Every tool and guide here comes out of an actual research workflow, not a content brief. This page is that workflow, end to end — plus who runs the site and how the material is made and checked.
A systematic-trading developer with a software background. I build and validate expert advisors on MetaTrader (MQL4/MQL5) and research agentic trading architectures, and I apply Marcos López de Prado's validation methodology — Deflated Sharpe Ratio, Probability of Backtest Overfitting, purged and embargoed walk-forward, and combinatorially purged cross-validation — to everything I keep. The tools on this site are the ones I reach for in my own work; the guides are what I learned the hard way.
I am not a financial adviser, and nothing here is a recommendation. I write about how to test whether a strategy is real — the engineering and statistics of validation — not about what to trade.
Quant Lab Tools is a set of diagnostics and explanations for people who build their own systematic strategies. It exists because the methods that actually separate a real edge from a lucky backtest live in dense papers and code libraries, and almost nowhere in a form you can just use.
It is calculators that implement published methods faithfully, and guides that explain them in plain language with real examples. It is not a signal service, a course funnel, or a place that tells you a number will go up. There are no trade ideas here and there never will be.
This is the order I move through, and the order the tools are meant to be used in. The sequence matters: each step exists to stop a specific way a backtest lies to you.
Before any search, I write down why an edge should exist — what inefficiency, what behaviour, on what instrument and timeframe. A reason invented after seeing a winner is a story, not a hypothesis, and it is the root of most overfitting.
Spread, commission and slippage are modelled before I read any performance number. I treat transaction-cost modelling as the single most critical element of a backtest, because I have watched a clearly profitable gross result flip to a net loss with nothing else changed.
Every variant, threshold and discarded idea is counted. That count feeds the Deflated Sharpe Ratio, because the honesty of a result depends entirely on how many tries stand behind it.
Validation uses purged, embargoed walk-forward — training samples whose outcomes overlap the test window are removed, with a buffer after. A holdout reused many times is no longer out-of-sample, so I treat it as a spending budget.
Where it matters, CPCV builds many backtest paths from combinations of purged folds, giving a distribution of out-of-sample outcomes rather than one. A genuine edge looks similar across paths; an overfit one falls apart on most.
A candidate has to clear the luck line — the Sharpe an unskilled strategy would reach given the trial count — with a Deflated Sharpe Ratio at or above 0.95. Below that, the result is labelled provisional, not promoted. PBO checks whether the selection process itself is picking noise.
Nothing touches live capital on the strength of a backtest. Strategies run forward on paper first, and automation is never given decision authority over live money until it has been validated forward — and even then, conservatively. The goal of the discipline is survival, not a leaderboard.
I think the honest version of this matters, so here it is:
The tools are meant to be used together. If you have a backtest in hand, the natural entry point is the overfitting check.