Exploring transaction data with pandas gave me a baseline. Counting things, grouping things, building a sense of what normal looks like. That was useful, but it left me staring at tables of numbers. What I wanted was to see the shape of what's happening over time.

I've spent time thinking about stablecoins, the Terra collapse, and the regulatory questions surrounding both. It gave me a better sense of where the industry might be headed and why regulators are taking a careful approach. But it also highlighted something I'd been noticing. The conversations happening around banking and the conversations happening inside a bank are very different.

I had what felt like a straightforward question. What are the actual rules around crypto?

Even after spending real time on this, I don't have a clean picture. Multiple agencies have authority over different pieces, the federal framework is still being developed, and the right thing to do is acknowledge that and think out loud about what I can see from where I sit.

Less than a year ago, I was genuinely optimistic about blockchain. The technology had real properties, real use cases, and I wanted to understand it properly before forming opinions. Then $40 billion evaporated in a week, and the picture changed.

When I first dug into blockchain, I focused on the technology. Distributed ledgers, consensus, immutability. What I didn't spend enough time on was the most obvious problem with Bitcoin as actual currency. It moves too much. Accept $100 in Bitcoin today, and it might be worth $85 by Friday. I built a payments company. That kind of volatility makes a currency useless for commerce. So people built something to fix it.

I wanted to try the same data tools on transaction-level records. Mortgage lending data is useful, but the day-to-day activity of a bank lives in its transactions. Deposits, withdrawals, transfers, loan payments. Every client interaction with their account generates a record.

Schedule RC, the balance sheet section of the public call report, is where a bank lays out what it owns and owes, so I pulled one up to see how it is put together. I've read balance sheets before from my startup days, but a bank's looks different in several ways.

The call report keeps coming up in any conversation about bank financials. I realized I didn't really know what was in it. So I pulled one up.

I've been wanting to dig into bank financial statements, but I kept realizing I was fuzzy on some basics. I've worked with financial statements before, built projections for investors, reviewed monthly P&Ls, tracked cash burn. But that was a while ago, and I never had formal accounting training. So before jumping into bank-specific financials, I wanted to ground the fundamentals.

With Jupyter, pandas, and matplotlib set up, let's put those tools to work on real data.