AI-Driven Quantitative Hedge Fund
Dnalyaw owns the full pipeline from signal to live execution — reinforcement learning agents generating trade ideas, language models surfacing the fundamental risk that breaks quantitative models, and a multi-gate risk engine holding absolute veto over every order.
Four-gate risk veto.
Every order passes through exposure, position limit, sector cap, and daily loss checks. The engine approves, rejects, reduces, or flattens — with no override path. By design, not by policy.
Backtest to live.
Two-layer pipeline: fast vectorized replay over years of tick data, then OMS-integrated simulation with realistic fills, slippage, and borrow costs before any capital goes live.
LLMs research. They never touch orders.
Language models continuously parse earnings calls, filings, social media momentum, institutional positioning shifts, and alternative data — surfacing structural risk signals that break systematic strategies before price reflects them.
The framework is public. Read the approach →Material risk — undisclosed class action
13F cluster — accelerating position cuts
ASC 606 deviation — accelerated booking
RL agents trade. Risk engine holds veto.
Reinforcement learning agents consume market state, portfolio exposure, and microstructure features to generate trade signals — constrained by a risk engine that holds absolute veto.
From backtest to live.
Real-time position tracking, order flow monitoring, and P&L with sub-second updates. The same OMS that runs in simulation runs in production.
Measure everything.
Transaction cost analysis from day one. Every fill captures arrival price vs fill price, slippage, and market impact — the gap between backtest and live.
Latency Histogram
Slippage Distribution
Fill Quality
Exchange-proximate infrastructure
Servers co-located with major exchanges — US equities and Hong Kong, exchange-proximate.
NY — NYSE / NASDAQ
New York region deployment, exchange-proximate to NYSE and NASDAQ matching engines.
HK — HKEX
Hong Kong region deployment near HKEX. Equities, derivatives, and Stock Connect access.
Normalized Feed
Heterogeneous data sources unified into a single schema. Quality gates filter spreads, stale quotes, and price spikes.
Built on years of independent research in reinforcement learning, market microstructure, and systematic execution — a research-first process run with institutional-grade discipline.
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