeperiod=6) dataframe['trend_open_1h'] = ta.EMA(dataframe['open'], timeperiod=12) dataframe['trend_open_2h'] = ta.EMA(dataframe['open'], timeperiod=24) dataframe['trend_open_4h'] = ta.EMA(dataframe['open'], timeperiod=48) dataframe['trend_open_6h'] = ta.EMA(dataframe['open'], timeperiod=72) dataframe['trend_open_8h'] = ta.EMA(dataframe['open'], timeperiod=96) dataframe['fan_magnitude'] = (dataframe['trend_close_2h'] / dataframe['trend_close_8h']) dataframe['fan_magnitude_gain'] = dataframe['fan_magnitude'] / dataframe['fan_magnitude'].shift(1) ichimoku = ftt.ichimoku(dataframe, conversion_line_period=20, base_line_periods=60, laggin_span=120, displacement=30) dataframe['chikou_span'] = ichimoku['chikou_span'] dataframe['tenkan_sen'] = ichimoku['tenkan_sen'] dataframe['kijun_sen'] = ichimoku['kijun_sen'] dataframe['senkou_a'] = ichimoku['senkou_span_a'] dataframe['senkou_b'] = ichimoku['senkou_span_b'] dataframe['leading_senkou_span_a'] = ichimoku['leading_senkou_span_a'] dataframe['leading_senkou_span_b'] = ichimoku['leading_senkou_span_b'] dataframe['cloud_green'] = ichimoku['cloud_green'] dataframe['cloud_red'] = ichimoku['cloud_red'] dataframe['atr'] = ta.ATR(dataframe) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions_long = [] conditions_short = [] # Long conditions if self.enter_long_params['buy_trend_above_senkou_level'] >= 1: conditions_long.append(dataframe['trend_close_5m'] > dataframe['senkou_a']) conditions_long.append(dataframe['trend_close_5m'] > dataframe['senkou_b']) if self.enter_long_params['buy_trend_above_senkou_level'] >= 2: conditions_long.append(dataframe['trend_close_15m'] > dataframe['senkou_a']) conditions_long.append(dataframe['trend_close_15m'] > dataframe['senkou_b']) if self.enter_long_params['buy_trend_above_senkou_level'] >= 3: conditions_long.append(dataframe['trend_close_30m'] > dataframe['senkou_a']) conditions_long.append(dataframe['trend_close_30m'] > dataframe['senkou_b']) if self.enter_long_params['buy_trend_above_senkou_level'] >= 4: conditions_long.append(dataframe['trend_close_1h'] > dataframe['senkou_a']) conditions_long.append(dataframe['trend_close_1h'] > dataframe['senkou_b']) if self.enter_long_params['buy_trend_above_senkou_level'] >= 5: conditions_long.append(dataframe['trend_close_2h'] > dataframe['senkou_a']) conditions_long.append(dataframe['trend_close_2h'] > dataframe['senkou_b']) if self.enter_long_params['buy_trend_above_senkou_level'] >= 6: conditions_long.append(dataframe['trend_close_4h'] > dataframe['senkou_a']) conditions_long.append(dataframe['trend_close_4h'] > dataframe['senkou_b']) if self.enter_long_params['buy_trend_above_senkou_level'] >= 7: conditions_long.append(dataframe['trend_close_6h'] > dataframe['senkou_a']) conditions_long.append(dataframe['trend_close_6h'] > dataframe['senkou_b']) if self.enter_long_params['buy_trend_above_senkou_level'] >= 8: conditions_long.append(dataframe['trend_close_8h'] > dataframe['senkou_a']) conditions_long.append(dataframe['trend_close_8h'] > dataframe['senkou_b']) # Trends bullish if self.enter_long_params['buy_trend_bullish_level'] >= 1: conditions_long.append(dataframe['trend_close_5m'] > dataframe['trend_open_5m']) if self.enter_long_params['buy_trend_bullish_level'] >= 2: conditions_long.append(dataframe['trend_close_15m'] > dataframe['trend_open_15m']) if self.enter_long_params['buy_trend_bullish_level'] >= 3: conditions_long.append(dataframe['trend_close_30m'] > dataframe['trend_open_30m']) if self.enter_long_params['buy_trend_bullish_level'] >= 4: conditions_long.append(dataframe['trend_close_1h'] > dataframe['trend_open_1h']) if self.enter_long_params['buy_trend_bullish_level'] >= 5: conditions_long.append(dataframe['trend_close_2h'] > dataframe['trend_open_2h']) if self.enter_long_params['buy_trend_bullish_level'] >= 6: conditions_long.append(dataframe['trend_close_4h'] > dataframe['trend_open_4h']) if self.enter_long_params['buy_trend_bullish_level'] >= 7: conditions_long.append(dataframe['trend_close_6h'] > dataframe['trend_open_6h']) if self.enter_long_params['buy_trend_bullish_level'] >= 8: conditions_long.append(dataframe['trend_close_8h'] > dataframe['trend_open_8h']) # Trends magnitude conditions_long.append(dataframe['fan_magnitude'] > 1) for x in range(self.enter_long_params['buy_fan_magnitude_shift_value']): conditions_long.append(dataframe['fan_magnitude'].shift(x+1) < dataframe['fan_magnitude']) # Short conditions if self.enter_short_params['short_trend_below_senkou_level'] >= 1: conditions_short.append(dataframe['trend_close_5m'] < dataframe['senkou_a']) conditions_short.append(dataframe['trend_close_5m'] < dataframe['senkou_b']) if self.enter_short_params['short_trend_below_senkou_level'] >= 2: conditions_short.append(dataframe['trend_close_15m'] < dataframe['senkou_a']) conditions_short.append(dataframe['trend_close_15m'] < dataframe['senkou_b']) if self.enter_short_params['short_trend_below_senkou_level'] >= 3: conditions_short.append(dataframe['trend_close_30m'] < dataframe['senkou_a']) conditions_short.append(dataframe['trend_close_30m'] < dataframe['senkou_b']) if self.enter_short_params['short_trend_below_senkou_level'] >= 4: conditions_short.append(dataframe['trend_close_1h'] < dataframe['senkou_a']) conditions_short.append(dataframe['trend_close_1h'] < dataframe['senkou_b']) if self.enter_short_params['short_trend_below_senkou_level'] >= 5: conditions_short.append(dataframe['trend_close_2h'] < dataframe['senkou_a']) conditions_short.append(dataframe['trend_close_2h'] < dataframe['senkou_b']) if self.enter_short_params['short_trend_below_senkou_level'] >= 6: conditions_short.append(dataframe['trend_close_4h'] < dataframe['senkou_a']) conditions_short.append(dataframe['trend_close_4h'] < dataframe['senkou_b']) if self.enter_short_params['short_trend_below_senkou_level'] >= 7: conditions_short.append(dataframe['trend_close_6h'] < dataframe['senkou_a']) conditions_short.append(dataframe['trend_close_6h'] < dataframe['senkou_b']) if self.enter_short_params['short_trend_below_senkou_level'] >= 8: conditions_short.append(dataframe['trend_close_8h'] < dataframe['senkou_a']) conditions_short.append(dataframe['trend_close_8h'] < dataframe['senkou_b']) # Trends bearish if self.enter_short_params['short_trend_bearish_level'] >= 1: conditions_short.append(dataframe['trend_close_5m'] < dataframe['trend_open_5m']) if self.enter_short_params['short_trend_bearish_level'] >= 2: conditions_short.append(dataframe['trend_close_15m'] < dataframe['trend_open_15m']) if self.enter_short_params['short_trend_bearish_level'] >= 3: conditions_short.append(dataframe['trend_close_30m'] < dataframe['trend_open_30m']) if self.enter_short_params['short_trend_bearish_level'] >= 4: conditions_short.append(dataframe['trend_close_1h'] < dataframe['trend_open_1h']) if self.enter_short_params['short_trend_bearish_level'] >= 5: conditions_short.append(dataframe['trend_close_2h'] < dataframe['trend_open_2h']) if self.enter_short_params['short_trend_bearish_level'] >= 6: conditions_short.append(dataframe['trend_close_4h'] < dataframe['trend_open_4h']) if self.enter_short_params['short_trend_bearish_level'] >= 7: conditions_short.append(dataframe['trend_close_6h'] < dataframe['trend_open_6h']) if self.enter_short_params['short_trend_bearish_level'] >= 8: conditions_short.append(dataframe['trend_close_8h'] < dataframe['trend_open_8h']) # Trends magnitude conditions_short.append(dataframe['fan_magnitude'] < 1) for x in range(self.enter_short_params['short_fan_magnitude_shift_value']): conditions_short.append(dataframe['fan_magnitude'].shift(x+1) > dataframe['fan_magnitude']) # Check for last position outcomes # pair = metadata['pair'] # if pair in self.last_positions: # last_position = self.last_positions[pair] # if last_position['type'] == 'long' and last_position['profit'] > 0: # conditions_long.append(dataframe['enter_long'] != 1) # Avoid long after profitable long # if last_position['type'] == 'short' and last_position['profit'] > 0: # conditions_short.append(dataframe['enter_short'] != 1) # Avoid short after profitable short if conditions_long: dataframe.loc[ reduce(lambda x, y: x & y, conditions_long), 'enter_long'] = 1 if conditions_short: dataframe.loc[ reduce(lambda x, y: x & y, conditions_short), 'enter_short'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions_long = [] conditions_short = [] # Exit long conditions conditions_long.append(qtpylib.crossed_below(dataframe['trend_close_5m'], dataframe[self.exit_long_params['exit_long_indicator']])) # Exit short conditions conditions_short.append(qtpylib.crossed_above(dataframe['trend_close_5m'], dataframe[self.exit_short_params['exit_short_indicator']])) if conditions_long: dataframe.loc[ reduce(lambda x, y: x & y, conditions_long), 'exit_long'] = 1 if conditions_short: dataframe.loc[ reduce(lambda x, y: x & y, conditions_short), 'exit_short'] = 1 return dataframe # def confirm_trade_exit(self, pair: str, trade: 'Trade', order_type: str, amount: float, rate: float, # time_in_force: str, custom_data: dict) -> bool: # self.last_positions[pair] = { # 'type': 'short' if trade.is_short else 'long', # 'profit': trade.calc_profit_ratio(rate) # } # return True # Allow the trade exit