# source: https://raw.githubusercontent.com/remiotore/ccxt-freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/KamaRsi2.py import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import merge_informative_pair from pandas import DataFrame, Series from functools import reduce from datetime import datetime from freqtrade.persistence import Trade """ Supertrend Indicator adapted for freqtrade from: https://github.com/freqtrade/freqtrade-strategies/issues/30 ref: https://www.tradingfuel.com/supertrend-indicator-formula-and-calculation/ ATR: https://github.com/freqtrade/technical/issues/26 """ def supertrend(dataframe, multiplier=3, period=10): df = dataframe.copy() df['TR'] = ta.TRANGE(df) df['ATR'] = df['TR'].ewm(alpha=1 / period).mean() st = 'ST_' + str(period) + '_' + str(multiplier) stx = 'STX_' + str(period) + '_' + str(multiplier) df['basic_ub'] = (df['high'] + df['low']) / 2 + multiplier * df['ATR'] df['basic_lb'] = (df['high'] + df['low']) / 2 - multiplier * df['ATR'] df['final_ub'] = 0.00 df['final_lb'] = 0.00 for i in range(period, len(df)): df['final_ub'].iat[i] = df['basic_ub'].iat[i] if df['basic_ub'].iat[i] < df['final_ub'].iat[i - 1] or df['close'].iat[i - 1] > df['final_ub'].iat[i - 1] else df['final_ub'].iat[i - 1] df['final_lb'].iat[i] = df['basic_lb'].iat[i] if df['basic_lb'].iat[i] > df['final_lb'].iat[i - 1] or df['close'].iat[i - 1] < df['final_lb'].iat[i - 1] else df['final_lb'].iat[i - 1] df[st] = 0.00 for i in range(period, len(df)): df[st].iat[i] = df['final_ub'].iat[i] if df[st].iat[i - 1] == df['final_ub'].iat[i - 1] and df['close'].iat[i] <= df['final_ub'].iat[i] else \ df['final_lb'].iat[i] if df[st].iat[i - 1] == df['final_ub'].iat[i - 1] and df['close'].iat[i] > df['final_ub'].iat[i] else \ df['final_lb'].iat[i] if df[st].iat[i - 1] == df['final_lb'].iat[i - 1] and df['close'].iat[i] >= df['final_lb'].iat[i] else \ df['final_ub'].iat[i] if df[st].iat[i - 1] == df['final_lb'].iat[i - 1] and df['close'].iat[i] < df['final_lb'].iat[i] else 0.00 df[stx] = np.where((df[st] > 0.00), np.where((df['close'] < df[st]), 'down', 'up'), np.NaN) df.drop(['basic_ub', 'basic_lb', 'final_ub', 'final_lb'], inplace=True, axis=1) df.fillna(0, inplace=True) return DataFrame(index=df.index, data={ 'ST' : df[st], 'STX' : df[stx] }) class Github_remiotore_ccxt_freqtrade__KamaRsi2__20260111_210550(IStrategy): """ PASTE OUTPUT FROM HYPEROPT HERE """ buy_params = { 'rsi-buy-trigger': 10, 'cci-buy-trigger': -100, 'buy-method': 'both', # trend, rsi2, both 'buy-price': 'ohlc4' # open, close, hl2, hlc3, ohlc4 } sell_params = { 'rsi-sell-trigger': 90, 'cci-sell-trigger': 100, 'sell-method': 'both', # trend, rsi2, both 'sell-price': 'ohlc4' # open, close, hl2, hlc3, ohlc4 } minimal_roi = { "0": 1 } stoploss = -1 """ END HYPEROPT """ timeframe = '5m' use_sell_signal = True sell_profit_only = False sell_profit_offset = 0.01 ignore_roi_if_buy_signal = True process_only_new_candles = False startup_candle_count: int = 200 def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['ohlc4'] = ta.AVGPRICE(dataframe) dataframe['hlc3'] = (dataframe['high'] + dataframe['low'] + dataframe['close']) / 3 dataframe['hl2'] = (dataframe['high'] + dataframe['low']) / 2 dataframe['kama-short'] = ta.KAMA(dataframe, timeperiod=5) dataframe['kama-long'] = ta.KAMA(dataframe, timeperiod=200) dataframe['cci'] = ta.CCI(dataframe, timeperiod=5) dataframe['rsi'] = ta.RSI(dataframe['ohlc4'], timeperiod=2) dataframe['sar'] = ta.SAR(dataframe) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: params = self.buy_params trades = False if self.config['runmode'].value in ('live', 'dry_run'): trades = Trade.get_trades([Trade.pair == metadata['pair'], Trade.is_open.is_(True),]).all() conditions = [] if not trades: if params['buy-method'] == 'rsi2' or params['buy-method'] == 'both': conditions.append(dataframe[params['buy-price']] > dataframe['kama-long']) conditions.append(dataframe['close'] < dataframe['kama-short']) conditions.append(dataframe['rsi'] < params['rsi-buy-trigger']) conditions.append(dataframe['cci'] < params['cci-buy-trigger']) if params['buy-method'] == 'trend' or params['buy-method'] == 'both': conditions.append(dataframe[params['buy-price']] > dataframe['sar']) else: conditions.append(dataframe['ohlc4'] > dataframe['sar']) conditions.append(dataframe['volume'] > 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: params = self.sell_params conditions = [] if params['sell-method'] == 'rsi2' or params['sell-method'] == 'both': conditions.append(dataframe['close'] > dataframe['kama-short']) conditions.append(dataframe['rsi'] > params['rsi-sell-trigger']) conditions.append(dataframe['cci'] > params['cci-sell-trigger']) if params['sell-method'] == 'trend' or params['sell-method'] == 'both': conditions.append(dataframe[params['sell-price']] < dataframe['sar']) conditions.append(dataframe['volume'] > 0) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'sell'] = 1 return dataframe """ Additional buy/sell timeout override if price drifts """ def check_buy_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool: ob = self.dp.orderbook(pair, 1) current_price = ob['bids'][0][0] if current_price > order['price'] * 1.01: return True return False def check_sell_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool: ob = self.dp.orderbook(pair, 1) current_price = ob['asks'][0][0] if current_price < order['price'] * 0.99: return True return False