Venue-Specific Latency: Why Deterministic Trading Infrastructure Must Be Calibrated Per Exchange
Table of Contents The 5-Microsecond Window That Separates Alpha from Adverse Selection Speed Is Fragmented: Why Venue Architecture Dictates Outcome…
Category: Market microstructure
How Josh Levine Built Island ECN: The Matching Engine That Rewrote Electronic Trading
Table of Contents The Carnegie Mellon Dropout Who Democratized Markets Why a FoxPro Database Changed Trading Forever The Maker-Taker Model:…
How the first true Low-Latency market was designed and architected.
Background: Stock Trading in the 1990s In the mid-1990s, U.S. stock trading was dominated by human intermediaries and fragmented systems.…
Leveraging Limit Order Book Imbalances for Profitable Trading: A Deep Dive into Recent Research and Practical Tools
Introduction Traders are always on the lookout for the slightest edge that can turn a profit. One crucial aspect that…
High-frequency trading through artificial intelligence for financial innovation
Professor Chien-Feng Huang, at the National University of Kaohsiung in Taiwan delves into high-frequency trading across Artificial Intelligence In the…
Do You Know What Your Algo is Doing at any given moment?
Algorithmic trading, often referred to as algo trading, involves using computer programs to follow a defined set of instructions for…
Market Micro-structure visualization (an open-source project)
If you have been following, you already know that I have always been focused on low-latency trading systems. Assuming that…
Case Study: Algorithmic Trading With Go
In this case study, we are excited to share an insider’s perspective and look under the hood of how a…
The 0+ HFT Strategy
Ever wondered how HFT strategies work? Today, we’ll explore the 0+ HFT strategy –  a strategy that has been practiced…
hedge fundsAnalyticsDatascienceDerivativesElectronic TradinginfrastructureMarket microstructureOTCQuant
Case Study: Bond Trading System
(By Jonathan Simon). Original article published here. It is easy to distance yourself from a large collection of patterns or…
Detailed performance analysis of a simple low-latency trading system
The video titled “A detailed performance analysis of a simple low-latency trading system” presents an overview of a simple low-latency…
Decoding the Quant Market
A Guide to Machine Learning in Trading This book has been created by ChatGPT-4 and promoted and published by Gautier…
Trading at light speed: designing low latency systems in C++ – David Gross – Meeting C++ 2022
[vc_row full_width=”stretch_row”][vc_column][vc_column_text] Making a trading system “fast” cannot be an afterthought. While low latency programming is sometimes seen under the…
Using Monte Carlo Simulation for Algorithmic Trading
In financial markets, quantitative traders use the most common Monte Carlo Simulation method to reshuffle the order of their historical trades to help them better understand how a trading system could have happened.
The Return Of Quant Investing
Curated content from here. By Stephan M Kessler, global head of quantitative investment strategies (QIS) research at Morgan Stanley, and Vishwanath…
How can AI and ML drive hedge funds?
AI is increasingly being adopted by hedge funds to generate investment ideas, help with portfolio and investment decisions, for signal generation, to manage risk, and in some cases execute trades. As interest from managers grows, and as the technologies underpinning these strategies continue to develop, what might AI mean for how hedge funds invest?
An Introduction to the Sequencer World
Original post by Aiden Pestell Hi tech savvies. My name is Aiden and I have spent the last 4 years…
Defensive Market Making Against Market Manipulators
https://miro.medium.com/max/1400/0*2arApVA3c-sEuK3E Photo by Keith Johnston on Unsplash Greetings! In a previous article entitled “Simplified Avellaneda-Stoikov Market Making” we discussed how to use such…
High-frequency trading firms can easily get to 64% accuracy in predicting direction of the next trade, Princeton study finds
High-frequency trading firms can easily get to 64% accuracy in predicting direction of the next trade, Princeton study finds