Boom · Jakarta Composite Index (1999)
After the crisis, the Jakarta Composite Index swung within a 24.5% drawdown over 150 trading days from June 1999 to January 2000. Replay each session, weigh policy-driven decisions across 26 points, and receive AI coach feedback in this simulated training environment.
150 daily bars · 26 decision points · 1999-06 to 2000-01 · Stocks
Key facts
This window spans 217 days with a peak-to-trough drawdown of 24.5% and a 36.6% run-up.
- Max drawdown
- −24.5%
- Max run-up
- +36.6%
- Duration
- 217 days
- Largest single-day move
- +5.5%
- 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 Jakarta Composite Index entered a fragile recovery phase after the Asian financial crisis, navigating the transition from the 1990s into the early 2000s. Between June 1999 and January 2000, 150 trading days presented investors and policymakers with the challenge of rebuilding confidence in Indonesian markets while managing persistent economic uncertainty. This period tested the ability to distinguish between genuine recovery signals and temporary price surges driven by sentiment rather than fundamentals.
Over the full data span, the index closed from 682.12 to 644.65, reflecting a net decline of -5.5%. However, this headline figure masks considerable turbulence: the market experienced a maximum drawdown of 24.5% and witnessed its largest single session swing of 5.5%. Participants faced 26 decision points throughout this stretch, with 9 deemed critical moments where positioning would significantly influence outcomes.
This scenario sharpens skills in policy reading and risk calibration during uncertain recoveries. Trainees must weigh competing signals—official statements against economic data, short-term momentum against long-term value—while managing the psychological pressure of whipsaw moves. The simulated environment, complete with AI coach feedback, allows learners to practice disciplined decision frameworks without real capital at stake, building habits essential for navigating volatile markets.