The reading list

Forty lessons have each ended with a source line, which means the course has quietly accumulated a bibliography that nobody has ever seen in one place. Here it is, extracted from the lessons themselves.

The spine

Three texts do most of the work, counted by how many lessons cite them:

   Larry Harris, Trading and Exchanges                      8 lessons
     the foundation for M0 and much of M1. If you buy one
     book from this course, this is it — it is about how
     markets WORK, written by someone who cares about
     institutions rather than equations.

   John Hull, Options, Futures, and Other Derivatives       5 lessons
     the derivatives standard, spanning M0.7 through M2.4.
     Comprehensive and slightly dull; use it as a reference
     rather than reading it through.

   Maureen O'Hara, Market Microstructure Theory             5 lessons
     the theory behind M1 — Glosten–Milgrom, inventory
     models, Kyle. Denser than Harris and worth it once
     the intuition is in place.

By module

Everything else is depth on one part of the course:

   M0   Harris ch.1–7
        Hull ch.1–2, 7, 10–12                 derivatives, options

   M1   O'Hara ch.1–4                         the models
        Bouchaud et al., Trades, Quotes
          and Prices                          the modern empirical bible
        Aldridge, High-Frequency Trading      practitioner mechanics

   M2   Chan, Quantitative Trading            start here — short, honest
        Chan, Algorithmic Trading             pairs trading as practised
        Joshi, The Concepts and Practice
          of Mathematical Finance             option-pricing intuition
        Sinclair, Volatility Trading          written by someone who has
                                              actually hedged the position
        Gatheral, The Volatility Surface      the surface, properly
        López de Prado, Advances in
          Financial Machine Learning          backtesting pathologies

   M3   Mandelbrot, The (Mis)behavior
          of Markets                          the roadmap's "required"
        Cont (2001), stylized facts           one paper, the whole of M3.1
        McNeil, Frey & Embrechts,
          Quantitative Risk Management        EVT, copulas, coherent risk
                                              measures — the reference
        Tsay, Analysis of Financial
          Time Series                         GARCH and friends
        Thorp, A Man for All Markets          Kelly, from its practitioner
        Falkenstein, The Missing
          Risk Premium                        statistically real,
                                              economically absent

   M4   Tuckman, Fixed Income Securities      duration and the curve
        Marks, Mastering the Market Cycle     the cycle-level view

   M5   Odd Lots, Flirting with Models        podcasts — practitioners
          (podcasts)                          describing their own
                                              business models
        Zuckerman, The Man Who Solved
          the Market                          Renaissance

You are not expected to read all of that. The list exists so that when a module turns out to matter for your work, you know where to go next.

Books for colour

The roadmap prescribes five, separately from the technical list, and they do something the textbooks cannot: they teach what it is like, and how things fail.

   Michael Lewis, Liar's Poker
     1980s Salomon Brothers. The culture of a trading floor
     before any of the machinery in this course existed.
     Funny, and the best single antidote to imagining
     finance as a meritocracy of models.

   Roger Lowenstein, When Genius Failed
     LTCM. Nobel laureates, a strategy that was probably
     right, and leverage that meant being right didn't
     matter — M3.6 and M0.7 as a narrative. If you read
     one, read this one.

   Sebastian Mallaby, More Money Than God
     a history of hedge funds. The best context for M5:
     how each business model came to exist, and what
     problem it was solving.

   Scott Patterson, The Quants
     the quant funds into 2007–08. Reads differently after
     M2.7 and M3.5 — you can see the multiple testing and
     the correlation breakdown coming.

   Gregory Zuckerman, The Man Who Solved the Market
     Renaissance and Jim Simons. Also cited in M5.4, and
     the best available account of the one track record
     that survives the survivorship critique.

Order matters for the narrative ones

The course has twice recommended reading something after the relevant module rather than before, and the reason generalises:

   Michael Lewis, Flash Boys        read AFTER M1.6
     the races are real; the framing as systematic theft
     from ordinary investors is not. You can only see
     which is which with the microstructure in place.

   Felix Salmon, "Recipe for
   Disaster" (Wired, 2009)          read AFTER M3.5
     the Gaussian copula story, told well and overstated.
     M3.5 gives you the actual defect — zero tail
     dependence — so you can enjoy the narrative without
     absorbing its conclusion.

Narrative journalism needs a villain, and mechanisms rarely supply one. That is not a criticism of the genre — it is why these books are worth reading for colour and not for models. Read them after the machinery, and they become much better: you spend the time noticing what is accurate, which is most of it, and what has been sharpened for the story.

What the roadmap says to avoid

Books titled How to Trade [Anything], finance Twitter/X, and trading YouTube. M6.3 is about why, and about the questions that make the reason obvious.

Source: this list is extracted from the source lines of all 40 lessons plus the roadmap’s own M6 selections. Every title above is cited somewhere in the course with a chapter or a reason, so if one looks interesting, the lesson that recommended it will tell you what for.