# source: https://raw.githubusercontent.com/remiotore/ccxt-freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/AlligatorStrategy.py import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter) import ta class Github_remiotore_ccxt_freqtrade__AlligatorStrategy__20260111_210550(IStrategy): """ Sources Crypto Robot : https://www.youtube.com/watch?v=tHYs5135jUA Github : https://github.com/CryptoRobotFr/TrueStrategy/blob/main/AligatorStrategy/Aligator_Strategy_backtest.ipynb freqtrade backtesting -s Github_remiotore_ccxt_freqtrade__AlligatorStrategy__20260111_210550 --timerange=20200903-20210826 --stake-amount unlimited -p EGLD/USDT --config user_data/config_binance.json --enable-position-stacking =============== SUMMARY METRICS ================ | Metric | Value | |------------------------+---------------------| | Backtesting from | 2020-09-11 11:00:00 | | Backtesting to | 2021-08-26 00:00:00 | | Max open trades | 1 | | | | | Total/Daily Avg Trades | 258 / 0.74 | | Starting balance | 1000.000 USDT | | Final balance | 14245.054 USDT | | Absolute profit | 13245.054 USDT | | Total profit % | 1324.51% | | Trades per day | 0.74 | | Avg. daily profit % | 3.81% | | Avg. stake amount | 829.268 USDT | | Total trade volume | 213951.164 USDT | | | | | Best Pair | EGLD/USDT 4287.66% | | Worst Pair | EGLD/USDT 4287.66% | | Best trade | EGLD/USDT 305.28% | | Worst trade | EGLD/USDT -10.69% | | Best day | 7078.313 USDT | | Worst day | -1052.860 USDT | | Days win/draw/lose | 8 / 275 / 19 | | Avg. Duration Winners | 9 days, 18:58:00 | | Avg. Duration Loser | 2 days, 9:00:00 | | Rejected Buy signals | 3045 | | | | | Min balance | 905.332 USDT | | Max balance | 16789.797 USDT | | Drawdown | 169.59% | | Drawdown | 2544.743 USDT | | Drawdown high | 15789.797 USDT | | Drawdown low | 13245.054 USDT | | Drawdown Start | 2021-08-12 18:00:00 | | Drawdown End | 2021-08-24 18:00:00 | | Market change | 497.97% | ================================================ 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_* """ INTERFACE_VERSION = 2 minimal_roi = { "0": 100 # inactive } stoploss = -0.99 # inactive trailing_stop = False buy_stoch_rsi = DecimalParameter(0.5, 1, decimals=3, default=0.82, space="buy") sell_stoch_rsi = DecimalParameter(0, 0.5, decimals=3, default=0.2, space="sell") timeframe = '1h' process_only_new_candles = False use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = False startup_candle_count = 200 # EMA200 order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } order_time_in_force = { 'buy': 'gtc', 'sell': 'gtc' } plot_config = { 'main_plot': { 'ema7':{}, 'ema30':{}, 'ema50':{}, 'ema100':{}, 'ema121':{}, 'ema200':{} }, 'subplots': { "STOCH RSI": { 'stoch_rsi': {} } } } 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 """ dataframe['stoch_rsi'] = ta.momentum.stochrsi(close=dataframe['close'], window=14, smooth1=3, smooth2=3) #Non moyenné dataframe['ema7']=ta.trend.ema_indicator(close=dataframe['close'], window=7) dataframe['ema30']=ta.trend.ema_indicator(close=dataframe['close'], window=30) dataframe['ema50']=ta.trend.ema_indicator(close=dataframe['close'], window=50) dataframe['ema100']=ta.trend.ema_indicator(close=dataframe['close'], window=100) dataframe['ema121']=ta.trend.ema_indicator(close=dataframe['close'], window=121) dataframe['ema200']=ta.trend.ema_indicator(close=dataframe['close'], window=200) 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['ema7'] > dataframe['ema30']) & (dataframe['ema30'] > dataframe['ema50']) & (dataframe['ema50'] > dataframe['ema100']) & (dataframe['ema100'] > dataframe['ema121']) & (dataframe['ema121'] > dataframe['ema200']) & (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['ema121'] > dataframe['ema7']) & (dataframe['stoch_rsi'] > self.sell_stoch_rsi.value) & (dataframe['volume'] > 0) ), 'sell'] = 1 return dataframe