Why this exists.

132 Engineering is a software firm based in Canada. We build tools — diagramming platforms, tipping bots, data pipelines. But outside of client work, we invest. Not aggressively. Not speculatively. We hold positions, we manage risk, we think in probabilities.

And for years, we ran our analysis in spreadsheets.

The spreadsheet problem

Every quarter, someone on the team would update a Google Sheet with fresh FRED data, re-run a DCF with manually entered growth assumptions, eyeball a correlation matrix, and call it risk analysis. It worked, technically. But it was slow, error-prone, and impossible to share meaningfully.

The professional tools existed — Bloomberg, FactSet, Capital IQ. But they cost $20,000 to $40,000 per year. For a small firm running its own capital, that's not a tool cost. That's a salary.

The consumer alternatives — Yahoo Finance, Seeking Alpha, TradingView — are excellent for what they do. But they don't do what we needed. No stochastic DCF. No Monte Carlo with fat tails for crypto. No automated stress testing against macro factors. No mean-reverting simulation for forex. No portfolio engine with Cholesky-correlated returns.

The missing middle

There's a gap in the market that nobody talks about. Between the institutional terminal and the consumer screener, there's an entire tier of analytical tools that simply doesn't exist at an accessible price point.

The methods themselves aren't secret. Geometric Brownian Motion is in every finance textbook. Stochastic DCF is standard practice on institutional desks. Factor regression against FRED data is what every quant fund runs. The papers are public. The math is known. The data is available from FMP and FRED for a fraction of what Bloomberg charges.

What was missing was someone putting it all together into a product that a normal person could use.

So we built it.

Market Risk started as an internal tool in early 2026. A Node.js server, some EJS templates, Plotly for charts, Supabase for the database. We added features as we needed them — DCF first (because that's what we used most), then Monte Carlo (because we wanted probability distributions, not point estimates), then factor regression (because we wanted to understand what was driving our returns).

Then crypto came along. Our portfolio had BTC and ETH positions, and running equity DCF on them was meaningless. So we built a crypto engine — Student-t distributed Monte Carlo with fat tails, NVT valuation, momentum regime detection. Same analytical rigour, different models.

Then forex. Some of our positions were currency-hedged. Currencies don't trend like equities — they mean-revert. So we built an Ornstein-Uhlenbeck simulation with rate differential and PPP deviation models.

Then the dividend capture calendar. Because we noticed that some of our best short-term trades were dividend captures, and we wanted a systematic way to scout for them.

What it is now.

Market Risk is a multi-asset research platform with 18 analytical methods running across equities, crypto, and forex. 20 sector templates. 15 risk categories. 19 interactive charts, each with educational overlays that explain what you're looking at. A portfolio engine. A stock screener. A dividend capture calendar. All running in the browser with data from FMP and FRED.

It is not financial advice. It does not tell you what to buy. It does not make predictions. It runs quantitative models and shows you the output, with full transparency about the methodology, the assumptions, and the limitations.

It is the tool we wished existed when we were staring at that spreadsheet.

If you invest seriously — not day-trading, not gambling, but actually thinking about risk and valuation — this is for you. If you understand what a Sharpe ratio means but your tools don't compute one, this is for you. If you've ever wished you could run a quick Monte Carlo on a position without opening Excel and spending an hour building the model from scratch, this is for you.

We use it every day. We hope you will too.