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Low-Level System Design For Trading Systems

Let's talk about low-level system design for trading systems
Components of a Low-Latency Trading Systems
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End To End Interview Preparation Guide

Core Software Engineering Preparation
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4.9/5
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Testimonials
5/5
This C++ guide is incredibly thorough, especially the breakdown of memory layout and vtable internals. It’s definitely not for absolute beginners trying to finish that core fundamentals section in 15 days is pretty aggressive if you're starting from scratch! But if you’re prepping for hardcore systems engineering or quant interviews, it’s exactly the checklist you need to expose your weak spots.
5/5
Great session. Learnt many things about how HFT domain works
5/5
The guide is really useful to my low latency learning journey. I am able to use the concepts in guide in Java.
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Very much clear and informative response.

About me

I am a part of the Algo team, responsible for managing different kinds of orders such as Limit, OCO, Iceberg, Bracket, etc. The most critical task of the team is to provide low latency to its client, along with the essential functionalities such as pausing the order, resuming the order, and changing different order parameters (order quantity, bid price, ask price, time in force, etc), recover the order in case of some accident, canceling the order, etc in the real-time. Currently, I single-handedly manage a large multithreaded, distributed system, where I add new functionalities to the product and fix different kinds of bugs in the system. I am a really good understanding of different HFT infrastructure components such as price server, connectivity engine, risk engine, etc. Most of the day-to-day work is mostly around C++ only as C++ is the go-to language for low-latency hft infrastructure development. Prior to joining ********, he completed his Master of Technology from the Indian Institute of Technology, Gandhinagar. There he worked extensively on various machine learning and deep learning applications and published a research paper in Cods-Comad 2020. His research topic was "Solar Energy Forecasting using Machine Learning". If we talk about his skills, he has a good command over C, C++, Python, Java, Data Pre-Processing, Natural Language Processing (LSTM, GRU, BERT, etc), Computer Vision (CNN, RCNN, FRCNN, etc) along with classical machine learning algorithms. He has implemented various classical machine learning algorithms from scratch.