Commodity Shock · Shenzhen Component Index (2014)
Replay the Shenzhen Component Index across 148 daily bars from August 2014 to March 2015, when it climbed 52.0% during a global commodity shock. Step through each session, navigate 9 critical decision points, and receive an AI coach review on your simulated calls.
148 daily bars · 25 decision points · 2014-08 to 2015-03 · Stocks
Key facts
This window spans 224 days with a peak-to-trough drawdown of 7.7% and a 57.2% run-up.
- Max drawdown
- −7.7%
- Max run-up
- +57.2%
- Duration
- 224 days
- Largest single-day move
- −6.6%
- Bars
- 148 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 2014, the Shenzhen Component Index entered a period of intense transformation. Across 148 daily bars extending to March 2015, the market operated within a global commodity shock while navigating 25 decision points, 9 of which were critical junctures. Investors faced the challenge of positioning within a complex environment where traditional correlations between oil and equities were being rewritten. The index was anything but quiet, demanding disciplined engagement through each session.
The numbers tell a story of counterintuitive strength. The close moved from 8007.76 to 12169.52, representing a 52.0% advance over the period. Yet this rise was punctuated by a maximum drawdown of 7.7% and a largest single-bar move of 6.6%, reminding participants that compounding gains and sharp reversals often coexist. The magnitude of intraday swings tested the conviction of even those positioned correctly on direction.
This replay cultivates decision-making skills essential for volatile markets. Participants learn to process ambiguous signals without flinching, manage exposure relative to realistic drawdown tolerances, and maintain psychological equilibrium when momentum defies expectations. The scenario proves invaluable for building systematic approaches that survive noise, because outcomes in replay directly inform how one constructs frameworks for the next uncertain episode.