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QEV

AI-native research and decision infrastructure

Turn data and evidenceinto validated decisions.

QEV is an AI-native operating system for researching ideas, testing models, managing risk, and moving validated decisions into controlled execution.

Starting with quantitative finance: strategy research, backtesting, paper trading, risk validation, and broker execution in one connected workflow.

Join Early AccessExplore the System

Evidence-to-execution cycle

QEV/OS · v0

EX-01 · The problem

Trading tools are fragmented. Decision systems should not be.

A strategy may begin in a notebook, move into a separate backtester, use another service for paper trading, and rely on custom scripts for execution. The evidence, assumptions, code, results, and live behavior become disconnected.

Today that workflow is assembled by hand from separate tools for:

  • research
  • data
  • notebooks
  • backtesting
  • broker APIs
  • deployment
  • monitoring
  • AI assistants
  • risk controls

QEV keeps the complete decision history connected — what was tested, why it was tested, what evidence supported it, and what happened after deployment.

EX-02 · The operating cycle

One continuous evidence-to-execution cycle.

Every stage feeds the next, and every stage writes back to the same record. Nothing is lost between the notebook and the order.

01

Discover

AI-assisted research over market data, papers, news, and user datasets.

02

Design

Convert ideas into explicit hypotheses, models, strategies, and test plans.

03

Validate

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

04

Simulate

Operate strategies against live data in controlled paper environments.

05

Execute

Deploy approved strategies through broker-neutral execution adapters.

06

Monitor

Track performance, drift, exposure, failures, and the evidence behind every decision.

EX-03 · Product layers

More than a backtester.

QEV is a layered platform. Each layer has one job; together they carry a decision from question to controlled execution.

Intelligence

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

Validation

Backtesting, robustness checks, bias detection, confidence and evidence scoring.

Quant

Strategies, portfolios, risk models, simulations, analytics.

Execution

Broker and exchange adapters, approval policies, order controls.

Memory

Datasets, experiments, assumptions, provenance, versions, outcomes.

EX-04 · First vertical

Built first for quantitative markets. Designed for broader decisions.

Enter the Quant Lab

Quantitative finance is the ideal proving ground for QEV: noisy data, measurable outcomes, strict risk constraints, and continuous feedback. Mistakes are expensive, and results are measurable — exactly the conditions a validation system is built for.

The same research, validation, and controlled-execution architecture can later support other evidence-intensive domains: forecasting, investment analysis, asset evaluation, operational decisions, and scenario simulation.

Quant is the proving ground. Decisions are the platform.

EX-05 · Control model

AI proposes. Deterministic systems control.

QEV uses AI to read papers, generate hypotheses, write experiments, and analyze failures. Risk limits, approvals, position sizing, and order execution stay outside the language model.

EX-001 · SAMPLE RECORD

Decision Record

VALIDATED
Hypothesis
Intraday momentum on liquid US equities persists after costs, out-of-sample, across regimes.
Evidence
  • — 5y daily bars, survivorship-adjusted
  • — Walk-forward windows, 63d
  • — Round-trip cost model @ 4.5 bps
  • — Out-of-sample: last 18 months
Validation
  • ✓ Transaction costs modeled
  • ✓ Look-ahead / leakage check
  • ✓ Multiple-testing control
  • ✓ Out-of-sample stability
  • ✓ Drawdown within limits
Outcome
Approved for paper → live-small, behind risk gateway.
  • 01AI cannot bypass position limits or risk rules.
  • 02Live deployment requires explicit human approval.
  • 03Every order has a traceable origin — hypothesis to fill.
  • 04Strategies move through controlled stages, never directly to production.
  • 05Emergency-stop and maximum-loss controls are mandatory, not optional.

Promotion lifecycle · every gate requires explicit approval

EX-06 · Builder philosophy

Build first. Learn theory when it unlocks the next capability.

QEV is designed for builders. It connects theory to an actual problem: improving a test, correcting a risk assumption, explaining a failed strategy, or making execution safer. Knowledge arrives in context, when it is needed.

  1. 01Build
  2. 02Test
  3. 03Find failure
  4. 04Learn the missing concept
  5. 05Improve
  6. ↺ repeat

EX-07 · Integrations

Use the infrastructure that already works.

QEV is not a replacement for QuantConnect or QuantRocket. It sits above quant engines, brokers, and data sources — connecting them into one decision record.

Research engines

  • LEAN
  • QuantConnect
  • Custom Python
  • QEV-native engines

QEV sits above quant engines; it does not replace them.

Brokers

  • Tradier
  • Interactive Brokers
  • Webull
  • Futu

Tradier is the first adapter target. Others are roadmap items.

Data

  • Broker feeds
  • Licensed datasets
  • User-supplied data
  • Research documents
  • Alternative data

Every dataset carries provenance and version history.

Integration roadmap — availability varies by development stage. No integration is claimed live until it is implemented.

EX-08 · Roadmap

What exists, what is next, what is further out.

Scope is stated plainly. The first working system is a connected prototype — not a finished platform.

Stage 1

IN DEVELOPMENT

Connected prototype

  • Real-time market data
  • Strategy execution loop
  • Local paper simulation
  • Tradier broker adapter
  • Risk controls
  • Dashboard and logs

Stage 2

PLANNED

Research platform

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

Stage 3

PLANNED

AI quant lab

  • Research agents
  • Paper-to-code workflows
  • Automated test generation
  • Evidence scoring
  • Strategy review agents
  • Controlled promotion pipelines

Stage 4

EXPLORING

Decision operating system

  • Broader asset research
  • Business and operational models
  • Cross-domain decision workflows
  • Enterprise deployments
Full roadmap →

Early access

Build decisions that can survive contact with reality.

Join the early-access list for product updates, prototype access, and future research partnerships.

Join QEV Early Access