Google Colab Notebooks from Medium blogs

.github/workflows/convert-notebooks.yml

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+name: Convert Notebooks to HTML
+
+on:
+  workflow_dispatch:  # Manual trigger only
+
+permissions:
+  contents: write      # Required to commit and push changes
+  actions: read        # Default safe read access
+  pages: write
+  id-token: write
+
+jobs:
+  convert:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+        with:
+          persist-credentials: true  # Needed for pushing commits
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.x'
+
+      - name: Install Jupyter
+        run: |
+          python -m pip install --upgrade pip
+          pip install jupyter
+
+      - name: Convert large notebooks to HTML
+        run: |
+          mkdir -p docs
+          # Find notebooks larger than 20KB (20 * 1024 bytes)
+          find notebooks -type f -name "*.ipynb" -size +20k | while read nb; do
+            echo "Converting $nb ..."
+            jupyter nbconvert "$nb" --to html --output-dir docs
+          done
+
+      - name: List generated files
+        run: ls -lh docs
+
+      - name: Commit and push changes
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        run: |
+          git config user.name "github-actions[bot]"
+          git config user.email "github-actions[bot]@users.noreply.github.com"
+          # Add and commit only if there are changes
+          git add docs/
+          if git diff --cached --quiet; then
+            echo "No changes to commit."
+          else
+            git commit -m "Automated: Convert Jupyter notebooks to HTML"
+            git push origin HEAD:${{ github.ref }}
+          fi

README.md

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-# Google-Colab-Notebooks
-Collection of Jupyter Notebooks from Google Colab
+<h1 align="center">Welcome to Google-Colab-Notebooks 👋</h1>
+<p>
+  <img alt="Version" src="https://img.shields.io/badge/version-1.0.0-blue.svg?cacheSeconds=2592000" />
+  <a href="https://github.com/shortthirdman-org/Google-Colab-Notebooks" target="_blank">
+    <img alt="Documentation" src="https://img.shields.io/badge/documentation-yes-brightgreen.svg" />
+  </a>
+  <a href="LICENSE" target="_blank">
+    <img alt="License: MIT" src="https://img.shields.io/badge/License-MIT-yellow.svg" />
+  </a>
+  <a href="https://twitter.com/ShortThirdMan93" target="_blank">
+    <img alt="Twitter: ShortThirdMan93" src="https://img.shields.io/twitter/follow/ShortThirdMan93.svg?style=social" />
+  </a>
+</p>
+
+> Collection of Jupyter Notebooks from Google Colab
+
+### 🏠 [Homepage](https://github.com/shortthirdman-org/Google-Colab-Notebooks)
+
+### ✨ [Demo](https://github.com/shortthirdman-org/Google-Colab-Notebooks)
+
+## Author
+
+👤 **Swetank Mohanty**
+
+* Website: https://shortthirdman.medium.com/
+* Twitter: [@ShortThirdMan93](https://twitter.com/ShortThirdMan93)
+* Github: [@shortthirdman](https://github.com/shortthirdman)
+* LinkedIn: [@shortthirdman](https://linkedin.com/in/shortthirdman)
+
+## 🤝 Contributing
+
+Contributions, issues and feature requests are welcome!<br />Feel free to check [issues page](https://github.com/shortthirdman-org/Google-Colab-Notebooks/issues). 
+
+## Show your support
+
+Give a ⭐️ if this project helped you!
+
+<a href="https://www.patreon.com/shortthirdman">
+  <img src="https://c5.patreon.com/external/logo/become_a_patron_button@2x.png" width="160">
+</a>
+
+## 📝 License
+
+Copyright © 2025 [Swetank Mohanty](https://github.com/shortthirdman).<br />
+This project is [MIT](LICENSE) licensed.
+
+***
+_This README was generated with ❤️ by [readme-md-generator](https://github.com/kefranabg/readme-md-generator)_

notebooks/Bayesian_Opt_Trading_Strategy.ipynb

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+{
+  "metadata": {
+    "kernelspec": {
+      "name": "python",
+      "display_name": "Python (Pyodide)",
+      "language": "python"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "python",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8"
+    }
+  },
+  "nbformat_minor": 4,
+  "nbformat": 4,
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": "### [Bayesian Optimization to Optimize a Trading Strategy on the Top 10 Most Active Stocks](https://wire.insiderfinance.io/i-used-bayesian-optimization-to-optimize-a-trading-strategy-on-the-top-10-most-active-stocks-97345ff731ed)",
+      "metadata": {}
+    },
+    {
+      "cell_type": "code",
+      "source": "import yfinance as yf\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom bayes_opt import BayesianOptimization\nimport time\nimport numpy as np\nimport calendar\n\nplt.style.use(\"dark_background\")\n\ntickers = [\"LCID\", \"NVDA\", \"INTC\", \"F\", \"TSLA\", \"HOOD\", \"WULF\", \"CIFR\", \"SOFI\", \"IREN\"]\nstart_date = \"2020-01-01\"\nend_date = \"2025-06-30\"\ntrain_cutoff_date = \"2023-12-31\"\ninitial_capital = 10\n\nresults = []",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "source": "def backtest_strategy_double_ma(data, short_window, long_window, initial_capital):\n    df = data.copy()\n    df['SMA_Short'] = df['Close'].rolling(window=short_window).mean()\n    df['SMA_Long'] = df['Close'].rolling(window=long_window).mean()\n    df['Signal'] = 0\n    df.loc[df['SMA_Short'] > df['SMA_Long'], 'Signal'] = 1\n    df['Position'] = df['Signal'].shift(1)\n    df['Return'] = df['Close'].pct_change()\n    df['Strategy_Return'] = df['Position'] * df['Return']\n    df['Equity_Curve'] = (1 + df['Strategy_Return']).cumprod() * initial_capital\n    final_value = df['Equity_Curve'].iloc[-1]\n    num_trades = df['Position'].diff().abs().sum()\n    return final_value, num_trades, df",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "source": "for symbol in tickers:\n    print(f\"Running for {symbol}...\")\n    df = yf.download(symbol, start=start_date, end=end_date)[['Close']]\n\n    df_train = df.loc[start_date:train_cutoff_date].copy()\n    df_test = df.loc[train_cutoff_date:end_date].copy()\n\n    def backtest_bo(short_window, long_window):\n        short_window = int(round(short_window))\n        long_window = int(round(long_window))\n        if short_window >= long_window:\n            return -1e10\n        final_value, _, _ = backtest_strategy_double_ma(df_train, short_window, long_window, initial_capital)\n        return final_value\n\n    pbounds = {'short_window': (5, 50), 'long_window': (55, 200)}\n    optimizer = BayesianOptimization(f=backtest_bo, pbounds=pbounds, random_state=42, verbose=0)\n    optimizer.maximize(init_points=5, n_iter=45)\n\n    best_params = optimizer.max['params']\n    best_short_window = int(round(best_params['short_window']))\n    best_long_window = int(round(best_params['long_window']))\n\n    _, _, df_test_result = backtest_strategy_double_ma(df_test, best_short_window, best_long_window, initial_capital)\n    df_test_result['Buy & Hold'] = (1 + df_test_result['Return']).cumprod() * initial_capital\n\n    final_strategy_value = df_test_result['Equity Curve'].iloc[-1]\n    final_bh_value = df_test_result['Buy & Hold'].iloc[-1]\n    strategy_return_pct = ((final_strategy_value / initial_capital) - 1) * 100\n    bh_return_pct = ((final_bh_value / initial_capital) - 1) * 100\n    num_trades = df_test_result['Position'].diff().abs().sum()\n\n    results.append({\n        \"Symbol\": symbol,\n        \"Short Window\": best_short_window,\n        \"Long Window\": best_long_window,\n        \"Strategy Final Value\": final_strategy_value,\n        \"Buy & Hold Final Value\": final_bh_value,\n        \"Strategy Return (%)\": strategy_return_pct,\n        \"Buy & Hold Return (%)\": bh_return_pct,\n        \"Trades\": int(num_trades)\n    })\n    \n    # Print the results for the current symbol\n    print(f\"{symbol} - Best Short Window: {best_short_window}, Best Long Window: {best_long_window}\")\n    print(f\"{symbol} - Strategy Final Value: ${final_strategy_value:.2f}, Buy & Hold Final Value: ${final_bh_value:.2f}\")\n    print(f\"{symbol} - Strategy Return: {strategy_return_pct:.2f}%, Buy & Hold Return: {bh_return_pct:.2f}%\")\n    print(f\"{symbol} - Number of Trades: {int(num_trades)}\\n\")\n\n    # Plot equity curve comparison\n    plt.figure(figsize=(12, 6))\n    plt.plot(df_test_result['Equity Curve'], label='Strategy', color='green')\n    plt.plot(df_test_result['Buy & Hold'], label='Buy & Hold', linestyle='--', color='orange')\n    plt.title(f\"{symbol} - Out-of-Sample Equity Curve\")\n    plt.xlabel(\"Date\")\n    plt.ylabel(\"Portfolio Value (USD)\")\n    plt.legend()\n    plt.grid(True, color='gray', linestyle='--', linewidth=0.5)\n    plt.tight_layout()\n    plt.savefig(f\"{symbol}_equity_curve.png\", dpi=300)\n    plt.show()",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "source": "results_df = pd.DataFrame(results)\nresults_df = results_df.set_index(\"Symbol\")\nresults_df = results_df.sort_values(\"Strategy Return (%)\", ascending=False)\nresults_df.to_csv(\"multi_ticker_strategy_comparison.csv\")\nresults_df",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "source": "plt.figure(figsize=(12, 6))\nx = np.arange(len(results_df))\nwidth = 0.35\n\nbars1 = plt.bar(x - width/2, results_df[\"Strategy Return (%)\"], width, label=\"Strategy\", color='red')\nbars2 = plt.bar(x + width/2, results_df[\"Buy & Hold Return (%)\"], width, label=\"Buy & Hold\", color='green')\n\nfor bars in [bars1, bars2]:\n    for bar in bars:\n        height = bar.get_height()\n        plt.text(bar.get_x() + bar.get_width()/2, height + np.sign(height)*2, f\"{height:.1f}%\", \n                 ha='center', va='bottom' if height >= 0 else 'top', fontsize=12)\n\nplt.xticks(x, results_df.index, rotation=45)\nplt.ylabel(\"Return (%)\")\nplt.title(\"Strategy vs Buy & Hold Performance (Test Set)\")\nplt.legend()\nplt.grid(True, linestyle='--', alpha=0.5)\nplt.tight_layout()\nplt.savefig(\"comparison_bar_chart.png\", dpi=300)\nplt.show()",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
+}