Minneapolis Historical Replay: 2009-11 to 2012-04
Replay Minneapolis housing from November 2009 to April 2012 as prices slid 11.1% through a 16.6% drawdown. Step through each historic bar, weigh six decision points, and receive an AI coach review of your simulated choices.
30 monthly bars · 6 decision points · 2009-11 to 2012-04 · Property
Key facts
Peak-to-trough drawdown of 16.6% with a 9.7% run-up.
- Max drawdown
- −16.6%
- Max run-up
- +9.7%
- Largest single-month move
- +3.6%
- Bars
- 30 Monthly bars
- Decision points
- 6
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 replay places you in Minneapolis from November 2009 to April 2012, a city still reeling from the global financial crisis that erupted two years earlier. Housing prices had already fallen sharply, yet the market showed no clear bottom. Six decision points were embedded across 30 monthly bars, with two marked as critical inflection moments where your choices could diverge from history.
The data shows prices sliding from 124.31 to 110.45, a decline of -11.1%. The maximum drawdown reached 16.6%, while the largest single-bar move hit 3.6%, illustrating that even within a broader downtrend, individual months could swing violently. This period, spanning the late 2000s and early 2010s, captures the final chapter of a prolonged housing collapse.
For decision training, this scenario sharpens your ability to manage risk in markets with no obvious direction. The challenge lies in distinguishing noise from signal, maintaining discipline through drawdowns, and avoiding reactive decisions. Successful navigation requires a clear process, sound risk calibration, and the psychological resilience to stay rational when prices drift lower month after month. This is the essence of learning from history without being paralyzed by it.
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