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Case Study — Fintech / Algorithmic Trading
We Built an Algorithmic Backtesting Engine That Processes 10 Years of Data in Seconds
A private trading firm was manually backtesting strategies on TradingView. We replaced hundreds of hours of manual work with a custom engine that validates any SMC strategy in under 2.5 seconds.
The command center: real-time equity curve, session filters, and live trade signals across multiple currency pairs.
66.7%
Win Rate
5.32
Profit Factor
< 2.5s
Backtest Speed
100M+
Ticks Processed
The Dashboard
A Complete Backtesting Command Center
Traders configure every parameter of their strategy — from entry conditions and kill zones to risk management — then get instant, data-backed results.
1
Strategy Builder
Define entry conditions using SMC concepts — Liquidity Sweeps, Market Structure Shifts, Fair Value Gaps, Order Blocks, and more.
2
Granular Parameters
Set timeframes, risk-reward targets, data periods, account settings, slippage, and session-specific kill zones.
3
Model Summary & Execution
Review the complete model configuration, then hit "Run Backtest" for instant results.
4
Results Dashboard
Full analytics — equity curve, session performance breakdown, win/loss streaks, max drawdown, and detailed PnL metrics.
The Challenge
Our client was manually backtesting Smart Money Concepts (SMC) strategies on TradingView. This process was slow, prone to human error, and impossible to scale. They needed a way to scientifically validate their edge across multiple currency pairs and timeframes without spending hundreds of hours on manual replay.
The Solution
We engineered a custom Python-based backtesting kernel wrapped in a Next.js dashboard. The system ingests tick-level data, simulates realistic orderbook conditions (slippage, spread), and executes specific SMC logic rules — Order Blocks, Fair Value Gaps, Liquidity Sweeps, Market Structure Shifts, and Displacement.
The frontend provides an interactive command center where traders adjust every parameter — risk percentage, stop loss buffer, session kill zones, timeframes — and instantly visualize the equity curve and full performance breakdown.
Built for Real Trading
- Realistic slippage and commission modeling — no inflated backtests
- Session-aware execution (London, New York, Asia kill zones)
- Multi-asset support — Forex, Gold (XAUUSD), Bitcoin, DXY
- Export to JSON and Pine Script for live deployment
Key Technologies
Next.jsPython / PandasPostgreSQLTailwind CSSRechartsCeleryRedis
Project Outcomes
- Eliminated manual testing completely
- New strategies validated in minutes, not days
- Identified optimal London + NY session pairs
- Deployed to live trading server
- 66.7% win rate with 5.32 profit factor confirmed
"We went from spending weekends manually replaying charts to validating any strategy in seconds. This changed how we trade."
— Client, Private Trading Firm
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We specialize in building high-performance fintech applications — backtesting engines, analytics dashboards, and automation tools for traders and funds.