Who this is for. Solo quants and small teams setting up or cleaning up their research environment. Assumes comfort with Python, SQL and the command line.
How to use it. Read the steps in order; each one assumes the previous. The “why here” line says what the step adds. Where a step is a tool, run it on your own numbers before moving on. The exemplar papers at the end are the archive’s highest-rigor papers for the path’s methods; read two of them with the checklists in hand.
Steps
- How to Build a Quant Research Pipeline: From Idea to Evidence, Repeatably
The seven-stage quant research pipeline — idea intake, data, features, backtest, validation, paper trading, production — with the artifact each stage must produce and the gate it must pass.
Why here: The end-to-end design: idea to evidence, repeatably.
- A Practical Quant Research Stack for a One-Person Shop (2026)
The complete toolchain for solo quant research in 2026: data, storage, backtesting, compute, and deployment — with honest costs and the mistakes to skip.
Why here: Concrete tool choices for one person.
- Market Data for Quant Research: How to Choose a Vendor (2026)
A decision framework for choosing market data vendors for quant research: survivorship bias, point-in-time integrity, licensing, and the real cost tiers.
Why here: Where the data comes from.
- When to Use Parquet, Postgres, or a Columnar Database
The three storage archetypes for quant research — files, relational, columnar-analytical — matched to workload shapes, with the two-tier default and the migration triggers.
Why here: Files or a database, and which database.
- Point-in-Time Data Architecture for Backtesting
Designing a point-in-time data layer: bitemporal modeling, the as-of query pattern, a practical schema for prices, fundamentals, universes and events, and the migration path from a naive store.
Why here: The storage design that prevents look-ahead bias by construction.
- Corporate Actions and Adjusted Price Data
Corporate actions in quant research: how adjustments work, the dividend/total-return distinction, the actions that break backtests (spinoffs, mergers, delistings), and the store-unadjusted principle.
Why here: The adjustment logic every price dataset needs.
- How to Version Datasets and Backtests
Versioning for quant research: content-addressed datasets, config-hashed backtests, the lineage chain that makes any result re-derivable, and the lightweight tooling that suffices.
Why here: Make every result reproducible from a hash.
- Reproducible Quant Research with Docker
Using Docker for reproducible quant research: what containers do and don't solve, the research-image pattern, determinism beyond the environment, and when containers are overkill.
Why here: Freeze the environment.
- A Minimal ML Experiment-Tracking Stack for Quant Research
Experiment tracking for quant ML: what to record, the finance-specific requirements (trial counts, temporal splits, leakage audits), tool tiers from SQLite to MLflow/W&B, and the minimal stack that suffices.
Why here: Track trials so the overfitting haircut is honest.
- Local GPU vs Cloud GPU for Financial NLP and LLM Research (2026)
When to buy a GPU and when to rent one for financial NLP and LLM research: break-even math, VRAM sizing, data-licensing constraints, and the hybrid default.
Why here: Compute decisions for NLP and deep-learning work.
- Quant Researcher Compute-Cost Calculator tool
Interactive calculator: local GPU vs cloud GPU vs API costs for quant research. Editable assumptions, break-even hours, and a monthly budget for your whole stack.
Why here: Price the compute.
- From Notebook to Production: A Minimal Automated-Strategy Operating Stack (2026)
The minimal operating stack for running an automated trading strategy: one VPS, Docker Compose, Postgres, cron done right, secrets, logs, dead-man alerting, backups, and incident response — with working configs.
Why here: From research code to a running strategy.
- Research-to-Production Checklist for an Automated Trading Strategy (2026)
The complete checklist for taking a backtested strategy live: validation gates, execution safety, kill switches, monitoring, reconciliation, and the go-live protocol.
Why here: The final gate.
Related
All paths: learning paths. Methods, with their own guides and exemplars: methods pages. Programmatic access to the papers: agents page.