bill
d794910697
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>
92 lines
3.5 KiB
Markdown
92 lines
3.5 KiB
Markdown
# Bill's Learning Track — Quant / Algorithmic
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**Background:** Data scientist, ML experience, cybersecurity/scripting skills
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**Role:** Build and validate — data pipelines, backtests, models, automation, alerts
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---
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## Phase 1 — Market Data Infrastructure
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**Goal:** Get clean, reliable data into your toolchain
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- [ ] TradingView Pine Script basics — indicators, alerts, screeners
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- [ ] Python market data: `yfinance`, `pandas-ta`, `ccxt` (crypto if relevant)
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- [ ] Free vs paid data sources — what's actually needed at this stage
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- [ ] Build a basic OHLCV data fetcher for a watchlist of tickers
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- [ ] Understand data quality issues: survivorship bias, split adjustments, gaps
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- [ ] Set up a local data store (SQLite or Parquet) for backtesting
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**Deliverable:** Script that pulls daily OHLCV for a watchlist and stores it locally
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---
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## Phase 2 — Indicator Implementation & Backtesting
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**Goal:** Implement and test common signals before trusting them
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- [ ] Implement SMA, EMA, RSI, MACD, ATR from scratch (don't just use a library — understand them)
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- [ ] Build a simple backtesting loop (vectorized, not event-driven to start)
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- [ ] Understand backtest traps: look-ahead bias, overfitting, survivorship bias
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- [ ] Walk-forward validation basics
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- [ ] Metrics: win rate, expectancy, Sharpe ratio, max drawdown, Calmar ratio
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- [ ] Pine Script: build a simple strategy and view equity curve in TradingView
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**Deliverable:** One complete backtest with proper train/test split and metrics reported
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---
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## Phase 3 — ML Signal Generation
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**Goal:** Apply ML where it actually adds value (not everywhere)
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- [ ] Feature engineering for price data (returns, rolling stats, lagged features)
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- [ ] Classification: predict direction (up/down/flat next N bars)
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- [ ] Avoid the common traps: data leakage, overfitting short samples
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- [ ] Evaluate with financial metrics, not just accuracy
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- [ ] Regime detection: is the market trending, ranging, or volatile?
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- [ ] Understand when ML helps vs when simple rules beat it
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**Deliverable:** A classifier that outputs trade signals with a documented edge (positive expectancy on out-of-sample data)
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---
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## Phase 4 — Automation & Alerting
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**Goal:** React to markets without staring at screens
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- [ ] TradingView alerts → webhook → Python handler
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- [ ] Build a market watch script (runs on schedule, flags setups from Colette's criteria)
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- [ ] Notification system (email, SMS, or Telegram bot)
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- [ ] Paper trading automation: auto-log triggered trades to journal
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- [ ] Understand execution risk: slippage, partial fills, latency (not HFT, but real)
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**Deliverable:** Script that monitors a watchlist and sends an alert when Colette's setup criteria are met
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---
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## Phase 5 — Prop Challenge Execution
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**Goal:** Apply the system under challenge conditions
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- [ ] Simulate challenge rules in paper trading: daily loss limit, max drawdown, profit target
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- [ ] Risk sizing formula locked in (never manual)
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- [ ] Dashboard: daily P&L, drawdown remaining, progress toward profit target
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- [ ] Post-trade review script: auto-generate stats from journal
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- [ ] Two clean simulated challenge passes before paying
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**Deliverable:** Automated prop challenge tracker with real-time status
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---
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## Tools & Stack
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| Purpose | Tool |
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|---------|------|
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| Charting | TradingView |
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| Data | yfinance, pandas, pandas-ta |
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| Backtesting | Custom vectorized or `backtesting.py` |
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| ML | scikit-learn, lightgbm |
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| Automation | Python scripts + cron or systemd |
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| Alerts | TradingView webhooks + custom handler |
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| Storage | SQLite or Parquet files |
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