Collapse · Shenzhen Component Index (2016)
Replay the Shenzhen Component Index tumble from August 18, 2015 to March 21, 2016, a 30.9% drawdown that tested policy reflexes. Walk through 142 daily bars, confront 25 decision points, and receive an AI coach review of every simulated call.
142 daily bars · 25 decision points · 2015-08 to 2016-03 · Stocks
Key facts
This window spans 216 days with a peak-to-trough drawdown of 30.9% and a 41.4% run-up.
- Max drawdown
- −30.9%
- Max run-up
- +41.4%
- Duration
- 216 days
- Largest single-day move
- −8.2%
- Bars
- 142 Daily bars
- Decision points
- 25
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
In August 2015, the Shenzhen Component Index stood at 12683.86, poised at the epicenter of a policy experiment that would reshape market psychology across Chinese equities. The newly introduced circuit breaker mechanism aimed to temper panic, yet it simultaneously introduced an untested dynamic: the magnetic attraction effect where falling prices accelerated toward the breaker threshold itself. For traders and risk managers, this era presented a rare case study in how regulatory interventions can create second-order behavioral consequences. The stakes were not merely financial; they involved calibrating judgment under a novel policy framework that had no historical precedent in Chinese markets.
The index closed at 10394.14 by March 21, 2016, representing a -18.1% decline over 142 daily bars. However, the headline figure concealed a more turbulent reality. The maximum drawdown reached 30.9%, while the largest single-bar move measured 8.2%, underscoring the regime of violent intraday swings that defined this period. Participants navigated 25 decision points across the dataset, of which 9 were flagged as critical, demanding precise timing and risk calibration. The scenario replay forces confrontations with these inflection moments, asking: given the information available at each bar, what is the appropriate response when policy signals and price dynamics interact in unexpected ways?
This replay cultivates decision training through exposure to policy-driven volatility. The magnetic attraction effect—where assets gravitate toward circuit breaker levels—challenges conventional mean-reversion assumptions and tests whether participants can recognize regime changes rather than fighting them. Risk management during this era required not just position sizing but also an understanding of how trading halts alter liquidity and amplify subsequent moves. Psychologically, the scenario trains emotional discipline: the discipline to reassess positions when the original thesis collides with a policy event rather than doubling down on conviction. Completing the full 142-bar journey with AI coach feedback transforms historical trauma into structured learning for future market participants.