Key Benefits
If diverging, re-optimize or pause strategy.</p> </div> </div> </section> </div>.
How It Works
Test Before You Trade
Would your strategy have made money last year? WinBee's backtesting engine lets you test any trading strategy against historical data before risking real money.
See exactly how your DCA bot, Grid strategy, or AI-enhanced approach would have performed during bull markets, bear markets, and flash crashes.
📊 Real Data, Real Results: Backtest using actual exchange data from 2017-2024 across 50+ cryptocurrencies.
No cherry-picked results.
Why Backtest?
🔬 Validate Your Idea
Think DCA into BTC every week would beat lump sum? Test it! Backtest shows if your hypothesis holds up against real market conditions.
Example: User tested weekly DCA vs monthly DCA 2020-2024.
Weekly won by 12% due to better crash averaging.
💡 Optimize Parameters
Should Grid bot have 10 or 20 levels? 5% spacing or 3%? Test all combinations to find optimal settings.
Example: Tested 50 Grid configurations.
Winner: 15 levels, 4% spacing = 47% annual return.
📉 Stress Test for Crashes
See how strategy performs during COVID crash (March 2020), Terra collapse (May 2022), FTX meltdown (Nov 2022).
Key Insight: If strategy survives worst crashes and still profitable, it's robust.
🎯 Build Confidence
Knowing your strategy made 200% in backtest gives psychological edge to stick with it during drawdowns.
Psychology: Backtested traders hold through -20% dips.
Non-tested panic sell.
Backtesting Process
Step 1: Configure Strategy
Set up your strategy parameters exactly as you'd run live:
- ✓ Strategy Type: DCA, Grid, AI-Enhanced, Custom
- ✓ Asset & Pair: BTC/USDT, ETH/USDT, etc.
- ✓ Trade Frequency: Daily, weekly, hourly
- ✓ Investment Amount: $100/week, $1000 lump sum, etc.
- ✓ Risk Controls: Stop-loss %, take-profit targets, position sizing
Step 2: Select Time Period
Choose historical date range to test against:
- ✓ Full History: 2017-2024 (7 years, includes all market cycles)
- ✓ Bull Market Only: 2020-2021 (see peak performance)
- ✓ Bear Market Only: 2022 (test worst-case scenario)
- ✓ Custom Range: Pick any start/end date
- ✓ Rolling Windows: Test every 365-day period and average results
Step 3: Run Simulation
Engine replays historical data tick-by-tick:
- ✓ Price Data: Uses actual OHLCV (Open, High, Low, Close, Volume) from exchanges
- ✓ Slippage Modeling: Simulates realistic fill prices (not perfect theoretical prices)
- ✓ Fee Inclusion: Deducts trading fees (0.1% maker, 0.1% taker)
- ✓ Order Execution: Simulates market/limit orders as they would've filled historically
- ✓ Rebalancing: Accounts for compound growth (profits reinvested)
Step 4: Analyze Results
Comprehensive performance report with 20+ metrics:
- ✓ Total Return: Percentage gain/loss over period
- ✓ Annual Return (CAGR): Compound annual growth rate
- ✓ Max Drawdown: Worst peak-to-trough decline
- ✓ Win Rate: Percentage of profitable trades
- ✓ Sharpe Ratio: Risk-adjusted returns
- ✓ Trade Log: Every single buy/sell with prices and timestamps
Advanced Features
🔄 Monte Carlo Simulation
Run 1000+ simulations with randomized entry points to see probability distribution of outcomes.
Shows: "85% chance of positive return, 15% chance of loss" instead of single result.
🎛️ Parameter Optimization
Automatically test 100s of parameter combinations to find best settings.
Example: Test DCA amounts $50-$500 in $10 increments, pick winner.
📊 Walk-Forward Testing
Optimize on past data, validate on future data to avoid overfitting.
Train on 2020-2022, test on 2023-2024 (prevents curve-fitting bias).
🔀 Multi-Asset Backtesting
Test portfolio strategies across BTC, ETH, and 10 altcoins simultaneously.
See diversification benefits vs single-asset concentration.
Sample Backtest Results
Example: Weekly DCA into BTC (2020-2024)
Key Insights
- ✓ Total invested: $10,400 ($50/week × 208 weeks)
- ✓ Final value: $34,700 (+247% vs lump sum +189%)
- ✓ Survived COVID crash, Terra collapse, FTX implosion
- ✓ Best year: 2021 (+312%), Worst year: 2022 (-64%)
- ✓ DCA outperformed lump sum by buying more during crashes
Important Warnings
⚠️ Backtesting Limitations
-
1.
Past Performance ≠ Future Results: Just because DCA made 200% in 2020-2024 doesn't guarantee it will in 2025-2029.
Markets change.
-
2.
Overfitting Risk: Optimizing too much finds settings that worked in past but fail in future.
Keep strategies simple.
-
3.
Slippage Underestimation: Backtest assumes you got your order filled.
Real trading has failed orders and worse fill prices during volatility.
-
4.
Survivorship Bias: Testing only BTC/ETH ignores dead coins.
Many 2017 altcoins went to zero (not in backtest data).
Use backtesting to validate ideas, not as guarantee of profit.
Always start with small real money before scaling up.
From Backtest to Live Trading
Once satisfied with backtest results:
Paper Trade First
Run strategy live with fake money for 30 days.
Verify backtest results hold in real-time.
Start Small
Deploy with 10% of intended capital.
If profitable after 90 days, scale up gradually.
Monitor & Adjust
Compare live results vs backtest monthly.
If diverging, re-optimize or pause strategy.
Perfect For
Busy Professionals
Trade without watching charts 24/7
Active Traders
Enhance strategies with AI insights
Beginners
Learn from AI-powered automation