Heracles Volatility-Ratio Strategy

Article Strategy library · Author: @Mablue (Masoud Azizi)

Summary

Heracles is a four-hour cryptocurrency strategy whose entry condition compares a shifted Donchian channel position value with a shifted Keltner channel width value. It opens a long position when their ratio falls within a configurable interval; the listed defaults set that interval from 0.16 to 0.75, with separate shifts of 15 and 9 candles. The strategy does not define an indicator-based exit signal, instead relying on a stepped return-on-investment schedule and a fixed stop loss.

The source includes a reported optimization snapshot of 25 trades with wins, draws, losses, average and median profit, total profit, and average duration. These figures are a single in-sample result and do not establish robustness or future performance. The code uses a BTC-oriented result comment, while the market universe and broader validation details are not established in the document. The ratio combines indicators with different scales, and the strategy's settings and results should be independently tested across markets and periods.

Key ideas

The strategy forms entries from the ratio of lagged Donchian channel position and Keltner channel width indicators.
A long position opens when that ratio lies within a configurable band, using separate indicator shifts.
Exits use a staged return threshold schedule and a fixed stop rather than an indicator-based exit signal.
The source reports one optimization snapshot, which is not evidence of out-of-sample robustness.

Tags

Crypto Futures Volatility Backtesting

Full text

# Heracles


# Heracles









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

## Source (GPL-3.0)

```python
# 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: A Volatility-Ratio Entry with a Time-Based ROI Exit

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