Python and SQL for Finance

Python for finance is the shared language of the catalog. Every practitioner course hands you data and asks for code, so this Python for finance course makes sure the code is not the obstacle.

What will I be able to build with Python after this course?

You will be able to take a raw file of prices, find and fix its errors, compute returns, volatility, correlation and drawdown, chart them, and run a simple strategy test that someone else can rerun and check. That is the daily work of a junior quant analyst, and it is where most mistakes in published backtests begin.

Why does this course spend so long on data cleaning?

Because market data is dirty. Prices arrive with gaps, split adjustments, stale quotes, time zones that disagree and holidays that differ between exchanges. A strategy test on uncleaned data measures the errors in the file, not the strategy. The course teaches you to look before you compute.

Why is a point-in-time database so important?

SQL for finance is the skill that turns a pile of price files into data you can trust. Prices, trades, positions and reference data all live in tables, and a researcher who can join and window them correctly avoids the quiet errors, such as missing delisted stocks, that make a backtest look better than it was.

Because a database built from today’s view of the world leaks the future. Index members are the ones that survived, company names are today’s names, and fundamentals are revised after they were first reported. The course shows how to store data as it was known on each date and how to query it with as-of joins.

Who is this course for?

Analysts who work in spreadsheets and want to move to code, students starting the practitioner path, and anyone heading for the machine learning track.

What you will be able to do

• Load, clean and align daily and intraday price data from files and public sources.
• Compute simple and log returns, rolling statistics and drawdowns with pandas and NumPy.
• Plot prices, returns and distributions clearly enough to spot data errors.
• Write small, tested functions instead of long notebooks.
• Run a first moving-average strategy test and read its result with suspicion.
• Write SELECT, JOIN, GROUP BY and window-function queries on price, trade and position tables.

Syllabus, module by module

1. Setting up

   Installing Python, managing environments, and working in notebooks and plain scripts.

2. The language

   Types, lists, dictionaries, loops, functions and errors, using financial examples throughout.

3. NumPy

   Arrays, vectorized arithmetic, random numbers and why loops over prices are slow.

4. pandas for market data

   Series and DataFrames, date indexes, resampling, joins across markets and missing values.

5. Returns and risk

   Simple and log returns, volatility, correlation, rolling windows and maximum drawdown.

6. Tables of market data

   Prices, trades, quotes, positions and reference data, and how they relate.

7. Querying

   SELECT, WHERE, ORDER BY and aggregates on daily and intraday prices.

What every course contains

Every paid course comes with the first seven and the certificate, whichever level it sits at. The proctored exam and the credential come when the course is taken as part of a Certificate, Diploma or Fellowship. Everything is self-paced: the material is yours to study in any order, at any hour, for the whole of the license.

1. Workbooks. Written to train the thinking patterns and the important concepts of the course, with worked problems and the reasoning behind each answer, so that you learn to think the way the subject is thought.
2. Audio edition. Focused, well-presented recordings with explainers and examples of the material, made to be interesting, relevant and entertaining, so that the general subject can be learned away from the desk.
3. Notes. Pre-produced notes with every fact the course relies on, written out in full, so that nothing has to be transcribed from the films or the audio.
4. Cheat sheet. One sheet with every important learning concept of the course on it, to keep beside you during the work and to revise from before the exam.
5. Named sources. Every fact in the notes names its source, the paper, the book or the dataset, for anyone who wants to dig in further.
6. Explainer films. Focused films, in the style of the sample films on the catalog page, that walk through the most difficult concepts of the course with visual explanations of a high standard.
7. Transcript. A full transcript of every film and every audio recording, searchable and quotable.
8. Proctored exam. Taken as part of a Certificate, Diploma or Fellowship, the course ends in a proctored multiple-choice questionnaire, with a pass mark of 60% and three retakes included. A course taken on its own is assessed by its coursework and has no exam.
9. Verifiable certificate. A certificate of completion that a third party can verify: an employer sends the code on it to the credentials office and receives written confirmation of the course, the date and the result.
10. Credential of the program. The Certificate, Diploma or Fellowship of the program, track or path the course belongs to, awarded when every course in it is passed. Which one depends on the program you choose.

How the certificate is earned

Unit quizzes and one graded problem set: you clean a messy multi-asset price file, compute a set of return and risk statistics, and submit a notebook that reruns from top to bottom. On a pass you receive the Course certificate in Python and SQL for Finance, issued by the Institute with a verification code. A course taken as part of a Certificate, Diploma or Fellowship ends in a proctored online exam with a pass mark of 60%. The fee includes three retakes; each attempt after that carries a reduced proctored exam fee. A course taken on its own is assessed by its coursework and has no exam. Read how certification works.

Before you start

No programming experience is required. You need a computer on which you can install software, and school algebra.