# source: https://raw.githubusercontent.com/nicnl31/pyalgotrader/c22b45dc6744b8dc3ea5c9e73858c81274686684/frameworks/freqtrade/user_data/strategies/SmoothedHA_STC_OBV_CHOP_ADX_DI.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 import pandas as pd from freqtrade.persistence import Trade from pandas import DataFrame from datetime import datetime, timedelta from typing import Optional, Union from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, IStrategy, merge_informative_pair, stoploss_from_absolute, timeframe_to_prev_date) # -------------------------------- # Add your lib to import here import talib.abstract as ta import pandas_ta as pta from technical import qtpylib import re class Github_nicnl31_pyalgotrader__SmoothedHA_STC_OBV_CHOP_ADX_DI__20240902_122600(IStrategy): """ This is a strategy template to get you started. More information in https://www.freqtrade.io/en/latest/strategy-customization/ You can: :return: a Dataframe with all mandatory indicators for the strategies - Rename the class name (Do not forget to update class_name) - Add any methods you want to build your strategy - Add any lib you need to build your strategy You must keep: - the lib in the section "Do not remove these libs" - the methods: populate_indicators, populate_entry_trend, populate_exit_trend You should keep: - timeframe, minimal_roi, stoploss, trailing_* """ # Strategy interface version - allow new iterations of the strategy interface. # Check the documentation or the Sample strategy to get the latest version. INTERFACE_VERSION = 3 # Optimal timeframe for the strategy. timeframe = '1h' # Can this strategy go short? can_short: bool = False # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". minimal_roi = { "0": 0.4 } # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". stoploss = -0.20 use_custom_stoploss = True # Trailing stoploss trailing_stop = False # trailing_only_offset_is_reached = False # trailing_stop_positive = 0.01 # trailing_stop_positive_offset = 0.0 # Disabled / not configured # Enable position adjustments position_adjustment_enable = True # Run "populate_indicators()" only for new candle. process_only_new_candles = True # These values can be overridden in the config. use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False # BUY PARAMETERS # ========================================================================== do_optimize = False buy_obv_ema_length = IntParameter(50, 100, default=50, optimize=do_optimize) buy_stc_length = IntParameter(10, 12, default=10, optimize=do_optimize) buy_stc_fast = IntParameter(23, 26, default=23, optimize=do_optimize) buy_stc_slow = IntParameter(45, 50, default=50, optimize=do_optimize) buy_stc_threshold_lower = IntParameter(20, 30, default=25, optimize=do_optimize) buy_stc_threshold_upper = IntParameter(70, 80, default=75, optimize=do_optimize) buy_stc_factor = 0.5 buy_chop_threshold = DecimalParameter(40.0, 50.0, default=50.0, decimals=1, optimize=do_optimize) buy_adx_threshold = DecimalParameter(20.0, 25.0, default=20.0, decimals=1, optimize=do_optimize) buy_ha_smooth_length1 = IntParameter(5, 15, default=10, optimize=do_optimize) buy_ha_smooth_length2 = IntParameter(5, 15, default=10, optimize=do_optimize) # SELL PARAMETERS # ========================================================================== sell_rr_ratio = DecimalParameter(1.0, 3.0, default=1.5, optimize=do_optimize) sell_rr_ratio_divisor = IntParameter(1, 3, default=3, optimize=do_optimize) # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 100 # Optional order type mapping. order_types = { 'entry': 'limit', 'exit': 'market', 'stoploss': 'market', 'stoploss_on_exchange': False } # Optional order time in force. order_time_in_force = { 'entry': 'GTC', 'exit': 'GTC' } @property def plot_config(self): return { # Main plot indicators (Moving averages, ...) 'main_plot': { 'ha_close_smooth': { 'color': '#38393b', 'fill_to': 'ha_open_smooth', 'fill_color': 'rgba(172, 208, 255, 0.8)' }, 'ha_open_smooth': {'color': '#38393b'} }, 'subplots': { # Subplots - each dict defines one additional plot "STC": { 'stc': {'color': ''} }, "OBV": { 'obv': {'color': ''}, f'obv_ema{self.buy_obv_ema_length.value}': {'color': ''} }, "CHOP": { 'chop': {'color': '#4e76ed'} }, "ADX": { 'adx': {'color': '#f0d84f'}, 'plus_di': {'color': '#37eb34'}, 'minus_di': {'color': '#eb3a34'} } } } def informative_pairs(self): """ Define additional, informative pair/interval combinations to be cached from the exchange. These pair/interval combinations are non-tradeable, unless they are part of the whitelist as well. For more information, please consult the documentation :return: List of tuples in the format (pair, interval) Sample: return [("ETH/USDT", "5m"), ("BTC/USDT", "15m"), ] """ return [] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Adds several different TA indicators to the given DataFrame Performance Note: For the best performance be frugal on the number of indicators you are using. Let uncomment only the indicator you are using in your strategies or your hyperopt configuration, otherwise you will waste your memory and CPU usage. :param dataframe: Dataframe with data from the exchange :param metadata: Additional information, like the currently traded pair :return: a Dataframe with all mandatory indicators for the strategies """ # # Set datetime index for the dataframe # dataframe.set_index('date', drop=False, inplace=True) # Smoothed Heiken Ashi Indicator # ------------------------------------ ha_smooth = heikenashi_smooth( dataframe, timeperiod1=self.buy_ha_smooth_length1.value, timeperiod2=self.buy_ha_smooth_length2.value ) dataframe['ha_open_smooth'] = ha_smooth['ha_open_smooth'] dataframe['ha_high_smooth'] = ha_smooth['ha_high_smooth'] dataframe['ha_low_smooth'] = ha_smooth['ha_low_smooth'] dataframe['ha_close_smooth'] = ha_smooth['ha_close_smooth'] # ADX-DI_PLUS-DI_MINUS Indicators # ------------------------------------ dataframe['adx'] = ta.ADX(dataframe) dataframe['plus_di'] = ta.PLUS_DI(dataframe) dataframe['minus_di'] = ta.MINUS_DI(dataframe) # Choppiness Index Indicator # ------------------------------------ dataframe['chop'] = pta.chop( dataframe['high'], dataframe['low'], dataframe['close'], length=14 ) # Schaff Trend Cycle Indicator # ------------------------------------ stc = pta.stc(dataframe['close'], tclength=self.buy_stc_length.value, fast=self.buy_stc_fast.value, slow=self.buy_stc_slow.value, factor=self.buy_stc_factor) dataframe['stc'] = stc[f"STC_{self.buy_stc_length.value}_{self.buy_stc_fast.value}_{self.buy_stc_slow.value}_{self.buy_stc_factor}"] # On-balance Volume Indicator # ------------------------------------ dataframe['obv'] = ta.OBV(dataframe) dataframe[f'obv_ema{self.buy_obv_ema_length.value}'] = ta.EMA(dataframe['obv'], timeperiod=self.buy_obv_ema_length.value) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the entry signal for the given dataframe :param dataframe: DataFrame :param metadata: Additional information, like the currently traded pair :return: DataFrame with entry columns populated """ # SCHAFF TREND CYCLE TRIGGER dataframe.loc[ ( (qtpylib.crossed_above(dataframe['stc'], self.buy_stc_threshold_lower.value)) & (dataframe['ha_close_smooth'] > dataframe['ha_open_smooth']) & (dataframe['obv'] > dataframe[f'obv_ema{self.buy_obv_ema_length.value}']) & (dataframe['chop'] < self.buy_chop_threshold.value) & (dataframe['adx'] > self.buy_adx_threshold.value) & (dataframe['plus_di'] > dataframe['minus_di']) & (dataframe['volume'] > 0) # Make sure Volume is not 0 ), ['enter_long', 'enter_tag']] = (1, 'buy_stc_trigger') # CHOPPINESS INDEX TRIGGER dataframe.loc[ ( (qtpylib.crossed_below(dataframe['chop'], self.buy_chop_threshold.value)) & (dataframe['ha_close_smooth'] > dataframe['ha_open_smooth']) & (dataframe['obv'] > dataframe[f'obv_ema{self.buy_obv_ema_length.value}']) & (dataframe['stc'] > self.buy_stc_threshold_upper.value) & (dataframe['adx'] > self.buy_adx_threshold.value) & (dataframe['plus_di'] > dataframe['minus_di']) & (dataframe['volume'] > 0) # Make sure Volume is not 0 ), ['enter_long', 'enter_tag']] = (1, 'buy_chop_trigger') return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the exit signal for the given dataframe :param dataframe: DataFrame :param metadata: Additional information, like the currently traded pair :return: DataFrame with exit columns populated """ dataframe.loc[ ( (dataframe['volume'] > 0) # Make sure Volume is not 0 ), 'exit_long'] = 0 return dataframe def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, after_fill: bool, **kwargs) -> Optional[float]: """ Set the custom stoploss as the previous smoothed Heiken Ashi bar's low. """ dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) # Retrieve the time of the candle before the trade candle previous_trade_date = timeframe_to_prev_date( self.timeframe, trade.open_date_utc - timedelta(minutes=extract_minutes(self.timeframe)) ) # Retrieve the candle before the trade candle previous_trade_candle = dataframe.loc[ dataframe['date'] == previous_trade_date] previous_trade_candle = previous_trade_candle.squeeze() stoploss = stoploss_from_absolute( stop_rate=previous_trade_candle['ha_low_smooth'], current_rate=current_rate, is_short=trade.is_short ) return stoploss def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> Optional[Union[str, bool]]: """ Set custom exit based on reward-risk ratio. """ dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) # Retrieve the time of the candle before the trade candle previous_trade_date = timeframe_to_prev_date( self.timeframe, trade.open_date_utc - timedelta( minutes=extract_minutes(self.timeframe)) ) # Retrieve the candle before the trade candle previous_trade_candle = dataframe.loc[ dataframe['date'] == previous_trade_date] previous_trade_candle = previous_trade_candle.squeeze() # Calculate the initial stoploss rate open_rate = trade.open_rate stoploss_rate = abs(previous_trade_candle['ha_low_smooth'] / open_rate - 1) if current_profit >= self.sell_rr_ratio.value * stoploss_rate: return f'{self.sell_rr_ratio.value}:1_rr' def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: Optional[float], max_stake: float, current_entry_rate: float, current_exit_rate: float, current_entry_profit: float, current_exit_profit: float, **kwargs) -> Optional[float]: """ Adjust the current position based on the current reward-risk ratio. """ dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe) # Retrieve the time of the candle before the trade candle previous_trade_date = timeframe_to_prev_date( self.timeframe, trade.open_date_utc - timedelta( minutes=extract_minutes(self.timeframe)) ) # Retrieve the candle before the trade candle previous_trade_candle = dataframe.loc[ dataframe['date'] == previous_trade_date] previous_trade_candle = previous_trade_candle.squeeze() # Calculate the initial stoploss and take profit rate open_rate = trade.open_rate stoploss_rate = abs( previous_trade_candle['ha_low_smooth'] / open_rate - 1) take_profit_rate = self.sell_rr_ratio.value * stoploss_rate partial_exit_stake_amount = -trade.stake_amount / self.sell_rr_ratio_divisor.value partial_take_profit_threshold = take_profit_rate / self.sell_rr_ratio_divisor.value for i in range(1, self.sell_rr_ratio_divisor.value): if (current_profit >= partial_take_profit_threshold * i) and (trade.nr_of_successful_exits == i-1): return partial_exit_stake_amount return None def heikenashi_smooth(dataframe, timeperiod1: int=10, timeperiod2: int=10) -> DataFrame: """ Calculates the Heikin Ashi Smooth Indicator. """ # Work on a copy of the DataFrame bars = dataframe.copy() # Calculate the EMA of the original candles bars['open'] = ta.EMA(bars['open'], timeperiod=timeperiod1) bars['high'] = ta.EMA(bars['high'], timeperiod=timeperiod1) bars['low'] = ta.EMA(bars['low'], timeperiod=timeperiod1) bars['close'] = ta.EMA(bars['close'], timeperiod=timeperiod1) # Calculate the Heikin Ashi candles from the EMA candles # -------------------------- bars['ha_close'] = (bars['open'] + bars['high'] + bars['low'] + bars['close']) / 4 for i in range(1, len(bars)): if (pd.isna(bars.at[i-1, 'open'])) and (not pd.isna(bars.at[i, 'open'])): bars.at[i, 'ha_open'] = (bars.at[i, 'open'] + bars.at[i, 'close'])/2 elif (not pd.isna(bars.at[i-1, 'open'])) and (not pd.isna(bars.at[i, 'open'])): bars.at[i, 'ha_open'] = (bars.at[i-1, 'ha_open'] + bars.at[i-1, 'ha_close'])/2 bars['ha_high'] = bars.loc[:, ['high', 'ha_open', 'ha_close']].max(axis=1) bars['ha_low'] = bars.loc[:, ['low', 'ha_open', 'ha_close']].min(axis=1) # Smooth out the HA candles one more time bars['ha_open_smooth'] = ta.EMA(bars['ha_open'], timeperiod=timeperiod2) bars['ha_high_smooth'] = ta.EMA(bars['ha_high'], timeperiod=timeperiod2) bars['ha_low_smooth'] = ta.EMA(bars['ha_low'], timeperiod=timeperiod2) bars['ha_close_smooth'] = ta.EMA(bars['ha_close'], timeperiod=timeperiod2) return bars[ [ 'ha_open_smooth', 'ha_high_smooth', 'ha_low_smooth', 'ha_close_smooth' ] ] def extract_minutes(timeframe): timescale = timeframe[-1] num = int(re.findall('\d+', timeframe)[0]) if timescale == 'm': return num elif timescale == 'h': return num * 60 elif timescale == 'd': return num * 1440 elif timescale == 'w': return num * 1440 * 7