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Building a Trading Alert Bot

A real-time alert system built to organize market context, make alerts easier to review, and keep human judgment at the center.

Follow the build decisions behind the dashboard, research layer, paper tracking, and alert review loop.

Last updated July 7, 2026 Start from the beginning
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Build updates

The decisions behind the work.

Follow the latest decisions, lessons, and build changes. Newest first.

05 #

Building the trading alert bot system

The Trading Bot shows how a real AI project becomes a system: market data, a research layer, paper tracking, a dashboard, and an alert review loop.

Lesson to take with you A useful AI build is not one prompt. It needs structure, data, a place to review the output, and a human deciding what it means.

Build highlights

  • Build specWhat I wanted the alert system to watch, organize, and bring back for review.
  • Research agent roleHow AI helps organize market context before I decide whether an alert deserves attention.
  • Dashboard previewHow the dashboard organizes the information I need to review.
04 #

Studying market data for better setup review

A major part of the build has been looking at market data and asking what makes a setup worth reviewing.

Lesson to take with you Good automation depends on better questions. Before a bot can surface useful information, you have to define what is worth paying attention to.

Build highlights

  • Reading the market contextI looked at market data to understand what the alert system should help me notice and review.
  • Choosing what deserves attentionThis part of the build focused on what a stronger setup needs to make clear before I pay attention to it.
03 #

Adding the research agent

The research agent helps turn market context into a clearer summary before an alert is reviewed.

Lesson to take with you AI is more useful when it has a specific job. In this build, the research layer helps organize context so a person can review it.

Build highlights

  • Research layerHow market context gets organized before it reaches the review screen.
  • Research agent roleWhat the research agent is responsible for in the build.
02 #

Adding paper tracking to review the bot

The paper-tracking feature gives the build a way to review how alerts behave after they appear.

Lesson to take with you Testing is part of the build. Paper tracking turns alerts into a review loop I can learn from.

Build highlights

  • Paper review loopHow alerts can be followed after they appear so the system can keep improving.
  • Dashboard learning layerHow the dashboard turns past alerts into something I can compare and improve.
01 #

Starting the trading alert bot foundation

The first version started with a simple goal: organize market information in one place so alerts could be reviewed with more context.

Lesson to take with you A useful AI project starts with the workflow before the model. First decide what the system needs to help a person review.

Build highlights

  • Building the first versionThe early pieces brought the dashboard, saved market data, and alert review into one workflow.
  • Human review pathA simple view of how the bot supports review instead of replacing judgment.