New York Historical Replay: 2021-02 to 2023-07
Replay New York's housing market from February 2021 to July 2023 across 30 monthly bars, watching prices climb 26.2% before a 2.0% drawdown. Step through each historic day, face 6 decision points, and receive an AI coach review of your choices in a fully simulated environment.
30 monthly bars · 6 decision points · 2021-02 to 2023-07 · Property
Key facts
Peak-to-trough drawdown of 2.0% with a 26.2% run-up.
- Max drawdown
- −2.0%
- Max run-up
- +26.2%
- Largest single-month move
- +1.9%
- Bars
- 30 Monthly bars
- Decision points
- 6
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
This scenario recreates New York's housing market from February 2021 to July 2023 across 30 monthly bars, immersing participants in a period of extraordinary volatility and shifting monetary policy. The era was defined by post-pandemic recovery, historic low interest rates, and the Federal Reserve's subsequent pivot toward aggressive rate hikes. Participants face the challenge of navigating a market where prices were climbing rapidly while the macro environment was beginning to shift beneath them.
The replay captures prices climbing from 228.99 to 289.08, a gain of 26.2%, though this trajectory was punctuated by a maximum drawdown of 2.0% and a largest single-bar move of 1.9%. Six decision points, including two critical junctures, test participants' ability to read evolving conditions and adjust strategy accordingly.
This replay teaches that even modest drawdowns and single-bar moves deserve respect. The training emphasizes process over prediction, requiring participants to manage risk consistently rather than react emotionally to price changes. By confronting these historical conditions in a simulated environment, learners can develop the psychological discipline and analytical frameworks needed to navigate similar market transitions with greater confidence and precision.
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