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CofiBit’s Stochastic and ADX Entry Strategy with EMA Exits

Code Freqtrade

Summary

This five-minute long-only strategy combines a fast stochastic oscillator, an ADX strength filter, and short EMAs. It enters when the opening price is below the five-period EMA of lows, the stochastic %K crosses above %D while both remain below a tunable threshold, and ADX exceeds its own tunable threshold. The default thresholds are 25, and the entry parameters can each be adjusted from 20 to 30.

An exit is triggered when the opening price reaches or exceeds the five-period EMA of highs, or when either stochastic line crosses above a tunable upper level, defaulting to 75. The configuration also specifies a tiered ROI schedule and a 25% stop loss. These are code settings, not evidence of performance: the document provides no backtest results, market selection, or risk-adjusted evaluation. The strategy’s source attribution is informal, and the snippet does not discuss position sizing, short entries, transaction costs, or how its parameters were selected.

Key ideas

  • The strategy seeks long entries after a low-level stochastic crossover while ADX indicates sufficient trend strength.
  • Entry also requires the candle opening price to be below the five-period EMA of lows.
  • Exits use either the five-period EMA of highs or an upper stochastic threshold.
  • The code specifies tunable indicator thresholds, a tiered ROI schedule, and a stop loss, but reports no performance evidence.

Tags

Technical indicatorsMomentumRisk managementBacktesting
Full text
# CofiBitStrategy.py


```py
# --- Do not remove these libs ---
import freqtrade.vendor.qtpylib.indicators as qtpylib
import talib.abstract as ta
from freqtrade.strategy import IStrategy
from freqtrade.strategy import IntParameter
from pandas import DataFrame


# --------------------------------


class CofiBitStrategy(IStrategy):
    """
        taken from slack by user CofiBit
    """

    INTERFACE_VERSION: int = 3
    # Buy hyperspace params:
    buy_params = {
        "buy_fastx": 25,
        "buy_adx": 25,
    }

    # Sell hyperspace params:
    sell_params = {
        "sell_fastx": 75,
    }

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi"
    minimal_roi = {
        "40": 0.05,
        "30": 0.06,
        "20": 0.07,
        "0": 0.10
    }

    # Optimal stoploss designed for the strategy
    # This attribute will be overridden if the config file contains "stoploss"
    stoploss = -0.25

    # Optimal timeframe for the strategy
    timeframe = '5m'

    buy_fastx = IntParameter(20, 30, default=25)
    buy_adx = IntParameter(20, 30, default=25)
    sell_fastx = IntParameter(70, 80, default=75)

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
        dataframe['fastd'] = stoch_fast['fastd']
        dataframe['fastk'] = stoch_fast['fastk']
        dataframe['ema_high'] = ta.EMA(dataframe, timeperiod=5, price='high')
        dataframe['ema_close'] = ta.EMA(dataframe, timeperiod=5, price='close')
        dataframe['ema_low'] = ta.EMA(dataframe, timeperiod=5, price='low')
        dataframe['adx'] = ta.ADX(dataframe)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the buy signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                (dataframe['open'] < dataframe['ema_low']) &
                (qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd'])) &
                (dataframe['fastk'] < self.buy_fastx.value) &
                (dataframe['fastd'] < self.buy_fastx.value) &
                (dataframe['adx'] > self.buy_adx.value)
            ),
            'enter_long'] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the sell signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                (dataframe['open'] >= dataframe['ema_high'])
            ) |
            (
                (qtpylib.crossed_above(dataframe['fastk'], self.sell_fastx.value)) |
                (qtpylib.crossed_above(dataframe['fastd'], self.sell_fastx.value))
            ),
            '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.