QuantDev.ai is an open-source research lab focused on market microstructure. We develop AI agents that bring sophisticated quantitative analysis, order flow intelligence, and execution research to retail traders and aspiring quant developers — all in the open.
The workflow
The workflow starts with raw venue data, but the objective is alpha from the beginning. We study the book to design predictive states, test those states at high-frequency horizons, build memory over the ones that survive, and then measure whether the signal has enough information and path economics to become a trading system.
We begin with the most granular feed we can get: market-by-order data, tick-level trades, quote updates, and order-book event streams. Candles are too compressed. Indicators are too late. The raw feed is where pressure, imbalance, and liquidity behavior first appear.
Raw data speaks before charts do.
Before there is a model, there is a market-state hypothesis. Is aggressive flow creating directional pressure? Is the book absorbing impact? Is liquidity refilling or disappearing? Is a move likely to continue, reverse, or fail? The alpha is designed from the beginning as a predictive state.
An alpha is a claim about market state.
We test the raw atoms behind the hypothesis at HFT horizons: order-flow imbalance, trade pressure, microprice deviation, queue pressure, near-vs-deep liquidity, absorption, replenishment, and failed impact. If the raw atom has no predictive structure, we do not pretend a downstream model will rescue it.
No raw edge, no downstream mythology.
Raw microstructure effects decay fast. The next step is memory: persistence, decay, agreement, disagreement, saturation, path conditioning, and regime interaction. Memory features are how short-lived pressure becomes a signal that can survive into economically realistic horizons.
Memory turns pressure into state.
A signal has to survive two separate tests. The information stack asks whether the feature has stable directional content: rank IC, bucket shape, daily stability, horizon profile, decay, sign flips, and sample health. The path stack asks whether the future path is actually extractable: terminal return, MFE, MAE, first-touch barriers, cost hurdle, time-to-pay, and day stability.
Information first. Extractability second. Policy last.
Only after the alpha survives research does it become a system: signal definition, gates, validation, execution assumptions, deployment constraints, monitoring, and live automation. The algorithm packages the alpha. It does not invent it.
The system is the final expression of the research.
The metric stack
A market-state idea does not become alpha because it sounds sophisticated. It becomes alpha only if the data supports it. The lab separates the process into three stages: information, path economics, and policy. Each stage answers a different question.
Does the feature predict direction?
Rank IC, bucket shape, daily stability, horizon profile, decay, sign-flip behavior, and sample health.
Did the future path contain extractable movement?
Terminal return, MFE, MAE, first-touch barriers, cost hurdle, time-to-pay, and day-level stability.
Can the signal become a trading system?
Execution logic, gates, sizing, cooldowns, validation, monitoring, and live deployment.
The research machine
The lab uses modern AI coding agents to move faster through the research loop. Codex, Claude Code, Cursor, Antigravity, and other agentic tools help write studies, refactor harnesses, generate diagnostics, document failures, and iterate on alpha ideas. The edge is not just one signal. It is the research machine around the signal.
Agents help create new atom studies, memory studies, metric passes, and diagnostics.
Agents help keep the Go research stack fast, reproducible, and easy to extend.
Failed ideas are not thrown away. They become evidence for the next pass.
The lab can test more alpha hypotheses without turning research into a hand-coded bottleneck.
Founder
Live
Operator
Dylan Siegel
QuantDev.ai is run by Dylan Siegel as an open-source microstructure alpha lab. The goal is to make serious market-state research visible: the raw data assumptions, alpha hypotheses, atom studies, metric stacks, failures, memory engineering, and systems that come out the other side.
Discipline
Microstructure
Approach
Open research
Stack
Go · agents
