HFT Multi-factor
Overview
The HFT Multi-factor strategy generates market-neutral returns by combining high-frequency trading with market making on major centralized exchanges. The system runs 100+ substrategies built upon machine-learning factor models — multi-factor time-series models that are iteratively refined based on market structure. Alpha is sourced evenly across the two engines: roughly 50% from high-frequency trading signals and 50% from market making.
The strategy focuses on major liquid assets and perpetual futures, validated under market stress conditions, with institutional-scale trading volume supported by in-house execution infrastructure.
How it works
Machine-learning factor framework — At the core of the strategy is a library of multi-factor time-series models. Signals are researched, deployed, and iteratively refined based on observed market structure, allowing the system to adapt as market regimes shift.
Diversified alpha signals — The 100+ substrategies combine market-making, momentum, and mean-reversion dynamics. Diversification across signal families and assets reduces reliance on any single source of return.
Maker-dominant execution — Approximately 90% of trade execution is placed as maker orders. The strategy earns bid-ask spread and maker rebates while minimizing taker fees and market impact, supported by high-turnover, institutional-scale execution infrastructure.
Leverage — The strategy operates within a 0.7x to 5x leverage range. The Neutral Trade vault mandate runs at 4.5x.
Venues
Centralized: Binance
Yield Sources
Bid-ask spread capture and maker rebates
Short-horizon alpha signals (momentum and mean reversion)
Short-term market inefficiencies
Risk Management
Quantitative Risk Framework — A multi-layer risk framework integrates volatility clustering, drawdown limits, correlation stress tests, and scenario simulations to maintain robust performance under varying market conditions.
Exposure & Monitoring Controls — Real-time exposure controls are paired with independent monitoring and audit mechanisms to enforce trading constraints, support transparent oversight, and navigate liquidity and volatility shifts.
Counterparty Risk Management — Counterparty risk is managed through a structured execution and custody framework across exchanges, on-chain infrastructure, and institutional providers, mitigating concentration risk.
Operational Risk Management — Strict operational safeguards — permission controls, workflow separation, automated reconciliation, and wash-trading prevention — minimize execution errors and enhance system reliability.
Why Vision Research?
Vision Research combines institutional asset-management pedigree with deep crypto-native engineering:
Arbitrage portfolio management is led by a former Blackstone quant and portfolio manager at a long-standing systematic crypto fund, with expertise in arbitrage strategies and DeFi trading, and a former lecturer at Columbia University.
Alpha research and strategy design is led by a quantitative strategist with 5+ years across centralized-exchange and on-chain markets, previously at Crypto.com after a multi-billion-dollar asset manager.
Off-chain systems are built by a former CTO of a New York asset-management firm, specializing in low-latency architecture and high-frequency trading systems for institutional-grade execution.
On-chain engineering is led by a full-stack developer with 8+ years of experience, including four in Web3 and prior work at Bytedance, focused on on-chain strategy engineering and protocol-level integration.
Vision Research is an alias. We've excluded the curator's full name to mitigate alpha leaking. Neutral Trade has completed thorough due diligence on the team and its strategy.
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