Free guides to learn, build and research with confidence
Tutorials, concept guides and a working glossary organized by topic, so you can find the right starting point for Python, market data, strategy testing and AI research.
Resource hub
Find resources by topic
Every link below points to Quantzivo content: blog guides, Academy course outlines and the planned Studio and AI modules. Pick a topic to jump straight to it.
Python tutorials
Market data
Financial analysis
Developer learning
Strategy development
Algo trading concepts
AI research
Foundations
Code, data and analysis skills
The building blocks behind every research workflow: writing Python, handling market data correctly and reading the numbers that move markets.
Python tutorials
Write Python for financial data
Start with loading and cleaning price data in pandas, then compute common indicators in vectorized code you can read and test.
- pandas
- NumPy
- Time series
- Cleaning market data with pandas: preparing raw price data for analysis
- Technical indicators in Python: computing common indicators in code
- Python Programming for Financial Markets: the Academy course outline
Market data resources
Understand and organize market data
Learn what OHLCV bars, corporate actions and survivorship mean for your results, and how to structure data so research stays reproducible.
- OHLCV
- Data quality
- Pipelines
- Building a market data pipeline: moving data from source to research-ready
- Cleaning market data with pandas: checks to run before any analysis
- Global Financial Markets Fundamentals: how instruments trade and prices form
Financial analysis tutorials
Read charts, statements and the economy
Connect price behavior, company fundamentals and macroeconomic releases, and learn what each type of analysis can and cannot tell you.
- Technical
- Fundamental
- Macro
- Reading macroeconomic indicators: what the major releases measure
- Technical analysis: the planned Quantzivo AI module (in development)
- Fundamental analysis: the planned Quantzivo AI module (in development)
- Macro intelligence: the planned Quantzivo AI module (in development)
Developer learning resources
Engineer research and fintech tools
For software engineers moving into finance: APIs, data processing, testing and the tooling that keeps quantitative code maintainable.
- APIs
- Tooling
- Testing
- Building Financial Dashboards & Applications: the Academy course outline
- Developer tools: the planned Quantzivo Studio tooling (in development)
- Algorithm development: the planned Python coding workspace in Studio
- Building a market data pipeline: a data engineering walkthrough
Strategy & research
Test ideas without fooling yourself
Guides on turning ideas into explicit rules, evaluating them honestly and using AI as a research aid rather than an oracle.
Strategy development guides
From idea to explicit rules
Define entries, exits, position sizing and risk limits precisely enough that a computer, and a skeptical reviewer, can check them.
- Designing rule-based strategies
- Seven backtesting pitfalls to avoid
- Factor Models & Quantitative Research course
- Strategy builder (Studio, in development)
Algo trading concepts
Backtests, validation and simulation
Understand out-of-sample testing, transaction costs and why a simulated result is only a starting point for further scrutiny.
- Walk-forward testing explained
- Algorithmic Trading with Python course
- Backtesting (Studio, in development)
- Paper trading (Studio, simulated only)
AI research guides
Use AI with healthy skepticism
Learn how language and sentiment models work, where they fail and how to validate machine learning on noisy financial data.
- How news sentiment models work
- Overfitting in financial machine learning
- Machine Learning for Financial Data course
- Sentiment analysis (Quantzivo AI, in development)
Structured learning
Prefer a guided sequence?
Free guides are a good way to explore. When you want a clear order of topics, projects and assessments, follow a learning path.
Explore the guides
Use this page and the blog to find the topics that interest you most.
Read the blog
Follow a learning path
Curated course sequences that build skills in a sensible order toward a goal.
See learning paths
Practice with projects
Apply what you learn in assessed course projects and, once available, in Quantzivo Studio.
Browse courses
Glossary
Key terms in plain language
Short definitions of terms that appear throughout our guides and courses.
| Term | What it means | Read more |
|---|---|---|
| Backtest | Running a strategy's rules over historical data to see how it would have behaved. The result is hypothetical and depends heavily on data quality and assumptions. | Backtesting |
| Drawdown | The decline from a portfolio's peak value to a subsequent low, usually shown as a percentage. Maximum drawdown is the largest such decline in a period. | Backtesting pitfalls |
| Sharpe ratio | Average excess return divided by the volatility of returns. It summarizes return per unit of risk but ignores tail risk and depends on the period measured. | Factor Models & Quantitative Research |
| Slippage | The difference between the price you expected to trade at and the price you would actually get, caused by spreads, market impact and delays. | Backtesting pitfalls |
| Look-ahead bias | Using information in a test that would not have been available at the time of the decision, such as revised data or the same bar's close. It inflates results. | Backtesting pitfalls |
| Walk-forward analysis | Repeatedly fitting a strategy on one window of data and testing it on the following, unseen window to check whether results hold out of sample. | Walk-forward testing |
| OHLCV | Open, high, low, close and volume: the standard summary of trading activity for each bar, such as a minute or a day. | Cleaning market data |
| Factor | A measurable characteristic, such as value, momentum or size, used to explain or group differences in returns across assets. | Factor Models & Quantitative Research |
| Sentiment score | A model's numeric estimate of how positive or negative a piece of text, such as a headline, reads. It is an estimate and can misread context or sarcasm. | News sentiment models |
| Paper trading | Simulating trades with virtual money against market prices. No real orders are placed, and simulated fills can differ from real executions. | Paper trading |
Educational content only: these resources explain concepts and methods. They are not investment advice or recommendations to buy or sell anything. See our Risk Disclaimer.
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