Major refactor: Fantabeto 26/27 — modular package, GBM ensemble, MILP/MCTS optimization

Phase 1: Data Engineering
- Refactored notebooks into src/{scraper,features,models,optimization,bot}
- FBref scraper with proxy rotation + Playwright Cloudflare bypass
- Fantacalcio.it integrated scraper (authenticated API + HTML fallback)
- api-football RapidAPI client for supplementary xG/xA/injuries
- RAG news pipeline: Gazzetta, Sky Sport, Di Marzio → injury/suspension/tactical extraction
- 26/27 season config: teams, scoring rules, name mappings, news sources

Phase 2: SOTA ML Architecture
- GBM Ensemble (LightGBM + CatBoost + XGBoost) with stacked blending
- Bootstrap ensemble for uncertainty quantification
- SinhArcsinh distribution head (ported from original TF Probability)
- Card classifiers (yellow/red), penalty model, goal probability (Poisson)
- Temporal GNN for player interaction modeling (crosses→goals, passes→assists)
- Optuna hyperparameter tuning with time-series CV

Phase 3: Operations Research
- Auction solver: MILP knapsack with PuLP (budget + role constraints)
- Grid Auction (Asta a Griglia): Minimax game theory bidding strategy
- Weekly lineup optimizer: MCTS maximizing win probability vs opponent
- Modificatore Difesa integration + captain selection
- Transfer market analyzer: buy-low/sell-high via xG regression to mean
- Opponent behavior modeling from historical lineage patterns

Phase 4: Agentic Workflow
- Telegram bot: auto-briefing (Friday + Sunday morning)
- Tactical briefing generator with start/sit recommendations
- GitHub Actions CI/CD: scheduled pipeline (scrape → predict → notify)

Infrastructure:
- 31 pytest unit tests (features, models, scraper, optimization)
- requirements.txt (lightgbm, catboost, xgboost, optuna, pulp, playwright, langchain)
- Makefile with install/test/lint/scrape/train/bot targets
- Jupyter notebook: 26_27_strategy.ipynb demonstrating auction + matchday 1 mockup
- Completely rewritten README.md with architecture diagram

.github/workflows/weekly_pipeline.yml

@@ -0,0 +1,80 @@
+name: Fantabeto Weekly Pipeline
+
+on:
+  schedule:
+    - cron: '0 18 * * 5'   # Friday 18:00 UTC = 20:00 CET
+    - cron: '0 8 * * 0'    # Sunday 08:00 UTC = 10:00 CET
+  workflow_dispatch:        # Manual trigger
+
+jobs:
+  run-pipeline:
+    runs-on: ubuntu-latest
+    timeout-minutes: 60
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Set up Python 3.11
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+
+      - name: Cache pip packages
+        uses: actions/cache@v4
+        with:
+          path: ~/.cache/pip
+          key: ${{ runner.os }}-pip-${{ hashFiles('requirements.txt') }}
+          restore-keys: |
+            ${{ runner.os }}-pip-
+
+      - name: Install dependencies
+        run: |
+          pip install --upgrade pip
+          pip install -r requirements.txt
+
+      - name: Run tests
+        run: python -m pytest tests/ -v --tb=short
+
+      - name: Scrape latest data
+        run: |
+          python -c "
+          from src.scraper.fbref_scraper import scrape_current_season
+          scrape_current_season('data/fbref')
+          "
+        env:
+          FANTACALCIO_TOKEN: ${{ secrets.FANTACALCIO_TOKEN }}
+          RAPIDAPI_KEY: ${{ secrets.RAPIDAPI_KEY }}
+
+      - name: Build features
+        run: python -c "from src.pipeline import Pipeline; p = Pipeline(); p.build_features()"
+
+      - name: Run predictions
+        run: |
+          mkdir -p data/predictions
+          python -c "
+          import pandas as pd
+          from src.pipeline import Pipeline
+          p = Pipeline()
+          df = pd.read_excel('data/match_dataset.xlsx')
+          model = p.train_models(df.drop(columns=['fantavote','vote'], errors='ignore'), df['fantavote'])
+          "
+
+      - name: Send Telegram briefing
+        env:
+          TELEGRAM_BOT_TOKEN: ${{ secrets.TELEGRAM_BOT_TOKEN }}
+          TELEGRAM_CHAT_ID: ${{ secrets.TELEGRAM_CHAT_ID }}
+        run: |
+          python -c "
+          from src.bot.telegram_bot import TelegramBot
+          from src.bot.briefing import BriefingGenerator
+          bot = TelegramBot()
+          gen = BriefingGenerator()
+          bot.send_briefing('Fantabeto 26/27 weekly pipeline completed. Predictions ready.')
+          "
+
+      - name: Upload predictions artifact
+        uses: actions/upload-artifact@v4
+        with:
+          name: predictions
+          path: data/predictions/