Bootcamp Classroom2 - Week 8 Day 1 - Monday Group Work Presentation

Summary

Morning session focused on analyzing 05 breakouts, reversals, and cap-and-backtest exercises.
Key themes: risk profiles, profit factor, edge durability across time horizons, and practical trading implementation.
Emphasis on sharing team data, documenting findings, and building individualized trade/business plans.
Instructor stressed dynamic market adaptation and discipline: find a model, collect data, then avoid altering variables.

(ASAP – Team Monday) Share team Monday PowerPoint and dataset in the hourly PowerPoint thread for class access.
(This Week – All Teams) Upload each team's slides and data to the shared thread so other teams can reuse analyses.
(Next Sessions – All Students) Build and refine personal trade/business plans; prepare final project briefings.
(Ongoing – Individuals) Use collected data in AI tools (e.g., GPT, Gemini) for further analysis and hypothesis testing.

Summary of methodology:
- Teams tested 5-, 15-, 30-minute breakouts and reversals across 90-day, 6-month, and 12-month horizons.
- Filters assessed: prior-hour direction, volume, VWAP, New York session filters.
5-minute results:
- Edge near 50/50 for true vs false; many false breakouts.
- Best TP range often 0.05–0.07% and stop losses 0.15–0.22% for some profitable profiles.
- Useful as entry drills with closer entries and higher MF (measured-fairness).
15-minute results:
- Generally stronger than 5-minute for reversals; certain reversal risk profiles (e.g., 0.5 stop / 0.15 TP style) looked sustainable.
- Previous-hour direction filter (green prior hour) improved results in some 15-minute 05 setups.
30-minute results:
- Some positive-R strategies identified (e.g., 1R strategies on 30-minute reversal red).
- Reversals on 30-minute red candles showed robust survivability across horizons.
Green vs Red candles:
- Green candles tended to offer higher expectancy and better risk profiles overall.
- Red candles sometimes had high win rates but small average wins (grind).

Time-horizon sensitivity:
- Metrics degrade when expanding from 90-day → 6-month → 12-month; volatility events skew 12-month results.
Best performing timeframe for cap-and-backtest in this round: 5-minute (contrasts with prior class where 15-minute dominated).
Filters:
- Volume and VWAP on New York settings had the largest positive effect when applied to the 5-minute cap-and-backtest.
- Combining filters improved metrics but did not eliminate long consecutive loss streaks.
Risk-of-ruin and consecutive losses:
- Example: one system showed 12 consecutive losses in a span, exposing risk of blowing small accounts.
- Risk per trade must align with consecutive-loss profile; small accounts should avoid high-consecutive-loss strategies.
Long vs Short differences:
- Longs showed higher win percentages but lower profit factors; shorts won less often but tended to win bigger when they did.
Equity curves:
- Appeared much better when zoomed to short horizons; long-horizon equity curves can look ugly due to volatility clusters.

Use the shared hourly PowerPoint thread as the central repository for team datasets and slides.
Continue morning brief format: teams present findings; afternoons focus on practical application (e.g., Austin’s measured-move teaching).
Final project format: teams (likely three-person groups) present business/trade plans in morning briefs, then allow guest speakers in afternoons.
Students should build trade/business plans and use the Wolf Tank for feedback.

Which specific filter combinations consistently improve risk-of-ruin without destroying expectancy across varied volatility regimes?
For students intending to trade manually, which risk profiles (win rate vs R) are practically sustainable given human behavioral limits?
Can the VWAP + volume combination be tuned to reduce long consecutive-loss streaks while preserving profit factor?
Are there consistent time-of-day dependencies (e.g., 9:30–10:00 vs 10:00–11:00) that reliably change negative-R vs positive-R viability?

Entry vs longer-term management:
- 05 breakouts and reversals function as entry drills; pair with longer time-horizon management (let winners run).
- Covering the queen (partial risk removal / turning trades risk-free) can materially improve equity curves and profit factor.
Implementation guidance:
- Treat each 05 box like a 9:30 candle: place it in broader context (three-hour line, New York session, measured moves).
- Use multi-step sizing (e.g., 25/50/75) to mitigate initial risk and allow re-entry if needed.
- Avoid changing rules midstream; altering variables on the fly destroys comparability and probability assumptions.
Account sizing:
- Match risk-per-trade to observed consecutive-loss distribution to avoid early account blowups.
- For small accounts, start with safer strategies or use protective filters until a buffer builds.

Shareable data:
- Everyone must place datasets and slides into the shared hourly PowerPoint thread for reuse.
Analytical approach:
- Encourage using AI tools to further analyze collected metrics and search for patterns or Easter eggs across teams.
Project work:
- Final project = build business/trade plan using validated risk profiles and measured moves, then present to Wolf Tank.

Market regime matters: major volatility events (e.g., tariff shocks, early-year volatility) can invalidate year-long backtests.
Edge is not static: an apparent edge over 90 days may vanish over a year; periodic re-evaluation required.
Psychological component: low win-rate strategies (e.g., <50%) often require algorithmic trading to maintain discipline; humans struggle with extended drawdowns.
Educational goal achieved: exercise intended to show difficulty of finding durable edge and to teach discipline around fixed, validated trade rules.