Multi-Timeframe Strategy with Ichimoku Cloud and ADX
Timeframe
15m
Direction
Long Only
Stoploss
-10.0%
Trailing Stop
Yes
ROI
0m: 10.0%
Interface Version
3
Startup Candles
N/A
Indicators
3
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 imports ---
import numpy as np
import pandas as pd
from pandas import DataFrame
from typing import Optional, Union
from freqtrade.strategy import (
BooleanParameter,
CategoricalParameter,
DecimalParameter,
IStrategy,
IntParameter,
merge_informative_pair,
)
# --------------------------------
from datetime import datetime
from freqtrade.persistence import Trade
import talib.abstract as ta
import technical.indicators as ftt
class MT_ICH_ADX_1(IStrategy):
"""
Multi-Timeframe Strategy with Ichimoku Cloud and ADX
"""
# Strategy interface version
INTERFACE_VERSION = 3
timeframe = "15m"
informative_timeframe = "1h"
can_short = False
# Minimal ROI designed for the strategy
minimal_roi = {"0": 0.1}
# Optimal stoploss
stoploss = -0.1
# Trailing stoploss
trailing_stop = True
trailing_stop_positive = 0.01
trailing_stop_positive_offset = 0.02
trailing_only_offset_is_reached = True
# Run "populate_indicators()" only for new candle
process_only_new_candles = True
# These values can be overridden in the config
use_exit_signal = True
exit_profit_only = False
ignore_roi_if_entry_signal = True
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 100
# --- Hyperparameters ---
# EMA (Higher Timeframe)
buy_ema_htf_period = IntParameter(10, 50, default=20, space="buy")
# Ichimoku Cloud (Lower Timeframe)
buy_ichimoku_conversion_period = IntParameter(5, 25, default=9, space="buy")
buy_ichimoku_base_period = IntParameter(20, 60, default=26, space="buy")
buy_ichimoku_span_b_period = IntParameter(40, 100, default=52, space="buy")
# ADX (Lower Timeframe)
buy_adx_ltf_period = IntParameter(10, 50, default=14, space="buy")
buy_adx_ltf_threshold = IntParameter(15, 50, default=25, space="buy")
# Exit Parameters
sell_adx_ltf_threshold = IntParameter(40, 80, default=60, space="sell")
def informative_pairs(self):
pairs = self.dp.current_whitelist()
informative_pairs = [(pair, self.informative_timeframe) for pair in pairs]
return informative_pairs
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Higher Timeframe Indicators
informative = self.dp.get_pair_dataframe(
pair=metadata["pair"], timeframe=self.informative_timeframe
)
informative["ema_htf"] = ta.EMA(informative, timeperiod=self.buy_ema_htf_period.value)
dataframe = merge_informative_pair(
dataframe, informative, self.timeframe, self.informative_timeframe, ffill=True
)
# Lower Timeframe Indicators
ichimoku = ftt.ichimoku(
dataframe,
conversion_line_period=self.buy_ichimoku_conversion_period.value,
base_line_periods=self.buy_ichimoku_base_period.value,
laggin_span=self.buy_ichimoku_span_b_period.value,
)
dataframe["tenkan_sen"] = ichimoku["tenkan_sen"]
dataframe["kijun_sen"] = ichimoku["kijun_sen"]
dataframe["senkou_span_a"] = ichimoku["senkou_span_a"]
dataframe["senkou_span_b"] = ichimoku["senkou_span_b"]
dataframe["adx_ltf"] = ta.ADX(dataframe, timeperiod=self.buy_adx_ltf_period.value)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(dataframe["close"] > dataframe[f"ema_htf_{self.informative_timeframe}"])
& (dataframe["tenkan_sen"] > dataframe["kijun_sen"])
& (dataframe["close"] > dataframe["senkou_span_a"])
& (dataframe["close"] > dataframe["senkou_span_b"])
& (dataframe["adx_ltf"] > self.buy_adx_ltf_threshold.value),
"enter_long",
] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[(dataframe["adx_ltf"] > self.sell_adx_ltf_threshold.value), "exit_long"] = 1
return dataframe