Foundational Time Series AI Models for Finance

Democratizing Market Predictions with AI

At Quantum Signals, we are working towards foundational time-series AI models for finance, with a focus on predicting short-term (1 to 30 minutes) price movements and market dynamics—volatility and liquidity. Our mission is twofold:

1. Deliver cutting-edge AI-driven market predictions that help traders and financial institutions make informed decisions.
2. Democratize access to advanced AI techniques, enabling novice and advanced users to not only consume curated predictions but also customize inputs, parameters, and biases to generate highly tailored insights.

The Challenge: Understanding Market Microstructure

For quantitative analysts and portfolio managers at investment banks, hedge funds, and retail banks, accurately forecasting intraday market behavior is a major challenge. These professionals depend on data-driven insights to optimize trading strategies, execute orders efficiently, and manage risk effectively.

At the core of short-term price movements lies market microstructure, the intricate interplay of supply and demand at the order book level. Understanding this microstructure is often considered one of the holy grails of finance—a critical but elusive piece in the puzzle of market behavior.

However, financial time series prediction is uniquely challenging due to:

• Non-stationarity: Market conditions evolve, making static models unreliable.
• High dimensionality & complexity: Market behavior is shaped by countless participants in a multi-player, zero-sum environment.
• Market microstructure noise: Short-term price fluctuations are often dominated by noise, making it difficult to extract meaningful trends.

Why AI? The Role of Transformer Models in Market Prediction

Recent breakthroughs in AI, particularly transformer models, have revolutionized natural language processing (NLP) by powering large language models (LLMs). These models have demonstrated remarkable capabilities in understanding, generating, and reasoning with text—effectively becoming foundational models for language. However, in the domain of time-series forecasting and financial modeling, no equivalent foundational models exist. Financial markets are dynamic, noisy, and highly contextual, making the development of general-purpose AI models far more challenging.

At Quantum Signals, we believe that similar advancements can be applied to financial time-series data. By leveraging transformer-based architectures and training on high-resolution market data, we aim to build a foundational time-series model for finance, capable of predicting short-term market dynamics with practical accuracy.

Our Approach: Leveraging Limit Order Book (LOB) Data

We are training our models using limit order book (LOB) data, which provides granular insights into market activity at different price levels. By analyzing posted, executed, and canceled orders, our models can detect patterns and anticipate short-term market movements.

We specifically focus on the 1 to 30-minute timeframe, avoiding both:

• Long-term forecasts (across days), which involve broader macroeconomic factors.
• High-frequency trading (<1 second), which is dominated by ultra-low-latency strategies.

Early Benchmarks & Future Improvements

Our initial benchmarks indicate that AI models can learn market microstructure dynamics to a meaningful extent, with prediction accuracy ranging between 55% and 65%. While promising, we see substantial room for improvement by:

• Incorporating more diverse securities and exchanges.
• Refining model architecture and feature engineering.
• Integrating proprietary transaction data from financial institutions for specialized tuning.

Beyond Predictions: A Platform for Custom AI-Driven Insights

Our vision extends beyond delivering off-the-shelf AI predictions. We are building an enterprise-grade platform that empowers financial professionals to develop and refine their own market predictions using cutting-edge AI models. This customization can take place in several ways:

• Feeding proprietary data: Financial institutions and quants can integrate their own transaction data, special order types, or internal signals to fine-tune the model for their specific needs.
• Introducing strategic biases: Users can adjust model parameters to align with their unique market views, ensuring predictions reflect their proprietary insights and risk preferences.
• Training with specific portfolios or strategies: The model can be tuned with a particular portfolio or trading strategy in mind, optimizing its predictions for asset allocation, execution, or risk management in line with the user's goals.

By providing this flexible, AI-driven platform, we enable financial professionals to go beyond generic market signals and build highly personalized predictive insights tailored to their trading objectives.

Looking Ahead

We are committed to advancing AI-driven market predictions and will continue sharing benchmarks, research findings, and insights on this blog. If you’re a quant, trader, or financial institution looking to explore AI’s potential in market forecasting, stay tuned—or reach out to collaborate!