Zeus Strategy: Ichimoku and KST

Code Freqtrade

Summary

This Freqtrade strategy uses a four-hour timeframe and computes an Ichimoku base line and the KST indicator’s difference. Each indicator is normalized across the available dataframe range. Entry and exit thresholds are exposed as tunable numeric values and comparison choices: the Ichimoku value drives long entries, while the KST difference drives exits.

The strategy also specifies a return-on-investment schedule and a stop loss, and its comments show an example configuration and reported trade statistics. Those figures are embedded in code comments and do not establish future performance. The document does not state the exchange, asset universe, data period, or validation method, and whole-dataframe normalization may make historical signal behavior depend on later observations if used without appropriate safeguards.

Key ideas

Long entries compare the normalized Ichimoku base line with a configurable threshold.
Exit signals compare the normalized KST difference with a separate configurable threshold.
The strategy includes a four-hour timeframe, return targets, and a stop loss.
The performance figures appear only in comments and lack enough context to support general conclusions.

Tags

Crypto Technical indicators Momentum Backtesting Risk management

Full text

# Zeus.py


```py
# Zeus Strategy: First Generation of GodStra Strategy with maximum
# AVG/MID profit in USDT
# Author: @Mablue (Masoud Azizi)
# github: https://github.com/mablue/
# IMPORTANT: INSTALL TA BEFORE RUN(pip install ta)
# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --spaces buy sell roi --strategy Zeus
# --- Do not remove these libs ---
import logging
from freqtrade.strategy import CategoricalParameter, DecimalParameter

from numpy.lib import math
from freqtrade.strategy import 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 Zeus(IStrategy):

    # *    1/43:     86 trades. 72/6/8 Wins/Draws/Losses. Avg profit  12.66%. Median profit  11.99%. Total profit  0.10894395 BTC ( 108.94Σ%). Avg duration 3 days, 0:31:00 min. Objective: -48.48793
    # "max_open_trades": 10,
    # "stake_currency": "BTC",
    # "stake_amount": 0.01,
    # "tradable_balance_ratio": 0.99,
    # "timeframe": "4h",
    # "dry_run_wallet": 0.1,

    INTERFACE_VERSION: int = 3
    # Buy hyperspace params:
    buy_params = {
        "buy_cat": "<R",
        "buy_real": 0.0128,
    }

    # Sell hyperspace params:
    sell_params = {
        "sell_cat": "=R",
        "sell_real": 0.9455,
    }

    # ROI table:
    minimal_roi = {
        "0": 0.564,
        "567": 0.273,
        "2814": 0.12,
        "7675": 0
    }

    # Stoploss:
    stoploss = -0.256

    buy_real = DecimalParameter(
        0.001, 0.999, decimals=4, default=0.11908, space='buy')
    buy_cat = CategoricalParameter(
        [">R", "=R", "<R"], default='<R', space='buy')
    sell_real = DecimalParameter(
        0.001, 0.999, decimals=4, default=0.59608, space='sell')
    sell_cat = CategoricalParameter(
        [">R", "=R", "<R"], default='>R', space='sell')

    # Buy hypers
    timeframe = '4h'

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Add all ta features

        dataframe['trend_ichimoku_base'] = ta.trend.ichimoku_base_line(
            dataframe['high'],
            dataframe['low'],
            window1=9,
            window2=26,
            visual=False,
            fillna=False
        )
        KST = ta.trend.KSTIndicator(
            close=dataframe['close'],
            roc1=10,
            roc2=15,
            roc3=20,
            roc4=30,
            window1=10,
            window2=10,
            window3=10,
            window4=15,
            nsig=9,
            fillna=False
        )

        dataframe['trend_kst_diff'] = KST.kst_diff()

        # Normalization
        tib = dataframe['trend_ichimoku_base']
        dataframe['trend_ichimoku_base'] = (
            tib-tib.min())/(tib.max()-tib.min())
        tkd = dataframe['trend_kst_diff']
        dataframe['trend_kst_diff'] = (tkd-tkd.min())/(tkd.max()-tkd.min())
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        IND = 'trend_ichimoku_base'
        REAL = self.buy_real.value
        OPR = self.buy_cat.value
        DFIND = dataframe[IND]
        # print(DFIND.mean())
        if OPR == ">R":
            conditions.append(DFIND > REAL)
        elif OPR == "=R":
            conditions.append(np.isclose(DFIND, REAL))
        elif OPR == "<R":
            conditions.append(DFIND < REAL)

        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:
        conditions = []
        IND = 'trend_kst_diff'
        REAL = self.sell_real.value
        OPR = self.sell_cat.value
        DFIND = dataframe[IND]
        # print(DFIND.mean())

        if OPR == ">R":
            conditions.append(DFIND > REAL)
        elif OPR == "=R":
            conditions.append(np.isclose(DFIND, REAL))
        elif OPR == "<R":
            conditions.append(DFIND < REAL)

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

        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.

Zeus Crypto Strategy Using Ichimoku and KST Indicators

Summary

Key ideas

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