Coding Bootcamp Starter Repo

This repository is the baseline codebase for the coding boot camp. The first student workflow is simple: open the project in PyCharm, select a Python interpreter, install the requirements, and run the root main.py file.

Project Layout

Coding_BootCamp/
├── main.py                     # Demo script to run first
├── requirements.txt            # Python package requirements
├── credentials.py              # Loads optional environment variables
├── Data/
│   ├── data_definition.py      # DataDefinition: one interface for data
│   └── sources/
│       ├── famafrench.py       # Fama-French data via requests
│       ├── fred.py             # FRED data via requests
│       └── yfin.py             # Yahoo Finance data via yfinance
├── Classes/
│   └── MGTF_402/
│       └── Assignment_1.py     # Intentionally blank; reserved for exercises
└── Utilities/
    └── tools.py                # Helper functions for returns and statistics

Setup In PyCharm

1. Open PyCharm.
2. Choose Open and select the Coding_BootCamp project folder.
3. Select or create a virtual environment:
   • macOS: PyCharm > Settings > Project > Python Interpreter
   • Windows: File > Settings > Project > Python Interpreter
   • Choose an existing interpreter or create a new virtual environment such as .venv.
4. Open the PyCharm terminal and install the project requirements:

python -m pip install -r requirements.txt

The main packages are:

Package	Used for
pandas	DataFrames and time series
numpy	Numerical operations
scipy	Summary statistics
matplotlib	Plotting
requests	Downloading Fama-French and FRED data
yfinance	Yahoo Finance market data
python-dotenv	Loading optional values from .env

Optional FRED API Key

You do not need a FRED_API_KEY just to open the repo, import most modules, or work with non-FRED examples. A FRED API key is only needed when you make live requests to the FRED API, such as pulling the NBER recession series.

If you need live FRED data, create a .env file in the project root:

FRED_API_KEY=your_key_here

Free keys are available from the FRED API documentation.

Run The Demo

Run the main.py file at the project root.

In PyCharm, right-click main.py and choose Run 'main'.

From the terminal, run:

python main.py

You should see progress messages and printed output in the PyCharm Run window or terminal. The demo is designed to show how DataDefinition, the data source modules, and the utility functions work together. When the live downloads succeed, the chart is saved to outputs/ff5_growth_of_dollar.png.

How Data Access Works

Data/data_definition.py contains the DataDefinition class. This is the main entry point for requesting data:

from Data.data_definition import DataDefinition

# Fama-French 5-Factor daily data
ff5 = DataDefinition(
    source="famafrench",
    item="F-F_Research_Data_5_Factors_2x3_daily",
    start="2000-01-01",
    end=None,
).extract()

# Yahoo Finance closing prices
spy = DataDefinition(
    source="yfin",
    item="SPY",
    start="2000-01-01",
    end=None,
).extract()

# FRED data; requires FRED_API_KEY for the live API request
recessions = DataDefinition(
    source="fred",
    item="USRECD",
    start="2000-01-01",
    end=None,
).extract()

Supported data sources live in Data/sources/:

Source	Module	Purpose
famafrench	Data/sources/famafrench.py	Fama-French factor and portfolio data
fred	Data/sources/fred.py	Federal Reserve Economic Data
yfin	Data/sources/yfin.py	Yahoo Finance prices through yfinance

Utilities

Utilities/tools.py contains helper functions used by the demo, including:

• compute_levels_from_returns
• compute_returns_from_levels
• return_descriptor
• convert_daily_to_weekly
• generate_date_list

Import only what you need:

from Utilities.tools import compute_levels_from_returns, return_descriptor

Adding A New Data Source

To add another source later:

1. Create a new module in Data/sources/.
2. Add the import and routing logic in Data/data_definition.py.
3. Use the new source through DataDefinition(source="your_source", ...).

Keep source modules focused on downloading/parsing data, and keep calculations in Utilities/tools.py or your exercise files.