# source: https://raw.githubusercontent.com/jaredrsommer/freqtradestrategies/9d66f71ce21f2f65feaad6bb967215be22a3cfd6/HUGE_FreqTrade_Strategy_Collection/TrixStrategy.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 import ta # This class is a sample. Feel free to customize it. class Github_jaredrsommer_freqtradestrategies__TrixStrategy__20260518_195114(IStrategy): """ Sources : Cripto Robot : https://www.youtube.com/watch?v=uE04UROWkjs&list=PLpJ7cz_wOtsrqEQpveLc2xKLjOBgy4NfA&index=4 Github : https://github.com/CryptoRobotFr/TrueStrategy/blob/main/Github_jaredrsommer_freqtradestrategies__TrixStrategy__20260518_195114/Trix_Complete_backtest.ipynb freqtrade backtesting -s Github_jaredrsommer_freqtradestrategies__TrixStrategy__20260518_195114 --timerange=20170817-20210919 --stake-amount unlimited -p ETH/USDT --config user_data/config_binance.json --enable-position-stacking --max-open-trades 1 =============== SUMMARY METRICS ================ | Metric | Value | |------------------------+---------------------| | Backtesting from | 2017-08-18 01:00:00 | | Backtesting to | 2021-09-19 00:00:00 | | Max open trades | 1 | | | | | Total/Daily Avg Trades | 910 / 0.61 | | Starting balance | 1000.000 USDT | | Final balance | 455182.175 USDT | | Absolute profit | 454182.175 USDT | | Total profit % | 45418.22% | | Trades per day | 0.61 | | Avg. daily profit % | 30.44% | | Avg. stake amount | 89051.996 USDT | | Total trade volume | 81037315.991 USDT | | | | | Best Pair | ETH/USDT 716.78% | | Worst Pair | ETH/USDT 716.78% | | Best trade | ETH/USDT 40.29% | | Worst trade | ETH/USDT -17.67% | | Best day | 69170.563 USDT | | Worst day | -49006.144 USDT | | Days win/draw/lose | 427 / 613 / 449 | | Avg. Duration Winners | 23:59:00 | | Avg. Duration Loser | 13:56:00 | | Rejected Buy signals | 17052 | | | | | Min balance | 1064.302 USDT | | Max balance | 466421.313 USDT | | Drawdown | 37.23% | | Drawdown | 149281.307 USDT | | Drawdown high | 453758.871 USDT | | Drawdown low | 304477.564 USDT | | Drawdown Start | 2021-05-15 02:00:00 | | Drawdown End | 2021-07-02 08:00:00 | | Market change | 1028.84% | ================================================ 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_buy_trend, populate_sell_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 = 2 # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". minimal_roi = { "0": 100 # inactive } # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". stoploss = -0.99 # inactive # 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 # Hyperoptable parameters buy_stoch_rsi = DecimalParameter(0.5, 1, decimals=3, default=0.8, space="buy") sell_stoch_rsi = DecimalParameter(0, 0.5, decimals=3, default=0.2, space="sell") # Optimal timeframe for the strategy. timeframe = '1h' # Run "populate_indicators()" only for new candle. process_only_new_candles = False # These values can be overridden in the "ask_strategy" section in the config. use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = False # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 21 # Optional order type mapping. order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } # Optional order time in force. order_time_in_force = { 'buy': 'gtc', 'sell': 'gtc' } plot_config = { 'main_plot': { 'trix': {}, }, 'subplots': { "STOCH RSI": { 'stoch_rsi': {}, }, "TRIX": { 'trix_pct': {}, 'trix_signal': {}, 'trix_histo': {}, }, } } 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 """ # Momentum Indicators # ------------------------------------ # # Stochastic RSI dataframe['stoch_rsi'] = ta.momentum.stochrsi(close=dataframe['close'], window=14, smooth1=3, smooth2=3) # Overlap Studies # ------------------------------------ # -- Trix Indicator -- trixLength = 9 trixSignal = 21 dataframe['trix'] = ta.trend.ema_indicator(ta.trend.ema_indicator(ta.trend.ema_indicator(close=dataframe['close'], window=trixLength), window=trixLength), window=trixLength) dataframe['trix_pct'] = dataframe['trix'].pct_change() * 100 dataframe['trix_signal'] = ta.trend.sma_indicator(dataframe['trix_pct'],trixSignal) dataframe['trix_histo'] = dataframe['trix_pct'] - dataframe['trix_signal'] return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the buy signal for the given dataframe :param dataframe: DataFrame populated with indicators :param metadata: Additional information, like the currently traded pair :return: DataFrame with buy column """ dataframe.loc[ ( (dataframe['trix_histo'] > 0) & (dataframe['stoch_rsi'] < self.buy_stoch_rsi.value) & (dataframe['volume'] > 0) ), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the sell signal for the given dataframe :param dataframe: DataFrame populated with indicators :param metadata: Additional information, like the currently traded pair :return: DataFrame with sell column """ dataframe.loc[ ( (dataframe['trix_histo'] < 0) & (dataframe['stoch_rsi'] > self.sell_stoch_rsi.value) & (dataframe['volume'] > 0) ), 'sell'] = 1 return dataframe