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>
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shared/ROADMAP.md
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# Trading Roadmap — Bill & Colette
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## Target Outcome
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Pass a prop firm challenge and trade a firm account for income.
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## Timeline (estimated)
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```
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Month 1 [Phase 1] Foundations + TradingView setup
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Month 2 [Phase 2] Technical Analysis
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Month 3-4 [Phase 3] Strategy Development + Backtesting
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Month 5-6 [Phase 4] Paper Trading (live simulation)
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Month 7 [Phase 5] Prop Challenge Prep
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Month 8+ [Phase 6] Challenge Attempt
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```
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This is a realistic timeline if you put in 5–10 hours/week.
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Rushing phases 3–5 is the most common mistake — a strategy needs real testing time.
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## Prop Firm Options (to decide by end of Sprint 01)
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| Firm | Challenge Cost | Account Size | Rules |
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|------|---------------|--------------|-------|
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| Apex Trader Funding | $167 | $50K sim | Popular for futures |
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| TopStep | $165 | $50K sim | Futures focused |
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| FTMO | ~$155 | $10K | Forex/stocks |
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| MyFundedFutures | varies | varies | Futures |
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## Key Principles
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1. **Never risk more than 1% per trade** during paper trading — practice the habit now
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2. **Journal every trade** — no journal = no improvement
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3. **Backtest before you trust** — a strategy that "feels right" is not a strategy
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4. **Paper trading results are optimistic** — live will be harder
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5. **Pass 2 simulated challenges before paying for a real one**
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## Current Sprints
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→ Bill: [bill/sprints/sprint-01.md](../bill/sprints/sprint-01.md) — Market Data Infrastructure
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→ Colette: [colette/sprints/sprint-01.md](../colette/sprints/sprint-01.md) — Chart Literacy
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## Completed Sprints
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_(none yet)_
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shared/curriculum/README.md
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shared/curriculum/README.md
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# Trading Curriculum — Overview
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## The Model
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Bill and Colette operate as a two-person trading team with distinct roles:
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| Role | Person | Approach |
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|------|--------|----------|
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| Quant / Engineer | Bill | ML models, scripts, backtesting, automation, signal validation |
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| Market Observer | Colette | News, macro indicators, sector themes, trade idea generation |
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**Workflow:** Colette spots opportunities (news catalyst, macro shift, chart pattern) → flags to Bill → Bill validates with data → team decides → Bill executes or automates
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This mirrors how small prop desks actually work. Neither role is more important — a great model with no ideas is useless, and good ideas with no discipline lose money.
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---
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## Bill's Track — Quant / Algorithmic
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See: [bill-track.md](bill-track.md)
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Phases:
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1. Market data infrastructure (APIs, data feeds, TradingView Pine Script)
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2. Technical indicator implementation and backtesting
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3. ML signal generation (classification, regression, feature engineering)
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4. Strategy automation and alerting
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5. Prop challenge execution and monitoring
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---
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## Colette's Track — Market Observer
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See: [colette-track.md](colette-track.md)
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Phases:
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1. Chart literacy and TradingView basics
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2. News and macro awareness (what matters, what's noise)
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3. Sector rotation and high-level indicators
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4. Generating and communicating trade ideas
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5. Prop challenge: managing the watchlist and flagging setups
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---
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## Shared Milestones
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| Milestone | Description |
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|-----------|-------------|
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| First paper trade | Both place one paper trade and review it together |
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| First flagged setup | Colette identifies a setup → Bill backtests it |
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| First automated alert | Bill's script triggers on a Colette-identified pattern |
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| Simulated challenge pass | Both run through a prop challenge simulation |
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| Real challenge attempt | When two simulated passes are clean |
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shared/curriculum/bill-track.md
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# 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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shared/curriculum/colette-track.md
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# Colette's Learning Track — Market Observer
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**Background:** News-aware, macro-focused, strong intuition for market narratives
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**Role:** Observe and flag — identify macro themes, news catalysts, sector strength, and trade ideas
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The goal is to build on the market awareness you already have and give it a framework.
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You're not learning to trade from scratch — you're learning to translate what you see into
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structured ideas that can be tested and acted on.
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---
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## Phase 1 — Chart Literacy (TradingView Basics)
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**Goal:** Read a chart and understand what it's showing — no indicators yet
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- [ ] What a candlestick shows (open, high, low, close)
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- [ ] What timeframes mean (daily vs weekly vs monthly)
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- [ ] How to spot a trend (is price making higher highs, or lower lows?)
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- [ ] What volume tells you (is this move significant?)
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- [ ] How to use TradingView: add a ticker, change timeframe, draw a line
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- [ ] What a 52-week high/low means and why people watch it
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**Exercise:** Pull up 3 tickers. For each: is it in an uptrend, downtrend, or sideways? Is volume rising or falling?
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**Milestone:** Can look at a chart and describe what the price is doing
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---
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## Phase 2 — News & Macro Awareness
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**Goal:** Connect the news you're already reading to what it means for price
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- [ ] What the Fed does and why interest rates matter to stocks
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- [ ] What earnings season is and how it affects price
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- [ ] What sector rotation means (money moving from tech → energy, etc.)
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- [ ] Understanding the difference between a news catalyst and a hype cycle
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- [ ] How to cross-reference a news story with the actual chart — was the move already priced in?
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- [ ] The DXYZ case study: what premium-to-NAV means, and what the historical pattern looks like
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**Key habit:** When you see a headline or hear a recommendation, check the chart first:
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1. Did the price already move before this news broke?
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2. What's the proposed exit — where would you get out if wrong?
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3. What does the broader sector look like?
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**Exercise:** Read a financial headline and check the chart of the ticker mentioned. Write one sentence: did the chart confirm the story, or had the move already happened?
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**Milestone:** Can explain why the DXYZ premium exists and what historical precedents look like
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---
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## Phase 3 — Sector & Macro Indicators
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**Goal:** Know the high-level picture before looking at individual stocks
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- [ ] The major S&P 500 sectors (XLK, XLE, XLF, XLV, etc.) and how to track them
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- [ ] What the VIX is and what elevated fear looks like on a chart
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- [ ] How to read a sector heatmap (TradingView has one built in)
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- [ ] What the yield curve is (simplified: are long rates higher than short rates?)
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- [ ] Dollar strength (DXY) and its relationship to commodities and international stocks
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- [ ] How to identify the strongest sector and focus there
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**Exercise:** Every Sunday, look at the sector ETFs for the past week. Which sector was strongest? Which was weakest? Write it down. Do this for 4 weeks in a row.
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**Milestone:** Can give a 2-minute sector summary each week
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---
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## Phase 4 — Generating Trade Ideas
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**Goal:** Turn observations into structured, communicable ideas for Bill to evaluate
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A strong trade idea connects what you're seeing in the news or macro picture to what's happening on the chart.
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**Example:**
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- Weak: "I read that SpaceX is doing something big."
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- Strong: "XLK has been the strongest sector 3 weeks running, NVDA just broke to a new 52-week high on heavy volume, and earnings are in 3 weeks — might be worth a look."
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**Format for flagging an idea:**
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```
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Ticker:
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Why I noticed it: (news, chart, sector strength)
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What the chart looks like: (uptrend / breakout / pullback to support)
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Timeframe: (daily / weekly)
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Questions for Bill: (anything to backtest or dig into)
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```
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- [ ] Submit 2 trade ideas per week using the format above
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- [ ] Review together: did the idea have merit? What happened?
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- [ ] Build a personal watchlist of 10–15 tickers you understand well
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**Milestone:** 8 trade ideas submitted, at least 3 that Bill validated as having a testable edge
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---
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## Phase 5 — Prop Challenge Support
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**Goal:** Be the eyes during the trading day while Bill's system runs
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- [ ] Monitor the watchlist for news that could affect open trades
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- [ ] Flag upcoming macro events (Fed meeting, CPI report, earnings)
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- [ ] Keep a simple daily log: what happened in the market today (2–3 sentences)
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- [ ] Review open positions: is the original thesis still intact?
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**Milestone:** Consistent daily market log for 30 days
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---
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## Recommended Resources
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| Resource | What It's Good For |
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|----------|-------------------|
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| TradingView (free) | Charts, sector heatmap, economic calendar |
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| Finviz.com | Sector/market overview at a glance |
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| FRED (St. Louis Fed) | Real macro data — interest rates, inflation, jobs |
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| Investopedia | Clear definitions for any term |
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**A useful filter for any recommendation you encounter:** Does the person showing you this also show you the data behind it? A good idea holds up to a chart check.
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