Backtesting & analysis for algo trading strategies
Don't risk capital on untested logic. We pressure-test your strategy against years of historical data with realistic slippage, brokerage fees, and walk-forward validation so you know exactly how it behaves before going live.
What makes our backtests different
Most backtests lie because they ignore friction. We model the exact conditions your strategy will face in production and flag every assumption that could break your edge.
Realistic friction modelling
Slippage scaled to volatility, maker/taker fees matching your broker tier, and STT/stamp duty for Indian markets.
Walk-forward validation
In-sample/out-of-sample splits prevent overfitting. We prove your edge holds on unseen data, not just the training set.
Monte Carlo stress tests
We randomise trade order and slippage to show worst-case drawdowns so you size capital with eyes wide open.
Multi-venue data coverage
NSE, BSE, MCX tick-level and minute-bar data across equities, options, futures, and commodities.
Corporate action handling
Splits, bonuses, and dividends adjusted in the historical dataset so your signals aren't distorted by data artefacts.
Regime-aware analysis
We tag performance by market regime (trending, choppy, high-vol) so you know when your strategy thrives and when to stand down.
Sample backtest metrics
Every report includes these metrics and more. Below is an anonymised snapshot from a recent Nifty options strategy engagement.
| Metric | Value | Notes |
|---|---|---|
| Total return | 42.3% | 2 years |
| Max drawdown | -8.7% | Worst peak-to-trough |
| Sharpe ratio | 1.84 | Annualised |
| Win rate | 61% | 573 trades |
| Profit factor | 1.92 | Gross P / Gross L |
| Avg slippage | 0.03% | Per trade |
How a backtesting engagement works
Share your strategy logic
Send us your strategy rules entry/exit conditions, position sizing, instruments, and timeframes. We accept pseudocode, Pine Script, Python, or even a plain-English description.
We configure the backtest environment
Our team sets up the data feed (NSE, BSE, MCX tick/minute/daily data), configures realistic slippage and brokerage fee models matching your broker tier, and defines the test window.
Run backtest with walk-forward validation
We execute the backtest across in-sample and out-of-sample periods using walk-forward analysis to detect overfitting. Monte Carlo simulations stress-test edge durability.
Deliver detailed results report
You receive a comprehensive report with equity curve, max drawdown, Sharpe ratio, win rate, profit factor, monthly returns heatmap, and per-trade logs with slippage analysis.
Review and iterate
We walk you through the results on a call, highlight weak points, and suggest parameter adjustments or structural improvements. Iterations are included until you're satisfied.
Turnaround time
- →Simple single-instrument backtest: 3–5 business days
- →Multi-leg options strategy with Monte Carlo: 7–10 business days
- →Portfolio-level analysis with regime tagging: 10–14 business days
Pricing
Backtesting engagements start at a fixed project fee based on strategy complexity and data requirements. Iterative refinements are included you don't pay extra for parameter tuning rounds. Contact us for a quote tailored to your strategy.
Learn how we can help.
Talk to our team about your project or product idea. We'll show
you how Arkalogi can make it real.