Initialize trading project — Bill & Colette learning tracks

Two-person trading team setup: Bill (quant/algorithmic) and Colette (market observer).
Includes phase-by-phase curriculum, separate sprint tracks, trade journal templates,
roadmap to prop firm challenge, and agent handoff documentation.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

shared/curriculum/bill-track.md

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+# Bill's Learning Track — Quant / Algorithmic
+
+**Background:** Data scientist, ML experience, cybersecurity/scripting skills
+**Role:** Build and validate — data pipelines, backtests, models, automation, alerts
+
+---
+
+## Phase 1 — Market Data Infrastructure
+
+**Goal:** Get clean, reliable data into your toolchain
+
+- [ ] TradingView Pine Script basics — indicators, alerts, screeners
+- [ ] Python market data: `yfinance`, `pandas-ta`, `ccxt` (crypto if relevant)
+- [ ] Free vs paid data sources — what's actually needed at this stage
+- [ ] Build a basic OHLCV data fetcher for a watchlist of tickers
+- [ ] Understand data quality issues: survivorship bias, split adjustments, gaps
+- [ ] Set up a local data store (SQLite or Parquet) for backtesting
+
+**Deliverable:** Script that pulls daily OHLCV for a watchlist and stores it locally
+
+---
+
+## Phase 2 — Indicator Implementation & Backtesting
+
+**Goal:** Implement and test common signals before trusting them
+
+- [ ] Implement SMA, EMA, RSI, MACD, ATR from scratch (don't just use a library — understand them)
+- [ ] Build a simple backtesting loop (vectorized, not event-driven to start)
+- [ ] Understand backtest traps: look-ahead bias, overfitting, survivorship bias
+- [ ] Walk-forward validation basics
+- [ ] Metrics: win rate, expectancy, Sharpe ratio, max drawdown, Calmar ratio
+- [ ] Pine Script: build a simple strategy and view equity curve in TradingView
+
+**Deliverable:** One complete backtest with proper train/test split and metrics reported
+
+---
+
+## Phase 3 — ML Signal Generation
+
+**Goal:** Apply ML where it actually adds value (not everywhere)
+
+- [ ] Feature engineering for price data (returns, rolling stats, lagged features)
+- [ ] Classification: predict direction (up/down/flat next N bars)
+- [ ] Avoid the common traps: data leakage, overfitting short samples
+- [ ] Evaluate with financial metrics, not just accuracy
+- [ ] Regime detection: is the market trending, ranging, or volatile?
+- [ ] Understand when ML helps vs when simple rules beat it
+
+**Deliverable:** A classifier that outputs trade signals with a documented edge (positive expectancy on out-of-sample data)
+
+---
+
+## Phase 4 — Automation & Alerting
+
+**Goal:** React to markets without staring at screens
+
+- [ ] TradingView alerts → webhook → Python handler
+- [ ] Build a market watch script (runs on schedule, flags setups from Colette's criteria)
+- [ ] Notification system (email, SMS, or Telegram bot)
+- [ ] Paper trading automation: auto-log triggered trades to journal
+- [ ] Understand execution risk: slippage, partial fills, latency (not HFT, but real)
+
+**Deliverable:** Script that monitors a watchlist and sends an alert when Colette's setup criteria are met
+
+---
+
+## Phase 5 — Prop Challenge Execution
+
+**Goal:** Apply the system under challenge conditions
+
+- [ ] Simulate challenge rules in paper trading: daily loss limit, max drawdown, profit target
+- [ ] Risk sizing formula locked in (never manual)
+- [ ] Dashboard: daily P&L, drawdown remaining, progress toward profit target
+- [ ] Post-trade review script: auto-generate stats from journal
+- [ ] Two clean simulated challenge passes before paying
+
+**Deliverable:** Automated prop challenge tracker with real-time status
+
+---
+
+## Tools & Stack
+
+| Purpose | Tool |
+|---------|------|
+| Charting | TradingView |
+| Data | yfinance, pandas, pandas-ta |
+| Backtesting | Custom vectorized or `backtesting.py` |
+| ML | scikit-learn, lightgbm |
+| Automation | Python scripts + cron or systemd |
+| Alerts | TradingView webhooks + custom handler |
+| Storage | SQLite or Parquet files |