This is an experiment.

Alpha with AI has always been a writing-first newsletter. That is not changing. But this week I wanted to try something different, a video podcast format, where the workflow comes alive on screen rather than on the page.

If you like it, I’ll do more of these alongside the written pieces. If you prefer the written format, that is coming back soon.

Either way, would love to know what you think in the comments.

This is Part 3 of the Claude Code series.

If you are new here, Part 1 was about why Claude Code is not just a coding tool. Part 2 was the full investment research setup: Obsidian, Playwright, Firecrawl, the whole stack.

This one is where the actual research begins.

Insider trade data is one of those signals everyone has an opinion on.

Half the community ignores it entirely. The other half treats every transaction like a gospel signal.

Peter Lynch settled this a long time ago — insiders sell for hundreds of reasons, but they buy for only one.

The data agrees. Out of all insider trade transactions in the US market over five years, ~ 95% are sells. Which means the sell data alone tells you almost nothing in isolation.

Alphabet insiders sold $395 million worth of stock over two years. The stock returned 143% in the same period.

There is also one more layer most people miss entirely. Insiders can only trade when they hold zero price-sensitive non-public information. At the most critical moments, launches, acquisitions, earnings surprises, they are literally prohibited from trading.

The silence in the data is often more important than the signal itself.

This is where Claude Code changes the workflow entirely.

What used to mean downloading CSVs, cross-referencing filings manually, and building Excel models from scratch, Claude Code does in minutes.

In this episode I walk through a live build:

* → Pulling recent US insider trade data directly

* → Classifying every transaction — Strong Buy, Moderate Buy, Strong Sell

* → Extracting sector and macro concentration patterns

* → Using a past Alpha with AI article as a reference framework fed directly into Claude Code via Obsidian

That last part is the one I keep coming back to. Your own past work, your own frameworks, your own research, Claude Code can use all of it as context. That is what makes this stack genuinely compounding over time.

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Resources from this episode:

Part 2:

Reference Article: Stop Reading Insider Filings Manually:

Research & Action — Full Workflow Templates:

https://shikshannivesh.com/research-in-action

Drop a comment or reply to this email directly for the prompts, signal classification framework, and Obsidian setup used in this build.

What’s coming next

This newsletter is part of a series. Now that the part 3 is done, the next pieces will go deep into specific finance workflows: a full equity research workflow that runs end to end inside Claude Code, a wealth management workflow for prioritizing client calls after a market event, a monthly accounting audit workflow, and a portfolio monitoring system that runs on a virtual server and pings you when something needs attention. Each of these will use the system you just built.

If you want the rest of the series as it comes out, you’re already in the right place by reading this.

Claude Code is for coding if you want to code. It’s for cooking if you want to cook. It’s for shopping if you want to shop. And if you’re really into finance, it’s the most powerful research tool you can have on your machine right now. The hour you spend setting this up is the hour your research stops leaking and starts compounding.

That’s the whole thing.

🧰 Free and Premium Resources from Shikshan Nivesh

If you want the frameworks, prompts, and workflows I use across my research stack, free and paid, they all live in one place: shikshannivesh.com. The industry research playbook, the $100K Research Framework, the Kill Report prompt, and more. Everything built for analysts and investors who’d rather spend their time thinking than clicking.

🤝 Help Us Grow This Circle

Thank you for reading and supporting Alpha with AI. If you share this edition with even one person who might find it valuable, it means the world to us and helps this project reach those who need it most.

At Shikshan Nivesh, our goal is simple, to make financial research faster, smarter, and more accessible.

We believe investing should start with understanding and every newsletter we write is built to reflect that.

Written by Shubham Borkar | Research & Insights by Shikshan Nivesh AI Team

Financial Clarity. Insightful Ideas.

Disclaimer

This Prompt Kit and its outputs are for educational and research purposes only. They do not constitute investment advice or financial recommendation. Always verify disclosures and consult qualified professionals before making investment or business decisions.