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IMIE dashboard and market watchlist
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AI assistant supporting structured market research
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Market scanner for focused opportunity review
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Market-session context inside the workbench
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Market analysis with economic calendar and chart
IMIE — The AI Quant Research Copilot for Individual Traders
Inspiration
Individual traders often research markets through disconnected tools: charts, calendars, scanners, notes, spreadsheets, and general-purpose AI assistants. Moving between them makes it difficult to preserve context, compare evidence, and follow a repeatable process.
IMIE was created to bring those activities into one structured research workbench.
What it does
IMIE helps a trader move from a market question to an evidence-based research view without handing control to an automated trading bot.
The working platform demonstrated for Build Week includes:
- a market dashboard and watchlist
- market scanning and saved scan history
- market-session awareness
- an integrated economic calendar
- chart-based market analysis
- an AI research assistant
- historical replay and replay results
These views are designed to work together. A user can check which market session is active, review upcoming economic events beside the chart, inspect market conditions, record a research view, and revisit prior scans or replay results.
IMIE is decision-support software. It does not place live trades, promise returns, or replace the trader’s judgment.
How we built it
IMIE had an existing research foundation before Build Week. During the event, we used GPT-5.6 and Codex to extend and present it as a coherent user-facing application.
GPT-5.6 helped with product reasoning, workflow design, review, and clear communication. Codex worked directly with the repository to inspect relevant code, implement bounded changes, diagnose failures, run verification, and keep documentation aligned with the working application.
The development process remained human-directed: changes were scoped, reviewed, and verified before acceptance. The submitted demo focuses only on capabilities that are visible in the running platform.
Challenges
The main challenge was turning a broad research system into a workflow that is understandable within a three-minute demonstration. We chose one practical story: understand the current market context, identify relevant events, inspect the chart, conduct focused analysis, and preserve the result for later comparison.
A second challenge was balancing useful AI assistance with responsible product boundaries. IMIE supports research and evidence organization while keeping decisions with the user.
Accomplishments
We are proud that IMIE is more than a chat interface. It connects market context, an economic calendar, chart analysis, scanning, AI-assisted research, and historical replay in one consistent workbench.
We are also proud of the Build Week development process. Codex accelerated repository work, but every important change remained subject to explicit human review and verification.
What we learned
AI-assisted engineering works best when the model is given clear boundaries, relevant repository context, and objective checks. The strongest results came from small, reviewable steps rather than broad autonomous changes.
We also learned that a complex platform should be explained through a simple user outcome. For IMIE, that outcome is better-structured research—not automated trading.
What’s next
Next steps include improving onboarding, expanding guided research workflows, strengthening evidence comparison across saved sessions, and testing the experience with more realistic user scenarios.
IMIE will continue to prioritize transparency, human control, and research quality.