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>
45 lines
1.6 KiB
Markdown
45 lines
1.6 KiB
Markdown
# Bill — Sprint 01: Market Data Infrastructure
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**Track:** Quant / Algorithmic — Phase 1
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**Duration:** 2 weeks
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**Goal:** Get a working data pipeline and understand the data landscape before building anything on top of it.
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---
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## Tasks
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### Environment Setup
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- [ ] Create Python virtual environment in `~/trading/bill/scripts/`
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- [ ] Install: `yfinance pandas pandas-ta matplotlib sqlite3 requests`
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- [ ] Verify TradingView Pine Script editor opens (Chart → Pine Editor)
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### Data Pipeline
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- [ ] Write a script that pulls daily OHLCV for a list of tickers via yfinance
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- [ ] Store results in SQLite or Parquet in `~/trading/bill/backtests/data/`
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- [ ] Handle edge cases: missing data, stock splits, delisted tickers
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- [ ] Pull at least 5 years of history for SPY, QQQ, and 3 tickers of your choice
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### TradingView Orientation
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- [ ] Write a basic Pine Script indicator (start with a simple EMA crossover)
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- [ ] Understand how TradingView alerts work and what webhook delivery looks like
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- [ ] Explore the built-in Strategy Tester — understand what the equity curve and metrics mean
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### Research
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- [ ] Survey free vs paid data sources (yfinance limitations, Alpaca, Polygon.io)
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- [ ] Understand what a prop firm's execution environment looks like — do they support algos?
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- [ ] Read one article on walk-forward validation vs simple train/test split
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---
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## Deliverable
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A script (`fetch_data.py`) that:
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1. Takes a list of tickers and a date range
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2. Fetches OHLCV from yfinance
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3. Saves to local storage
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4. Prints a summary: rows fetched, date range, any gaps flagged
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---
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## Notes
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_(add as you go)_
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