# source: https://raw.githubusercontent.com/remiotore/ccxt-freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/BTCEMABounce.py from freqtrade.strategy.interface import IStrategy from freqtrade.strategy.strategy_helper import merge_informative_pair from typing import Dict, List from functools import reduce from pandas import DataFrame from freqtrade.strategy.hyper import CategoricalParameter, DecimalParameter, IntParameter import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy # noqa import math class Github_remiotore_ccxt_freqtrade__BTCEMABounce__20260111_210550(IStrategy): """ Applies the EMABounce strategy, but using BTC/USD data to find buy points 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_buy_trend, populate_sell_trend You should keep: - timeframe, minimal_roi, stoploss, trailing_* """ buy_long_period = IntParameter(20, 100, default=50, space="buy") buy_short_period = IntParameter(5, 15, default=10, space="buy") buy_diff = DecimalParameter(0.01, 0.10, decimals=3, default=0.065, space="buy") buy_macd_enabled = CategoricalParameter([True, False], default=False, space="buy") sell_diff = DecimalParameter(0.01, 0.10, decimals=3, default=0.057, space="sell") sell_hold = CategoricalParameter([True, False], default=False, space="sell") minimal_roi = { "0": 0.161, "15": 0.088, "54": 0.028, "82": 0 } stoploss = -0.144 trailing_stop = True trailing_stop_positive = 0.199 trailing_stop_positive_offset = 0.201 trailing_only_offset_is_reached = True timeframe = '5m' process_only_new_candles = False use_sell_signal = True sell_profit_only = True ignore_roi_if_buy_signal = False startup_candle_count: int = 30 order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } order_time_in_force = { 'buy': 'gtc', 'sell': 'gtc' } plot_config = { 'main_plot': { 'ema': {}, 'sma': {}, 'sar': {'color': 'white'}, }, 'subplots': { "MACD": { 'macdhist': {'color': 'blue'} }, "RSI": { 'rsi': {'color': 'red'}, }, } } 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/ETH", "15m"), ] """ return [("BTC/USD", "5m")] 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 """ if not self.dp: return dataframe inf_tf = '5m' btc_dataframe = self.dp.get_pair_dataframe(pair="BTC/USD", timeframe=inf_tf) btc_macd = ta.MACD(btc_dataframe) btc_dataframe['macd'] = btc_macd['macd'] btc_dataframe['macdsignal'] = btc_macd['macdsignal'] btc_dataframe['macdhist'] = btc_macd['macdhist'] btc_dataframe['ema'] = ta.EMA(btc_dataframe, timeperiod=self.buy_long_period.value) btc_dataframe['ema_short'] = ta.EMA(btc_dataframe, timeperiod=self.buy_short_period.value) btc_dataframe['ema_angle'] = ta.LINEARREG_SLOPE(btc_dataframe['ema_short'], timeperiod=3) / (2.0 * math.pi) btc_dataframe['ema_diff'] = (((btc_dataframe['ema'] - btc_dataframe['close']) / btc_dataframe['ema'])) \ - self.buy_diff.value dataframe = merge_informative_pair(dataframe, btc_dataframe, self.timeframe, "5m", ffill=True) dataframe['rsi'] = ta.RSI(dataframe) macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] dataframe["bb_percent"] = ( (dataframe["close"] - dataframe["bb_lowerband"]) / (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) ) dataframe["bb_width"] = ( (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"] ) dataframe['ema'] = ta.EMA(dataframe, timeperiod=self.buy_long_period.value) dataframe['ema_short'] = ta.EMA(dataframe, timeperiod=self.buy_short_period.value) dataframe['ema_angle'] = ta.LINEARREG_SLOPE(dataframe['ema_short'], timeperiod=3) / (2.0 * math.pi) dataframe['ema_diff'] = (((dataframe['ema'] - dataframe['close']) / dataframe['ema'])) - self.buy_diff.value dataframe['sma'] = ta.SMA(dataframe, timeperiod=self.buy_long_period.value) dataframe['sar'] = ta.SAR(dataframe) 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 """ conditions = [] conditions.append(dataframe['volume'] > 0) if self.buy_macd_enabled.value: conditions.append(dataframe['macdhist'] >= 0) conditions.append(qtpylib.crossed_above(dataframe['ema_angle_5m'], 0)) conditions.append(dataframe['ema_diff'] > 0.0) 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: """ 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 buy column """ conditions = [] if self.sell_hold.value: dataframe.loc[(dataframe['close_5m'].notnull() ), 'sell'] = 0 else: conditions.append(qtpylib.crossed_below(dataframe['ema_angle_5m'], 0)) conditions.append(dataframe['close'] > dataframe['ema']) conditions.append(dataframe['ema_diff'] >= -self.sell_diff.value) if conditions: dataframe.loc[reduce(lambda x, y: x & y, conditions), 'sell'] = 1 return dataframe