Bubble · Shenzhen Component Index (2015)
Replay the 2015 Shenzhen Component Index bubble across 150 daily bars, where price climbed 15.8% before a punishing 39.0% drawdown. Step through each session, face 26 decision points, and receive AI coach feedback in this simulated, educational market-replay.
150 daily bars · 26 decision points · 2015-01 to 2015-08 · Stocks
Key facts
This window spans 219 days with a peak-to-trough drawdown of 39.0% and a 68.0% run-up.
- Max drawdown
- −39.0%
- Max run-up
- +68.0%
- Duration
- 219 days
- Largest single-day move
- −8.2%
- Bars
- 150 Daily bars
- Decision points
- 26
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 Shenzhen Component Index, a benchmark representing China's tech-heavy southern exchange, entered a period of extraordinary speculative intensity in early 2015. The market was fueled by aggressive margin lending, retail investor exuberance, and policy-driven liquidity that disconnected prices from fundamentals. Spanning January to August 2015, this era tested every participant's ability to navigate an environment where momentum dominated reasoning and risk awareness eroded under the promise of easy gains.
Across the 150 daily bars from 2015-01-07 to 2015-08-14, the index rose from 11613.36 to 13445.87, a nominal 15.8% advance. Yet this headline figure concealed violent intraperiod swings, including an 8.2% single-session move. The episode ultimately delivered a 39.0% drawdown from peak to trough as the bubble burst. Trainees faced 26 decision points, with 9 deemed critical to outcomes, forcing repeated choices under evolving market conditions.
This replay sharpens decision-making under uncertainty by exposing how cognitive biases—anchoring on entry prices, herding with crowd momentum, and recency weighting—compound during bubble phases. The concentration of critical inflection points illustrates that a handful of choices drive most of the result. Practicing systematic risk protocols, pre-defined exit rules, and emotional discipline in this simulated environment builds resilience applicable to any volatile market context.