About

Who I Am

I'm JJ — a data engineer and independent market analyst based in the U.S. (You can find my engineering and portfolio work on my personal site.) By day I work with large-scale data pipelines; by night I apply the same quantitative rigor to financial markets. I've been studying market cycles, historical price behavior, and long-term trends for over a decade, with a particular focus on U.S. equities, crypto assets, and global macro.

What This Site Is

JJ International Financial Analysis is a personal research blog built on one conviction: most short-term market noise disappears when you zoom out far enough. Here you'll find long-horizon charts and data-driven narratives — not daily tips or hot takes.

Coverage includes:

• U.S. Equities (S&P 500) — monthly data back to 1927, trend channels, historical drawdowns, and cycle positioning
• Bitcoin & Crypto — halving cycle analysis, log-scale regression bands, on-chain context
• Macro — yield curves, inflation regimes, dollar cycles, and how they interact with risk assets

Analytical Philosophy & Methodology

Markets are non-linear and span multiple orders of magnitude over decades. Linear charts exaggerate recent volatility and compress ancient moves — they distort more than they reveal. I use logarithmic scale throughout because a 50 % drawdown looks the same in 1932 and in 2008, and that comparability is what makes historical context meaningful.

The core toolkit:

• Log-linear regression channels — fit to decades of monthly closes to identify the secular trend and measure how far price deviates from it
• Cycle & halving overlays — for Bitcoin, four-year halving windows mapped onto price and on-chain metrics
• Macro regime tagging — rate cycles, recession bands, and Fed policy shifts overlaid on asset returns
• Data-first, narrative-second — every chart is built from raw public data (FRED, Yahoo Finance, CoinGecko) before any narrative is attached

One pattern I keep seeing in markets: investors obsess over price (the Y-axis) while completely overlooking time (the X-axis). How fast to react, when to sit tight — emotion crowds out the one variable that is entirely objective and impossible to manipulate. This site is built to fix that blind spot.

My goal is to help investors quantify uncertainty rather than react to noise — to plan rather than panic-trade. I want to see fewer people chasing leveraged short-term patterns that erode both capital and peace of mind. I also want to push back against generic rules-of-thumb that ignore asset characteristics entirely: "buy at the knee, sell at the shoulder," "sell when price touches the 200-day MA" — advice that sounds clean but gives beginners false precision without context.

What I offer instead: log-scale trend lines, time gaps between historical peaks and troughs, and cycle-aware positioning — tools grounded in common sense and historical evidence, not pattern-matching for its own sake.

Why Trust This Analysis

I won't claim I can predict markets — nobody can reliably.

Why Prediction Has Real Limits

In late July 2026, Amazon raised its capex plan to $22 billion and the stock rose 15%. Around the same time, Google reported 82% cloud revenue growth — a far stronger number — yet fell 5–7% on capex guidance alone. Same kind of news, opposite reactions. The explanations that followed were all written after the fact.

That same period, a leveraged hedge fund called Situational Awareness was forced to liquidate most of its AI-infrastructure holdings after a margin call. The unwind, and the rebound that followed, moved semiconductor and AI stocks broadly for days. Nothing in public filings would have flagged this fund's existence or its leverage before it happened.

Around the same time, as the U.S.–Iran conflict eased, Houthi attacks on Red Sea shipping pushed oil prices back above $100. Even a well-supported long-term trend can be interrupted by a shock outside the model entirely.

I don't think this kind of uncertainty is easy to quantify. So I'd rather readers not spend more time and energy than necessary trying to call things that are genuinely hard to call.

What I can offer is transparent, reproducible analysis: every chart shows its data source and time range, every trend line can be re-derived from the underlying dataset, and I update charts with live data rather than cherry-picking favorable snapshots.

My background in large-scale data engineering means I'm comfortable with the full pipeline — from raw data ingestion and cleaning to statistical fitting and interactive visualization. The numbers you see here haven't passed through a black box.

I also have no broker affiliation, no sponsored positions, and no incentive to push you toward any trade. This site exists to share frameworks, not to sell signals.

A Message to Readers

Long-term investing is won or lost not in the moment of entry, but in the years of holding through volatility. The goal of every chart on this site is to give you historical perspective before panic or euphoria sets in.

When markets fall 30 % and headlines scream crisis, a log-scale channel chart can show you that price is still well within normal historical range. When Bitcoin doubles in a month and optimism peaks, the same framework can show where we are in the cycle.

Use this site as a reference lens — one of many inputs in your own decision-making process. Question the charts, verify the data, and always size risk appropriately for your own situation.

All content represents personal research and opinion. Nothing here constitutes investment advice. Always consult a qualified financial professional before making investment decisions.