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

requirements.txt

@@ -0,0 +1,55 @@
+# Fantabeto 2026/27 — Core dependencies
+python-dateutil>=2.8
+requests>=2.31
+beautifulsoup4>=4.12
+lxml>=5.0
+
+# Data processing
+pandas>=2.1
+numpy>=1.26
+openpyxl>=3.1
+scipy>=1.11
+pyyaml>=6.0
+
+# ML models
+lightgbm>=4.3
+catboost>=1.2
+xgboost>=2.0
+scikit-learn>=1.4
+
+# Hyperparameter tuning
+optuna>=3.5
+
+# Graph neural network (optional, for T-GNN)
+torch>=2.2
+torch-geometric>=2.5
+
+# Optimization
+pulp>=2.8
+
+# Browser automation (Playwright fallback for Cloudflare)
+playwright>=1.42
+
+# RAG & LLM (optional, for news pipeline)
+langchain>=0.1
+langchain-community>=0.1
+chromadb>=0.4
+feedparser>=6.0
+
+# LLM providers (choose one)
+openai>=1.12
+# anthropic>=0.20
+# ollama>=0.1
+
+# Visualization
+matplotlib>=3.8
+seaborn>=0.13
+plotly>=5.18
+
+# Development
+pytest>=8.0
+black>=24.0
+ruff>=0.3
+
+# Bot
+python-telegram-bot>=21.0