Health Care Sel SPDR Downtrend Replay (2018-11-30 to 2018-12-24)
Replay the 2018 health care ETF correction as the XLV dropped from 92.04 to 85.96 over 32 daily bars. You step through each authentic trading day, face 7 decision points, and receive an AI coach review of your simulated choices.
32 daily bars · 7 decision points · 2018-11 to 2019-01 · Stocks
Key facts
This window spans 49 days with a peak-to-trough drawdown of 15.8% and a 7.2% run-up.
- Max drawdown
- −15.8%
- Max run-up
- +7.2%
- Duration
- 49 days
- Largest single-day move
- +4.4%
- Bars
- 32 Daily bars
- Decision points
- 7
Every figure above is computed programmatically from this replay’s own candle series — no editorial input, no LLM. Methodology: Methodology
The market advances day by day. You only see candles up to the current day — as if you were really there.
At key decision points choose Buy / Trim / Exit / Hold. Each decision advances the timeline by one day.
After settlement you get a six-dimension score and an AI coach review. Your result enters the scenario leaderboard.
Event Replay
What happened — and what it teaches
The Health Care Select Sector SPDR (XLV) entered a sharp correction phase in late November 2018, at a time when broader market volatility was escalating across US equities. This 32-daily-bar replay spans from late November through late December, placing participants in the shoes of investors navigating an unexpected sector rotation. With the healthcare sector historically viewed as defensive, the magnitude of the decline tested conventional assumptions about portfolio protection.
The authentic price data reveals a decline from 92.04 to 85.96, representing a -6.6% loss over the replay period. Participants faced a maximum drawdown of 15.8% and witnessed the largest single-bar move of 4.4%, creating moments of intense intraday pressure. Seven decision points were embedded throughout, with three flagged as critical junctures where directional conviction and risk management were most severely tested.
This replay sharpens decision-making under uncertainty by confronting traders with the psychological challenge of a falling market in a sector they may have trusted. The gap between expected stability and actual volatility reveals how risk estimation errors compound. For training purposes, the scenario emphasizes disciplined position sizing, pre-defined exit triggers, and emotional regulation when the thesis breaks down.