RiskLabAI.py — Tutorial Notebooks

Runnable Python tutorials for RiskLabAI.py (≥ 2.0.1), the financial-machine-learning library based on López de Prado's Advances in Financial Machine Learning and Machine Learning for Asset Managers. Each notebook is exhaustive over its module, runs top-to-bottom against a pinned environment, and mirrors the matching Julia tutorial in Notebooks.jl.

Getting started

New to this? This gets you from a fresh computer to running any tutorial. You only do Step 1 once.

Step 1 — install the tools (once)

1a. VS Code. Download and install Visual Studio Code (Windows or macOS) and open it once. Then install these extensions (left sidebar → Extensions icon → search → Install):

• Python (publisher: Microsoft)
• Jupyter (publisher: Microsoft)

1b. Anaconda (Python + Jupyter). Download and install Anaconda (Windows: run the installer with defaults; macOS: run the .pkg). This gives you Python and Jupyter ready to go.

Step 2 — run a notebook

1. Get the notebooks. Go to https://github.com/RiskLabAI/Notebooks.py → green Code button → Download ZIP → unzip somewhere easy. (Or, with git: git clone https://github.com/RiskLabAI/Notebooks.py.)
2. Open the folder in VS Code: File → Open Folder → choose the Notebooks.py folder.
3. Install the packages. Open a terminal in VS Code (Terminal → New Terminal) and run:

   pip install -r requirements.txt

   This installs RiskLabAI (from PyPI) plus Jupyter, pandas, matplotlib, and the data tools. (First time takes a few minutes.)
4. Open a notebook (e.g. optimization/portfolio_construction.ipynb), click Select Kernel (top-right) → Python Environments → choose the Anaconda/base Python, then click Run All.

The FRED API key (two notebooks)

The fractional differentiation and triple-barrier labeling tutorials pull real data from FRED (the Federal Reserve's free database). To run them:

1. Get a free API key (instant): https://fredaccount.stlouisfed.org/apikey
2. Set it as an environment variable, then open a new terminal so it takes effect:
   • Windows: setx FRED_API_KEY "your_key_here"
   • macOS: add export FRED_API_KEY="your_key_here" to ~/.zshrc, then source ~/.zshrc
3. Verify in a new terminal: echo %FRED_API_KEY% (Windows) or echo $FRED_API_KEY (macOS).

All other tutorials run fully offline and need no key.

Optional — the PDE notebook only

The Deep-BSDE notebook (pde/deep_bsde.ipynb) needs PyTorch (a large download). Only if you want to run it:

pip install -r requirements-pde.txt

Flagship tutorials

#	Notebook	Topic
1	optimization/portfolio_construction.ipynb	Robust portfolio construction — covariance denoising (Marčenko–Pastur), HRP, and NCO vs naive Markowitz
2	data/structures/financial_data_structures.ipynb	Financial data structures — time, tick, volume, dollar, imbalance & run bars (offline AAPL data)
3	data/differentiation/fractional_differentiation.ipynb	Fractional differentiation — the minimum d for stationarity via an ADF sweep (FRED S&P 500)
4	data/labeling/triple_barrier_labeling.ipynb	Triple-barrier labeling + meta-labeling + trend scanning (FRED S&P 500)
5	backtest/cross_validation_and_pbo.ipynb	Cross-validation — purged K-Fold, CPCV, walk-forward, and the Probability of Backtest Overfitting (PBO)
6	features/feature_importance/feature_importance.ipynb	Feature importance — MDI, MDA, SFI, orthogonal (PCA) features, and weighted Kendall-τ
7	pde/deep_bsde.ipynb	Deep-BSDE PDE solver (Han, Jentzen & E 2018) — four financial PDEs, checked vs Monte-Carlo / closed-form references

Setup

RiskLabAI installs straight from PyPI — no clone of the library needed.

git clone https://github.com/RiskLabAI/Notebooks.py.git
cd Notebooks.py
python -m venv .venv
source .venv/bin/activate          # Windows: .venv\Scripts\activate
pip install -r requirements.txt    # RiskLabAI (PyPI) + notebook deps
jupyter notebook                   # launch, then open any tutorial

Run a notebook headless:

jupyter nbconvert --to notebook --execute --inplace <path-to-notebook>

Optional — Deep-BSDE PDE notebook only. Tutorial 7 (pde/deep_bsde.ipynb) needs PyTorch, kept out of the light core environment:

pip install -r requirements-pde.txt

Tutorials 3–4 read the S&P 500 from FRED; set the FRED_API_KEY environment variable (a free key from https://fred.stlouisfed.org). All other data is either synthetic or the committed input_data/AAPL_OHLCV_1m_Data.csv snapshot, so the notebooks stay reproducible and offline.