# source: https://raw.githubusercontent.com/remiotore/ccxt-freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/FRAYSTRAT_BTCUSDT_1H.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 talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib class Github_remiotore_ccxt_freqtrade__FRAYSTRAT_BTCUSDT_1H__20260111_210550(IStrategy): """ This is a sample strategy to inspire you. 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_* """ INTERFACE_VERSION = 2 minimal_roi = { "0": 0.4, "32": 0.152, "208": 0.19, "525": 0.2 } stoploss = -0.229 trailing_stop = True trailing_only_offset_is_reached = True trailing_stop_positive = 0.02 trailing_stop_positive_offset = 0.03 # Disabled / not configured buy_rsi = IntParameter(low=10, high=50, default=15, space='buy', optimize=True, load=True) sell_rsi = IntParameter(low=50, high=100, default=70, space='sell', optimize=True, load=True) cooldown_lookback = IntParameter(2, 90, default=3, space="protection", optimize=True) stop_duration = IntParameter(12, 200, default=3, space="protection", optimize=True) use_stop_protection = BooleanParameter(default=True, space="protection", optimize=True) @property def protections(self): return [ { "method": "CooldownPeriod", "stop_duration_candles": self.cooldown_lookback.value }, { "method": "MaxDrawdown", "lookback_period_candles": self.cooldown_lookback.value, "trade_limit": 5, "stop_duration_candles": self.stop_duration.value, "max_allowed_drawdown": 0.7 }, { "method": "StoplossGuard", "lookback_period_candles": 20, "trade_limit": 3, "stop_duration_candles": 4, "only_per_pair": False }, { "method": "LowProfitPairs", "lookback_period_candles": 24, "trade_limit": 2, "stop_duration_candles": 4, "required_profit": 0.01 } ] timeframe = '15m' process_only_new_candles = False use_sell_signal = True sell_profit_only = True ignore_roi_if_buy_signal = False startup_candle_count: int = 8 order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } order_time_in_force = { 'buy': 'gtc', 'sell': 'gtc' } plot_config = { 'main_plot': { 'tema': {}, 'sar': {'color': 'blue'}, 'ema7':{'color': 'red'}, 'ema12':{'color': 'yellow'} }, 'subplots': { "MACD": { 'macd': {'color': 'blue'}, 'macdsignal': {'color': 'orange'}, 'macdhist':{} }, "FISHERS RSI":{ 'fisher_rsi':{'color':'green'}, } } } 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['adx'] = ta.ADX(dataframe) dataframe['rsi'] = ta.RSI(dataframe) rsi = 0.1 * (dataframe['rsi'] - 50) dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1) stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] dataframe['mfi'] = ta.MFI(dataframe) bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=18, 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['ema7'] = ta.EMA(dataframe, timeperiod=7) dataframe['ema30'] = ta.EMA(dataframe, timeperiod=30) dataframe['ema12'] = ta.EMA(dataframe, timeperiod=12) dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) dataframe['sar'] = ta.SAR(dataframe) dataframe['tema'] = ta.TEMA(dataframe, timeperiod=7) hilbert = ta.HT_SINE(dataframe) dataframe['htsine'] = hilbert['sine'] dataframe['htleadsine'] = hilbert['leadsine'] """ if self.dp: if self.dp.runmode.value in ('live', 'dry_run'): ob = self.dp.orderbook(metadata['pair'], 1) dataframe['best_bid'] = ob['bids'][0][0] dataframe['best_ask'] = ob['asks'][0][0] """ 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[ ( (qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value)) & (dataframe['close'] < dataframe['bb_middleband']) & # Guard: tema below BB middle (dataframe['tema'] > dataframe['tema'].shift(1)) & # Guard: tema is raising (dataframe['tema'] < dataframe['ema7']) & (dataframe['volume'] > 0) # Make sure Volume is not 0 )| ( (dataframe['ema7'] < dataframe['ema12']) & (dataframe['rsi'] > 51 ) & (dataframe['rsi'] > dataframe['rsi'].shift(1)) & (dataframe['macdsignal'] > dataframe['macd']) & (dataframe['volume'] > 0) )| ( (dataframe['macdsignal'] < dataframe['macd']) & (dataframe['ema12'] < dataframe['ema7']) & (dataframe['tema'] > dataframe['tema'].shift(1)) & (dataframe['volume'] > 0) # Make sure Volume is not 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[ ( (qtpylib.crossed_above(dataframe['rsi'], self.sell_rsi.value)) & (dataframe['close'] > dataframe['bb_middleband']) & # Guard: tema above BB middle (dataframe['tema'] < dataframe['tema'].shift(1)) & # Guard: tema is falling (dataframe['volume'] > 0) # Make sure Volume is not 0 )| ( (dataframe['ema7'] > dataframe['ema12']) & (dataframe['tema'] > dataframe['ema7']) & (dataframe['rsi'] < dataframe['rsi'].shift(1)) & (dataframe['macdsignal'] > dataframe['macd']) & (dataframe['volume'] > 0) )| ( (dataframe['macdsignal'] < dataframe['macd']) & (dataframe['tema'] > dataframe['ema7']) & (dataframe['tema'] < dataframe['tema'].shift(2)) & (dataframe['volume'] > 0) # Make sure Volume is not 0 ), 'sell'] = 1 return dataframe