README.md

Before you start  ·  The four weeks  ·  Course map  ·  The project  ·  What it uses  ·  Series front page

Course 1: Python Foundations

Four weeks from your first line of code to a month-end portfolio review you run yourself.

On day one you store a bond: its ticker, its coupon, its maturity. Four weeks later a month-end holdings extract arrives that does not tie to its cover note. You find what is wrong with it, fix it, state every decision, and write up what the book holds and how it differs from its benchmark. The project gives you the questions. The code and the conclusion are yours.

That is the course: from storing one bond to reviewing a whole book on your own.

Every skill is taught on the job it does on a desk. Loops total a portfolio. Joins attach issuers and ratings to positions, with a check on the row count and the face amount around each one. Charts show where the spread and the duration sit. Your first time series is Treasury yields since 2015. Your first text analysis is on FOMC statements.

Two habits run through all of it.

Habit	What it means
The Excel bridge	Every new idea sits beside the formula you would write today, so you are translating something you know.
Check before you trust	You work out what the answer should be before you rely on code, yours or an AI assistant's. Each day ends with code that runs cleanly and is wrong, and you find the bug.

You need no coding experience. You do need to know what a coupon, a spread, and a duration are. This is the first course in Python for Fixed Income.

Status: 21 of 21 notebooks are ready, and so is the course project.

This course is educational content created in a personal capacity. Nothing here is investment advice or a recommendation to buy or sell any security. All portfolio data is synthetic.

Before you start

Setup is on the series front page. Pick one of two ways to run the notebooks.

Way	What to do
Google Colab, nothing to install	Open any notebook in the course map and click the Open in Colab badge at the top.
Your own Windows machine	Follow the three steps on the front page. The install line for this course is below.

python -m pip install -r course1_python_foundations\requirements.txt

Each day's folder holds three files with the same name: the notebook, a short overview PDF to read first, and a solutions notebook. How a day works explains the pattern.

The four weeks

Week	Theme	By the end you can
1	Python foundations	Write variables, loops, functions, and a class, read an error, and turn an email of holdings into a table
2	NumPy and pandas	Hold a portfolio in a table, filter and group it, and join it to issuer and rating tables with checks around each join
3	Visualization and exploratory data analysis	Draw finished charts, test a new file before trusting it, and work with a time series
4	Consumer credit, text, and the project	Analyze a yes or no outcome, turn text into numbers, keep your work in a file you can run again, and report on a month-end extract on your own

Course map

Week 1: Python foundations

Day	Notebook	You learn	Status
00	Setup: Python, VS Code, and Colab	Step by step, with a screenshot of each step: install Python and VS Code, get the course, make the course environment, and use Colab	Ready
0	Start Here: Python, Notebooks, and Libraries	Ground zero, for someone who has never opened a notebook: what Python is, notebooks and their code and markdown cells, .py files, and libraries	Ready
1	Introduction to Python	Variables, f-strings, lists, dictionaries, conditions, loops, and functions, on four bonds	Ready
2	Files and Folders	Finding, creating, writing, and reading the desk's holdings file	Ready
3	Debugging	Reading an error, the common error types, bugs that raise no error, and checks that stop the run with assert	Ready
4	Classes: A Bond Object	A bond that carries its own data and calculations	Ready
5	Case Study: Organizing a Small Portfolio	One job from start to finish: an email of holdings becomes a table	Ready

Week 2: NumPy and pandas

Day	Notebook	You learn	Status
1	NumPy	Arrays, arithmetic on a whole portfolio at once, and pricing a bond from its cash flows	Ready
2	pandas: Series and DataFrames	Tables with names, selecting, filtering, and sorting 200 holdings	Ready
3	pandas: Combining and Loading	Joining tables, reading and saving CSV files and Excel workbooks, summaries, and dates	Ready
4	Case Study: A First Look at the Data	Three linked tables: positions, issuers, and ratings, checked before any join	Ready
5	Case Study: Joining the Tables	Joining the three tables, checking each join, and answering the desk's questions from the result	Ready

Week 3: Visualization and exploratory data analysis

Day	Notebook	You learn	Status
1	Visualization	Histograms, box plots, bar charts, line plots, scatter plots, pair plots, and heatmaps, on the 200 holdings	Ready
2	More Visualization	Regression plots, joint plots, violin, strip and swarm plots, interactive and 3D charts with plotly, and customizing a chart	Ready
3	Exploratory Data Analysis	Sanity checks, missing values, distributions, outliers, and relationships, on a holdings file with problems planted in it	Ready
4	Case Study: Treasury Yields	A first time series: Treasury par yields by tenor and year, a spread between two tenors, and daily changes	Ready
5	Case Study: A Trade Blotter	The week's capstone: a month of trades checked, summarized, joined to the holdings, and reported	Ready

Week 4: Consumer credit, text, and the project

Day	Notebook	You learn	Status
1	Case Study: Consumer Credit	A full analysis of a public credit card default dataset: 30,000 accounts, a yes or no outcome, and rates with their counts	Ready
2	Analyzing Text	Regular expressions, cleaning, stopwords, stemming, and turning text into numbers, on FOMC statements	Ready
3	Case Study: Statement Sentiment	Turning the wording of FOMC statements into a score with three word lists, and how far to trust it	Ready
4	From Notebook to Module	Putting the cleaning and checking steps into a .py file, importing it, and running the same lines on a second file	Ready
5	Course Project	A new month-end extract, answered on your own, ending in a written picture of the book. A worked version sits beside it	Ready

The project

Day 5 of week 4 is yours, and it is more than one sitting: plan on two or three. A new month-end extract arrives with a cover note, and 43 questions take you from the structure of the file to a written picture of the book. Nothing tells you what is wrong with the extract. You test it against the cover note and the desk's rules, fix what is an error, flag what you cannot fix, and keep what is real.

You hand in four things: a cleaning function with checks that stop the run, an Excel workbook with the cleaned book and its summary, a short log of any AI assistant use, and a written conclusion.

File	What it is
course1_project.ipynb	The brief, the questions, and a check cell for every answer that can be checked. A check says correct or not yet, and never shows the answer
course1_project_overview.pdf	The brief as slides: the scenario, the files, the rules, and how the checks work
course1_project_solutions.ipynb	The worked version, with every decision stated. Open it after you finish

The conclusion you write describes the data. It never recommends, ranks, or forecasts a bond, an issuer, or a sector.

What it uses

Library	Used for	First appears
NumPy	Arithmetic on whole columns at once	Week 2, Day 1
pandas	Tables: loading, filtering, grouping, joining	Week 2, Day 2
openpyxl	Reading and writing Excel workbooks from pandas	Week 2, Day 3
matplotlib and seaborn	Charts	Week 3, Day 1
plotly	Interactive and 3D charts	Week 3, Day 2
nltk	Stopwords, stemming, and a published sentiment word list	Week 4, Day 2

Exact versions are pinned in requirements.txt.

Data	Kind	Used in
The desk's portfolio: holdings, benchmark, issuers, ratings, trades, and the project extract	Invented for this course	Weeks 1 to 3, Week 4, Day 4, and the project
U.S. Treasury daily par yield curve rates	Real, public	Week 3, Day 4 and Week 4, Day 3
UCI Default of Credit Card Clients	Real, public, CC BY 4.0	Week 4, Day 1
FOMC post-meeting statements	Real, public	Week 4, Days 2 and 3

Sources and terms for every file are in the data page.

What comes next

Course 2 is Bond Math: three weeks on pricing, yield, accrued interest, duration, convexity, and spread, built by hand and checked against Excel.

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Created by Jeff Lenamon