Appendix D: Annotated Bibliography
This appendix lists the primary sources cited or implicitly referenced throughout the book, organized by topic. Each entry includes a one-paragraph annotation noting what the source contributes and where the book's framework draws from it.
D.1 Market Microstructure
Easley, D., López de Prado, M., O'Hara, M.
Multiple papers, including: - "Flow Toxicity and Liquidity in a High-Frequency World." Review of Financial Studies 25 (5), 2012. - "The Volume Clock: Insights into the High-Frequency Paradigm." Journal of Portfolio Management 39 (1), 2012. - "VPIN and the Flash Crash: A Critical Review." Journal of Empirical Finance 27, 2014.
The trio's body of work on order-flow toxicity and Bulk Volume Classification (BVC) is the academic foundation for the book's order-flow chapters (Ch 3, 11). BVC is the inference engine that allows screen traders without true tick-side data to estimate aggressive flow direction. VPIN is a related construct (volume probability of informed trading) used in some institutional work. Read for: the rigorous derivation of BVC and the framework for thinking about flow-based measures. Skip: the more abstract VPIN-as-prediction-tool work; useful in some institutional applications but less directly relevant to discretionary screen trading.
Hasbrouck, J., Saar, G.
"Low-Latency Trading." Journal of Financial Markets 16 (4), 2013.
Foundational work on the impact of HFT on intraday market dynamics, particularly during low-volume overnight windows. Documents bid-ask asymmetry and quote staleness. Relevant for Chapter 1's RTH/ETH session asymmetry treatment. Read for: understanding why standard volatility estimators are biased on overnight data. Skip: the more technical discussion of latency arbitrage; relevant primarily to HFT firms.
Menkveld, A.
"High Frequency Trading and the New Market Makers." Journal of Financial Markets 16 (4), 2013.
Documents the segmentation of institutional flow into market makers, HFTs, and "fundamental" (slower) institutional flow. Each leaves different fingerprints on the tape. Relevant for Chapter 11's framework on reading institutional intent from order flow. Read for: the segmentation framework. Note: the empirical results are now decade-old; HFT participation has increased since.
Brogaard, J., Hendershott, T., Riordan, R.
"High-Frequency Trading and Price Discovery." Review of Financial Studies 27 (8), 2014.
Empirical study of HFT participation and its effect on price discovery. Documents that HFT contributes to price discovery despite controversy. Relevant for understanding modern microstructure. Read for: evidence-based view of HFT's role. Skip: if you only need a high-level summary; the technical details are less relevant for a discretionary trader.
Bouchaud, J.-P., Bonart, J., Donier, J., Gould, M.
Trades, Quotes and Prices: Financial Markets Under the Microscope. Cambridge University Press, 2018.
Comprehensive monograph on market microstructure from a physics-influenced perspective. Treats order book dynamics, market impact, and limit-order strategies. Relevant for Chapter 18 (execution) and for understanding order book behavior generally. Read for: rigorous treatment of order book dynamics. Skip: sections on market making strategy; tangential to discretionary futures trading.
D.2 Volume Profile and Market Profile
Steidlmayer, J. P., Hawkins, S. B.
Markets and Market Logic. Porcupine Press, 1986.
Steidlmayer's original Market Profile work, developed at the Chicago Board of Trade. The TPO (Time Price Opportunity) construction predates Volume Profile and remains the cleanest framework for day-type classification. Read for: the philosophical and operational foundations of profile-based reading. Note: dated in language and examples; the modern Dalton treatment is often more accessible.
Dalton, J., Dalton, R., Jones, E.
Mind Over Markets: Power Trading with Market Generated Information (3rd ed.). Wiley, 2007.
The institutional standard for Market Profile pedagogy. Treats day type classification, naked POCs, value area dynamics, and the operational use of profile in active trading. Cited extensively in Chapter 9. Read for: the operational framework. Note: the day-type taxonomy and naked-POC retest framing are the most enduring contributions.
Dalton, J., Dalton, R.
Markets in Profile: Profiting from the Auction Process. Wiley, 2007.
A more accessible companion to Mind Over Markets; treats the same concepts with broader contextual framing. Useful as an introduction. Read for: the auction-process framing of price discovery.
D.3 Technical Analysis (Empirical Treatment)
Bulkowski, T.
Encyclopedia of Chart Patterns (3rd ed.). Wiley, 2021.
Comprehensive empirical study of chart patterns, with reaction rates and failure rates documented for each. The author's quality-score weights for patterns (Chapter 5) are calibrated against Bulkowski's published reaction rates. Read for: evidence-based assessment of which patterns have empirical support and which do not. Skip: if you are skeptical of pattern recognition; Bulkowski himself is honest about the modest magnitude of effects.
Lo, A. W., Mamaysky, H., Wang, J.
"Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation." Journal of Finance 55 (4), 2000.
The canonical academic study of whether technical patterns extract information beyond the null. Found that conditional kernel-regression-based pattern recognition does extract incremental information, but effect sizes are small and only emerge with thousands of trials. Read for: the rigorous statistical treatment of TA's existence question. Note: humble in scope; supports "some TA edge exists" but does not endorse specific patterns.
Faber, M.
"A Quantitative Approach to Tactical Asset Allocation." Journal of Wealth Management 9 (4), 2007.
Documents that simple "be in asset above 10-month SMA, in cash otherwise" rule applied across an ensemble of asset classes produces favorable risk-adjusted returns. Key insight: the edge is portfolio-level, not single-instrument. Relevant for Chapter 6's treatment of MA filters. Read for: evidence-based perspective on MA-based regime filters at long horizons.
Hurst, B., Ooi, Y. H., Pedersen, L. H.
"A Century of Evidence on Trend-Following Investing." Journal of Portfolio Management 44 (1), 2017.
Documents that time-series momentum (a generalization of MA cross) has produced durable returns across 67 markets and a century of data, at the portfolio level. Relevant for Chapter 6 and for understanding the institutional case for trend-following strategies. Read for: the rigorous case for trend-following at portfolio scale. Caveat: the per-instrument intraday case is not supported by this work.
D.4 Risk Management and Sizing
Kelly, J. L.
"A New Interpretation of Information Rate." Bell System Technical Journal 35, 1956.
The original Kelly Criterion paper. Derives the optimal fraction of capital to bet given known edge. Foundational for sizing theory. Read for: the math. Note: the practical application uses fractional Kelly (0.25 to 0.5), not full Kelly, because of estimation uncertainty.
Vince, R.
The Mathematics of Money Management. Wiley, 1992.
Practitioner-focused treatment of position sizing, including extensions to Kelly. Covers Optimal-f and other variants. Read for: the practitioner application of sizing math. Note: dated in language but the math is durable.
Thorp, E. O.
A Man for All Markets: From Las Vegas to Wall Street. Random House, 2017.
Memoir of a quantitative pioneer. Includes practical perspective on Kelly fractional sizing and risk management. Read for: the practitioner perspective. Skip: if you want only the mathematical content; this is mostly biographical.
Schwager, J.
Market Wizards (and sequels). HarperCollins, multiple volumes.
Interviews with successful traders. The risk management discipline themes are remarkably consistent across the interviews. Read for: the consistent practitioner emphasis on risk management as the dominant edge. Note: anecdotal, not statistical.
D.5 Statistical Validation
Bailey, D., López de Prado, M.
"The Deflated Sharpe Ratio: Correcting for Selection Bias, Backtest Overfitting, and Non-Normality." Journal of Portfolio Management 40 (5), 2014.
The DSR formula derivation. Foundational for honest reporting of backtest results. Cited extensively in Chapter 16. Read for: the derivation. Implementation: available in mlfinlab Python library.
Bailey, D., Borwein, J., López de Prado, M., Zhu, Q. J.
"The Probability of Backtest Overfitting." Journal of Computational Finance 20 (4), 2016.
PBO computation via combinatorially symmetric cross-validation (CSCV). The most direct measure of overfitting. Read for: the methodology. Implementation: mlfinlab.
Harvey, C., Liu, Y., Zhu, H.
"...and the Cross-Section of Expected Returns." Review of Financial Studies 29 (1), 2016.
Documents the multiple-testing problem in factor research: more than 300 published factors are likely contaminated by selection bias. The companion paper "Lucky Factors" (Harvey, Liu 2018) introduces methods for honest reporting. Read for: rigorous treatment of multiple-testing correction in finance. Note: factor-research focused, but the principles apply directly to TA strategy validation.
Pardo, R.
The Evaluation and Optimization of Trading Strategies (2nd ed.). Wiley, 2008.
Practitioner-focused treatment of walk-forward analysis. Foundational for the institutional discipline. Read for: the operational walk-forward methodology. Note: pre-DSR/PBO, so the modern statistical methods are not covered; pair with the López de Prado work.
López de Prado, M.
Advances in Financial Machine Learning. Wiley, 2018.
Comprehensive treatment of statistical methods for finance, including backtesting, validation, and feature engineering. Read for: the rigorous modern treatment of finance ML. Note: dense; a long-term reference rather than a quick read.
D.6 Volatility Estimation
Yang, D., Zhang, Q.
"Drift-Independent Volatility Estimation Based on High, Low, Open, and Close Prices." Journal of Business 73 (3), 2000.
The Yang-Zhang estimator derivation. Most efficient single-source volatility estimator under reasonable assumptions. Cited in Chapter 8. Read for: the derivation. Note: the estimator is the right default for futures because it correctly weights overnight gaps.
Garman, M. B., Klass, M. J.
"On the Estimation of Security Price Volatilities from Historical Data." Journal of Business 53 (1), 1980.
Earlier high-low-based volatility estimators. Less efficient than Yang-Zhang but historically important. Read for: the historical context. Skip: if you only need the modern method.
Andersen, T. G., Bollerslev, T.
"Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts." International Economic Review 39 (4), 1998.
Established the use of high-frequency intraday data to compute "realized volatility" and improve forecast accuracy. Foundational for modern volatility forecasting. Read for: the methodology. Note: the realized volatility framework underlies much of modern econometric volatility forecasting.
D.7 Behavioral Finance and Trader Psychology
Lo, A. W.
"The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective." Journal of Portfolio Management 30 (5), 2004.
Frames the underlying problem of edge decay: edges are not stationary; they decay as participants discover them. Highly relevant for Chapter 15 and 16. Read for: the philosophical framing of why TA works sometimes and not others.
Kahneman, D.
Thinking, Fast and Slow. Farrar, Straus and Giroux, 2011.
The behavioral economics treatment of cognitive biases relevant to trading. Read for: the bias catalog (anchoring, loss aversion, etc.) that affects discretionary trading. Note: non-trading-specific but directly applicable.
Tversky, A., Kahneman, D.
"Judgment under Uncertainty: Heuristics and Biases." Science 185 (4157), 1974.
The foundational heuristics-and-biases paper. Trader-relevant biases (representativeness, availability, anchoring) all derive from this work. Read for: the original cognitive bias research.
D.8 Order Flow Pedagogy (Practitioner)
Harnett, T.
MarketDelta educational materials and webinars; various dates.
Practitioner-grade pedagogy on footprint and order flow. Less rigorous than academic literature but useful for operational technique. Read for: the practical pattern recognition. Note: most material is video-based and proprietary; quality is uneven.
Internal Circle Trader (ICT) materials
Various practitioner materials by Michael Huddleston (Inner Circle Trader). Available freely or by paid course.
ICT pedagogy on liquidity sweeps, FVGs, and order block concepts. Read for: the practitioner framing of liquidity-based reading. Caveat: the material is uneven; some claims overreach. The underlying observations about liquidity sweeps and FVG retests are empirically defensible on liquid index futures, but the broader theoretical framework is unsupported.
D.9 General Reference
CME Group
Daily volume data, contract specifications, margin requirements, calendars. Available at cmegroup.com.
The primary source for CME-listed futures information. Use: verify all contract specifications, settlement times, roll dates against CME's published documents.
CFTC, SEC
Reports on participant types, position limits, and regulatory developments.
Useful for understanding the regulatory and structural constraints on futures markets. Read for: the institutional and regulatory context.
Investopedia
The best free general-finance reference. Some entries are excellent; others are surface-level.
Use: as a starting point for any unfamiliar concept. Caveat: verify specific quantitative claims against primary sources.
D.10 The author's own work
Trend & Levels (Pine v6)
The author's quality-score implementation in Pine Script v6, the operational manifestation of Chapter 5's framework. Available in the book's project directory.
Futures Institutional Edge (Pine v6)
The author's institutional-stack overlay, implementing concepts from Chapters 9, 10, 11, 12, 13, and 14 in a single Pine v6 file. Available in the book's project directory.
Futures Institutional Quant Panel (Pine v6)
A more compact dashboard implementation for traders who want the regime composite and key signals without the full overlay.
These are reference implementations. They are not commercial products; they are educational artifacts that show what the book's framework looks like in code on a specific platform.
D.11 Reading suggestions by experience level
For the trader new to futures:
Start with: Dalton, Markets in Profile (introduction to profile-based reading). Then: Bulkowski, Encyclopedia of Chart Patterns (evidence-based assessment of patterns). Avoid: practitioner ICT materials until the foundations are solid.
For the experienced discretionary trader:
Start with: Easley, López de Prado, O'Hara papers (rigorous order-flow framing). Then: Bailey, López de Prado on DSR/PBO (statistical validation). Use Dalton as the reference for profile concepts.
For the systematic researcher:
Start with: López de Prado, Advances in Financial Machine Learning. Then: Pardo, Evaluation and Optimization for the practitioner walk-forward framework. Cross-reference Yang-Zhang for volatility estimation, Lo et al. for the conditional-pattern-recognition framing.
For the trader debugging a losing streak:
Start with: Kahneman, Thinking Fast and Slow (cognitive bias catalog). Then: re-read this book's Chapter 19 (failure modes). The bias is more often in the trader's head than in the framework.
D.12 The intentionally short list
Many sources are not in this bibliography. Examples:
- The dozens of self-help trading books on Amazon. Most are derivative; a few are useful but rarely required reading.
- "The X book on futures trading." Most are dated and surface-level. Dalton and Bulkowski cover the durable content.
- Most YouTube content. Quality is highly variable; selection requires significant filtering effort.
- Most paid trading courses. Quality varies; the best are excellent, the average is poor.
The trader who reads the entries in this bibliography (a manageable list) plus this book has a comprehensive foundational education. Adding 20 more sources adds noise more than signal.
This bibliography is the durable reading list. The book stands on these sources; the trader who masters this book will benefit from selectively going deeper into the primary sources cited.