Sources : Cripto Robot : https://www.youtube.com/watch?v=uE04UROWkjs&list=PLpJ7cz_wOtsrqEQpveLc2xKLjOBgy4NfA&index=4 Github : https://github.com/CryptoRobotFr/TrueStrategy/blob/main/TrixStrategy/Trix_Complete_backtest.ipynb
Timeframe
1h
Direction
Long Only
Stoploss
-31.0%
Trailing Stop
No
ROI
0m: 55.3%, 423m: 14.4%, 751m: 5.9%, 1342m: 0.0%
Interface Version
2
Startup Candles
N/A
Indicators
6
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from freqtrade.strategy import (merge_informative_pair,
BooleanParameter,
CategoricalParameter,
DecimalParameter,
IStrategy,
IntParameter,
informative)
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
from functools import reduce
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.persistence import PairLocks, Trade
from datetime import datetime
# This class is a sample. Feel free to customize it.
class TrixV23Strategy(IStrategy):
"""
Sources :
Cripto Robot : https://www.youtube.com/watch?v=uE04UROWkjs&list=PLpJ7cz_wOtsrqEQpveLc2xKLjOBgy4NfA&index=4
Github : https://github.com/CryptoRobotFr/TrueStrategy/blob/main/TrixStrategy/Trix_Complete_backtest.ipynb
"""
# 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".
minimal_roi = {
"0": 0.553,
"423": 0.144,
"751": 0.059,
"1342": 0
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
stoploss = -0.31
# Trailing stoploss
trailing_stop = False
# trailing_only_offset_is_reached = True
# trailing_stop_positive = 0.02
# trailing_stop_positive_offset = 0.9 # Disabled / not configured
# Optimal timeframe for the strategy.
timeframe = '1h'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
# These values can be overridden in the "ask_strategy" section in the config.
use_sell_signal = True
sell_profit_only = True
ignore_roi_if_buy_signal = False
use_custom_stoploss = True
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 200
# Optional order type mapping.
order_types = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optional order time in force.
order_time_in_force = {
'buy': 'gtc',
'sell': 'gtc'
}
#---------------------------#
#-- Hyperspace parameters --#
#---------------------------#
buy_params = {
"buy_trix_signal_type": "trigger",
"buy_trix_src": "low",
"buy_trix_timeperiod": 8,
"buy_trix_signal_timeperiod": 19,
"buy_stoch_rsi_enabled": True,
"buy_rsi_timeperiod": 14,
"buy_stoch_rsi_timeperiod": 14,
"buy_stoch_rsi": 0.901,
"buy_ema_enabled": True,
"buy_ema_src": "open",
"buy_ema_timeperiod": 10,
"buy_ema_multiplier": 0.85,
"buy_btc_ema_enabled": True,
"buy_btc_ema_multiplier": 0.996,
"buy_btc_ema_timeperiod": 184,
}
sell_params = {
"sell_trix_signal_type": "trailing",
"sell_trix_src": "high",
"sell_trix_timeperiod": 10,
"sell_trix_signal_timeperiod": 19,
"sell_stoch_rsi_enabled": True,
"sell_rsi_timeperiod": 14,
"sell_stoch_rsi_timeperiod": 14,
"sell_stoch_rsi": 0.183,
"sell_atr_enabled": True,
"sell_atr_timeperiod": 30,
"sell_atr_multiplier": 4.99,
}
#------------------------------#
#-- Hyperoptables parameters --#
#------------------------------#
# buy
buy_trix_signal_type = CategoricalParameter(['trailing', 'trigger'], default='trigger', space="buy", optimize=False, load=True)
buy_trix_src = CategoricalParameter(['open', 'high', 'low', 'close'], default='close', space="buy", optimize=False, load=True)
buy_trix_timeperiod = IntParameter(5, 25, default=9, space="buy", optimize=False, load=True)
buy_trix_signal_timeperiod = IntParameter(5, 25, default=21, space="buy", optimize=False, load=True)
buy_stoch_rsi_enabled = BooleanParameter(default=True, space="buy", optimize=False, load=True)
buy_rsi_timeperiod = IntParameter(5, 25, default=14, space="buy", optimize=False, load=True)
buy_stoch_rsi = DecimalParameter(0.6, 0.99, decimals=3, default=0.987, space="buy", optimize=False, load=True)
buy_stoch_rsi_timeperiod = IntParameter(5, 25, default=14, space="buy", optimize=False, load=True)
buy_ema_enabled = BooleanParameter(default=False, space="buy", optimize=False, load=True)
buy_ema_timeperiod = IntParameter(9, 100, default=21, space="buy", optimize=False, load=True)
buy_ema_multiplier = DecimalParameter(0.8, 1.2, decimals=2, default=1.00, space="buy", optimize=False, load=True)
buy_ema_src = CategoricalParameter(['open', 'high', 'low', 'close'], default='close', space="buy", optimize=False, load=True)
buy_btc_ema_enabled = BooleanParameter(default=False, space="buy", optimize=True, load=True)
buy_btc_ema_timeperiod = IntParameter(150, 250, default=200, space="buy", optimize=True, load=True)
buy_btc_ema_multiplier = DecimalParameter(0.8, 1.0, decimals=3, default=0.97, space="buy", optimize=True, load=True)
# sell
sell_trix_signal_type = CategoricalParameter(['trailing', 'trigger'], default='trailing', space="sell", optimize=False, load=True)
sell_trix_src = CategoricalParameter(['open', 'high', 'low', 'close'], default='close', space="sell", optimize=False, load=True)
sell_trix_timeperiod = IntParameter(5, 25, default=9, space="sell", optimize=False, load=True)
sell_trix_signal_timeperiod = IntParameter(5, 25, default=21, space="sell", optimize=False, load=True)
sell_stoch_rsi_enabled = BooleanParameter(default=True, space="sell", optimize=False, load=True)
sell_rsi_timeperiod = IntParameter(5, 25, default=14, space="sell", optimize=False, load=True)
sell_stoch_rsi = DecimalParameter(0.01, 0.4, decimals=3, default=0.048, space="sell", optimize=False, load=True)
sell_stoch_rsi_timeperiod = IntParameter(5, 25, default=14, space="sell", optimize=False, load=True)
sell_atr_enabled = BooleanParameter(default=True, space="sell", optimize=False, load=True)
sell_atr_timeperiod = IntParameter(9, 30, default=14, space="sell", optimize=False, load=True)
sell_atr_multiplier = DecimalParameter(0.7, 9.0, decimals=3, default=4.0, space="sell", optimize=False, load=True)
plot_config = {
'main_plot': {
'trix_b_8': {'color': 'blue'},
'trix_s_10': {'color': 'orange'},
'ema_b_signal': {'color': 'red'},
'btc_usdt_close_1h': {'color': 'purple'},
'btc_usdt_ema_184_1h': {'color': 'yellow'},
},
'subplots': {
"TRIX BUY": {
'trix_b_pct': {'color': 'blue'},
'trix_b_signal_19': {'color': 'orange'},
},
"TRIX SELL": {
'trix_s_pct': {'color': 'blue'},
'trix_s_signal_19': {'color': 'orange'},
},
"STOCH RSI": {
'b_stoch_rsi': {'color': 'blue'},
's_stoch_rsi': {'color': 'orange'},
},
}
}
@informative('1h', 'BTC/{stake}')
def populate_indicators_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
#---------#
#-- BTC --#
#---------#
for val in self.buy_btc_ema_timeperiod.range:
dataframe[f'ema_{val}'] = ta.EMA(dataframe, timeperiod=val)
return dataframe
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
#------------------------#
#-- ATR based stoploss --#
#------------------------#
if self.sell_atr_enabled.value == False:
return 1
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = dataframe.iloc[-1].squeeze()
stoploss_price = last_candle['low'] - last_candle[f'atr_{self.sell_atr_timeperiod.value}'] * self.sell_atr_multiplier.value
if stoploss_price < current_rate:
return (stoploss_price / current_rate) - 1
# return maximum stoploss value, keeping current stoploss price unchanged
return 1
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
#----------------#
#-- Indicators --#
#----------------#
# Trix Indicator
for val in self.buy_trix_timeperiod.range:
dataframe[f'trix_b_{val}'] = ta.EMA(ta.EMA(ta.EMA(dataframe[self.buy_trix_src.value], timeperiod=val), timeperiod=val), timeperiod=val)
dataframe['trix_b_pct'] = dataframe[f'trix_b_{self.buy_trix_timeperiod.value}'].pct_change() * 100
for val in self.buy_trix_signal_timeperiod.range:
dataframe[f'trix_b_signal_{val}'] = ta.SMA(dataframe['trix_b_pct'], timeperiod=val)
for val in self.sell_trix_timeperiod.range:
dataframe[f'trix_s_{val}'] = ta.EMA(ta.EMA(ta.EMA(dataframe[self.sell_trix_src.value], timeperiod=val), timeperiod=val), timeperiod=val)
dataframe['trix_s_pct'] = dataframe[f'trix_s_{self.sell_trix_timeperiod.value}'].pct_change() * 100
for val in self.sell_trix_signal_timeperiod.range:
dataframe[f'trix_s_signal_{val}'] = ta.SMA(dataframe['trix_s_pct'], timeperiod=val)
# Stochastic RSI
for val in self.buy_rsi_timeperiod.range:
dataframe['b_rsi'] = ta.RSI(dataframe, timeperiod=val)
for val in self.buy_stoch_rsi_timeperiod.range:
dataframe['b_stoch_rsi'] = (dataframe['b_rsi'] - dataframe['b_rsi'].rolling(val).min()) / (dataframe['b_rsi'].rolling(val).max() - dataframe['b_rsi'].rolling(val).min())
for val in self.sell_rsi_timeperiod.range:
dataframe['s_rsi'] = ta.RSI(dataframe, timeperiod=val)
for val in self.sell_stoch_rsi_timeperiod.range:
dataframe['s_stoch_rsi'] = (dataframe['s_rsi'] - dataframe['s_rsi'].rolling(val).min()) / (dataframe['s_rsi'].rolling(val).max() - dataframe['s_rsi'].rolling(val).min())
# EMA
for val in self.buy_ema_timeperiod.range:
dataframe[f'ema_b_{val}'] = ta.EMA(dataframe[self.buy_ema_src.value], timeperiod=val)
dataframe['ema_b_signal'] = dataframe[f'ema_b_{self.buy_ema_timeperiod.value}'] * self.buy_ema_multiplier.value
# ATR
for val in self.sell_atr_timeperiod.range:
dataframe[f'atr_{val}'] = ta.ATR(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod=val)
dataframe['stoploss_price'] = dataframe['low'] - dataframe[f'atr_{self.sell_atr_timeperiod.value}'] * self.sell_atr_multiplier.value
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
stake = self.config['stake_currency'].lower()
#-----------------------#
#-- Guards and trends --#
#-----------------------#
# For backtesting & Hyperopt
conditions.append(dataframe['volume'] > 0)
# Can't send a buy signal at the same time as a sell signal
conditions.append(dataframe['trix_s_pct'] > dataframe[f'trix_s_signal_{self.sell_trix_signal_timeperiod.value}'])
# If BTC is not going well, don't buy
if self.buy_btc_ema_enabled.value:
conditions.append(dataframe[f'btc_{stake}_close_1h'] > (dataframe[f'btc_{stake}_ema_{self.buy_btc_ema_timeperiod.value}_1h'] * self.buy_btc_ema_multiplier.value))
# Stoch RSI
if self.buy_stoch_rsi_enabled.value:
conditions.append(dataframe['b_stoch_rsi'] < self.buy_stoch_rsi.value)
# Trend check
if self.buy_ema_enabled.value:
conditions.append(dataframe['close'] > dataframe['ema_b_signal'])
# Probably less efficient than trigger mode
if self.buy_trix_signal_type.value == 'trailing':
conditions.append(dataframe['trix_b_pct'] > dataframe[f'trix_b_signal_{self.buy_trix_signal_timeperiod.value}'])
#--------------#
#-- Triggers --#
#--------------#
# Main trigger : trix indicator
if self.buy_trix_signal_type.value == 'trigger':
conditions.append(qtpylib.crossed_above(dataframe['trix_b_pct'], dataframe[f'trix_b_signal_{self.buy_trix_signal_timeperiod.value}']))
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
#-----------------------#
#-- Guards and trends --#
#-----------------------#
# For backtesting & Hyperopt
conditions.append(dataframe['volume'] > 0)
# Stoch RSI
if self.sell_stoch_rsi_enabled.value:
conditions.append(dataframe['s_stoch_rsi'] > self.sell_stoch_rsi.value)
# Main indicator : Trix
if self.sell_trix_signal_type.value == 'trailing':
conditions.append(dataframe['trix_s_pct'] < dataframe[f'trix_s_signal_{self.sell_trix_signal_timeperiod.value}'])
#--------------#
#-- Triggers --#
#--------------#
# Main indicator. We probably want trailing mode
if self.sell_trix_signal_type.value == 'trigger':
conditions.append(qtpylib.crossed_below(dataframe['trix_s_pct'], dataframe[f'trix_s_signal_{self.sell_trix_signal_timeperiod.value}']))
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'sell'] = 1
return dataframe