LightTrader: A Standalone High-Frequency Trading System with Deep Learning Inference Accelerators and Proactive Scheduler
Bibliographic record
Abstract
Recent research shows that artificial intelligence (AI) algorithms can dramatically improve the profitability of high-frequency trading (HFT) with accurate market prediction, overcoming the limitation of conventional latency-oriented approaches. However, it is challenging to integrate the computationally intensive AI algorithm into the existing trading pipeline due to its excessively long latency and insufficient throughput, necessitating a breakthrough in hardware. Furthermore, harsh HFT environments such as bursty data traffic and stringent power constraint make it even more difficult to achieve system-level performance without missing crucial market signals.In this paper, we present LightTrader, the world’s first AI-enabled HFT system that incorporates an FPGA and custom AI accelerators for short-latency-high-throughput trading systems. Leveraging the computing power of brand-new AI accelerators fabricated in TSMC’s 7nm FinFET technology, LightTrader optimizes the tick-to-trade latency and response rate for stock market data. The AI accelerators, adopting Coarse-Grained Reconfigurable Array (CGRA) architecture, which maximizes the hardware utilization from the flexible dataflow architecture, achieve a throughput of 16 TFLOPS and 64 TOPS. In addition, we propose both workload scheduling and dynamic voltage and frequency scaling (DVFS) scheduling algorithms to find an optimal offloading strategy under bursty market data traffic and limited power condition. Finally, we build a reliable and rerunnable simulation framework that can back-test the historical market data, such as Chicago Mercantile Exchange (CME), to evaluate the LightTrader system. We thoroughly explore the performance of LightTrader when the number of AI accelerators, power conditions, and complexity of deep neural network models change. As a result, LightTrader achieves 13.92× and 7.28× speed-up of AI algorithm processing compared to existing GPU-based, FPGA-based systems, respectively. LightTrader with multiple AI accelerators achieves up to 99.5% response rates, while LightTrader with the proposed workload scheduling and DVFS scheduling algorithm relieves the miss rate from 17.1% to 23.1%.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".