XTX Founder’s AI Trading Adds £19.89M Daily

Today's Highlights

FinanceToolkit Major Upgrade: Massive Free, Keyless Official Data

FinanceToolkit is an open-source Python financial data and analysis library. With 5,410 GitHub stars, it integrates market data, financial reports, economic and fixed-income data, along with valuation, technical indicators, and portfolio optimization calculations, making it popular among quantitative researchers.

On October 10, version v2.2.2 was released.

The standout feature is the addition of massive, free, keyless official data: new endpoints include monthly inflation/CPI/unemployment rates, quarterly GDP, daily policy rates, government bond yield curves for nine major economies, and 10-year yields for EU countries. Mortgage rates and industrial output data no longer rely on FRED. Additional endpoints cover FMP executive compensation, M&A and stock splits, ETF holdings, and earnings call transcripts, alongside credit spreads, EIOPA risk-free curves, stress tests, and climate scenario inputs.

In terms of performance, external data is cached by default. GARCH computation time dropped from 186 seconds to 13 seconds, and option Monte Carlo simulations from 66 seconds to 2.5 seconds, improving import speed by over 60%. It also fixes calculation errors in WACC, Graham numbers, and barrier options. ¹

FinanceDatabase Update: New MCP Service and Significant Performance Boost

FinanceDatabase is an open-source, free foundational database for financial instruments, covering over 300,000 assets including stocks, ETFs, mutual funds, indices, forex, and cryptocurrencies. It requires no API Key and has garnered 9,451 GitHub stars.

Version 2.5.0, released on October 10, is the most significant update since version 2.0:

1. Added an MCP server supporting both hosted and local deployment, enabling natural language queries across the entire database.
2. Switched to Polars lazy loading and local caching, reducing show_options execution time from ~8 seconds to 0.1 seconds. Added Parquet format support, requiring Python 3.11+.
3. Comprehensive data cleaning and completion: stock ISIN coverage increased from 26% to 78%; added ~15,000 stocks and 8,000 ETFs; corrected all ISINs and renamed companies; marked ~17,000 invalid codes as delisted; fixed NA misreadings; and cleaned ~35,000 empty records. ¹

Quantitative Research

How Does an LLM Learn to Switch Strategies in Options Trading?

A single stock often corresponds to thousands of option contracts. An AI agent must not only select contracts but also construct portfolios. How does an LLM learn to switch strategies in response to market conditions?

A recent study published on arXiv proposes a SOTA framework: it abstracts the vast options space into strategy-level decisions, leaving portfolio implementation to a deterministic resolver, and trains a Qwen3.8-27B model fine-tuned via supervised learning and reinforcement learning.

In a 6-month out-of-sample test on 9 major US stocks and SPY options, the SOTA framework achieved a total return of 18.3%, a Sharpe ratio of 1.60, and a max drawdown of 8.96%, outperforming rule-based and machine learning selectors in the same environment. The study also found asymmetric effects from news: while helpful for teacher trajectories, retaining news during the reinforcement learning phase reduced out-of-sample returns from 18.3% to -2.7%.

This work has been accepted for the NeurIPS 2026 Agenthon Workshop. ³

When Correct Rewards Are Not Enough: Diagnosing and Guiding PPO in Analytically Solvable Broker-Trader Games

In complex market dynamics, reinforcement learning is often directly applied to find optimal execution. But can it truly leverage existing analytical solutions?

This research places PPO within a continuous-time broker-trader game with an analytical solution: the broker chooses trading speed, facing informed traders and random uninformed order flows, and derives finite-step rewards from continuous-time returns.

Results: In noise-free order flows, PPO-FFNN approximates reference actions. However, with random uninformed flows, PPO (FFNN/LSTM) becomes significantly inaccurate—diagnostics show its critic cannot reliably rank nearby actions, and potential-based reward shaping is ineffective. Under partial information, a deterministic equivalent controller based on observable history is closer to optimal. The study then freezes the analytical policy and fine-tunes PPO, stabilizing a 2.22% gap reduction when execution costs are halved.

This paper has been accepted for ICAIF 2026. ⁴

Quant Career

Is the Tower North Moore Team Worth Joining?

A post on r/quant asks about the Tower North Moore team.

Tower, founded by Mark Gorton in 1998, is a ultra-low-latency proprietary HFT firm using a structure of semi-autonomous teams sharing infrastructure. It has a large engineering base in India and settled with US authorities over spoofing allegations, paying substantial fines.

According to aggregated netizen information, the team has nearly 100 members (quants/devs/ops), trading most markets including crypto. The US pod is the largest and contributes most of the PnL, with one desk generating nine-figure profits this year. A base salary of ~$200k for 3 years of experience comes with a peculiar bonus structure entirely dependent on which pod you are in. The Indian market is an unexpanded experimental venture. Work-life balance is slightly better than Limestone’s but still requires at least 60 hours/week. It is purely merit-based, with politics having no effect.

Others warn the company is gradually pushing employees to sign worse contracts.

According to ratings on TierMarker, the company ranks at a B+ level.

46-Year-Old Math PhD Rises to UK’s Top Wealthy List via AI Trading

Alex Gorsky recently trended domestically, cited as rising to the UK’s top wealthy list through AI stock trading. In reality, he did not get rich trading stocks personally but is a successful entrepreneur.

Gorsky founded the AI quantitative market-making firm XTX Markets in 2015, holding 75% of the shares. The company’s systems process over 400PB of data, analyzing over 50,000 instruments across stocks, forex, commodities, and cryptocurrencies, capturing micro-price patterns.

With only 250+ employees and no traditional pit traders, the company achieves a daily trading volume of $250 billion. It has no external investors, with profits primarily going to partners.

In 2026, Gorsky ranked 8th on the UK’s rich list with £16.006 billion. His wealth grew from £8.745 billion to £16.006 billion in the past year, an increase of £7.261 billion, equivalent to an average daily appreciation of £19.89 million.

Born in Moscow in 1979, Gorsky holds a PhD in mathematics from Moscow State University, with research in commutative algebra and homological dimension, and is registered as a mathematician by the Mathematics Genealogy Project.

After graduation, Gorsky moved to London, engaging in quantitative trading at Deutsche Bank and GSA Capital, where he headed the foreign exchange trading department.

The so-called “AI stock trading” is actually quantitative market making, earning bid-ask spreads by providing liquidity rather than betting on price directions. He also established Axiom Maths and the AIMO Prize to support mathematics education and AI research.