algorithmic-trading

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…

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…

I am trying to generate a ql.HestonSLVMCModel vol surface for FX (to generate paths and price TARFs). To start with I need a local vol surface. In FX, strikes are not fixed but quoted in delta. This complicates the use of ql.BlackVarianceSurface because it requires a rectangular grid of vols. In a post by StackG I found a proposed method ( in the comment of user35980 ) whereby the original vols a…

What are some quantitative strategies that can be applied to BTC contract trading or spot trading with decent returns?

Tradeweb Markets Inc., a global leader in electronic trading across asset classes, reported total trading volume for the month of May 2026 of $62.3 trillion (tn). Average daily volume for the month was $3.0tn, an increase of 18.3 percent (%) year-over-year (YoY). Record Highlights: For May of 2026, Tradeweb records included: - ADV in rates futures - ADV in repurchase agreements May 2026 Highlight…

These are quite simple models, so forgive me If my question is basic. I am implementing Monte Carlo simulation for European call option pricing under two setups: Constant volatility (GBM with σ = 0.2) Uncertain volatility where σ is sampled from a lognormal distribution per path. The mean is 0.2 and standard deviation 0.05. In theory, I expected the uncertain volatility case to produce a higher o…

What is AutoWealth AI? AutoWealth AI is an open-source, multi-agent investment analysis engine built entirely in Python. It uses three specialized AI agents that analyze stocks from different perspectives, then combines their signals through a weighted voting mechanism. GitHub Repository | 482 tests passing | 11 major features The Core Architecture Three AI Agents Working Together Technical Analy…

A comprehensive deep dive into the mathematical bridge between risk management and alpha prediction in algorithmic trading systems. From the Fundamental Law of Active Management to conditional factor models, explore how machine learning transforms static betas into dynamic prediction engines. 📊 Deep Research Topics: quantitative finance, investment analysis, financial education, financial resea…

assuming the usual FX vol surface conventions are correctly set for the given market: how do I extract the 3D Bloomberg implied vol surface. I understand that the data from their implied vol table form the basis of our interpolation and that you can export to Excel. However, I would like to fit the implied vol surface and see how different methods behave relative to Bloomberg or Murex's surface -…

What Happened BigFinanceBench (928 expert-authored tasks) and Hedge-Bench (102 real hedge-fund analyst tasks) dropped simultaneously, giving the market its first rigorous, rubric-graded measurement of where AI agents actually stand. Best-in-class models hit 58.8% on BigFinanceBench — and below 16% on the harder hedge-fund tasks. Both benchmarks grade the derivation , not just the final answer, wh…

I am trying to smooth a 13 period EMA Elder Force Index in c++, and nobody really describes this as anything more than : Force Index(1) = {Close (current period) - Close (prior period)} x Volume. Force Index(13) = 13-period EMA of Force Index(1). I must be a bit thick, but when you use a moving average with a period, you get an average of that period, and then use that as a reference for furthe…

I built a multi-agent BTC research pipeline that runs 3 specialized agents and outputs a daily signal BTC at $76,099. Down 40.8% from the $125,835 ATH. RSI neutral at 54. Sitting 8% below the 200-day EMA. Most retail traders and even most bots handle this kind of sideways chop with rules-based buy-the-dip logic. But rules miss context — when exchange reserves are at 7-year lows and $2.5B in ETF m…

Marex announced that it has been onboarded as a broker on Deribit, one of the leading platforms for digital asset derivatives trading. The onboarding further expands Marex’s institutional digital assets offering and enhances client access to global crypto derivatives markets. Expanding institutional access to crypto derivatives markets Through integration, Marex will provide institutional clients…

TradingAgents just hit 82,356 GitHub stars and 15,978 forks in roughly 17 months — and yet most engineers who clone it only run the default propagate("NVDA", "2026-01-15") call once, stare at the verdict, and never touch the parts that make this framework special. TradingAgents is the only open-source multi-agent LLM trading framework backed by a peer-reviewed arXiv paper (2412.20138) where the p…