From: thepipeline_xyz
The development of high-performance systems for high-frequency trading (HFT) served as a foundational inspiration for addressing performance challenges in blockchain technology [02:10:57].
High-Frequency Trading at Jump Trading
Beginning in 2014, engineers worked on a high-frequency trading team at Jump Trading, focusing on building comprehensive trading systems from scratch [00:00:17], [00:00:25].
The core function of these systems involved:
- Ingesting large volumes of data packets from exchanges [00:00:28].
- Executing rapid trading decisions [00:00:31].
- Sending orders back to the exchange swiftly [00:00:34].
HFT is a highly competitive field where speed is paramount [00:00:39]. The system that can decide and send an order faster than a competitor gains the advantage, whether by taking liquidity with an aggressive order or securing a position in the queue for a passive order [00:00:44]. This environment is characterized by extreme latency competitiveness [00:01:01].
Performance Optimization in HFT
The competitive nature of high-frequency trading necessitated continuous iterations of system improvements, focusing on:
- Shaving off latency [00:01:07].
- Building highly performant systems from the ground up [00:01:09], [00:02:06].
Transition to Blockchain Performance
By 2021, the team transitioned to the crypto sector within Jump, engaging with DeFi projects [00:01:18], [00:01:24]. This work, particularly with Solana DeFi, highlighted a significant demand for more performant blockchain technology [00:01:35], [00:01:41].
A critical observation was the inefficiency of existing Ethereum Virtual Machine (EVM) implementations, which had not undergone the extensive optimization seen in HFT systems [00:01:46], [00:01:51]. The team’s background in building high-performance systems for HFT was recognized as ideal for tackling these challenges in blockchain performance [00:02:00].
This led to the realization that substantial optimization was required in the EVM space, providing the initial inspiration for projects like Monad [00:02:36], [00:02:41]. The experience in optimizing traditional trading systems directly informed the approach to enhancing blockchain performance [02:16:00].