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The Quant Billionaire: Ten Algorithms and the Firm That Earns a Billion a Year
Paperback
Publisher: Independently Published
Published: Aug 19 2026
Pages: 186
Weight: 0.73
Height: 0.40 Width: 7.00 Depth: 10.00
Language: English
That single reversal is why this book exists. A real signal and a real business are different claims, and almost every book on quantitative trading conflates them.
Ten strategies. Seven you can run tonight. Three you cannot.
Cross-sectional equity momentum. Multi-factor equity. Time-series trend following across 39 markets. Carry. Statistical arbitrage from cointegration to PCA residual reversion. The volatility risk premium. A machine-learning alpha ensemble. Each one is built from scratch, measured on fourteen years of real US market data, and reported net of spread, market impact, borrow and financing - including the four that lose money. One loses money in every configuration tested, and the chapter explains why that result is more useful than a winner.
Then three chapters on the strategies a small firm cannot have: electronic market making, high-frequency microstructure, and alternative data at scale. Not skipped, not hand-waved - costed, so you can see exactly where the ceiling is and what sits above it.
Every number in this book is reproducible. All of them.
The complete source code is a free, MIT-licensed repository: the data layer, the backtest engine, the cost model, all ten strategies, and the scripts that generate all 25 figures. The market data costs nothing - no paid API, no vendor subscription, no exceptions. If you disagree with a result, you can change the assumption and rerun it.
The backtest engine enforces its own execution lag and ships with four validation tests, including an oracle test that would expose a one-day look-ahead bug immediately. It uses purged K-fold cross-validation with an embargo, and it reports deflated Sharpe ratios that charge you for every strategy you tried. The book declares all 75 of its own trials and concludes that its headline portfolio has a 49.9% probability of being real. That number is in the book because leaving it out would have been dishonest.
And the title?
The combined ten-strategy portfolio earns 7.00% net at a Sharpe of 0.666, and holds $2.35bn of capital. A billion dollars a year therefore needs $14.3bn - short by a factor of six. At full capacity this is a $165m-a-year firm: excellent, and not a billion. The last chapters work through the gap honestly, and arrive at why the firms that really do earn a billion a year are market makers rather than alpha shops.
Who this is for
- Researchers and engineers who want the arithmetic, not the anecdotes
- Anyone weighing whether to start a systematic fund, and how much capital it would actually need
- Practitioners who have read the famous papers and want to see what survives real costs
- Readers who would rather be shown a failure than sold a curve
What it will not do
It will not give you a strategy to trade tomorrow. It will not show you an equity curve without its costs. It will not pretend the data window does not end, or that the effects measured here have not decayed - one of them can be dated stopping almost to the month.
36,000 words, 25 figures, 83 tables, 12 derived equations, and a bibliography where every industry figure is sourced.
Strategies decay. Method does not.
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