Test your trading strategies with our free backtesting platform before risking real capital. Validate rules using historical data across crypto and stocks with detailed performance metrics.
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The free backtesting platform lets you validate trading strategies using years of historical data before deploying real capital. Run your DCA rules against past BTC cycles or test RSI strategies on ETH volatility from 2020-2024. See exactly how your automation would have performed during market crashes, bull runs and sideways action without risking funds.
Access minute-by-minute price data going back years across major cryptocurrencies and stocks. Test strategies on 1-minute scalping setups or monthly DCA patterns with the same granularity. The system processes candle data from Binance, Coinbase and other exchanges to mirror real trading conditions.
Every backtest generates detailed reports showing win rate, maximum drawdown, Sharpe ratio and profit factor. Compare how the same strategy performs across different assets or time periods. Identify which market conditions favor your approach and which ones to avoid.
Strategy validation tools
Upload your trading rules and watch them execute against historical market movements. If your RSI strategy shows consistent losses during trending markets, you discover this in testing rather than live trading. The platform highlights periods where your logic would have struggled.
No coding required for backtests
Build strategies using visual rule builders then backtest immediately. Drag indicators like moving averages or RSI onto charts, set your entry and exit conditions, then run the simulation across months or years of data.
Buy The Dips
Purchases assets when price drops below moving average support with RSI confirmation. Exits on bounce back above resistance levels. Effective during volatile but trending markets.
Moving Average Scalper
Enters long when fast MA crosses above slow MA on short timeframes. Quick exits on opposite crossover. Works best in trending conditions with clear directional momentum.
RSI Oversold Rebound
Buys when RSI drops below 30 and starts climbing back up. Sells when RSI reaches 70 or higher. Simple mean reversion approach for range-bound markets.
Volatility Breakout
Triggers entries when price breaks above recent high with volume confirmation. Uses ATR-based stops. Captures momentum moves in both crypto and stock markets.
DCA Accumulator
Dollar cost averages into positions over time regardless of price. Reduces timing risk by spreading entries. Most effective during long-term accumulation phases.
Buy The Dips
Purchases assets when price drops below moving average support with RSI confirmation. Exits on bounce back above resistance levels. Effective during volatile but trending markets.
Moving Average Scalper
Enters long when fast MA crosses above slow MA on short timeframes. Quick exits on opposite crossover. Works best in trending conditions with clear directional momentum.
RSI Oversold Rebound
Buys when RSI drops below 30 and starts climbing back up. Sells when RSI reaches 70 or higher. Simple mean reversion approach for range-bound markets.
Volatility Breakout
Triggers entries when price breaks above recent high with volume confirmation. Uses ATR-based stops. Captures momentum moves in both crypto and stock markets.
DCA Accumulator
Dollar cost averages into positions over time regardless of price. Reduces timing risk by spreading entries. Most effective during long-term accumulation phases.
Grid Trading
Places buy and sell orders at regular intervals above and below current price. Profits from range-bound price action. Requires careful risk management in trending markets.
Supertrend Momentum
Follows Supertrend indicator signals for trend direction. Enters long above Supertrend line, exits below. Adapts to volatility using ATR calculations.
Bollinger Band Mean Reversion
Buys near lower Bollinger Band, sells near upper band. Assumes price returns to mean over time. Most profitable in sideways or range-bound conditions.
Breakout Plus Retest
Enters after price breaks resistance then retests successfully. Confirms strength before position entry. Higher win rate but fewer total signals than pure breakout strategies.
Support And Resistance Bounce
Identifies key support and resistance levels then trades bounces off these zones. Uses multiple timeframe confirmation. Effective in established trading ranges.
Run multiple backtests simultaneously to compare how different approaches perform during the same time periods. Your DCA strategy might excel during bear markets while momentum strategies shine in bull runs. Side-by-side comparisons reveal which tools work when.
Adjust strategy parameters like RSI levels or moving average periods then re-run backtests to see impact on performance. Find the optimal RSI threshold for your trading style or discover which moving average combination works best for your target assets.
Include exchange fees, slippage and spread costs in backtest calculations for accurate performance projections. A strategy showing 15% returns might drop to 8% after realistic cost assumptions. Better to discover this in testing than live trading.
Download backtest data as CSV files for additional analysis in Excel or other tools. Review individual trade details, monthly performance breakdowns and drawdown periods. Share results with trading partners or advisors for feedback.
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Historical testing reveals the best times to enter and exit positions based on your strategy logic. Discover whether your DCA approach works better with weekly or monthly intervals. Find the RSI levels that generate the highest win rates for your target assets.
Test stop-loss levels, position sizing and portfolio allocation rules against past market crashes and volatility spikes. See how different risk controls would have protected capital during major drawdowns. Adjust parameters based on historical stress testing results.
Every approach has market conditions where it struggles. Testing identifies these weak periods so you can pause strategies or adjust parameters when similar conditions arise. Trend-following systems might fail in choppy markets while mean reversion struggles during strong trends.
Seeing your strategy succeed across multiple market cycles builds the psychological confidence needed for live trading. When your live bot hits a drawdown period, you know from testing whether this is normal behavior or a signal to intervene.
Use backtest results to refine your approach through systematic iteration. Test small parameter changes, add filters to reduce false signals, or combine multiple strategies for better risk-adjusted returns. Each iteration teaches you more about market behavior.
Once testing validates your approach, deploy the same logic to live markets through automated execution. Your historical testing provides realistic expectations for future performance while identifying the market conditions where your edge is strongest.
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