Volatility-Ratio Entry Strategy

Code Freqtrade

Summary

Heracles is a four-hour crypto strategy that computes a Donchian channel percentage band and Keltner channel width, then enters long when a ratio of their shifted values falls within tunable bounds. The indicator shifts and ratio limits are exposed as hyperparameters, and the accompanying instructions suggest optimizing buy conditions and the return-on-investment schedule with Freqtrade's hyperopt. The supplied configuration also sets a stop loss and staged profit targets; the exit signal itself is disabled, leaving ROI and stop loss to govern exits.

The document reports one optimization result: 25 trades, with 18 wins, four draws, and three losses, alongside average and median profit, total BTC profit, average duration, and an objective score. These figures describe a single reported run, not evidence of out-of-sample performance. The sample, market period, pair selection, fees, slippage, and validation procedure are not documented, so the result cannot establish robustness or live profitability. The strategy also requires the external technical-analysis package and an age filter in pair selection.

Key ideas

The strategy enters long when a shifted Donchian percentage-band value divided by a shifted Keltner width lies within optimized bounds.
Its indicator shifts and ratio thresholds are configurable hyperparameters.
A four-hour timeframe, ROI schedule, and stop loss define the supplied trading configuration.
The reported optimization run is small and lacks details needed to assess out-of-sample or live performance.

Tags

Crypto Technical indicators Backtesting Volatility

Full text

# Heracles.py


```py
# Heracles Strategy: Strongest Son of GodStra
# ( With just 1 Genome! its a bacteria :D )
# Author: @Mablue (Masoud Azizi)
# github: https://github.com/mablue/
# IMPORTANT:Add to your pairlists inside config.json (Under StaticPairList):
#   {
#       "method": "AgeFilter",
#       "min_days_listed": 100
#   },
# IMPORTANT: INSTALL TA BEFORE RUN(pip install ta)
#
# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --spaces roi buy --strategy Heracles
# ######################################################################
# --- Do not remove these libs ---
from freqtrade.strategy import IntParameter, DecimalParameter, IStrategy
from pandas import DataFrame
# --------------------------------
# Add your lib to import here
# import talib.abstract as ta
import pandas as pd
import ta
from ta.utils import dropna
import freqtrade.vendor.qtpylib.indicators as qtpylib
from functools import reduce
import numpy as np


class Heracles(IStrategy):
    ########################################## RESULT PASTE PLACE ##########################################
    # 10/100:     25 trades. 18/4/3 Wins/Draws/Losses. Avg profit   5.92%. Median profit   6.33%. Total profit  0.04888306 BTC (  48.88Σ%). Avg duration 4 days, 6:24:00 min. Objective: -11.42103

    INTERFACE_VERSION: int = 3
    # Buy hyperspace params:
    buy_params = {
        "buy_crossed_indicator_shift": 9,
        "buy_div_max": 0.75,
        "buy_div_min": 0.16,
        "buy_indicator_shift": 15,
    }

    # Sell hyperspace params:
    sell_params = {
    }

    # ROI table:
    minimal_roi = {
        "0": 0.598,
        "644": 0.166,
        "3269": 0.115,
        "7289": 0
    }

    # Stoploss:
    stoploss = -0.256

    # Optimal timeframe use it in your config
    timeframe = '4h'

    ########################################## END RESULT PASTE PLACE ######################################

    # buy params
    buy_div_min = DecimalParameter(0, 1, default=0.16, decimals=2, space='buy')
    buy_div_max = DecimalParameter(0, 1, default=0.75, decimals=2, space='buy')
    buy_indicator_shift = IntParameter(0, 20, default=16, space='buy')
    buy_crossed_indicator_shift = IntParameter(0, 20, default=9, space='buy')

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe = dropna(dataframe)

        dataframe['volatility_kcw'] = ta.volatility.keltner_channel_wband(
            dataframe['high'],
            dataframe['low'],
            dataframe['close'],
            window=20,
            window_atr=10,
            fillna=False,
            original_version=True
        )

        dataframe['volatility_dcp'] = ta.volatility.donchian_channel_pband(
            dataframe['high'],
            dataframe['low'],
            dataframe['close'],
            window=10,
            offset=0,
            fillna=False
        )

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Buy strategy Hyperopt will build and use.
        """
        conditions = []

        IND = 'volatility_dcp'
        CRS = 'volatility_kcw'
        DFIND = dataframe[IND]
        DFCRS = dataframe[CRS]

        d = DFIND.shift(self.buy_indicator_shift.value).div(
            DFCRS.shift(self.buy_crossed_indicator_shift.value))

        # print(d.min(), "\t", d.max())
        conditions.append(
            d.between(self.buy_div_min.value, self.buy_div_max.value))

        if conditions:
            dataframe.loc[
                reduce(lambda x, y: x & y, conditions),
                'enter_long']=1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Sell strategy Hyperopt will build and use.
        """
        dataframe.loc[:, 'exit_long'] = 0
        return dataframe

```

Shown in full with attribution under the source's licence. Licence: GPL-3.0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.

Heracles Strategy: Delayed Volatility-Ratio Entries with Hyperopt

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