China National (Real Estate Climate Index, 1998+) Historical Replay: 2006-10 to 2009-04
Replay China's national real-estate index from October 2006 to April 2009, a 30-month stretch that ended with a close of 94.76 after an 8.4% decline. Step through each monthly bar, face six decision points, and receive an AI coach review in this simulated training scenario.
30 monthly bars · 6 decision points · 2006-10 to 2009-04 · Property
Key facts
Peak-to-trough drawdown of 11.1% with a 5.3% run-up.
- Max drawdown
- −11.1%
- Max run-up
- +5.3%
- Largest single-month move
- −2.0%
- 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
This replay transports participants to China's national real-estate climate index from October 2006 to April 2009, a 30-month period that captured the build-up, eruption, and aftermath of the 2007 global financial crisis. The scenario unfolds across 30 monthly bars, challenging players to navigate uncertainty with six decision points, two of which the system flags as critical junctures where outcomes diverged most sharply.
Over the full span, the index retreated from 103.4 to 94.76, a decline of 8.4%. The maximum drawdown reached 11.1%, meaning portfolios that entered at local peaks endured double-digit erosion before recovering. The largest single monthly swing measured 2.0%, illustrating that even measured volatility can test conviction when positions are sized aggressively or when leverage is applied without sufficient margin of safety.
For decision training, this episode sharpens several core competencies. First, it forces participants to distinguish temporary dislocations from structural regime shifts when volatility spikes but magnitude stays contained. Second, the six decision points—two critical—train calibration under pressure, asking players to articulate entries, exits, and risk controls before outcomes are known. Third, the psychological dimension emerges when a measured-looking decline of 8.4% delivers an 11.1% drawdown, revealing how drawdown geometry differs from simple price change. These lessons transfer across asset classes, reinforcing disciplined process over prediction.