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DryRunStrategy

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freqle/uploads/user-90abf0da8f3178f3/DryRunStrategy_converted.py · first seen 2026-07-28

Basics mode: spot timeframe: 5m interface version: 3
Settings stoploss: -0.3 has minimal roi
Indicators Bollinger_Bands RSI SAR Stochastic talib
15 related strategies (⧉ identical code, ≈ similar name)

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# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy  # noqa

class DryRunStrategy(IStrategy):
    INTERFACE_VERSION = 3
    '\n    Strategy 002\n    author@: Gerald Lonlas\n    github@: https://github.com/freqtrade/freqtrade-strategies\n\n    How to use it?\n    > python3 ./freqtrade/main.py -s Strategy002\n    '
    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi"
    minimal_roi = {'60': 0.01, '30': 0.03, '20': 0.04, '0': 0.05}
    # Optimal stoploss designed for the strategy
    # This attribute will be overridden if the config file contains "stoploss"
    stoploss = -0.3
    # Optimal ticker interval for the strategy
    timeframe = '5m'

    def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
        """
        Adds several different TA indicators to the given DataFrame

        Performance Note: For the best performance be frugal on the number of indicators
        you are using. Let uncomment only the indicator you are using in your strategies
        or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
        """
        # Stoch
        stoch = ta.STOCH(dataframe)
        dataframe['slowk'] = stoch['slowk']
        # RSI
        dataframe['rsi'] = ta.RSI(dataframe)
        # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
        rsi = 0.1 * (dataframe['rsi'] - 50)
        dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
        # Bollinger bands
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        # SAR Parabol
        dataframe['sar'] = ta.SAR(dataframe)
        # Hammer: values [0, 100]
        dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame) -> DataFrame:
        """
        Based on TA indicators, populates the buy signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        dataframe.loc[(dataframe['rsi'] < 30) & (dataframe['slowk'] < 20) & (dataframe['bb_lowerband'] > dataframe['close']) & (dataframe['CDLHAMMER'] == 100), 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame) -> DataFrame:
        """
        Based on TA indicators, populates the sell signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        dataframe.loc[(dataframe['sar'] > dataframe['close']) & (dataframe['fisher_rsi'] > 0.3), 'exit_long'] = 1
        return dataframe