Activity Heatmap
The activity heatmap shows when positions were closed across weekdays and hours. It helps you see timing patterns, but it is context, not a trading signal by itself.
What this is
This article explains how to read the current Analytics heatmap and how to use it with the Trade Log.
When to use it
Use this article if:
- positions cluster around specific sessions
- losses appear at the same time of day
- a strategy behaves differently during weekends or funding windows
- you need to compare bot behavior across periods
Before you start
Important current product behavior:
- the heatmap is based on closed positions in the selected bot and period
- rows represent weekdays
- columns represent hours
- the API projects buckets into the browser timezone when a valid timezone is available, otherwise it falls back to UTC
- empty or pale cells mean low activity, not necessarily weak performance
Step by step
Step 1: Check the selected scope
Before reading the heatmap, confirm the selected bot and period. A 7-day view can look noisy, while a 90-day view can reveal repeated timing patterns.
Step 2: Find clusters
Darker cells mean more closed positions in that weekday-hour bucket. A cluster can be useful when it matches a real market session, but it can also be an artifact of how the strategy exits.
Common patterns include:
- concentrated exits during high-volume sessions
- weekend quiet periods
- repeated funding-window activity
- many exits immediately after a volatile candle window
Step 3: Compare timing with outcomes
The heatmap shows count, not quality. A busy hour can contain wins, losses, or both.
Use Trade Distribution and Trade Log to answer:
- did this time window make or lose money
- were trades long or short
- did funding or fees matter
- did the same symbol repeat
Step 4: Avoid using the heatmap alone
Do not add or remove a trading window from the heatmap alone. Confirm timing changes with backtests, trade details, and enough closed-position samples.
What you should see
After reviewing the heatmap, you should know:
- whether activity is spread out or clustered
- which weekday-hour buckets deserve deeper review
- whether the selected period has enough data
- which related analytics view should be opened next
Common mistakes
- treating activity count as profitability
- forgetting the selected timezone or fallback to UTC
- making decisions from a short period
- ignoring the Trade Log behind a dark cell
- confusing live Analytics timing with historical Backtest timing
Related articles
- Trade Distribution
- Trade Journal And Export
- Equity Curve
- Why Live And Backtest Results Differ