Integrating HFT Beta Factory V0 With QMTL: Key Requirements

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Integrating HFT Beta Factory v0 with QMTL: Key Requirements

Hey everyone! Get ready to dive deep into something super exciting for all you quant developers out there. We're talking about the crucial integration of HFT Beta Factory v0 with QMTL, a move designed to truly empower our high-frequency trading (HFT) strategies with robust, scalable factor computation. This isn't just about connecting two systems; it's about building a more powerful, flexible, and efficient framework for developing and deploying cutting-edge HFT strategies.

Our main goal here is straightforward yet ambitious: we want to ensure that the ongoing development of HFT Beta Factory v0 within the hft-factory-strategies repository can run naturally and seamlessly on top of QMTL. To achieve this, we're meticulously defining the minimum necessary hooks, interfaces, and extension requirements that qmtl will need to provide. This isn't just a technical exercise; it's about laying the groundwork for a future where creating sophisticated HFT factors is as smooth as possible. We're aiming to make qmtl the go-to platform, guys, by making it incredibly easy to plug in advanced analytical components like our Beta Factory.

This initiative is part of a larger, well-thought-out roadmap. For those of you who want to explore the bigger picture, check out our Workspace roadmap, which outlines the grand vision. We've also got an Idea repository where we brainstorm and refine concepts, and specifically for the Beta Factory, we've been brewing some fascinating ideas. This includes foundational concepts like simple market beta (your classic regression against a market index), more nuanced factors like sector/industry beta (understanding how specific sectors react), and even granular, high-frequency ideas such as intraday liquidity beta (capturing short-term market depth changes) and short-term volatility beta (how quickly price fluctuations occur within a trading day). These specific betas are critically important for HFT because they allow us to understand and predict market movements at a very granular level, which is essential for strategies that capitalize on fleeting opportunities. Each of these ideas, developed in hft-factory-ideas, is a testament to our commitment to delivering high-quality, actionable factors. Finally, the strategic skeleton for Beta Factory v0 itself lives in the hft-factory-strategies repository. This collaborative effort ensures that our work is well-aligned and poised for maximum impact, bringing significant value to the entire quant community.

Current State: Where HFT Beta Factory v0 Stands Today

Alright, let's talk about where we are right now with the HFT Beta Factory v0 project. We've made some solid progress, and a foundational skeleton for the v0 Beta Factory has already been integrated into the hyophyop/hft-factory-strategies repository. This is a big step, as it provides the initial structure for what will become a powerful factor computation engine. You can find the core components within the strategies/beta_factory/ package, which is designed to be modular and extensible.

At the heart of this initial implementation are a few key objects that form the backbone of our beta calculation process. These include the BetaFactory itself, which orchestrates the factor generation, and MarketBetaConfig, which is crucial for managing the parameters and settings of our market beta calculations. Perhaps the most central piece is the compute_market_beta function. This function is designed to take essential inputs like asset_returns_by_symbol (the return time series for individual assets) and benchmark_returns (the returns of the market index we're comparing against) to calculate the market beta. This function is the workhorse of our current setup, providing the core logic for how betas are actually derived from raw return data.

However, it's important to acknowledge that our current implementation has a significant, though temporary, limitation. As it stands, the HFT Beta Factory v0 is built purely on Python, focusing solely on simple Ordinary Least Squares (OLS)-type market beta. While OLS is a robust and widely understood statistical method for calculating beta, serving as an excellent foundational component, this pure Python approach means that the factory is not yet directly connected to qmtl's execution or data model. This is a crucial point, guys. Without this connection, we're missing out on a ton of benefits that qmtl offers, such as its optimized data handling, powerful backtesting capabilities, and the ability to operate within a real-time execution context. Imagine trying to run complex HFT strategies without qmtl's efficient infrastructure—it would be incredibly challenging to achieve the necessary speed and scale.

The implications of this pure Python state are clear: while we have a solid computational core, it's not yet integrated into the high-performance environment it needs to truly shine. We need qmtl's power to handle vast amounts of data efficiently, manage computational pipelines, and enable us to rigorously test and deploy these factors. The MarketBetaConfig object, for instance, currently manages parameters like lookback_length and min_observations directly within the Python code. While effective for a standalone module, integrating this configuration seamlessly into qmtl's broader system is the next logical step. This skeleton, though robust in its mathematical core, is just the beginning; its full potential will only be unlocked once it's harmonized with the qmtl ecosystem. This integration will transform a powerful calculation module into a true engine for high-frequency trading innovation.

Key Requirements from the QMTL Perspective: Charting the Integration Path

Alright, folks, let's get down to the nitty-gritty of what we need from QMTL to make this integration sing. This section is all about defining the necessary interfaces and hooks that qmtl must provide to ensure the HFT Beta Factory v0 can operate seamlessly. We're not talking about implementation details just yet; our focus here is on a clear, actionable design. This collaborative discussion is paramount to charting a smooth path forward.

Standard Data Access Path for Beta/Factor Computation

One of the most critically important requirements for any robust factor computation engine, including our HFT Beta Factory v0, is a standardized data access path. Without a consistent and efficient way to retrieve financial data, calculating factors becomes a messy, error-prone, and ultimately, a performance-hindering endeavor. We need to avoid data siloing and ensure that strategists can easily tap into the data they need.

For the v0 iteration, we have a few minimum essential data elements:

  • First, we need asset-specific return time series. This means having consistent access to the historical returns for individual assets. Initially, we're focusing on daily or other low-frequency data, but it's vital to design this with an eye towards future expansion to intraday data. Why? Because for high-frequency trading, intraday data is where the real edge often lies, offering higher granularity and faster insights into market movements. Returns are the fundamental building blocks for many quantitative factors, making their standardized access paramount.
  • Second, we require benchmark (market index) return time series. This is crucial for calculating market betas, as it provides the comparative context against which individual asset performance is measured.

Our Proposal: To address these needs, we suggest defining a common utility or a specific node type within qmtl's existing data model and node system. This utility would provide a