# source: https://raw.githubusercontent.com/jaredrsommer/freqtradestrategies/705e6fe460ed917b0491607b3bd81f2d67f46f74/tacos.py import logging import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame from sqlalchemy.orm.base import RELATED_OBJECT_OK from sqlalchemy.sql.elements import or_ import talib.abstract as ta import pandas_ta as pta import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.exchange import timeframe_to_minutes from freqtrade.persistence import Trade from technical import indicators from datetime import datetime, timezone from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, RealParameter, IStrategy, IntParameter, merge_informative_pair) class github_jaredrsommer_freqtradestrategies__tacos__20230120_025345(IStrategy): # Add some logging logger = logging.getLogger(__name__) # Strategy interface version - allow new iterations of the strategy interface. # Check the documentation or the Sample strategy to get the latest version. INTERFACE_VERSION = 2 # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". # ROI1 table: minimal_roi = { "0": 0.309, "60": -0.01 } # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". # use_custom_stoploss = True stoploss = -0.015 # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 30 #30 # Optimal timeframe for the strategy. timeframe = '1d' # hyperopt params sell_rsil = DecimalParameter(45, 90, default=70) sell_rsiu = DecimalParameter(65, 100, default=80) sell_wavetrend = DecimalParameter(0, 5, default=0) buy_wavetrend = DecimalParameter(-10, -1, default=0) buy_rsil = DecimalParameter(0, 40, default=40) buy_rsiu = DecimalParameter(40, 65, default=50) @property def plot_config(self): return { "main_plot": { }, "subplots": { "obv": { "OBV": { "color": "#1b61ab", "type": "line" }, "OBVSlope": { "color": "#f18b7a", "type": "line" } }, "wavetrend": { "wave_t1": { "color": "#1b61ab", "type": "line" }, "wave_t2": { "color": "#f18b7a", "type": "line" } }, } } def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # WaveTrend ap = (0.25 * (dataframe['high'] + dataframe['low'] + dataframe["close"] + dataframe["open"])) esa = ta.EMA(ap, timeperiod = 10) d = ta.EMA(abs(ap - esa), timeperiod = 10) dataframe["wave_ci"] = (ap-esa) / (0.015 * d) dataframe["wave_t1"] = ta.EMA(dataframe["wave_ci"], timeperiod = 21) dataframe["wave_t2"] = ta.SMA(dataframe["wave_t1"], timeperiod = 4) dataframe["wave_t1_pc"] = round((dataframe["wave_t1"] - dataframe["wave_t1"].shift()) / abs(dataframe["wave_t1"]) * 100, 2) # # Bollinger! # bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) # dataframe['bb.lower'] = bollinger['lower'] # dataframe['bb.middle'] = bollinger['mid'] # dataframe['bb.upper'] = bollinger['upper'] # Added PCB Style OBV dataframe['OBV'] = ta.OBV(dataframe) dataframe['OBVSlope'] = pta.momentum.slope(dataframe['OBV']) # VWMA # vwma_period = 13 # dataframe['vwma'] = ((dataframe["close"] * dataframe["volume"]).rolling(vwma_period).sum() / # dataframe['volume'].rolling(vwma_period).sum()) # VWAP # vwap_period = 20 # dataframe['vwap'] = qtpylib.rolling_vwap(dataframe, window=vwap_period) # # VPCI # dataframe['vpci'] = indicators.vpci(dataframe, period_long=14) # #williamsR # dataframe['williamspercent'] = indicators.williams_percent(dataframe) # # ADX # dataframe['adx'] = ta.ADX(dataframe) # dataframe['plus.di'] = ta.PLUS_DI(dataframe) # dataframe['minus.di'] = ta.MINUS_DI(dataframe) # dataframe['plus.di.slope'] = pta.momentum.slope(dataframe['plus.di']) # RSI dataframe['rsi'] = ta.RSI(dataframe) dataframe['rsi_slope'] = pta.momentum.slope(dataframe['rsi']) dataframe['rsi_ma'] = ta.EMA(dataframe['rsi'], timeperiod = 5) dataframe['rsi_ma_slope'] = pta.momentum.slope(dataframe['rsi_ma']) # # MACD # macd = ta.MACD(dataframe) # dataframe['macd'] = macd['macd'] # dataframe['macdsignal'] = macd['macdsignal'] # dataframe['macdhist'] = macd['macdhist'] # # Stochastic Fast # stoch_fast = ta.STOCHF(dataframe) # dataframe['fastd'] = stoch_fast['fastd'] # dataframe['fastk'] = stoch_fast['fastk'] # # Stochastic Slow # stoch_slow = ta.STOCH(dataframe) # dataframe['slowd'] = stoch_slow['slowd'] # dataframe['slowk'] = stoch_slow['slowk'] # # Perc # dataframe['perc'] = ((dataframe['high'] - dataframe['low']) / dataframe['low']*100) # dataframe['avg3_perc'] = ta.EMA(dataframe['perc'], 3) # dataframe['perc_norm'] = (dataframe['perc'] - dataframe['perc'].rolling(50).min())/(dataframe['perc'].rolling(50).max() - dataframe['perc'].rolling(50).min()) return dataframe def do_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # # Bollinger! # bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) # dataframe['bb.lower'] = bollinger['lower'] # dataframe['bb.middle'] = bollinger['mid'] # dataframe['bb.upper'] = bollinger['upper'] # WaveTrend ap = (0.25 * (dataframe['high'] + dataframe['low'] + dataframe["close"] + dataframe["open"])) esa = ta.EMA(ap, timeperiod = 10) d = ta.EMA(abs(ap - esa), timeperiod = 10) dataframe['wave_ci'] = (ap-esa) / (0.015 * d) dataframe['wave_t1'] = ta.EMA(dataframe['wave_ci'], timeperiod = 21) dataframe['wave_t2'] = ta.SMA(dataframe['wave_t1'], timeperiod = 4) dataframe['wave_t1_pc'] = round((dataframe['wave_t1'] - dataframe['wave_t1'].shift()) / abs(dataframe['wave_t1']) * 100, 2) # Added PCB Style OBV dataframe['OBV'] = ta.OBV(dataframe) dataframe['OBVSlope'] = pta.momentum.slope(dataframe['OBV']) # VWMA # vwma_period = 13 # dataframe['vwma'] = ((dataframe["close"] * dataframe["volume"]).rolling(vwma_period).sum() / # dataframe['volume'].rolling(vwma_period).sum()) # VWAP # vwap_period = 20 # dataframe['vwap'] = qtpylib.rolling_vwap(dataframe, window=vwap_period) # # VPCI # dataframe['vpci'] = indicators.vpci(dataframe, period_long=14) # #williamsR # dataframe['williamspercent'] = indicators.williams_percent(dataframe) # # ADX # dataframe['adx'] = ta.ADX(dataframe) # dataframe['plus.di'] = ta.PLUS_DI(dataframe) # dataframe['minus.di'] = ta.MINUS_DI(dataframe) # dataframe['plus.di.slope'] = pta.momentum.slope(dataframe['plus.di']) # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod = 3) dataframe['rsi_slope'] = pta.momentum.slope(dataframe['rsi']) dataframe['rsi_ma'] = ta.EMA(dataframe['rsi'], timeperiod = 5) dataframe['rsi_ma_slope'] = pta.momentum.slope(dataframe['rsi_ma']) # # MACD # macd = ta.MACD(dataframe) # dataframe['macd'] = macd['macd'] # dataframe['macdsignal'] = macd['macdsignal'] # dataframe['macdhist'] = macd['macdhist'] # # Stochastic Fast # stoch_fast = ta.STOCHF(dataframe) # dataframe['fastd'] = stoch_fast['fastd'] # dataframe['fastk'] = stoch_fast['fastk'] # # Stochastic Slow # stoch_slow = ta.STOCH(dataframe) # dataframe['slowd'] = stoch_slow['slowd'] # dataframe['slowk'] = stoch_slow['slowk'] # # Perc # dataframe['perc'] = ((dataframe['high'] - dataframe['low']) / dataframe['low']*100) # dataframe['avg3_perc'] = ta.EMA(dataframe['perc'], 3) # dataframe['perc_norm'] = (dataframe['perc'] - dataframe['perc'].rolling(50).min())/(dataframe['perc'].rolling(50).max()-dataframe['perc'].rolling(50).min()) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['wave_t1'] >= dataframe['wave_t2']) & (dataframe['rsi_ma'] <= self.buy_rsiu.value) & (dataframe['rsi_ma'] >= self.buy_rsil.value) & (dataframe['rsi_ma_slope'] > 0) ), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['wave_t1_pc'] <= self.sell_wavetrend.value) # (dataframe['wave_t1'] <= dataframe['wave_t2']) & (dataframe['rsi_ma'] >= self.sell_rsil.value) & (dataframe['rsi_ma'] <= self.sell_rsiu.value) & (dataframe['rsi_ma_slope'] <= 0) ), 'sell'] = 1 return dataframe # "All watched over by machines with loving grace..."