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Quantzivo Blog

Practical guides to markets, code & research

Clear, hands-on articles on backtesting, Python, quantitative research, financial data and AI, written to help you learn and test ideas honestly.

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Featured article
Backtesting4 min read

Seven backtesting pitfalls to avoid

A backtest is only as honest as its assumptions. Here are seven common mistakes that make historical results look better than they really are, and how to catch each one.

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All articles

Practical guides on Python, backtesting, quantitative research and AI in finance.

Python Programming5 min read

Cleaning market data with pandas

Raw price files are rarely ready for analysis. This walkthrough shows how to parse, deduplicate, validate and resample OHLCV data in pandas so your research starts from a trustworthy base.

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Quantitative Research4 min read

Walk-forward testing explained

Walk-forward testing repeatedly fits a strategy on past data and evaluates it on the period that follows. Learn how it works, how to choose windows, and what it can and cannot tell you.

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AI in Finance4 min read

How news sentiment models work

News sentiment models turn headlines and articles into structured scores. This guide explains the main approaches, from word lists to language models, and the limitations every researcher should understand.

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Financial Data Engineering4 min read

Building a reliable market data pipeline

Research is only as good as the data feeding it. Learn the core stages of a market data pipeline, from raw ingestion to validated, point-in-time storage, and the design choices that keep it reliable.

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AI in Finance4 min read

Overfitting in financial machine learning

Financial data is noisy, limited and always changing, which makes machine learning models especially easy to overfit. Learn the warning signs, the most common sources of leakage, and practical defenses.

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Educational content: articles on this blog explain concepts and techniques. They are not investment advice and do not recommend buying or selling any security. Code examples are simplified for learning.

What we write about

Eight topics across the research workflow

Each category maps to a stage of turning a market idea into something you can test, explain and review.

Backtesting

How to simulate rules on historical data without look-ahead, survivorship or cost-free illusions.

Python Programming

Practical pandas and NumPy techniques for loading, cleaning and analyzing market data.

Quantitative Research

Validation methods, statistics and research habits that separate evidence from noise.

AI in Finance

How machine learning and language models work on financial data, and where they fall short.

Market Research

Reading macro releases, company information and market context with the right caveats.

Algorithm Development

Turning ideas into explicit, testable rules for entries, exits, sizing and risk limits.

Financial Data Engineering

Pipelines, storage and point-in-time data design that keep research reproducible.

Trading Technology

Indicators in code, market structure and the systems that move data and orders.

Go beyond articles

Learn in a structured way

A blog post can explain one idea. Building real skill takes a sequence: the right order of topics, practice on real data, and feedback on your work. That is what Quantzivo Academy is built for.

  • 50 courses across ten disciplines, from market fundamentals to FinTech engineering
  • Learning paths that sequence courses toward a specific goal
  • Coding exercises and projects using Python and historical market data
  • Honest validation with costs, drawdowns and overfitting covered throughout
Browse courses See learning paths
Illustrative backtest report using sample data, with summary metrics and stated cost, slippage and fill assumptions
Where to start

Match the reading to a course

New to coding for markets?

Pair the pandas and indicator articles with Python Programming for Financial Markets.

Testing strategy ideas?

The backtesting and walk-forward guides lead into Factor Models & Quantitative Research.

Exploring AI and ML?

The sentiment and overfitting articles connect to Machine Learning for Financial Data.

Quantzivo Academy

Turn reading into structured skills

The Academy takes the topics on this blog further with sequenced lessons, coding exercises and assessed projects. Register your interest to hear when enrollment opens.

Register your interest Explore learning paths
Not investment adviceEducation & software only