ORVINUSBacktesting & Risk Modeling
A backtest that ignores costs and slippage is fiction. We build validation engines that tell you the truth before capital is at stake.
Backtesting and risk modeling is the validation layer of a trading system: simulating a strategy against historical market data under realistic execution assumptions — commissions, slippage, spread, latency — and modeling the risks it carries before real capital does. Done honestly, it is the difference between deploying a strategy and deploying a hope.
Most backtests are optimistic by construction: look-ahead bias, survivorship bias, fills at prices no one would have gotten, and parameters tuned until history looks beautiful. We engineer against every one of these failure modes, because a flattering backtest is worse than none — it funds conviction the market won't honor.
Risk modeling extends past a single strategy: position-level limits, portfolio-level exposure and drawdown controls, and optimization that allocates capital across strategies and instruments. Our institutional trading deployment pairs 80% strategy-execution accuracy with robust risk modeling — precision and protection engineered together, which is the only way either one holds.
Backtesting and risk modeling engines validate trading strategies against historical market data with realistic cost, slippage, and fill assumptions, and model drawdown, exposure, and position-sizing risk before live deployment. ORVINUS provides backtesting engines, walk-forward validation, code-enforced risk frameworks, and cost-aware portfolio optimization for quantitative trading operations.
Backtesting & Risk Modeling Engines
We build backtesting engines that simulate how a strategy would actually have traded: event-driven replay of historical data, order fills modeled with slippage and spread, commissions and fees per instrument, and latency between signal and execution. Data quality is treated as an engineering problem — cleaning, adjusting for splits and dividends, and handling gaps — because a backtest is only as honest as the data underneath it. Walk-forward analysis and out-of-sample testing are standard, so parameters that merely fit history get exposed before they lose money.
Risk modeling runs alongside: maximum drawdown analysis, value-at-risk estimation, exposure limits per instrument and sector, position sizing driven by volatility, and stress tests against historical shocks — the sessions where correlations spike and liquidity vanishes. The output is a risk framework wired into the strategy itself: limits enforced in code, not in policy documents, so the system cannot take the trade its own risk model forbids. Every assumption is documented, because a risk number you can't interrogate is not a risk number.
Portfolio Optimization
Portfolio optimization decides how capital divides across strategies and instruments: allocation that targets return per unit of risk rather than raw return. We implement the classical machinery — mean-variance optimization, risk parity, Kelly-fraction sizing — with the practical corrections that make it usable in production: robust covariance estimation that survives noisy short histories, turnover and transaction-cost penalties inside the objective, and hard constraints for position limits, sector exposure, and liquidity. An optimizer without those corrections produces elegant allocations that cost more to hold than they earn.
Optimization is validated the same way strategies are: allocation policies backtested with rebalancing costs included, sensitivity analysis on estimation windows, and honest comparison against naive baselines like equal weighting — because an optimizer that can't beat 1/N after costs shouldn't ship. The result is an allocation engine that recomputes on your schedule, respects your constraints, and hands the execution layer exact target weights rather than a research notebook someone has to interpret.
Built With
The technologies we reach for on this work — and why we use each one.
See what we use every tool for in the full technology stack.
What You Get
Backtesting engine
Event-driven historical simulation with realistic cost, slippage, and fill modeling — runnable by your team on new ideas.
Risk framework
Drawdown, exposure, and position-sizing limits enforced in code, with stress tests against historical shocks.
Validation report
Walk-forward and out-of-sample results with every assumption documented — the evidence a funding decision deserves.
Portfolio optimizer
An allocation engine with cost-aware optimization and constraints, producing target weights on your rebalancing schedule.
Other Algorithmic Trading Specializations
Trading Strategy Development
Trading ideas engineered into precise, testable strategy logic — indicator systems, multi-timeframe rules, and pattern recognition.
Explore Strategy DevelopmentAutomated Execution Systems
From signal to filled order without a human in the loop — low-latency execution with failsafes, monitoring, and 24/7 operation.
Explore Automated ExecutionOptions & Derivatives Pricing
Pricing models, Greeks, and volatility analytics for options and derivatives — engineered for production, not the textbook.
Explore Derivatives PricingTrading Analytics & Reporting
Performance dashboards, P&L tracking, and real-time charting — the instrumentation a serious trading operation runs on.
Explore Trading AnalyticsWhere This Work Lands
Common Questions
Why do live results differ from backtest results?
Usually because the backtest was too kind: fills at mid-price, zero slippage, ignored fees, or subtle look-ahead in the data. We model costs and slippage explicitly and validate out-of-sample precisely to shrink that gap. Some difference always remains — markets change — which is why staged live rollout with monitoring follows every backtest.
How do you prevent overfitting?
Discipline over optimism: strict train/test separation, walk-forward analysis, limits on parameter count, and sensitivity analysis showing whether performance survives small parameter changes. We treat any equity curve that looks too smooth with suspicion. If a strategy only works with one exact setting on one exact period, we tell you before your capital finds out.
What historical data do you use?
Whatever fits the market and strategy: exchange or vendor OHLC and tick data, adjusted for splits and dividends, with quality checks for gaps and bad prints before anything runs. If you already license data we build on it; otherwise we help select a source appropriate to your instruments, timeframe, and budget.
Can you backtest our existing strategy?
Yes. We take strategy logic in whatever form it exists — code, spreadsheet, or written rules — encode it precisely, and run it through the full validation stack under NDA. The engagement starts with a free discovery call to scope the strategy, the data requirements, and the questions you need the backtest to answer.
Ready for Backtesting & Risk?
Free discovery call — we scope the work, name the trade-offs, and respond within 24 hours.