China National (Real Estate Climate Index, 1998+) Historical Replay: 2014-03 to 2016-10
Replay 30 monthly bars from 2014-03 to 2016-10 as China's national property index absorbed a 4.1% drawdown and a 6.3% single-bar shock. Step through six decision points, including two critical ones, with simulated capital and receive an AI coach review.
30 monthly bars · 6 decision points · 2014-03 to 2016-10 · Property
Key facts
Peak-to-trough drawdown of 4.1% with a 9.2% run-up.
- Max drawdown
- −4.1%
- Max run-up
- +9.2%
- Largest single-month move
- +6.3%
- 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
China's national property market entered a complex phase in 2014, with the National Housing Index navigating an environment characterized by high inventory levels and downward price pressure. From March 2014 to October 2016, monthly data captured 30 bars as policymakers and market participants confronted supply-demand imbalances and sector uncertainty. The decade of the 2010s was proving particularly challenging for China's real estate ecosystem, setting the stage for a nuanced historical replay.
The index closed at 100.75, reflecting a 4.5% appreciation from its starting point of 96.4. Yet the journey was far from tranquil. The maximum drawdown hit 4.1%, and the largest single-month shock reached 6.3%, underscoring that modest aggregate returns can coexist with meaningful internal volatility. Six decision points punctuated this period, with two deemed critical, inviting participants to test their conviction and risk management under pressure.
This scenario sharpens decision training around process discipline when facing sustained uncertainty. The interplay between a 4.1% drawdown and a 6.3% single-bar shock teaches that aggregate performance metrics can obscure turbulent intraperiod episodes. Participants must practice calibrating position sizing, managing emotional responses to volatility spikes, and adhering to pre-defined risk rules rather than reacting to short-term price swings. Such historical context grounds the learning in authentic market behavior without suggesting any investment prescription for the present.