US National (FHFA, 1975Q1+) Historical Replay: 2012-07 to 2019-10
Replay 30 months of US national property data from 2012-07 to 2019-10, capturing a 42.0% climb and six decision points. Walk through each historical day, weigh policy-driven choices, and receive an AI coach review in this simulated training environment.
30 monthly bars · 6 decision points · 2012-07 to 2019-10 · Property
Key facts
- Max run-up
- +42.0%
- Largest single-month move
- +2.2%
- 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 replay covers 30 months of US national property data from 2012-07 to 2019-10, a period of post-crisis recovery as the Federal Reserve began normalizing monetary policy after years of emergency stimulus. Players encounter six decision points, including two critical nodes, requiring them to navigate policy-driven market shifts without the benefit of hindsight. The scenario tests whether participants can read evolving conditions and adjust positioning as the decade-long expansion matured.
Over the full span, prices climbed from 312.23 to 443.49, delivering a 42.0% cumulative return. Remarkably, the max drawdown was 0.0%, and the largest single monthly move measured just 2.2%. This was not a volatile boom but a steady grind higher, punctuated by brief pauses that demanded careful interpretation of whether momentum was faltering or merely catching its breath before resuming.
For decision training, the lesson centers on discipline in trending but measured markets. With no severe pullbacks to test conviction, the challenge becomes recognizing false signals and avoiding overtrading. The two critical decision points illustrate how policy cues can shift sentiment quickly, teaching players to weigh incomplete information, manage position sizing, and control the psychological urge to chase price after extended runs. Calm analysis outperforms reactive decisions in such environments.