Using AI for investment?

I built TradingSpy: A completely local, privacy-first AI trading research assistant & backtester (Fully Open Source).

Disclaimer: just an individual side project, using all free tier api/library for personal use purpose. Share for more awareness about loop engineering power trading agents and knowing about the trading technologies
Like many of you here, my weekends usually involve digging through charts, checking insider buying patterns, scraping news catalysts, and trying to write/test Backtrader strategies.

A few months ago, I realized I was spending more time wiring together five different data APIs, Jupyter notebooks, and custom scripts than actually researching strategies. Worse, most commercial platforms out there charge a fortune, lock you into their ecosystem, or harvest your strategies and data.
Since I couldn't find a privacy-first, all-in-one local environment that did what I wanted, I decided to build it myself.
I call it TradingSpy, and I just open-sourced it.

What is it?
TradingSpy is a local-first AI trading research workstation running entirely in Docker. It combines traditional data visualization (heatmaps, charts, indicators) with loop-engineered AI agents that act as your research companion.
It is not a broker and does not place trades. It is purely a sandbox for strategy generation, market analysis, and backtesting.

Why use it?
* Zero Data Privacy Concerns: Everything runs locally on your machine via Docker. Your strategy ideas and data remain yours.
* AI Strategy Generation (The Fun Part): You can describe a strategy in plain English, and the system generates a working Backtrader strategy, validates the syntax, and automatically runs parameter sweeps against historical data.
* Loop Engineering Agents: You can give it a goal (e.g., "Improve EMA_Trend for TQQQ until it beats the baseline"). The agent will autonomously iterate, test, tweak the code, and discard failed attempts without you having to babysit it.
* Aggregated Market Intelligence: Pulls real-time quotes, sector heatmaps, insider transactions, fundamentals, and academic papers (via arXiv) into a single interface.
* Supports Free/Local LLMs: Works out of the box with free-tier providers (Google AI Studio, OpenRouter) or completely offline local models via Ollama (qwen2.5-coder, etc.).

Why am I sharing this?
Right now, I am a solo developer building this on zero budget because it’s a tool I genuinely needed. I’m opening it up to the community because I want to make quantitative research accessible to everyone without the data privacy paranoia.
I would love to get your feedback, feature requests, or bug reports!
GitHub: https://github.com/mrhustlex/TradingSpy-TradingAgentService/
(Note: It’s experimental software meant for educational research, not financial advice!)