Quantitative trading software development, from backtest to risk desk
We build backtesting engines, options analytics and risk tooling your researchers can trust, with assumptions you can inspect and results you can reproduce.
A backtest is only as honest as its fills, its data and its assumptions. Look-ahead bias, survivorship gaps, mid-price fills on illiquid strikes and ignored early exercise all produce equity curves that never appear in a live account. Aurion Labs builds quantitative trading software that makes those assumptions explicit: configurable slippage and fill models, point-in-time data, and every simulated trade available to step through. We hold our own research to the same standard, from LEAN studies of long-dated SPX put structures to a desktop options backtester with surface models and a rules engine.
Clients come to us for three kinds of work. Research teams want a backtesting engine development partner who can model their actual instruments. Options desks want options analytics with surfaces, higher-order Greeks and scenario grids. Risk owners want risk analytics that update during the session, not in tomorrow's report. We write in Python, C# and .NET, store results in DuckDB or Postgres, and connect the output to your trading platform or market-data pipeline when the research is ready for production.
What we deliver
- Point-in-time datasets that avoid look-ahead and survivorship bias
- Bid-ask aware option fills instead of mid-price assumptions
- Implied-volatility solvers that stay stable in the deep wings
- Volatility surface fitting across strikes and expiries
- Higher-order Greeks including vanna, charm, vomma and speed
- Rules engines for delta, DTE and P&L based exits
- Walk-forward and out-of-sample validation of parameter choices
- Columnar result storage in DuckDB for fast slicing
- Reproducible runs with versioned code, data and configuration
- Earnings-event backtests for income and volatility strategies
Quant Analytics, built for production.
Backtesting engines
Event-driven engines that replay historical bars, ticks or full option chains through your strategy logic, with pluggable fill, slippage and commission models and a step-through mode for auditing individual trades.
Options analytics
Pricing and analytics for options books: Black-Scholes and Black-76, implied-volatility solvers, smoothed volatility surfaces, higher-order Greeks such as vanna and charm, and scenario heatmaps across spot and volatility.
Risk analytics
Portfolio risk views that aggregate positions across accounts: beta-weighted delta, vega by expiry bucket, stress tests on gap moves and volatility shocks, and limit checks that flag breaches as they happen.
Screeners and scan frameworks
Configurable scans across option chains and equities that rank candidates by expected value, probability of profit and return on capital, with earnings-aware filters and backtests of each scan's historical behaviour.
Performance statistics
Sharpe, Sortino, drawdown and rolling return statistics computed consistently across lookbacks and net of the risk-free rate, with benchmark comparison against the S&P 500, Nasdaq, Dow and Russell 2000.
LEAN and research tooling
QuantConnect LEAN algorithms, custom data adapters and research notebooks, with parameter sweeps run as walk-forward tests rather than one in-sample optimisation, so you see how a rule holds up out of sample.
How we deliver it.
Define the hypothesis
We pin down exactly what is being tested, on which instruments, at what frequency and with which costs. Vague research questions produce vague software, so this specification comes before any engine code.
Audit the data
We source and inspect the history you need from vendors such as Polygon or ThetaData, checking for gaps, splits, stale quotes and corporate actions before a single result is trusted.
Build and verify the models
Pricing models, fill logic and strategy rules are written as tested modules and checked against published reference values and hand-calculated cases before we run them across full histories.
Validate and hand over
Results are stress-tested out of sample and across market regimes, then delivered with notebooks, reports and documented assumptions. If the research graduates, we wire it into execution or your platform.
Built by us, not borrowed.
Relevant systems from the 30 we have engineered. Product and client names are withheld.
Options strategy backtester
Quant and analyticsDesktop engine with Black-Scholes, Black-76, implied-volatility and surface models, higher-order Greeks, scenario heatmaps, a step-through backtester and a rules engine for delta, DTE and P&L exits with combo order routing.
Options income screener
Quant and analyticsA scan framework for income strategies, with 80 scans specified across five strategy families, expected-value and probability maths, P&L profiles, earnings backtests and an optimiser.
Long-dated SPX structure research
Quant and analyticsLEAN backtests of a rolling four-leg SPX put structure with staggered entries, position caps and DTE-based exits.
Multi-period Sharpe and Sortino indicator
Quant and analyticsAnnualised risk-adjusted return across eight lookbacks from 30 days to full history, computed on daily closes net of the risk-free rate.
Portfolio benchmark backtester
Quant and analyticsCompares a portfolio's monthly returns against the S&P 500, Dow, Nasdaq and Russell 2000.
Chart pattern recognition engine
Quant and analyticsDetects chart patterns, candlestick formations and Fibonacci levels, then fans alerts out to email, Telegram and Discord.
The difference is operating experience.
Tools we built for our own lab
Our options backtester, income screener and LEAN studies exist because our own lab needed them. We know where backtests mislead because we have caught our own doing it, and we design engines to expose that.
Models you can inspect
We do not ship black boxes. Pricing, fill and risk assumptions are documented, configurable and unit-tested against reference values, so your quants can challenge them and your risk team can sign them off.
Research that reaches production
Because we also build execution, data pipelines and DevOps, a validated model does not stall at the notebook stage. The same team can carry it into a live, monitored service without a rewrite by strangers.
Technology we work with
Quant Analytics questions, answered.
Still deciding? A 30-minute call usually answers the rest.
What affects the cost of quantitative trading software?
How long does a backtesting engine take to build?
Who owns the models and the code?
Can you work with our existing data vendors and brokers?
Will your software tell us which strategies to trade?
Do you support quant tools after delivery?
Often built alongside.
Trading Platforms
We design, build and operate custom trading software: browser and desktop terminals, order management and broker integrations that hold up when the market moves fast.
ExploreData Engineering
We design and run the pipelines that move prices, options chains and fundamentals from vendors and brokers into the systems that trade, research and report on them.
ExploreAlgorithmic Trading
We turn trading rules into MT5 Expert Advisors, TradingView indicators, trade copiers and execution services that behave the same on a news spike as they did in the tester.
ExploreStart your quant analytics project.
Tell us what you are building. You will get a written scope, a clear estimate and a working build early.
Prefer email or phone? hello@aurionlabs.io · +44 7832 617626