Code & Kapital
We build research-grade financial data systems, backtesting infrastructure, portfolio analytics, and applied quant research designed for builders, allocators, and technically sophisticated investors.
Built for
Quants, engineers, PMs, allocators, and serious investors who care about process quality as much as the result.
Focus
Practical implementation over abstract theory, with clear standards for data integrity, validation, and operational rigor.
What We Do
Code & Kapital sits between institutional research, quant engineering, and premium financial software. The objective is not just to publish ideas, but to build the systems that make those ideas defensible.
Research
Applied work on factors, signals, portfolio construction, and implementation. Every conclusion is framed through robustness, regime awareness, and practical execution.
Tools
Software for financial data, signal evaluation, regime modeling, portfolio diagnostics, backtesting, and data workflow visibility.
Infrastructure
Python-based research systems, data pipelines, and validation workflows built for repeatability instead of one-off notebooks and fragile assumptions.
Education
Serious educational media for quants, engineers, and investors who care about implementation quality as much as the strategy idea itself.
Featured Research
Work spanning backtesting integrity, factor design, regime modeling, strategy validation, portfolio construction, and the data engineering standards required to support credible results.
How backtesting biases emerge in real research pipelines, why they are difficult to detect, and what it takes to structure a process that produces trustworthy results.
Inverse volatility weighting changes portfolio behavior by scaling risk instead of capital, revealing how construction decisions reshape returns in systematic strategies.
Why ticker-based systems fail, how OpenFIGI creates a stable identity layer, and where FIGI belongs inside a research-grade data pipeline.
Platform Preview
The platform centers research, diagnostics, and operational visibility to shorten the path from idea to dependable workflow.
Publishing, research systems, analytics, and operational workflows all sit on the same technical foundation, so the work stays consistent from idea generation through implementation.
Research harnesses that make assumptions explicit, track validation states, and reduce false confidence from fragile studies.
Exposure inspection, turnover analysis, concentration controls, and scenario-level portfolio diagnostics.
Cross-sectional diagnostics, decay analysis, crowding context, and implementation-aware interpretation of signals.
Structured review workflows for testing signal quality, comparing alternatives, and pressure-testing assumptions before formal strategy promotion.
Data Systems
Systematic investing breaks when data handling is casual. The stack is designed around daily workflows, structured datasets, entity resolution, and research outputs that can still be explained months later.
Research outputs depend on timestamp integrity, corporate action handling, security mapping, and reproducible joins.
Pipelines are designed around repeatable ingestion, validation, and issue detection rather than ad hoc data pulls.
Security master quality matters. Symbol changes, delistings, and identifier mapping cannot be an afterthought.
Analyses should be explainable months later. Inputs, transforms, and assumptions need a clear trail.
Research cadence
Weekly publication flow
Data operations
Daily validation mindset
Core stack
Python, SQL, reproducible pipelines
Education
The YouTube channel exists to extend the mission, not dilute it. Topics focus on quantitative research methods, implementation details, research workflows, and the engineering standards behind durable quant work.
Build a security master around FIGI identifiers instead of tickers, and see why durable instrument mapping matters in research systems.
Learn how inverse volatility weighting works, how portfolio weights are assigned, and which trade-offs matter in real implementation.
Why look-ahead bias, survivorship bias, and unrealistic execution assumptions quietly destroy otherwise promising studies.
Receive applied research, workflow notes, and updates on premium reports, tools, and data offerings. The focus is quality over volume.
Explore the research publication
Articles are written for serious readers who care about how quant workflows actually behave once they leave the deck and enter production.
Talk to the team
Early conversations across research, enterprise data, media, and tooling are welcome.
