import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta from freqtrade.persistence import Trade from freqtrade.strategy.interface import IStrategy from pandas import DataFrame from datetime import datetime, timedelta from freqtrade.strategy import merge_informative_pair from freqtrade.strategy import stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter import pandas as pd pd.options.mode.chained_assignment = None import math def hma(dataframe, source, length): return ta.WMA( 2 * ta.WMA(dataframe[source], int(math.floor(length/2))) - ta.WMA(dataframe[source], length), int(round(np.sqrt(length))) ) def calc_streaks(series: pd.Series): # logic tables geq = series >= series.shift(1) # True if rising eq = series == series.shift(1) # True if equal logic_table = pd.concat([geq, eq], axis=1) streaks = [0] # holds the streak duration, starts with 0 for row in logic_table.iloc[1:].itertuples(): # iterate through logic table if row[2]: # same value as before streaks.append(0) continue last_value = streaks[-1] if row[1]: # higher value than before streaks.append(last_value + 1 if last_value >= 0 else 1) # increase or reset to +1 else: # lower value than before streaks.append(last_value - 1 if last_value < 0 else -1) # decrease or reset to -1 return streaks class Sylv(IStrategy): #WinRatioAndProfitRatioLoss # ROI table: minimal_roi = { "0": 10, } INTERFACE_VERSION = 2 # Buy hyperspace params: buy_params = { "dev_low_buy": 1.166, "deviation_2_buy": 0.707, "deviation_buy": 1.657, "period_1_buy": 4, "period_2_buy": 4, } # Sell hyperspace params: sell_params = { "dev_low_sell": 0.763, "deviation_2_sell": 1.137, "deviation_sell": 3.14, "period_1_sell": 17, "period_2_sell": 6, } deviation_buy = DecimalParameter(1, 4, default=3.55, space='buy') dev_low_buy = DecimalParameter(0.7, 1.3, default=0.9, space='buy') period_1_buy = IntParameter(2, 18, default=13, space='buy') period_2_buy = IntParameter(2, 13, default=8, space='buy') deviation_2_buy = DecimalParameter(0.5, 1.2, default=1, space='buy') deviation_sell = DecimalParameter(1, 4, default=3.55, space='sell') dev_low_sell = DecimalParameter(0.7, 1.3, default=0.9, space='sell') period_1_sell = IntParameter(2, 18, default=13, space='sell') period_2_sell = IntParameter(2, 13, default=8, space='sell') deviation_2_sell = DecimalParameter(0.5, 1.2, default=1, space='sell') stoploss = -0.99 # effectively disabled. timeframe = '5m' # Sell signal use_sell_signal = True sell_profit_only = False sell_profit_offset = 0.001 # it doesn't meant anything, just to guarantee there is a minimal profit. ignore_roi_if_buy_signal = False # Trailing stoploss trailing_stop = False trailing_only_offset_is_reached = False trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.025 # Custom stoploss use_custom_stoploss = False # Run "populate_indicators()" only for new candle. process_only_new_candles = False # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 50 # Optional order type mapping. order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } plot_config = { # Main plot indicators (Moving averages, ...) 'main_plot': { 'sylvain_upper': {'color': 'blue'}, 'med_avg': {'color': 'red'}, 'sylvain_lower': {'color': 'blue'}, }, 'subplots': { # Subplots - each dict defines one additional plot "Stochastic": { 'srsi_k': {'color': 'blue'}, 'd': {'color': 'red'}, }, "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/USDT", "15m"), ] """ return [] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['typical'] = dataframe['close'] if dataframe['close'].iloc[-1] >= dataframe['close'].iloc[-2]: dataframe['typical'] = dataframe['close'] - dataframe['low'].shift(1) else: dataframe['typical'] = dataframe['close'].shift(1) - dataframe['low'] return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['deviation'] = self.deviation_buy.value * ta.SMA(dataframe['typical'], timeperiod=int(self.period_1_buy.value)) dataframe['pv_dev'] = dataframe['deviation'] * dataframe['volume'] dataframe['dev_high'] = ta.SMA(dataframe['pv_dev'], timeperiod=int(self.period_2_buy.value)) /ta.SMA(dataframe['volume'], timeperiod=int(self.period_2_buy.value)) dataframe['dev_low'] = self.dev_low_buy.value * dataframe['dev_high'] dataframe['pv_med'] = dataframe['close'] * dataframe['volume'] dataframe['med_avg'] = ta.SMA(dataframe['pv_med'], timeperiod=int(self.period_2_buy.value)) /ta.SMA(dataframe['volume'], timeperiod=int(self.period_2_buy.value)) dataframe['pv_avg'] = dataframe['med_avg'] * dataframe['volume'] dataframe['sylvain_lower'] = (ta.SMA(dataframe['pv_avg'], timeperiod=int(self.period_2_buy.value)) /ta.SMA(dataframe['volume'], timeperiod=int(self.period_2_buy.value))) - (dataframe['dev_low']*self.deviation_2_buy.value) dataframe.loc[ ( (qtpylib.crossed_below(dataframe['close'], dataframe['sylvain_lower'])) & # (qtpylib.crossed_above(dataframe['tsi'], dataframe['tsi_ema'])) & (dataframe['volume'] > 0) ), 'buy' ] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['deviation'] = self.deviation_sell.value * ta.SMA(dataframe['typical'], timeperiod=int(self.period_1_sell.value)) dataframe['pv_dev'] = dataframe['deviation'] * dataframe['volume'] dataframe['dev_high'] = ta.SMA(dataframe['pv_dev'], timeperiod=int(self.period_2_sell.value)) /ta.SMA(dataframe['volume'], timeperiod=int(self.period_2_sell.value)) dataframe['dev_low'] = self.dev_low_sell.value * dataframe['dev_high'] dataframe['pv_med'] = dataframe['close'] * dataframe['volume'] dataframe['med_avg'] = ta.SMA(dataframe['pv_med'], timeperiod=int(self.period_2_sell.value)) /ta.SMA(dataframe['volume'], timeperiod=int(self.period_2_sell.value)) dataframe['pv_avg'] = dataframe['med_avg'] * dataframe['volume'] dataframe['sylvain_upper'] = (ta.SMA(dataframe['pv_avg'], timeperiod=int(self.period_2_sell.value)) /ta.SMA(dataframe['volume'], timeperiod=int(self.period_2_sell.value))) + (dataframe['dev_high']*self.deviation_2_sell.value) dataframe.loc[ ( (qtpylib.crossed_above(dataframe['close'], dataframe['sylvain_upper'])) & (dataframe['volume'] > 0) ) , 'sell' ] = 1 return dataframe