Algorithmic Trading
Infrastructure
OMS, risk engine, execution, and normalized market data. From backtest to shadow trading to live. Multi-market, multi-strategy.
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.
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 parse 10-Ks, earnings calls, filings, and social sentiment for lawsuits, management changes, and accounting red flags that break cointegration — the alpha traditional quants miss.
Material risk — undisclosed class action
C-suite turnover — 3rd in 18 months
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 deployed in the same region as major exchanges for reduced latency. Direct colocation available on demand.
NY — NYSE / NASDAQ
New York region deployment near NYSE and NASDAQ matching engines. Equinix colocation available on demand.
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.