Can I backtest a FreqTrade strategy online without installing Docker or FreqTrade?

Yes. Backpulse runs your FreqTrade IStrategy through a managed engine in the cloud — no server to set up, no Docker, no deployment, no local FreqTrade installation. Paste your strategy and get results in seconds.

What is the difference between backtesting on Backpulse and FreqTrade's built-in backtester?

FreqTrade's built-in backtester requires a local Python environment, Docker, a server, data downloads and CLI commands. Backpulse runs the same IStrategy in the cloud with zero setup — no server, no Docker, no deployment. Paste your strategy and get a visual tearsheet in seconds.

What FreqTrade strategy format does Backpulse support?

Backpulse supports FreqTrade's IStrategy format. Your class must inherit from IStrategy and implement populate_indicators, populate_entry_trend and populate_exit_trend. Supports pandas_ta, TA-Lib, and can_short for short strategies.

What market data does the FreqTrade backtester use?

Backpulse fetches OHLCV candlestick data from Binance Futures (perpetual swaps). Data is available from 2019 onward across timeframes from 1 minute to 1 week.

Is FreqTrade backtesting on Backpulse free?

Yes — the FreqTrade backtest tool is available on the free tier. Pro and Scale plans unlock longer date ranges, more parallel jobs and priority queue access.

What performance metrics does Backpulse show after a FreqTrade backtest?

Backpulse returns a full tearsheet including: equity curve, underwater drawdown chart, monthly return heatmap, Sharpe ratio, Sortino ratio, CAGR, max drawdown, win rate, average trade duration, profit factor and more.

Backtest

Backtest de Estrategias Freqtrade

BETA

Pega tu IStrategy — freqrunner la ejecutará en un contenedor Docker aislado y calculará métricas completas.

Clúster Docker en la nube

Freqtrade 2026.x

Binance Futures & Spot OHLCV

Cero configuración local

Escribe una clase que herede de IStrategy según la estructura de Freqtrade. Define populate_entry_trend y populate_exit_trend para generar señales. Soporta pandas_ta, ta-lib y can_short.

La clase debe heredar de IStrategypopulate_entry_trend → genera columnas enter_long / enter_shortpopulate_exit_trend → genera columnas exit_long / exit_short

⚡

from freqtrade.strategy import IStrategy
from pandas import DataFrame
import talib.abstract as ta

class MyStrategy(IStrategy):

    timeframe = '15m'

    # set the initial stoploss to -10%
    stoploss = -0.10

    # exit profitable positions at any time when the profit is greater than 1%
    minimal_roi = {"0": 0.01}

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # generate values for technical analysis indicators
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # generate entry signals based on indicator values
        dataframe.loc[
            (dataframe['rsi'] < 30),
            'enter_long'] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # generate exit signals based on indicator values
        dataframe.loc[
            (dataframe['rsi'] > 70),
            'exit_long'] = 1

        return dataframe

from freqtrade.strategy import IStrategy
from pandas import DataFrame
import talib.abstract as ta

class MyStrategy(IStrategy):

    timeframe = '15m'

    # set the initial stoploss to -10%
    stoploss = -0.10

    # exit profitable positions at any time when the profit is greater than 1%
    minimal_roi = {"0": 0.01}

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # generate values for technical analysis indicators
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # generate entry signals based on indicator values
        dataframe.loc[
            (dataframe['rsi'] < 30),
            'enter_long'] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # generate exit signals based on indicator values
        dataframe.loc[
            (dataframe['rsi'] > 70),
            'exit_long'] = 1

        return dataframe

Configuración del backtest

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