ATX Downtrend Replay (1997-10-03 to 1997-10-28)
Replay 36 trading days from 1997-09-19 to 1997-11-07 as the ATX slides 8.4% from 1388.44 to 1272.13, with a 15.5% drawdown testing your conviction. Navigate seven decision points day-by-day under an AI coach and learn how historical corrections unfolded in simulated capital only.
36 daily bars · 7 decision points · 1997-09 to 1997-11 · Stocks
Key facts
This window spans 49 days with a peak-to-trough drawdown of 15.5% and a 8.5% run-up.
- Max drawdown
- −15.5%
- Max run-up
- +8.5%
- Duration
- 49 days
- Largest single-day move
- −8.3%
- Bars
- 36 Daily bars
- Decision points
- 7
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 the autumn of 1997, the Austrian ATX entered a corrective phase that would test the resolve of any participant willing to follow it bar by bar. Over thirty-six trading sessions from September 19 to November 7, the index gave back a measured but uncomfortable portion of its prior advance, and the question facing each learner is how to behave when a familiar uptrend begins to crack. The setup matters: this is a developed European equity benchmark moving through a period of global turbulence, where drawdowns can deepen quickly before any structural narrative resolves.
What unfolds across the replay is an 8.4% slide from 1388.44 to 1272.13, with a maximum drawdown of 15.5% that reminds participants how peak-to-trough pain can run far deeper than the closing print suggests. A largest single-bar move of 8.3% punctuates the path, the kind of session that separates reactive trading from disciplined process. Seven decision points, three of them flagged as critical, are scattered through the descent, forcing the learner to choose between holding, reducing, or repositioning at moments when conviction is hardest to maintain and information is scarcest.
The training value lies less in calling the bottom than in rehearsing the rhythm of a real correction: how volatility clusters, how drawdowns are measured, and how psychological pressure builds as losses accumulate. By stepping through each decision under simulated capital and an AI coach, participants practice sizing exposure, defining invalidation, and recording rationale before the next session opens. The lesson is procedural — corrections are inevitable, their magnitudes are uneven, and survival depends on a written plan executed with consistency rather than on forecasting the exact turning point.