China National (Real Estate Climate Index, 1998+) Historical Replay: 2016-11 to 2019-06
Replay 30 monthly bars of China's national property index from 2016-11 to 2019-06, navigating a 1.4% drawdown era. You will face six decision points, two marked critical, and receive an AI coach review of each simulated historical choice.
30 monthly bars · 6 decision points · 2016-11 to 2019-06 · Property
Key facts
Peak-to-trough drawdown of 1.4% with a 1.3% run-up.
- Max drawdown
- −1.4%
- Max run-up
- +1.3%
- Largest single-month move
- −1.3%
- 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 transports you to China's national property sector in late 2016, a period when the market had already experienced years of expansion following the 2015 leverage-fueled rally. Over the subsequent 30 months, the national property sentiment index would navigate a delicate phase between sustained growth and emerging headwinds. Six decision points await, two of which the scenario flags as critical junctions where positioning choices carry heightened consequence for portfolio trajectory.
From a starting level of 100.88 in November 2016, the index closed at 101.09 by June 2019—a cumulative advance of merely 0.2% across the entire 30-month window. The journey proved more eventful than the flat finish suggests, with the largest single monthly swing reaching 1.3% and peak-to-trough drawdown extending to 1.4%. These figures reveal a market caught in a compressed volatility range, where sharp but brief dislocations repeatedly reversed course.
This scenario sharpens decision training in three dimensions. First, process discipline: navigating low-conviction environments demands strict adherence to rules rather than intuition. Second, risk calibration: a 1.4% drawdown appears modest on paper yet tested patience through repeated 1.3% intrabar spikes. Third, psychological resilience: the flat terminal return means that timing errors at any of the six decision points created permanent impairment, underscoring that in range-bound markets, the cost of poor process is measured not in spectacular crashes but in quiet erosion of capital and opportunity cost.
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