Quantitative research contains many tasks that agents can accelerate: locating relevant work, inspecting unfamiliar datasets, generating analytical code, running experiments and documenting results. Yet speed can also magnify familiar research failures. An agent may invent a source, leak future information, overfit repeated experiments or silently change assumptions between runs.
This course shows how to place agents inside a disciplined research process. Participants will design bounded tools, maintain provenance, capture environments and require evidence at each stage. Agents are used to expand a researcher’s capacity—not to bypass statistical judgement or approve their own conclusions.
The course is hands-on and deliberately separates research assistance from autonomous trading. The methods apply across alpha research, risk analytics, portfolio analysis and research-platform engineering.
Learning Outcomes
Upon completion of this course, participants will be able to:
- Identify quantitative research tasks that are suitable for agent assistance
- Build a controlled agent workflow for literature, data and code tasks
- Require traceable sources and evidence for research claims
- Prevent common forms of temporal leakage and experimental contamination
- Use agents to generate, execute and review reproducible experiments
- Evaluate agent-produced code, analysis and narrative independently
- Capture provenance across data, assumptions, models and results
- Define governance boundaries between research agents and production systems
Course Outline
Agentic Quantitative Research
- Where agents can assist across the research lifecycle
- Differences between research copilots, workflows and autonomous agents
- Dynamic planning versus deterministic research pipelines
- Setting goals, non-goals and stopping conditions
- Preserving researcher responsibility and scepticism
- Separating research support from investment authority
Literature and Evidence Agents
- Searching papers, documentation and internal research archives
- Tracking citations and distinguishing primary from secondary evidence
- Verifying that quoted findings are present in the source
- Extracting methods, assumptions and limitations systematically
- Comparing conflicting evidence without forced consensus
- Building an auditable research evidence pack
Financial Data Discovery and Governance
- Finding datasets and understanding their intended use
- Schema, frequency, timezone and corporate-action considerations
- Point-in-time availability and survivorship bias
- Data entitlements, licensing and confidential information
- Data lineage and versioning for repeatable experiments
- Permission-aware agent access to research data
Hypothesis Development and Research Design
- Using agents to generate questions rather than conclusions
- Translating ideas into falsifiable hypotheses
- Recording assumptions, mechanisms and expected observations
- Designing baselines and meaningful comparisons
- Identifying confounders and alternative explanations
- Predefining evaluation criteria and stopping rules
Agent-Assisted Analysis and Coding
- Generating analytical code within approved libraries and patterns
- Inspecting transformations for leakage and look-ahead
- Verifying units, joins, missing data and numerical stability
- Using tests and invariants for financial calculations
- Running code in isolated, reproducible environments
- Reviewing dependencies and generated configuration
Experiment Orchestration
- Parameterised and repeatable experiment pipelines
- Capturing code, data versions, environments and random seeds
- Scheduling bounded parallel experiments
- Preventing agents from repeatedly optimising against a holdout set
- Tracking failed and negative experiments as well as successes
- Managing compute budgets and stopping unproductive searches
Backtesting and Statistical Validation
- Train, validation and genuinely untouched test periods
- Transaction costs, liquidity, capacity and implementation assumptions
- Multiple testing, selection bias and backtest overfitting
- Robustness across regimes, assets and parameter choices
- Comparing agent suggestions with simple baselines
- Requiring independent review before accepting a result
Research Memory and Knowledge Systems
- Storing decisions, evidence and experiment outcomes
- Distinguishing durable facts from provisional interpretations
- Avoiding contamination between projects and evaluation periods
- Correcting or expiring stale research memory
- Retrieving prior negative results and known pitfalls
- Protecting confidential research and intellectual property
Communicating Results
- Generating transparent research summaries from recorded evidence
- Distinguishing observed results from interpretation
- Reporting uncertainty, sensitivity and limitations
- Producing reproducible charts, tables and notebooks
- Reviewing narratives for overclaiming and omitted counterevidence
- Creating artefacts for peer review and governance
Practical Capstone
- Define a bounded quantitative research question
- Configure an agent with controlled evidence and data tools
- Produce a traceable research and experiment plan
- Run a reproducible analysis with anti-leakage checks
- Evaluate the result against statistical and operational criteria
- Present the evidence, limitations and recommendation for further work
Intended audience
This course is designed for quantitative researchers, data scientists, research engineers, quantitative developers and technical leads working with financial data and systematic research. It is also suitable for experienced software engineers supporting research platforms who want to understand agent-assisted quant workflows.
Prerequisites
Those attending this course should meet the following:
- Proficiency in Python for data analysis
- Working knowledge of statistics and quantitative research methods
- Familiarity with financial time-series data and backtesting
- Experience using Git, notebooks or reproducible analytical workflows