China National (Real Estate Climate Index, 1998+) Historical Replay: 2019-10 to 2022-03
Replay China's national real-estate climate index from October 2019 through March 2022, a span that absorbed a 4.7% drawdown. Make month-by-month decisions across six branching points, then receive an AI coach review of your reasoning in this simulated historical exercise.
30 monthly bars · 6 decision points · 2019-10 to 2022-03 · Property
Key facts
Peak-to-trough drawdown of 4.7% with a 4.1% run-up.
- Max drawdown
- −4.7%
- Max run-up
- +4.1%
- Largest single-month move
- −3.5%
- 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 national real-estate climate index tracks China's property sector vitality, and from October 2019 through March 2022 it offered a compressed window into the industry's transition from late-cycle momentum to mounting uncertainty. Over thirty monthly bars spanning two decades, participants faced a market flagged as a bubble environment with elevated volatility concerns. The central question for any analyst entering this replay was whether the index's structural support would hold—or whether momentum had definitively shifted.
What the data revealed was a modest but persistent contraction: the index closed at 96.62, down from 101.28, representing a -4.6% decline over the full period. The maximum drawdown reached 4.7%, while the largest single monthly move registered 3.5%—a reminder that even subdued trends can produce sharp intraperiod spikes. Across six decision points, including two classified as critical junctions, the market tested participants' ability to distinguish noise from signal amid a declining but range-bound trajectory.
This replay is most valuable as a study in probabilistic thinking under structural uncertainty. The modest absolute decline masked genuine regime-change risks that real-estate analysts were actively debating during this era. Participants learn to calibrate position sizing against drawdown tolerance, maintain discipline when volatility clusters around inflection points, and resist the cognitive trap of overinterpreting small-sample moves as directional confirmations. The goal is not to predict the next move but to build a repeatable process that survives ambiguity.