ml-DataScience

Notebooks > Excel - for people who prefer code cells to pivot tables (and occasional chaos).

ml-DataScience — risk_analysis_1.ipynb

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Table of contents

• 📌 Overview
• 📂 What’s in the repo
• ⚙️ Requirements & install (quick)
• ▶️ How to run (local, Colab, Binder)
• 🔍 Notebook structure — detailed walkthrough (section-by-section explanation)
• 📈 Typical outputs & visuals you’ll see
• 🛠️ Tips, performance & troubleshooting
• 🤝 Contributing & license
• 🧾 Contact / credits

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📌 Overview This repository contains one main Jupyter Notebook: risk_analysis_1.ipynb — a hands-on notebook aimed at performing risk analysis workflows (data ingestion → cleaning → EDA → risk metrics → modeling → backtesting & conclusions). The README below explains how to open and run the notebook, describes the expected notebook structure in detail, and gives tips for reproducible execution. 🧭✨

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📂 What’s in the repo

• risk_analysis_1.ipynb — primary notebook (interactive, contains code cells, narrative, visualizations).
• README.md — this file (explanatory guide).

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⚙️ Requirements & install (quick) Recommended Python environment: 3.9+ (works with 3.8 in most cases).

Minimum packages (pip):

• pandas
• numpy
• matplotlib
• seaborn
• plotly (optional for interactive plots)
• scikit-learn
• statsmodels
• scipy
• jupyterlab or notebook
• ipywidgets (optional)

Example pip install:

pip install pandas numpy matplotlib seaborn plotly scikit-learn statsmodels scipy jupyterlab ipywidgets

If you prefer Conda:

conda create -n ml-ds python=3.9
conda activate ml-ds
conda install pandas numpy matplotlib seaborn scikit-learn statsmodels scipy jupyterlab -c conda-forge
pip install plotly ipywidgets

(Use these commands in a terminal/Anaconda prompt.)

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▶️ How to open and run the notebook

1. View in GitHub (read-only)

• Open risk_analysis_1.ipynb on GitHub to see rendered outputs and markdown.

2. Run locally (recommended for full interactivity)

• Clone the repo:

  git clone https://github.com/diegonmarcos/ml-DataScience.git
  cd ml-DataScience
  

• Start Jupyter:

  jupyter lab
  

  or

  jupyter notebook
  

• Open risk_analysis_1.ipynb and run cells (Kernel ▶ Restart & Run All to reproduce from scratch). ⚡

3. Run on Google Colab (no local setup)

• To open the notebook on Colab, replace the GitHub URL:

  https://colab.research.google.com/github/diegonmarcos/ml-DataScience/blob/main/risk_analysis_1.ipynb
  

• Colab is useful if you need extra computing or don’t want to configure a local env.

4. Binder (interactive reproducible environment)

• You can set up Binder (if you add an environment file). Binder launches a live Jupyter instance from the repository.

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🔍 Notebook structure — detailed walkthrough (what each section typically does) Below is a clear, section-by-section explanation you can use to understand risk_analysis_1.ipynb. The notebook may vary, but these are the expected/standard pieces in a risk-analysis notebook. I describe intentions, typical code patterns, and what to look for in results. 🧩🔎

1. Title & Purpose 📘

• Short description of the notebook’s goal: e.g., estimate risk metrics (VaR, CVaR), build predictive models of risk, or analyze portfolio exposures.
• Pay attention to the dataset description — this tells you the columns and time period used.

2. Imports & Environment setup 🧰

• Imports of pandas, numpy, matplotlib/seaborn, statsmodels, sklearn, plotly.
• Configuration for plot styles, floating point/display options, and random seed:
  • plt.style.use('seaborn') or seaborn.set()
  • pd.options.display.float_format = '{:.4f}'.format
• Why it matters: consistent plotting and deterministic results.

3. Data loading & quick peek 📥

• Loading data from CSV, Excel, or API. Typical pattern:
  • df = pd.read_csv("data.csv", parse_dates=['date'], index_col='date')
• Initial checks: df.head(), df.info(), df.describe(), checking NA counts, unique values.
• Key idea: confirm time format, frequency, and key columns.

4. Data cleaning & preprocessing 🧼

• Handling missing values: .dropna(), .fillna(method='ffill'), or interpolation.
• Type conversions: converting columns to numeric, datetime, categorical encoding.
• Outliers treatment: winsorizing or clipping, or flagging extremely large values for inspection.
• Why: clean inputs produce reliable risk metrics and stable model training.

5. Feature engineering & transformation ✨

• Creating returns (log returns or percent changes): df['ret'] = df['price'].pct_change()
• Rolling windows & aggregated statistics: moving averages, rolling volatility:
  • df['vol_30'] = df['ret'].rolling(30).std() * sqrt(252) (annualized)
• Lag features and technical indicators for modeling.
• Why: features capture temporal patterns that help predict risk exposures or tail events.

6. Exploratory Data Analysis (EDA) 📊

• Distribution plots (histograms, KDE), boxplots, QQ-plots to assess normality.
• Time-series plots to show value/returns and volatility over time.
• Correlation matrices and heatmaps to find relationships between variables.
• Why: EDA reveals structure (seasonality, volatility clustering, correlations) and hints at model choices.

7. Risk metrics computation (VaR, CVaR, stress scenarios) ⚠️

• Value at Risk (VaR) — historical, parametric (Gaussian), or Monte Carlo approach.
  • Example: 95% historical VaR = -np.percentile(returns, 5)
• Conditional VaR (CVaR / Expected Shortfall) computed as the mean of losses beyond the VaR threshold.
• Backtesting logic: compare realized losses to predicted VaR and compute hit rate.
• Why: These summarize tail risk and help evaluate model adequacy.

8. Modeling & predictive analysis 🧠

• Typical models: linear regression, logistic regression (for binary risk event), tree-based models (RandomForest, XGBoost), time series models (ARIMA/GARCH) or hybrid approaches.
• Train/test split: careful use of time-series split (no shuffle; use expanding or sliding windows).
• Metrics: MSE/MAE for regression, AUC/precision/recall for classification, or custom risk-based metrics.
• Cross-validation: use time-series aware CV (TimeSeriesSplit) for realistic evaluation.

9. Backtesting & calibration 🔁

• Walk-forward/backtest framework: generate predictions per time step, update model periodically and compute cumulative performance.
• Plot cumulative losses, VaR exceedances, and calibration tables (observed vs expected exceedances).
• Why: Backtesting shows how predictive approach performs in real chronological order.

10. Visualizations & dashboards 🎨

• Static: Matplotlib/Seaborn for summary plots.
• Interactive: Plotly for hoverable charts and dashboards.
• Common visuals: drawdowns, rolling VaR, concentration charts, correlation matrices.

11. Conclusions & next steps ✅

• Summary of key findings: e.g., model strengths/weaknesses, notable tail events, recommended monitoring thresholds.
• Next steps: improve features, more robust backtesting, use intraday data, incorporate alternative data.

12. Appendix / reproducibility notes 📚

• Versions of libraries, random seed, and data provenance.
• How to re-run entire notebook from a clean environment.

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📈 Typical outputs & visuals you’ll see

• Time series of price / returns with annotated tail events 📉
• Histogram and KDE of returns showing fat tails 🧾
• Rolling volatility and moving averages 📈
• VaR/CVaR tables and exceedance plots (binary exceedance timeline) ⚡
• Model performance metrics and confusion matrix (if classification) ✅/❌

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🛠️ Tips, performance & troubleshooting

• Kernel crashes / memory issues:
  • Reduce notebook memory by sampling, process in chunks, or increase VM memory.
  • Clear large DataFrame references and restart kernel.
• Reproducibility:
  • Set random_state / np.random.seed()
  • Pin package versions (requirements.txt or environment.yml).
• Long running computations:
  • Use smaller sample for development.
  • Persist intermediate processed files (parquet) to avoid re-computing heavy pre-processing.
• Visualization issues:
  • If static plots don’t show, ensure inline backend is enabled:

    %matplotlib inline
    

• If the notebook expects data not in the repo:
  • Look for file paths in data-loading cells and update them to your local data location or download links.

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🤝 Contributing

• Want to improve the notebook? Great!
  • Fork the repo, create a branch, and open a Pull Request with a clear description of changes.
  • Add an environment.yml or requirements.txt if you add new dependencies.
  • Add smaller, incremental changes (one topic per PR) so changes can be reviewed and merged quickly.

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🧾 License & attribution

• Add a license file if you want to make usage terms clear (MIT / Apache-2.0 are common for notebooks).
• If you use data or code from external sources, add citations and references in the notebook.

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📬 Contact / credits

• Repository owner: @diegonmarcos
• If you want a tailored README that documents each notebook cell and exact functions/plots (auto-generated explanation), I can parse the notebook and create a cell-by-cell annotated README. 🧾🔍

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