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 Stratégie Freqtrade

BETA

Collez votre IStrategy — freqrunner l'exécute dans un conteneur Docker sécurisé et calcule des métriques précises.

Clúster Docker en la nube

Freqtrade 2026.x

Binance Futures & Spot OHLCV

Cero configuración local

Écrivez une classe héritant de IStrategy selon les standards Freqtrade. Définissez populate_entry_trend et populate_exit_trend pour générer des signaux. Support natif de pandas_ta, ta-lib et can_short.

La classe doit hériter de IStrategypopulate_entry_trend → génère les colonnes enter_long / enter_shortpopulate_exit_trend → génère les colonnes 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

Configuration du Backtest

Exchange

Mercado

Paire

BTCUSDT.P

Limite atteinte — Mettre à niveau

Unité de temps

Rango de fechas

Date de débutHasta

Capital (USDT)

Tamaño de orden

⚠ Exécute de véritables conteneurs Freqtrade dans le cloud.

📊

Résumé des performances

Prêt à être exécuté sur les véritables carnets d'ordres Binance Futures et Spot. Lancez un backtest ou consultez l'exemple ci-dessous.

Documentation Officielle Freqtrade

Guides et références officielles pour concevoir et optimiser vos stratégies IStrategy.

Démarrage rapide — IStrategy

Structure de classe, populate_entry_trend, populate_exit_trend, tables ROI et stoploss.

Indicateurs techniques

pandas_ta, ta-lib, qtpylib — intégration et calcul d'indicateurs dans votre stratégie.

Hyperopt — réglage des paramètres

IntParameter, DecimalParameter, espaces d'exploration, époques et analyse des résultats.

Guide de backtesting

Lancement de backtests, plages temporelles, config.json et analyse des performances.

Référence de configuration (Config)

Tous les paramètres config.json — exchange, paires, table ROI, etc.

Futures & effet de levier (can_short)

Activation de can_short, levier, mode de trading futures et marge.

Freqtrade sur GitHub

Code source officiel, suivi des anomalies (Issues) et stratégies communautaires.

Bibliothèque de stratégies

Dépôt communautaire avec des exemples concrets de IStrategy prêts à l'emploi.