Trading Sidecar

Type: Side Project
Platform: macOS
Status: In Development
Built with: Figma, Claude Code, Swift, AI Integration

I've tried building something like this at least four or five times. I love to trade and wanted AI to help me analyze charts in real time, suggesting trade ideas, profit targets, and when to move stop losses. Every previous attempt hit a wall. This time, the approach finally clicked.

Trading Sidecar with TradingView charts and AI suggestion cards

Four Failed Attempts

The first attempt used raw stock data APIs. The pure numerical data wasn't what AI needed to understand chart patterns. It couldn't "see" the chart. Next I tried a Chrome extension to take screenshots of TradingView, but browser security sandboxing made it unreliable. Then I built a Node.js instance on a PC that would screenshot the session, send it to Firebase, and have a React app check for new images and generate a response. It worked, technically, but the round trip was so slow it wasn't faster than just manually screenshotting and uploading to Claude in the browser.

Then it hit me: I've been building native iOS apps with Claude Code without issue. Why not build a native macOS app that does this directly? No browser extensions, no Node servers, no Firebase round trips. Just a native app with a built-in web view and an AI sidebar.

Overview of Trading Sidecar iterations showing live analysis, chat history, and AI-powered trading suggestions

The Breakthrough

In a single weekend, I had a working prototype. A macOS app with a main web view supporting multiple browser tabs for different charts and brokerages, custom prompt creation, configurable screenshot capture intervals, and a suggestions engine that analyzes the active tabs and generates trading insights.

Evolving the Suggestion Engine

The suggestion UI went through three distinct design iterations, each informed by real trading sessions:

v1 plain text suggestion showing raw analysis output

v1: Plain Text

The first version returned raw text analysis. Useful information, but hard to scan quickly when markets are moving. Trading requires fast decision-making and walls of text don't support that.

v2 action cards showing OPEN LONG and STAY OUT suggestions with color-coded status

v2: Action Cards

Restructured into card-based suggestions with clear actions: OPEN LONG, STAY OUT, EXIT. Each card shows the ticker, P&L, entry/current/stop-loss prices, and a specific recommended action like "Move Stop to 173.70." Color-coded for instant parsing. Green for active positions, amber for caution.

v3 strategy card showing STAY OUT recommendation with Watch For conditions and Long/Short setups with entry, stop, and target levels

v3: Strategy Cards

The current iteration goes beyond single-action suggestions. AI now generates scenario-based strategy cards that outline a range of possibilities. For example: "Watch for: break above 276 MA Ribbon resistance with volume for long, or breakdown below 274 support for short." This gives precise price levels and conditions to watch for, letting you prepare for multiple outcomes instead of reacting to one prediction.

Other Features

App Settings

Configurable appearance, layout, and behavior. Theme support (system, light, dark), adjustable chat text size, flexible panel positioning (chat left or right), screenshot quality settings, custom prompts, auto analysis intervals, and API configuration.

Trading Sidecar settings screen showing theme, text size, layout, and screenshot quality options

Tab and Multi-Chart Layout

The top bar with tabs mirrors a browser but with trading-specific affordances. The web view supports multiple tabs so you can have different charts and brokerages open simultaneously. The app intelligently captures screenshots from all tabs or specific ones.

Trading Sidecar showing tabbed layout with TradingView charts and trading sidebar

Designed in Figma, Built with AI

Trading Sidecar represents the most complex AI integration across my side projects. It's not just using AI to build the app but the app itself is an AI product. The design challenge is fundamentally about how humans and AI collaborate on real-time decisions with real stakes.

Every design iteration came from actual use. The evolution from text to action cards to strategy cards. It was driven by trading with the tool and finding the gaps. That tight loop between designing, building, and using the product myself is what makes these side projects valuable: the feedback is immediate and honest.