algorithmic-trading

Most retail algorithmic trading bots rely heavily on legacy technical analysis indicators—think RSI, MACD, or Bollinger Bands. While these indicators are easy to calculate, they suffer from a fatal flaw: they are lagging metrics derived entirely from historical price adjustments. In high-frequency, institutional environments, relying on simple moving averages is like trying to drive a car while l…

Every quant developer knows the feeling: you write an algorithmic strategy, run it against a basic backtesting script, and the equity curve looks like a flawless, vertical rocket ship. You feel like a market genius. But then you deploy that exact same strategy against a high-fidelity system—or live capital—and it immediately bleeds money. What happened? The strategy worked perfectly on paper beca…

This video explores how harness engineering provides the operational infrastructure that powers autonomous AI agents in quantitative finance — from execution runtimes and secure sandboxes to memory compaction and recursive skill orchestration. 🎥 Video Tutorial 🎥 Watch Video: https://youtu.be/_fcDzh04ntc Topics: quantitative finance, investment analysis, financial education, financial education…

CME Group, the world’s leading derivatives marketplace, along with Morningstar, a leading provider of independent investment insights, announced that they have entered into a multi-year licensing agreement for CME Group to launch derivatives products based on key Morningstar equity index benchmarks, including the Morningstar US Total Market, Large Cap, Large Cap Value, Large Cap Growth, Mid Cap, …

This article was originally published on the FlyTradr blog . Paper Trading vs Live Trading: Why Your Results Will Always Differ Paper trading is the step between backtesting and live trading. You run your strategy in real market conditions, with real prices and real market hours, but without any actual money at risk. It is a genuinely useful tool, and it should be a required step in any systemati…

In the hyper-competitive landscape of quantitative finance, raw LLMs are fundamentally ill-equipped for rigorous, fault-intolerant environments. The competitive moat has shifted to the operational infrastructure that wraps around them: The Harness. Explore execution runtimes, secure sandboxes, memory compaction, authorization fabrics, and the recursive autonomy of skills calling skills. 📊 Deep R…

What are the sources one can search for or view / download research articles and other publications on quantitative finance in addition to the Internet search engines?

Quantitative finance has a reputation for being gatekept behind expensive certificates and heavy math. Some of the math is real. But the fastest way in is not a reading list, it is building the core models yourself in Python until they are no longer mysterious. Here is the path that works, and what you are really learning at each step. Start with returns and risk Everything begins with returns. S…

I am implementing the layer-by-layer calibration of the time-dependent SABR model described in Hagan, Lesniewski and Woodward, Managing Vol Surfaces . In particular, I am using the effective-parameter formulas around equations (3.7)–(3.8) and the piecewise-constant integral update formulas in equation (3.11) of the paper. For each expiry $T_j$ , I first calibrate a standard SABR smile and obtain …

Intercontinental Exchange, Inc., one of the world’s leading providers of financial market technology and data powering global capital markets, announced the launch of ICE Compass, an AI-powered trading analytics platform that gives buy-side fixed income trading desks prioritized trader counterparty rankings and price estimates before executing trades. T. Rowe Price, which provided valuable feedba…

This video traces the evolution of portfolio construction from Markowitz's mean-variance framework to modern information-theoretic paradigms, exploring how entropy methods and the Entropy Pooling framework have transformed the way institutional investors build optimal portfolios. 🎥 Video Tutorial 🎥 Watch Video: https://youtu.be/gr4Z7fOsVk0 Topics: quantitative finance, investment analysis, fin…

86 minds. 32 teams. 1,007 submissions. One question, can a human out-predict the machine? We threw down a challenge to the brightest student minds in data science and finance: beat our algorithm at its own game. The response blew us away. Over the course of the AlgoSports23 Sports Prediction Competition, hosted on Kaggle, 86 entrants across 32 teamsfired off 1,007 submissions, each one a fresh at…

Over the past few months, I've been experimenting with automated trading systems on Polymarket. One of the more interesting ideas I explored was what I call a Last-Entry Probability Capture Strategy. The concept sounds simple: Instead of trying to predict market direction, wait until the final moments before resolution and look for markets where the remaining uncertainty appears overpriced. In th…

OpenQuant Newsletter - Edition #172 Quant News, Jobs & Internships, Upcoming Events, Puzzles and More! Hello, fellow Quants! Welcome to the OpenQuant newsletter - a publication dedicated to democratizing quantitative finance by sharing the latest news, jobs & internships, educational opportunities, and upcoming events in the industry. Here’s what we have in this week’s edition: - Quant News - cat…

The result is correct but challenges core norms of mathematics: checking proofs, crediting ideas and keeping research open to everyone.

The historical evolution from rigid mean-variance frameworks to flexible information-theoretic paradigms. Explore the deep intuition of the Entropy Pooling framework and its mapping to the classical Black-Litterman model. 📊 Deep Research Topics: quantitative finance, investment analysis, financial education, financial research, market analysis

Quantitative Finance (Quant): The Comprehensive Learning Path Introduction Quantitative Finance (Quant) is the application of mathematical and statistical methods to financial and risk management problems. Quants are the "rocket scientists of Wall Street," blending deep mathematical rigor, financial theory, and computer science to price complex derivatives, manage risk, and identify profitable al…

Algo Trading on US Equities: What Indian Traders Need to Know Before Starting Indian retail traders have had access to US equity markets for several years now through international brokerage platforms. The ability to buy shares in US companies is relatively straightforward. Algorithmic trading on US markets from India is a different and more complex conversation. This guide covers the key structu…

This video breaks down the architecture of quantitative factor models — from the mathematical bridge between risk management and alpha generation to the distinction between systematic beta exposure and true alpha in ML-driven trading. 🎥 Video Tutorial 🎥 Watch Video: https://youtu.be/3FS6Yqd-zDU Topics: quantitative finance, investment analysis, financial education, financial education video, t…

I want to price FX TARFs using the Quantlib library in Python. I want to generate paths using the ql.HestonSLVMCModel vol surface. Then, I want to compare the result to Bloomberg and see if the results are in line. In Bloomberg I can retrieve the SLV parameters clearly: per tenor I can find correlation, vol of vol and mixing fraction. Is it possible in Quantlib to also retrieve it from the SLV m…