# source: https://raw.githubusercontent.com/nicnl31/pyalgotrader/c22b45dc6744b8dc3ea5c9e73858c81274686684/frameworks/freqtrade/user_data/strategies/ichiV3.py # --- Do not remove these libs --- 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 # noqa pd.options.mode.chained_assignment = None # default='warn' 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_nicnl31_pyalgotrader__ichiV3__20240902_122600(IStrategy): """ This is the next version of ichiVx, and the previous version was ichiV2_5. ============================== Summary of changes from ichiV2_5: 1. Does not alter the original OHLC data. The Heikin Ashi data is now only for calculating signals. 2. The buy and sell parameters are the same as in the original ichiV1 version. """ INTERFACE_VERSION = 2 # NOTE: settings as of the 25th july 21 # Buy hyperspace params: buy_params = { "buy_trend_above_senkou_level": 5, "buy_trend_bullish_level": 6, "buy_fan_magnitude_shift_value": 3, "buy_min_fan_magnitude_gain": 1.002 # "buy_min_fan_magnitude_gain": 1.0013 # NOTE: Good value (Win% ~70%), alot of trades #"buy_min_fan_magnitude_gain": 1.008 # NOTE: Very save value (Win% ~90%), only the biggest moves 1.008, } # Sell hyperspace params: # NOTE: was 15m but kept bailing out in dryrun sell_params = { "sell_trend_indicator": "trend_close_2h", } # # ROI table: # minimal_roi = { # "0": 0.3 # } # Stoploss: stoploss = -0.06 # Optimal timeframe for the strategy timeframe = '5m' startup_candle_count = 96 process_only_new_candles = False trailing_stop = True trailing_stop_positive = 0.03 trailing_stop_positive_offset = 0.4 trailing_only_offset_is_reached = True use_sell_signal = True sell_profit_only = False # ignore_roi_if_buy_signal = True plot_config = { 'main_plot': { # fill area between senkou_a and senkou_b 'senkou_a': { 'color': 'green', #optional 'fill_to': 'senkou_b', 'fill_label': 'Ichimoku Cloud', #optional 'fill_color': 'rgba(255,76,46,0.2)', #optional }, # plot senkou_b, too. Not only the area to it. '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'}, 'trend_open_5m': {'color': '#4D7BF5'}, 'trend_open_15m': {'color': '#1E8C38'}, 'trend_open_30m': {'color': '#ABA6D0'}, 'trend_open_1h': {'color': '#631B69'}, 'trend_open_2h': {'color': '#A553FF'}, 'trend_open_4h': {'color': '#B10C78'}, 'trend_open_6h': {'color': '#389E03'}, 'trend_open_8h': {'color': '#571E60'} }, 'subplots': { 'fan_magnitude': { 'fan_magnitude': {} }, 'fan_magnitude_gain': { 'fan_magnitude_gain': {} } } } def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Populate Heikin Ashi candles heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] # dataframe['close'] = heikinashi['close'] # dataframe['ha_high'] = heikinashi['high'] # dataframe['ha_low'] = heikinashi['low'] dataframe['trend_close_5m'] = dataframe['close'] dataframe['trend_close_15m'] = ta.EMA(dataframe['trend_close_5m'], timeperiod=3) dataframe['trend_close_30m'] = ta.EMA(dataframe['trend_close_5m'], timeperiod=6) dataframe['trend_close_1h'] = ta.EMA(dataframe['trend_close_5m'], timeperiod=12) dataframe['trend_close_1.5h'] = ta.EMA(dataframe['trend_close_5m'], timeperiod=18) dataframe['trend_close_2h'] = ta.EMA(dataframe['trend_close_5m'], timeperiod=24) dataframe['trend_close_4h'] = ta.EMA(dataframe['trend_close_5m'], timeperiod=48) dataframe['trend_close_6h'] = ta.EMA(dataframe['trend_close_5m'], timeperiod=72) dataframe['trend_close_8h'] = ta.EMA(dataframe['trend_close_5m'], timeperiod=96) # Use Heikin Ashi open as the open trend tracker: it is the average of # the open and close of the last period dataframe['trend_open_5m'] = dataframe['ha_open'] dataframe['trend_open_15m'] = ta.EMA(dataframe['trend_open_5m'], timeperiod=3) dataframe['trend_open_30m'] = ta.EMA(dataframe['trend_open_5m'], timeperiod=6) dataframe['trend_open_1h'] = ta.EMA(dataframe['trend_open_5m'], timeperiod=12) dataframe['trend_open_2h'] = ta.EMA(dataframe['trend_open_5m'], timeperiod=24) dataframe['trend_open_4h'] = ta.EMA(dataframe['trend_open_5m'], timeperiod=48) dataframe['trend_open_6h'] = ta.EMA(dataframe['trend_open_5m'], timeperiod=72) dataframe['trend_open_8h'] = ta.EMA(dataframe['trend_open_5m'], timeperiod=96) dataframe['fan_magnitude'] = (dataframe['trend_close_1h'] / 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'] # do not use this in live, it has lookahead bias 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_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] # Trending market if self.buy_params['buy_trend_above_senkou_level'] >= 1: conditions.append(dataframe['trend_close_5m'].shift(1) > dataframe['senkou_a'].shift(1)) conditions.append(dataframe['trend_close_5m'].shift(1) > dataframe['senkou_b'].shift(1)) if self.buy_params['buy_trend_above_senkou_level'] >= 2: conditions.append(dataframe['trend_close_15m'].shift(1) > dataframe['senkou_a'].shift(1)) conditions.append(dataframe['trend_close_15m'].shift(1) > dataframe['senkou_b'].shift(1)) if self.buy_params['buy_trend_above_senkou_level'] >= 3: conditions.append(dataframe['trend_close_30m'].shift(1) > dataframe['senkou_a'].shift(1)) conditions.append(dataframe['trend_close_30m'].shift(1) > dataframe['senkou_b'].shift(1)) if self.buy_params['buy_trend_above_senkou_level'] >= 4: conditions.append(dataframe['trend_close_1h'].shift(1) > dataframe['senkou_a'].shift(1)) conditions.append(dataframe['trend_close_1h'].shift(1) > dataframe['senkou_b'].shift(1)) if self.buy_params['buy_trend_above_senkou_level'] >= 5: conditions.append(dataframe['trend_close_2h'].shift(1) > dataframe['senkou_a'].shift(1)) conditions.append(dataframe['trend_close_2h'].shift(1) > dataframe['senkou_b'].shift(1)) if self.buy_params['buy_trend_above_senkou_level'] >= 6: conditions.append(dataframe['trend_close_4h'].shift(1) > dataframe['senkou_a'].shift(1)) conditions.append(dataframe['trend_close_4h'].shift(1) > dataframe['senkou_b'].shift(1)) if self.buy_params['buy_trend_above_senkou_level'] >= 7: conditions.append(dataframe['trend_close_6h'].shift(1) > dataframe['senkou_a'].shift(1)) conditions.append(dataframe['trend_close_6h'].shift(1) > dataframe['senkou_b'].shift(1)) if self.buy_params['buy_trend_above_senkou_level'] >= 8: conditions.append(dataframe['trend_close_8h'].shift(1) > dataframe['senkou_a'].shift(1)) conditions.append(dataframe['trend_close_8h'].shift(1) > dataframe['senkou_b'].shift(1)) # Trends bullish if self.buy_params['buy_trend_bullish_level'] >= 1: conditions.append(dataframe['trend_close_5m'].shift(1) > dataframe['trend_open_5m']) if self.buy_params['buy_trend_bullish_level'] >= 2: conditions.append(dataframe['trend_close_15m'].shift(1) > dataframe['trend_open_15m']) if self.buy_params['buy_trend_bullish_level'] >= 3: conditions.append(dataframe['trend_close_30m'].shift(1) > dataframe['trend_open_30m']) if self.buy_params['buy_trend_bullish_level'] >= 4: conditions.append(dataframe['trend_close_1h'].shift(1) > dataframe['trend_open_1h']) if self.buy_params['buy_trend_bullish_level'] >= 5: conditions.append(dataframe['trend_close_2h'].shift(1) > dataframe['trend_open_2h']) if self.buy_params['buy_trend_bullish_level'] >= 6: conditions.append(dataframe['trend_close_4h'].shift(1) > dataframe['trend_open_4h']) if self.buy_params['buy_trend_bullish_level'] >= 7: conditions.append(dataframe['trend_close_6h'].shift(1) > dataframe['trend_open_6h']) if self.buy_params['buy_trend_bullish_level'] >= 8: conditions.append(dataframe['trend_close_8h'].shift(1) > dataframe['trend_open_8h']) # Trends magnitude conditions.append(dataframe['fan_magnitude_gain'].shift(1) >= self.buy_params['buy_min_fan_magnitude_gain']) conditions.append(dataframe['fan_magnitude'].shift(1) > 1) for x in range(self.buy_params['buy_fan_magnitude_shift_value']): conditions.append(dataframe['fan_magnitude'].shift(x+2) < dataframe['fan_magnitude'].shift(1)) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] conditions.append( qtpylib.crossed_below( dataframe['trend_close_5m'].shift(1), dataframe[self.sell_params['sell_trend_indicator']] ) ) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'sell'] = 1 return dataframe