Signal research and algorithmic strategy development held to institutional standards — where every result survives out-of-sample testing before it ever reaches capital.
Currently accepting new engagementsMost quantitative ideas that look profitable on paper are artifacts of the data — a lucky window, a leaked variable, or a model tuned until history agreed with it. We build systematic strategies the other way around: starting from an economic rationale, testing it against the harshest evidence we can construct, and discarding what does not hold up. The goal is not a backtest that dazzles, but a strategy whose behavior you can trust when it is deployed on capital you are accountable for.
We work across equities, futures, fixed income, and alternative datasets, translating raw signals into rules a trading or investment team can actually run, monitor, and defend to a risk committee. Every engagement is documented to a standard that survives internal review and external scrutiny alike.
A documented set of candidate signals with economic rationale, statistical significance testing, decay profiles, and correlation to existing factors — so you know not just whether a signal works, but why and for how long.
Fully specified entry, sizing, and exit rules with out-of-sample and walk-forward results, transaction-cost and slippage assumptions made explicit, and capacity estimates for the strategy at your intended scale.
Deflated Sharpe ratios, multiple-testing corrections, parameter-sensitivity maps, and regime analysis that quantify how much of the observed edge is real versus data-mined — the evidence a risk committee needs to sign off.
A reproducible, version-controlled backtesting pipeline with clean data lineage, deterministic runs, and a live-monitoring harness that flags when realized performance drifts from research expectations.
We start from an economic or behavioral rationale and the decision it is meant to inform, then define success and failure criteria before touching the data — so the research cannot quietly move the goalposts.
We build features from clean, point-in-time data, guard against look-ahead and survivorship bias, and evaluate each signal for statistical significance, stability, and independence from what you already trade.
Every promising result is stress-tested with out-of-sample and walk-forward analysis, multiple-testing adjustments, and realistic cost assumptions — deliberately trying to break the strategy before the market does.
We deliver documented rules, reproducible code, and a monitoring plan, then support the transition to live trading with clear thresholds for when a strategy should be reviewed, scaled, or retired.
Start with a free discovery call — a quick chat to pinpoint where AI can create value in your business and map the smartest first step.