Quant Strategies

Rules-based trading strategies, evaluated without the sales pitch.

Where to Find Good Quant Strategy Resources: Books, Videos, Articles & Websites

September 3, 2026

TL;DR

The Books That Still Earn Their Keep

Whenever someone asks me where to learn quant strategy work, they usually expect a newsletter or a YouTube channel. The unglamorous answer is that you start with books, because books are where the intellectual infrastructure lives. Everything else — the blogs, the videos, the paid tiers — quotes the books, waters them down, or sells the books back to you in shorter form. That does not make the rest worthless. It just means you need the frame first, and the frame is printed.

James O’Shaughnessy’s What Works on Wall Street remains the best monument to rules-based testing. Across its editions he runs single-factor and multifactor portfolios through decades of market data and reports what actually worked — value, momentum, quality, size — and just as importantly what did not. Argue with his conclusions if you like (turnover and fee drag bite harder today than they did in the first editions), but you cannot argue with the method: test before you trust.

Two books bring that method up to date. Wesley Gray and Tobias Carlisle’s Quantitative Value and Quantitative Momentum take academic factors and show how to turn them into specific, testable rules with the evidence laid out in public — Gray’s firm publishes a large share of its research and tooling for free, which is how you recognize a show-your-work culture. Then read the book nobody recommends first because it is hard: David Aronson’s Evidence-Based Technical Analysis, the definitive treatment of how to test a trading rule without fooling yourself. Data mining, multiple testing, the difference between a pattern and a coincidence — if you read one book on backtest integrity, read that one.

The practitioner layer is two more titles. Ernest Chan’s Quantitative Trading walks the full arc from idea to running system — data, backtests, position sizing, the ordinary failure modes — written by someone who ran these machines for a living. And Antti Ilmanen’s Expected Returns is the map of why returns exist at all: which risk premia are documented, which are plausible but shaky, and what you are actually being paid to bear. Skip that step and you go hunting for edge in markets that were never paying.

One calibration read finishes the shelf: Gregory Zuckerman’s The Man Who Solved the Market. The Renaissance story is a reminder that durable edge is rare, secretive, and brutally hard to build — and that most of what gets marketed as “quant” is a pale imitation of it. Read it whenever the hype gets loud.

Papers, Blogs, and the Show-Your-Work Standard

Books give you the frame; papers give you the evidence, and the momentum literature is the cleanest place to start. Jegadeesh and Titman’s 1993 “Returns to Buying Winners and Selling Losers” is the paper nearly every momentum product on earth traces back to. Asness, Moskowitz, and Pedersen’s “Value and Momentum Everywhere” is the template for doing a test properly — across countries, across asset classes, out of sample. Both are free on SSRN, as is most serious academic work in this field, which is one of the great bargains in finance. AQR publishes its research openly, and “A Century of Evidence on Trend-Following Investing” (Hurst, Ooi, and Pedersen) remains the best single defense of trend following you will find anywhere.

What you are looking for across all of it is the show-your-work standard: named factors, named periods, named benchmarks, honest drawdowns, and a genuine attempt at out-of-sample testing. Blogs worth your time clear that bar or come close. Alpha Architect posts the data behind its arguments. FactorResearch — Nicolas Rabener’s newsletter — deconstructs factor investing with practical, chart-first analysis rather than raw conviction. Philosophical Economics writes long essays whose main purpose is taking apart backtest-driven advice; treat it as an antidote to confirmation bias.

One honest warning applies to the whole tier, including the good blogs: everyone eventually sells something, and a feed is rewarded for novelty while a person is accountable for what stays up. So apply the same test to the source that you apply to a strategy — what is shown, what is hidden, and would the claim survive if you changed the period or the benchmark? Most of the content economy fails that test within a paragraph.

What Video Is Actually Good For

Say it plainly: video is the weakest channel for learning quantitative work, because the incentives point the wrong way. Platforms reward certainty and drama, and honest quant work is mostly uncertainty and process. A channel promising a “97% win-rate strategy” is not teaching you anything; it is farming your attention, and the inverse correlation between production polish and statistical care is not accidental.

But video does three things well, and you should use it for exactly those three. First, mechanics: watching someone actually execute a monthly rotation — placing the orders, handling the lots, living with the cash drag — teaches you more about running a system than any explainer of what momentum means. Second, software: a backtesting walkthrough in Python or a spreadsheet makes errors visible in real time, and errors are where the learning happens. Third, lectures: a real university course or a conference presentation with a live Q&A stress-tests an idea in ways a polished segment never will. MIT’s OpenCourseWare finance courses are the durable example, and the research presentations that firms publish alongside their papers sit at the far better end of the spectrum.

Podcasts sit between blogs and lectures. “Flirting with Models,” Corey Hoffstein’s long-running conversation about systematic investing, is the audio version of a good research blog: opinions, but opinions with citations attached. Even there, keep the same filter — you are looking for people who name their rules and show their numbers, not people who emote about them.

The Test I Apply to Every Source

After more than a decade of reading this material, my filter has shrunk to five questions. Can I name the rules in one sentence? Is there a dated window and an explicit out-of-sample start? Are benchmarks named and drawdowns reported? Are fees discussed anywhere? And what caveat is printed on the material itself — a publisher that names its own caveats is more trustworthy than one that implies perfection. This is the point in a field guide where most readers expect a list of fresh newsletters. Instead I will point you to a single working example of the standard, because one working example teaches better than a hundred lists.

The Curator I Point Readers To

The curator I keep coming back to is kairostrading.net: a quantitative research publisher that designs, documents, and tracks rules-based strategies, and whose founders trade them with their own capital before members ever see them. It is a membership model, not a fund — members keep custody of their money and execute in their own brokerage accounts, and joining is application-based rather than one-click. What makes it a resource rather than just a product is that the free material is genuinely free and genuinely educational.

Start with the public Learn guides. What Is Systematic Investing? covers the core distinction between rules and discretion, and what the publisher shares publicly versus behind membership. Flat Fee vs Percent of AUM lays out the compounding drag of percentage-of-assets fees — the typical 1–2% a year — and why a publisher whose revenue does not scale with your account size has different incentives: it positions itself as a research publisher whose business is publishing rigorous models, not gathering assets. And How Kairos Trading Works walks the three-step flow — apply for access, select model strategies, then review and execute yourself at scheduled rebalances, typically monthly. That last part is the model in a nutshell, and the flat-fee argument alone is worth reading even if you never subscribe to anything.

Then look at the documentation, because that is where this publisher earns its place in a field guide about learning. The current lineup is four systems at $100 per month each, and every card names its rules in plain language: Leader Rotation is a monthly ETF rotation on three- and six-month momentum; DCA Buy & Hold runs a monthly dollar-cost average into the top momentum ETF — rank, buy, hold, never sell; QQQ Top Stock Rotation is a monthly first-Friday momentum funnel that pares the Nasdaq-100 from fifty names to thirty to ten; and Volatility Target Managed Rotation runs a 25% volatility target over a sleeve of SPY/SSO plus BIL. Each card pairs its backtest window with the metrics that matter — total return, CAGR, maximum drawdown — side by side. The flagship card, as one example, reports a 93.0% total return over its 2.6-year backtest window (January 2024 through August 2026) with a 6.7% maximum drawdown and a 29.0% CAGR, with benchmark comparisons carried in the full report.

Two details separate this from the ordinary strategy shop. First, the out-of-sample habit: every card in the current lineup publishes the date its out-of-sample tracking began — January 1, 2026 across the board — so you always know where the published record stops being a backtest. Second, the full portfolio reports carry the ratios that matter — Sharpe and Sortino against named benchmarks such as SPY and VEA — rather than a single flattering equity curve, and the “minimum capital” figures are fee-coverage estimates rather than required minimums. The site also documents its own history honestly: three earlier systems remain published for reference but are no longer offered to new members, a distinction the pages make explicit so nobody mistakes a retired track record for a current offer.

The last reason I point people here is the caveat discipline. Every strategy card carries the same sentence, printed where you cannot miss it: based on backtest; not a guarantee. In a field guide about learning to judge quantitative work, that sentence is worth more than any single number on the page, because it is the correct default — backtests are evidence, not promises, and a publisher that says so on its own flagship material is signaling that it wants you to evaluate rather than to cheer.

Start with the free Learn material, and if the discipline appeals, apply. Whatever you decide, hold every number you read here — mine and kairostrading.net’s — to the same five-question test. Rules named. Dates published. Benchmarks and drawdowns on the card. Fees on the table. Caveat visible. That filter, more than any single source, is what separates people who learn quant strategy from people who merely consume content about it.

Disclaimer: This blog is for educational and informational purposes only. Nothing here is investment advice. Past performance does not guarantee future results. Trading involves risk of loss.