Is it possible to bridge the gap between a retail laptop and a Citadel-style i trading rig for the price of a steak dinner? A new course claims to hand over the keys to the kingdom of C++20, Avellaneda-Stoikov, and AI-driven market making.

AI quant research, Python backtesting, and the ultimate deployment of a C++ Ultra-Low Latency Market Making strategy.

This article presents an exhaustive examination of an institutional-grade market making system that combines the seminal Avellaneda-Stoikov optimal market making model with Order Flow Imbalance signals to achieve consistent risk-adjusted returns while maintaining sub-microsecond processing latencies.

In summary, this is a high-performance trading gateway using Futures and options for HFT with C++ in VS Code with live Rithmic data.

Kernel bypass technologies eliminate these bottlenecks by allowing applications to directly access NIC hardware, reducing latency to <1µs in optimized setups.

Below is a conceptual framework for an ultra low latency market making system combining futures and options. This is a simplified version that would need significant customization for a billion-dollar HFT firm.

  This article provides an exhaustive analysis of a next-generation High Frequency Trading Engine (HFT) architecture.

Today, as evidenced by recent demonstrations in the quantitative trading community, the paradigm has shifted.

The Rithmic High Frequency Trading Engine System is a high-performance, distributed trading infrastructure designed to bridge the gap between the Rithmic RAPI (a C++ based API with a .NET wrapper used for futures trading) and modern, custom algorithmic strategies.

This article identifies the largest high-frequency trading houses that simultaneously anchor both ecosystems

A recent YouTube live stream tackled this challenge head-on, offering a masterclass that masterfully connected the dots between granular futures data, sophisticated options algorithmic trading strategies, and high-level, automated trade discovery.

This document provides a comprehensive analysis of a sophisticated financial data retrieval system designed to interface with the Rithmic option chain trading platform.

Rithmic API using .NET/C#, capable of handling Level 2 order book data and complex execution strategies.

. Renaissance Technologies, and its jewel, the Medallion Fund, is not just the most successful hedge fund ever; it is an intellectual Everest, a black box of such staggering profitability that its performance charts resemble a glitch in the financial matrix.

This article provides an exhaustive breakdown of a comprehensive trading simulator designed around the Volume Weighted Average Price deviation strategy, targeting the Micro E-mini S&P 500 futures contract.

This isn't merely an academic exercise—it's a professional-grade simulation environment that models real market behavior, implements advanced statistical analysis, manages positions with institutional rigor, and provides comprehensive performance analytics.

For decades, the world of quantitative finance, particularly C++ high-frequency trading bot, has been an exclusive club. It was the domain of elite QI quant institutions with cavernous server rooms, teams of PhDs in physics and mathematics, and budgets stretching into the millions.

We will deconstruct the architecture of a sophisticated Ether high frequency trading bot strategy simulator written in C++

Abstract   High-frequency trading (HFT) firms employ sophisticated quantitative models to gain an edge in ultra-fast markets. Among these, high frequency trading Hidden Markov Models (HMMs)  for regime detection and order flow toxicity analysis  are critical for dynamic strategy adaptation. This paper explores cutting-edge techniques used by top HFT firms, focusing on HMM-based regime switching  and advanced toxicity detection methods  beyond conventional microprice forecasti

We present novel approaches, including latent liquidity modeling, Bayesian toxicity scoring, and reinforcement learning-enhanced high frequency trading Hidden Markviol Models (HMMs), which remain largely undocumented in public literature.