As markets evolve, traditional algorithmic trading systems face new challenges—from fragmented liquidity across chains to adversarial on‑chain bots. In The End of Algorithmic Trading, Steven Paul examines the architectural limits of automated strategies and teaches you how to design resilient, adaptive systems that can operate reliably in decentralized and centralized environments.
• Analyze failure points in common trading algorithms under high‑volatility and low‑liquidity conditions.
• Build fallback mechanisms using on‑chain oracles and multi‑exchange routing.
• Implement real‑time monitoring scripts with code examples to detect and mitigate flash crashes and front‑running bots.
This workbook‑style guide includes hands‑on exercises, sample code, and system diagrams—ideal for quantitative developers, blockchain engineers, and technical architects who need to future‑proof their trading infrastructure.
As markets evolve, traditional algorithmic trading systems face new challenges—from fragmented liquidity across chains to adversarial on‑chain bots. In The End of Algorithmic Trading, Steven Paul examines the architectural limits of automated strategies and teaches you how to design resilient, adaptive systems that can operate reliably in decentralized and centralized environments.
• Analyze failure points in common trading algorithms under high‑volatility and low‑liquidity conditions.
• Build fallback mechanisms using on‑chain oracles and multi‑exchange routing.
• Implement real‑time monitoring scripts with code examples to detect and mitigate flash crashes and front‑running bots.
This workbook‑style guide includes hands‑on exercises, sample code, and system diagrams—ideal for quantitative developers, blockchain engineers, and technical architects who need to future‑proof their trading infrastructure.

The End of Algorithmic Trading: Building Resilient Automated Systems in Evolving Markets

The End of Algorithmic Trading: Building Resilient Automated Systems in Evolving Markets
Product Details
BN ID: | 2940181702769 |
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Publisher: | Adaptive Publishing Group |
Publication date: | 05/04/2025 |
Sold by: | Draft2Digital |
Format: | eBook |
File size: | 307 KB |