Pandemic · S&P 500 Index (2020)
Relive 142 trading days from October 2019 to May 2020, watching the S&P 500 close fall 4.8% with a 33.9% max drawdown. Step through daily decision points, make your own calls, and receive an AI coach review in this simulated educational replay.
142 daily bars · 25 decision points · 2019-10 to 2020-05 · Stocks
Key facts
This window spans 205 days with a peak-to-trough drawdown of 33.9% and a 31.4% run-up.
- Max drawdown
- −33.9%
- Max run-up
- +31.4%
- Duration
- 205 days
- Largest single-day move
- −12.0%
- Bars
- 142 Daily bars
- Decision points
- 25
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 S&P 500 entered this 142-trading-day window in late October 2019 at 2995.99, navigating an otherwise mature bull market cycle that few anticipated would soon face its most severe stress test in over a decade. For traders and investors, this period represented a collision between record-long expansion, emerging global health concerns, and the rapid deterioration of economic expectations—testing the ability to adapt under mounting uncertainty. The stage was set for a volatility regime that would expose both the fragility of positioning and the discipline required to manage risk through crisis.
What followed was a -4.8% net decline over the full span, yet that deceptively modest headline number masked a 33.9% peak-to-trough drawdown and a largest single-day move of 12.0%—signatures of panic liquidation and the extraordinary speed of price discovery during dislocation. Market structure transformed within weeks, with sentiment shifting from complacency to fear as policy responses struggled to keep pace with evolving conditions. Liquidity dynamics and correlation breakdowns challenged even experienced practitioners, making this window a critical case study in how averages conceal distribution.
For decision training, this replay illuminates three enduring lessons: the importance of probabilistic thinking when confronting tail-risk scenarios, the danger of anchoring to recent price history during regime shifts, and the psychological discipline required to avoid reactive selling at market lows. Engaging with the 25 decision points—including 9 critical junctures—allows learners to practice calibrating position sizing, managing drawdown tolerance, and distinguishing between signal and noise in fast-moving environments. The historical authenticity of the dataset ensures that learners grapple with the same information asymmetry and emotional pressure that defined this period, building resilience through deliberate practice rather than hindsight.