Policy · KOSPI Index (2013)
Replay 144 days of KOSPI policy risk from 2012-11-07 to 2013-06-07, navigating a 6.5% drawdown. You make day-by-day decisions at 25 points and receive an AI coach review of your simulated choices.
144 daily bars · 25 decision points · 2012-11 to 2013-06 · Stocks
Key facts
This window spans 212 days with a peak-to-trough drawdown of 6.5% and a 9.2% run-up.
- Max drawdown
- −6.5%
- Max run-up
- +9.2%
- Duration
- 212 days
- Largest single-day move
- +2.0%
- Bars
- 144 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 late 2012, the KOSPI index entered a period defined by escalating policy risk in Northeast Asia. The scenario spans 144 trading days from 2012-11-07 to 2013-06-07, placing participants inside a market where geopolitical headlines could shift sentiment overnight. The core challenge was navigating uncertainty without the clarity of knowing which policy development would next dominate trading desks. Decision-makers faced a landscape where the distinction between noise and signal required constant recalibration.
The index closed the period at 1923.85, a net decline of -0.7% from an opening level of 1937.55. Beneath this modest headline figure lay more turbulent terrain: a maximum drawdown of 6.5% tested portfolio values, while the largest single trading day produced a 2.0% swing. These statistics reveal that the aggregate outcome masked genuine intraperiod volatility. Participants encountered 25 decision points, nine of which were flagged as critical, demanding responses to sudden shifts in risk appetite.
The replay trains several essential competencies. First, it reinforces the discipline of sizing positions against known drawdown tolerances before events unfold. Second, it highlights how confirmation bias can distort reading of policy signals, especially when narratives feel compelling but data is incomplete. Third, it pressures emotional regulation under the compounding stress of consecutive critical nodes. The lasting takeaway is that managing policy risk is fundamentally a process problem: the goal is not prediction but building robust frameworks that remain functional when the map is incomplete.
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