AI-native quantitative research infrastructure

Turn data and evidence into validated decisions.

QEV is an operating system for quantitative research, model validation, risk control, and controlled execution — with the whole decision kept on the record, from evidence to live behaviour.

Starting with quantitative finance. Research a strategy, test its assumptions, simulate live behaviour under costs, and deploy through controlled broker connections — in one connected workflow.

Request early access → Read the method

STRATEGY RECORD · QEV-0417 · rev.6

“20-day cross-sectional momentum, liquid US equities”

Paper → Live·small

Validation batteryResultStatus

Out-of-sample2015–2024Pass

Transaction costsedge survivesPass

Slippage modelappliedPass

Look-aheadnone foundPass

Multiple-testingde-flatteredPass

Drawdown / regimewithin limitPass

dataset eod.us.liquid@a3f19c · code v6 · 4e8b1d0 · gate human approval required · next live · small

§ 01The problem

Exhibit 1 A strategy scattered across tools loses the one thing that makes it defensible: its record.

A decision is more than code.
Today its evidence goes missing.

Research begins in one notebook, backtests run in another service, execution lives in a broker-specific script. Assumptions vanish, results can't be reproduced, and live behaviour drifts away from the evidence that justified it.

Today — disconnected desk

notebookdata vendorbacktester paper servicebroker APIcron scripts monitoringrisk sheetAI assistant

With QEV — one bound record

01 Evidence & assumptions
02 Model & code version
03 Validation results
04 Live behaviour & outcomes
05 The full decision history

§ 02The method

Exhibit 2 The name is the loop: Question, Evaluate, Validate — then execute under control.

One continuous loop, from question to controlled execution.

Question

Ask precisely

State the hypothesis, the data it needs, and what would prove it wrong.

Evaluate

Weigh evidence

Test the model against history, costs, and the ways it could be fooling you.

Validate

Earn the decision

Confirm it out-of-sample and under stress before any capital is committed.

Discover

AI-assisted research over data, papers, news, and your own datasets.

Design

Turn ideas into explicit hypotheses, models, and test plans.

Validate

Historical, out-of-sample, walk-forward, cost, and stress tests.

Simulate

Operate against live data in controlled paper environments.

Execute

Deploy approved strategies through broker-neutral adapters.

Monitor

Track drift, exposure, failures, and the evidence behind each decision.

§ 03The proving ground

Note Markets are chosen first because results are measurable and mistakes are expensive.

Quant is the proving ground.
Decisions are the platform.

Quantitative finance is the hardest honest test for a system like this: noisy data, measurable outcomes, strict risk constraints, and relentless feedback. If the record holds here, it holds.

The same research, validation, and controlled-execution framework is built to extend to other evidence-intensive decisions later — forecasting, asset evaluation, operational calls. The homepage stays concrete about markets; the architecture is deliberately broader.

§ 04The platform

Table 1 Five layers. QEV sits above the engines and brokers, not beside them.

More than a backtester.

IIntelligenceResearch agents, paper analysis, hypothesis generation, model explanation.

IIValidationBacktesting, robustness checks, bias detection, confidence & evidence scoring.

IIIQuantStrategies, portfolios, risk models, simulations, analytics.

IVExecutionBroker & exchange adapters, approval policies, order controls.

VMemoryDatasets, experiments, assumptions, provenance, versions & outcomes.

§ 05Safety & control

Exhibit 3 Nothing reaches live capital without passing every gate — and a human at the last one.

AI proposes.
Deterministic systems control.

› AI may read, hypothesise, and draft — it may not bypass position limits.
› Live deployment requires explicit human approval.
› Risk rules run outside the language model.
› Every order carries a traceable origin.
› Emergency-stop and maximum-loss controls are mandatory.

01ResearchGate

02BacktestGate

03PaperGate

04Human reviewApproval

05Live · smallGate

06ScaleGate

§ 06Integrations

Appendix Named as targets, not certifications. Status is stated, never implied.

Built on the infrastructure that already works.

Research engines

Custom Python Prototype
LEAN Planned
QuantConnect Planned
QEV-native Planned

Brokers

Tradier Prototype
Interactive Brokers Planned
Webull Planned
Futu Planned

Data

Broker feeds Prototype
Licensed datasets Planned
User-supplied data Planned
Research documents Planned

Integration roadmap — availability varies by development stage.

§ 07Roadmap

Note What runs today is marked. Nothing else is claimed as built.

A path stated plainly.

Stage 1Connected prototypeIn progress

Real-time data
Strategy execution loop
Local paper simulation
Tradier adapter
Risk controls
Dashboard & logs

Stage 2Research platformPlanned

Historical datasets
LEAN / QuantConnect link
Experiment tracking
Strategy registry
Walk-forward testing
Model comparison

Stage 3AI quant labPlanned

Research agents
Paper-to-code
Automated test generation
Evidence scoring
Review agents
Controlled promotion

Stage 4Decision OSVision

Broader asset research
Operational models
Cross-domain workflows
Enterprise deployments

Early access

Build decisions that survive contact with reality.

Request early access for product updates, prototype access, and future research partnerships. We read every submission.

› No profit promises. QEV evaluates strategies; it does not guarantee returns.
› Current prototype scope is stated separately from the roadmap.
› Your details are used only to contact you about QEV.