QuantSandbox: integrated quant research, in production.

Quant research, the way most analysts and PMs do it day-to-day, is fragmented. You pull market data in one notebook, fit a GARCH model in another, run portfolio optimization in a third, price options in a fourth, and string together a backtest by hand. The work is real; the workflow is brittle. Tabs proliferate, results stop reconciling, and the feedback loop from idea to validated strategy takes weeks longer than it should.

quantsandbox.orientedplatforms.com is the alternative we've shipped: one integrated application that consolidates the research-to-validation loop into a single session.

The shape of the application

Eleven research modules, organized around the phases of a quant workflow rather than around the underlying libraries:

• Market data — price charts, technical indicators, correlation matrices, custom universes.
• Portfolio optimization — Markowitz mean-variance, Black-Litterman, risk parity. Each method exposes its actual decision points (covariance estimation, view construction, risk budgets) rather than hiding them behind a one-click solver.
• Risk analytics — VaR, CVaR, Sharpe / Sortino, drawdown analysis, tail-risk metrics. Computed against the portfolios the user has actually constructed, not against canned demos.
• Options pricing — Black-Scholes, the Greeks, implied-volatility surfaces, binomial / lattice methods, Monte Carlo. The implied-vol surface in particular is the kind of artifact that's easy to generate badly; the calibration knobs are exposed.
• Time-series analysis — ARIMA, GARCH, cointegration tests, stationarity diagnostics, autocorrelation. The methods you actually need before throwing a model at price data.
• Strategy workshop and backtest studio — see The backtest engine below.
• Futures terminal — futures-specific workflows for the quant subset that lives there.

Plus modules covering factor analysis, pairs trading and hedging diagnostics, and the cross-cutting statistical-testing surface.

The point isn't the count. It's that one user, with one session and one mental model, can move from raw market data through hypothesis and modeling to a backtested strategy without changing tools — and without losing context every time they switch.

Methods exposed

The library underneath is intentionally opinionated about what gets exposed. Around thirty quantitative statistical tests are accessible inside the app (stationarity, cointegration, normality, autocorrelation, regime change, distribution-fit, and so on) — not because users always need them, but because skipping them is the failure mode that produces spurious results.

For portfolio construction:

For options:

• Black-Scholes for European pricing baselines.
• Binomial / lattice methods for American exercise.
• Monte Carlo for path-dependent or exotic structures.
• Implied-volatility surface construction with explicit smile / term-structure handling.
• The Greeks (Δ, Γ, Θ, Vega, Rho) on any structure the user prices.

For risk and validation: historical, parametric, and Monte Carlo VaR; CVaR; Sharpe and Sortino; max drawdown and drawdown duration; tail-risk indicators.

The backtest engine

The piece that takes the longest to build correctly, and the one most quant tools handle worst.

QuantSandbox uses an event-driven simulation: for each strategy under test, the engine processes a stream of market events in chronological order, and the strategy reacts to each one as it would in production. No look-ahead. No vectorized batch tricks that accidentally use Friday's close to compute Monday's signal.

On top of that:

• Walk-forward validation. Fit the strategy on a rolling window, evaluate on the next out-of-sample window, advance. Repeat across the full history. The result is a series of out-of-sample evaluations rather than a single in-sample fit — which is the only way to estimate how a strategy will degrade in live conditions.
• Execution cost modeling. Slippage, commissions, market impact priced realistically. Backtests that ignore these consistently overstate returns; backtests that include them are usually the moment a "promising" strategy stops being promising.
• Pre-built strategy templates — momentum, mean reversion, pairs, factor, an options overlay, and a buy-and-hold benchmark — each editable and serving as a starting point rather than a black box.

The metrics surface (total return, Sharpe, max drawdown, win rate, exposure profile) reads at a glance and reconciles against the underlying trade log. The two should always agree; the user can verify they do.

Asset class coverage

Equities (demonstrated against FAANG and broader US universes by default), futures contracts, and options. Strategies can compose across these — an equity portfolio with options overlays, a pairs trade hedged with futures — inside a single workspace.

Who QuantSandbox is for

The financially-literate user who would otherwise run this analysis across seven tools and lose half their time to plumbing. Concretely: quant researchers, factor PMs, students of systematic strategies, and engineers evaluating or building quant systems for their own desks.

It is not a retail-broker dashboard, not a black-box "AI picks stocks" gimmick, and not a closed platform. The methods are exposed. The decision points are visible. The results reconcile.

Why we built it

QuantSandbox started as the in-house tool we wanted: somewhere to evaluate methods quickly without rewriting the same scaffolding — data loaders, statistical tests, backtest harness — for every new project. Once it became real software, putting it online for any financially-literate user to access made more sense than keeping it private.

It is the canonical example of the kind of work Oriented Platforms ships: data and intelligence, integrated and operationalized, for users with stakes.

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