This comprehensive guide documents the complete journey of building a functional Pub/Sub messaging system using SQLite as the message broker and .NET as the development framework.

Abstract   In the domain of high-frequency trading (HFT) and algorithmic execution, the decoupling of market data ingestion from strategy execution is a critical architectural pattern. This article details the programming logic required to construct a robust trading gateway using C# and the Rithmic .NET API. The system described utilizes a "Tensor Field" approach to Limit Order Book (LOB) analysis, calculating higher-order derivatives of price and quantity to detect instituti

Before a trading application is allowed to connect to live markets through Rithmic, it must typically pass a Rithmic API conformance test.

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.

After months of rigorous backtesting, parameter optimization, and simulated execution in controlled environments, we stand at the threshold of a significant milestone: the deployment of a live futures trading platform infrastructure powered by the Rithmic API

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

Executive Summary This Python script represents a sophisticated financial analysis platform built with Streamlit, designed to perform comprehensive technical analysis, portfolio metrics calculation, and forecast ranking across multiple assets. The application combines traditional technical indicators with advanced portfolio risk metrics and machine learning-based forecasting to provide institutional-grade investment analysis capabilities. The system processes time-series fina

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.

Quantitative trading has transformed the way financial markets operate. It uses mathematical models and algorithms to make trading decisions, often at speeds and volumes impossible for human traders. This approach leverages data, statistics, and technology to identify trading opportunities and manage risk efficiently. Whether you are a beginner or someone curious about the mechanics behind modern trading, understanding quantitative trading basics is essential. Understanding Q

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.

In the context of Ethereum Markets (ETH) and other cryptocurrencies, HFT strategies are increasingly sophisticated, often employing proprietary and undisclosed quantitative models

I've analyzed four futures contracts using advanced quantitative techniques including volatility modeling, momentum decomposition, microstructure analysis, and regime detection. Below is a detailed analysis of each contract followed by a comparative ranking with forecasted portfolio metrics. 1. S&P 500 E-mini (SPZ5) - 15 Minute RTH Statistical Profile The contract spans from November 5 to December 2, 2025, with the most recent close at 6842.75. Over this period, price ranged

this kind of AI powered developer assistant—a sophisticated yet elegantly designed system that merges a local Large Language Model, a custom knowledge base, and an interactive user

The pitch is seductive: why painstakingly read through documentation when AI can simply know it for you? Should you be reading API documentation?