How I Wrote a Technical Book on Wealth Distribution and Coin Holdings: From Zero to O’Reilly Author

So, I wrote a technical book for O’Reilly. About coin collections and wealth allocation. Sounds niche, right? But here’s the thing: it wasn’t *really* about coins. It was about building a real business around a technical idea—and using the book as the foundation. I want to tell you how I did it, mistakes and all.

Why Coins? (And Why You Should Care)

Everyone talks about stocks and crypto. But what about the 1913 Liberty Head nickel in your grandpa’s safety deposit box? Or that first-edition comic you can’t bring yourself to sell?

I kept seeing the same angry forum threads: “My spouse says my coin collection is a waste!” “I want to sell, but it’s worth less than I paid!” “How do I pass this down without my kids fighting?”

These weren’t just collector gripes. They were real financial problems hiding in plain sight. I realized: we have zero tools to model passion assets fairly. That’s where the book idea was born: Wealth Allocation Modeling: Quantifying Passion Assets in a Modern Portfolio.

The Niche That Wasn’t

Sure, most finance books cover ETFs and real estate. But collectibles? Trillions in global value—6.2% of ultra-high-net-worth portfolios, per Knight Frank—and we still treat them like hobbies, not assets.

By using coins as the lens, I could map the same problems to art, vintage watches, or even NFTs. The trick? Find the universal in the specific. This is how technical authors turn obscure topics into must-read books.

How I Structured the Book (Without Drowning in Theory)

O’Reilly expects depth. But readers want to *do* something. So I built the book on three pillars:

• Why it matters: The real cost of illiquid assets
• How to measure it: Frameworks, not rules of thumb
• What to do: Code, spreadsheets, and real decisions

The chapters fell into place:

• Chapter 1: The Illiquidity Tax – Why your coin collection isn’t like your index fund
• Chapter 2: Behavioral Accounting – How we track value vs. how we feel value
• Chapter 3: Allocation Frameworks – From “just a hobby” to 25% of net worth: the tipping points
• Chapter 4: Liquidity Modeling – What happens when you *need* the cash?
• Chapter 5: Tax and Estate – The IRS doesn’t care if you love your coins
• Chapter 6: Case Studies – Real people, real trade-offs

The Tool Everyone Actually Uses

Readers loved the Wealth Allocation Matrix (WAM). It’s simple, but it works:

WAM = (Asset Value / Net Liquid Assets) × (Liquidity Score) × (Emotional Weight Factor)

Let’s say your coin collection is worth $150K. Your other liquid assets? $1M. You could sell in 3 years (score = 3). And honestly? Those coins mean a lot to you (emotional factor = 0.9).

WAM = (150,000 / 1,000,000) × 3 × 0.9 = 0.405

Here’s the rule: if WAM > 0.3, it’s time to rethink. Either reduce the position, or plan an exit. No opinions. Just math.

Who I Wrote For (And Why It Worked)

This wasn’t for your average retiree. I targeted tech founders, VCs, and financial engineers—people who think in models, not maxims.

Why? They’re already managing complex portfolios. They understand risk, volatility, and behavioral finance. And they’re suddenly asking: “How do I account for my vintage guitar collection when I exit my startup?”

Code That Did the Talking

No vague advice. Just working Python. Here’s a snippet from Chapter 4:

import pandas as pd
import numpy as np

def simulate_allocation(coin_pct=0.05, liquidity_days=1095, n_sims=1000):
    returns = []
    for _ in range(n_sims):
        market_return = np.random.normal(0.07, 0.15)
        coin_return = np.random.normal(0.03, 0.25) if liquidity_days <= 365 else -0.02
        portfolio_return = (1 - coin_pct) * market_return + coin_pct * coin_return
        returns.append(portfolio_return)
    return np.percentile(returns, [5, 50, 95])

print(f"5th/50th/95th percentile: {simulate_allocation()}")
# Output: e.g., (-12.3%, 6.7%, 18.1%)

This code landed in GitHub repos, Slack channels, and even a few hedge fund backtests. That’s the power of practical technical content.

Pitching Publishers: The 6-Month Pre-Game

You can’t just email O’Reilly with “I have an idea.” You need proof.

Step 1: Prove the Demand

• Wrote 12 LinkedIn posts on “The Tech of Collecting” (with code)

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• Built a free WAM calculator (10K+ users in 90 days)

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• Spoke at fintech meetups with live demos

Step 2: The Proposal That Got the Yes

• Competitive Analysis: “No book connects behavioral finance with code for passion assets.”
• Chapter Outline: With word counts and GitHub links
• Author Platform: 25K LinkedIn followers, speaking gigs, newsletter
• Marketing Plan: Webinar, free tool, API access

O’Reilly responded fast. But here’s the key: they didn’t just buy a book. They bought a platform.

Writing: 6 Months of Real Work

First draft? Three months. One chapter a week. I used Scrivener, but the real trick was this: write for a beta reader.

The Process That Saved Time

• Real data: Anonymized numbers from the calculator made examples concrete
• Jupyter notebooks: Hosted on GitHub, with live code
• Peer review: Sent drafts to CFOs, data scientists, estate planners

O’Reilly’s technical editor caught three model flaws. Fixed them. That’s why you need experts.

Production and Launch

• Used O’Reilly’s Atlas for real-time edits
• Launched with a free 30-day email course to build buzz

Beyond the Book: Turning Content Into a Business

The book was just the start. I turned the WAM into a freemium tool:

• Free: Basic portfolio scan
• Premium: $20/month for estate plans, tax scenarios

Result? 8K users in six months. Invited to speak at three major conferences. And yes—my next O’Reilly book was signed before this one even shipped.

The Real Lesson: You’re Not Writing a Book

I didn’t just write about wealth allocation. I built a tool. A business. A platform.

• Structure beats stories: Frameworks > opinions
• Code > commentary: Give readers something they can run
• Publishers want platforms: They need to know who will buy it
• Monetize after launch: The real ROI is in tools, services, and speaking

Whether you’re in fintech, AI, or DevOps, this works. Stop writing books. Build products that happen to be books. That’s how you move from author to authority.

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