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Atlas Seasonal Returns Heatmap
Logo "Atlas Seasonal Returns Heatmap"
21/09/2025
1
Desktop
Since 13/08/2025
Lượt bán
17
Free installs
246
Ảnh "Atlas Seasonal Returns Heatmap" được tải lênẢnh "Atlas Seasonal Returns Heatmap" được tải lên

🧭 Time your trades by the clock: Atlas reveals when markets tend to move by month, weekday, and hour. 🧭

See when your market tends to move — by Month, Day-of-Week, or Hour-of-Day.
Atlas computes forward returns over a horizon you choose and paints a symmetric heatmap (Down → Zero → Up). Each tile shows a metric (Mean, T-stat, or Sharpe) plus hit% | n. Use it to time entries/exits, plan sessions, and filter risk.

🎯 Why traders use it

  • Spot green windows to lean in and red windows to stand down.
  • Plan sessions around statistically favorable hours/days.
  • Add a clean “when” layer to discretionary and systematic strategies.

⚙️ How it works

  • Looks ahead over a window you choose and summarizes how price tends to behave in that window.
  • Groups the results into time buckets (Month / Weekday / Hour) and respects your timezone.
  • Applies outlier protection and a robust strength score per bucket (plus sample size).
  • Paints a balanced heatmap around neutral using your Up / Zero / Down colors.

👥 Who it’s for

  • Discretionary traders — better timing and sizing by session.
  • System & bot builders — a time filter / time-sizing layer for strategies.
  • Crypto & index traders — quick behavioral patterns across days/hours.
  • Beginners — simple read: greener = more positive bias over your chosen horizon.

🚀 Quick start

  • Pick Asset Preset (Forex/Crypto/Index/Gold).
  • Set Horizon (e.g., 1 Hour for HOD, 1 Day for DOW/Month).
  • Tune Lookback, Min Observations, and colors.
  • Read clusters (several adjacent green/red tiles) for the strongest cues.

ℹ️ Seasonality isn’t a guarantee of future results. It’s a statistical pattern that depends on the period and methodology. Use the indicator as context for your entry/exit rules and risk management, test robustness across multiple lookback windows, and avoid relying on bins with low n (sample size).


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