Autonomous quantitative research system
Bittensor AutoResearch
Creator & System Designer
An end-to-end system for researching whether machine-learning signals could predict relative performance across Bittensor subnet markets—and whether those signals survived realistic portfolio construction and unseen data.
THE PROJECT
Building the research infrastructure, not just the model
The project began with a quantitative trading question but developed into a broader research system. It needed to ingest and clean changing market data, engineer comparable features across subnet markets, train and evaluate models, simulate portfolio decisions and preserve strict boundaries between research data and unseen evaluation periods.
The objective was not to find the most attractive historical backtest. It was to build a process capable of testing, rejecting and improving ideas reproducibly.
The objective was to create a repeatable system that could test market hypotheses, evaluate whether apparent predictive signals survived unseen data and translate model outputs into realistic portfolio decisions.
Defining the research objective
Designing the architecture and evaluation process
Directing AI-assisted implementation
Working with Python, data pipelines and model evaluation
Selecting and comparing research approaches
Reviewing outputs and challenging invalid assumptions
Determining whether the system justified capital deployment
WHAT I BUILT
Four connected layers
A research system is only as credible as the infrastructure, evaluation and decision policy around the model.
Data infrastructure
A repeatable market-data foundation that could survive a changing network.
- Canonical market datasets
- Cleaning and validation
- Repeatable data refreshes
- Subnet identity and lifecycle handling
- Feature-generation pipelines
Machine-learning research
Comparable experiments focused on ranking relative market performance.
- Ridge regression
- XGBoost
- Cross-sectional market ranking
- Multiple targets and feature groups
- Out-of-sample model evaluation
Portfolio simulation
A policy layer that translated model output into capital decisions.
- Candidate ranking
- Position sizing
- Liquidity constraints
- Trading costs
- AMM-based entry and exit simulation
- Residual capital held in TAO
Recursive research workflow
Controlled iteration without outsourcing evaluation or judgement.
- Bounded LLM proposals and implementation
- Deterministic tests and evaluation
- Repeatable experiment outputs
- Human review of assumptions and results
I built the environment in which models could be proposed, tested, rejected and audited—not merely one model producing one forecast.
HOW THE SYSTEM WORKED
Recursive research within defined boundaries
Automation accelerated the loop. Objectives, constraints and deployment judgement remained human responsibilities.
Human judgement remained outside the automated loop.
Market data, feature generation, modelling, evaluation and portfolio simulation were repeatable system responsibilities. Research objectives, invalid assumptions and deployment decisions remained human ones.
Objectives, architecture and judgement
Research proposals and implementation assistance
Tests, metrics, simulations and reproducible outputs
VISUALISING THE RESEARCH
Signal had to survive time and execution
Historical fit, unseen-data performance and real portfolio mechanics were evaluated as separate questions.
The model was repeatedly trained only on information available at the time, then evaluated on later periods it had not seen.
The campaign explored a broad, controlled space. It did not imply that every feature, horizon or configuration was retained.
A model forecast was only one component of the final investment outcome. Portfolio decisions could succeed or fail at every downstream layer.
RESULT AND JUDGEMENT
Promising research. Insufficient stability for deployment.
Development results initially suggested that useful predictive relationships might exist. However, the underlying Bittensor network and its subnet markets were changing quickly, and performance did not generalise with enough consistency to justify deploying capital through the strategy.
The durable result was the infrastructure: a reusable system for ingesting data, testing models, simulating portfolio policies and determining when an apparently strong result was not reliable enough.
WHAT IT DEMONSTRATES
System building with judgement
Technical systems building
Turning a broad research idea into functioning infrastructure.
Quantitative judgement
Separating model performance, portfolio construction and market execution.
AI-assisted development
Using LLMs to accelerate implementation without outsourcing evaluation or judgement.
Research discipline
Testing promising results against unseen data before committing capital.
The project combined product architecture, quantitative research, technical implementation and practical investment judgement inside one independently built system.