Validate algorithmic strategies with high-throughput simulation pipelines capable of replaying years of tick-by-tick order books. Engineered with realistic slippage modeling, zero lookahead bias, and institutional-grade risk metrics.
Move past simplistic open-high-low-close (OHLC) assumptions. I build high-fidelity backtesting systems that mirror exchange matching engines down to microsecond execution queues.
Simulates actual order book state, partial volume fills, latency queues, and cancellation latency so your live PnL matches backtest projections.
Ultra-fast storage architectures using ClickHouse or compressed Parquet formats, processing 100M+ trade records in seconds across multi-core CPU clusters.
Walk-forward cross-validation prevents overfitting, while Monte Carlo resampling evaluates probability of ruin, tail-risk VaR, and max consecutive drawdown.
Stop deploying strategies based on flawed retail backtesting tools. Get bespoke simulation software with verified mathematical integrity.
Asset managers needing bespoke multi-asset simulation environments with custom borrow fee schedules, portfolio rebalancing rules, and institutional investor tear-sheets.
Desks developing statistical arbitrage, momentum, or liquidity-provision strategies needing microsecond-accurate fill modeling against historical tick data.
Traders executing high-frequency signals across fragmented liquidity pools who require exchange-specific fee and latency modeling.
Complete, production-tested software packages with automated test suites verifying mathematical accuracy.
Milestone-based delivery with intermediate backtest benchmarks delivered at every phase.
We analyze your execution rules, entry/exit criteria, required historical tick granularity (L1 top-of-book vs. L2/L3 full order book), and transaction fee structure. You receive an architecture plan and fixed delivery price.
I build the core simulation state machine and historical tick data parsers. Millions of market events are processed in seconds with zero lookahead bias or survivorship distortion.
We calibrate realistic exchange latency, order fill probabilities, dynamic borrow costs for short legs, and multi-thread parameter sweep algorithms across CPU cores.
Complete statistical validation, Monte Carlo simulation suites, clean modular code packaging, and a live deployment walkthrough with your quantitative research team.
Insights on backtesting speed, tick data storage architectures, and live execution bridge readiness.
Vectorized backtesters (e.g., pandas/NumPy matrix calculations) evaluate entire price arrays simultaneously, making them ultra-fast for preliminary signal discovery. However, they cannot model real-world trading realities like order queuing, intraday limit fill delays, partial fills, or path-dependent trailing stops. An event-driven backtester processes tick-by-tick market state sequentially, accurately simulating order lifecycle events, exchange latency, and real-time portfolio margin without lookahead bias.
Our simulation engines strictly enforce temporal isolation: signals generated at timestamp T can only observe information available up to T, with fills scheduled at T + simulated latency. To combat survivorship bias, our data pipelines ingest point-in-time constituent lists and delisted asset databases so failed instruments are included in historical universe queries.
Yes. When building event-driven systems, we structure the core strategy class using an interface-agnostic event bus. The exact same signal generation and risk management code executed during the backtest runs against live WebSocket exchange feeds without rewriting mathematical logic.
You backtest a strategy on today's S&P 500 components going back ten years, and the results look outstanding. What you probably didn't account for is that dozens of companies in that index ten years ago went bankrupt, got delisted, or were quietly dropped for underperforming.
You spend weeks building a strategy, run it through years of historical data, and the equity curve looks perfect: steady climb, small drawdowns, a win rate north of 70%. Then you go live and the results don't match at all.
Discover complementary portfolio management systems, personal finance apps, and free quantitative tools.
Real-time multi-exchange balance aggregation, NAV calculations, and automated risk monitors.
Multi-currency banking dashboards, automated transaction categorization, and budget forecasting.
Rapid turnaround trading journal templates and financial monitoring boilerplates.
Simulate dollar-cost averaging strategies with real Binance historical prices and equity curves.
Engineering guides on low-latency trading engines, tick data parsing, and order books.
Speak directly with the engineer to discuss your execution logic, data feeds, and timeline.
