19. References

Ho, T. & Stoll, H. (1981). "Optimal dealer pricing under transactions and return uncertainty." Journal of Financial Economics 9(1), 47–73.

Glosten, L. & Milgrom, P. (1985). "Bid, ask and transaction prices in a specialist market with heterogeneously informed traders." Journal of Financial Economics 14(1), 71–100.

Kyle, A. (1985). "Continuous auctions and insider trading." Econometrica 53(6), 1315–1336.

Avellaneda, M. & Stoikov, S. (2008). "High-frequency trading in a limit order book." Quantitative Finance 8(3), 217–224.

Guéant, O., Lehalle, C-A. & Fernandez-Tapia, J. (2013). "Dealing with the inventory risk: a solution to the market making problem." Mathematics and Financial Economics 7(4), 477–507. (arXiv:1105.3115)

Guéant, O. (2017). "Optimal market making." Applied Mathematical Finance 24(2). (The modern general formulation of the GLFT family.)

Bergault, P., Evangelista, D., Guéant, O. & Vieira, D. (2021). "Closed-form approximations in multi-asset market making." Applied Mathematical Finance 28(2). (arXiv:1810.04383 — the multi-asset extension used when quoting correlated markets.)

Barzykin, A., Bergault, P. & Guéant, O. (2023). "Algorithmic market making in dealer markets with hedging and market impact." Mathematical Finance 33(1), 41–79. (The rigorous theory behind quote-here-hedge-there.)

Cartea, Á., Jaimungal, S. & Penalva, J. (2015). Algorithmic and High-Frequency Trading. Cambridge University Press. (The standard textbook for the stochastic-control view.)

Stoikov, S. (2018). "The micro-price: a high-frequency estimator of future prices." Quantitative Finance 18(12), 1959–1966.

Kolm, P., Turiel, J. & Westray, N. (2023). "Deep order flow imbalance: extracting alpha at multiple horizons from the limit order book." Mathematical Finance 33(4), 1044–1081. (The deep-learning successor to microprice-style signals.)

Easley, D., López de Prado, M. & O'Hara, M. (2012). "Flow toxicity and liquidity in a high-frequency world." Review of Financial Studies 25(5). (VPIN; for the critique see Andersen, T. & Bondarenko, O. (2014), "VPIN and the flash crash," Journal of Financial Markets 17.)

Nison, S. (1991). Japanese Candlestick Charting Techniques. New York Institute of Finance. (The book that brought Homma's candlesticks west.)

Lo, A., Mamaysky, H. & Wang, J. (2000). "Foundations of technical analysis: computational algorithms, statistical inference, and empirical implementation." Journal of Finance 55(4), 1705–1765. (The honest academic appraisal of chart patterns.)

J.P. Morgan / Reuters (1996). RiskMetrics — Technical Document, 4th ed. (The EWMA volatility estimator every quoting engine still runs.)

Gašperov, B., Begušić, S., Posedel Šimović, P. & Kostanjčar, Z. (2021). "Reinforcement learning approaches to optimal market making." Mathematics 9(21), 2689.

Gašperov, B. & Kostanjčar, Z. (2021). "Market making with signals through deep reinforcement learning." IEEE Access 9, 61611–61622.

Spooner, T., Fearnley, J., Savani, R. & Koukorinis, A. (2018). "Market making via reinforcement learning." AAMAS 2018.

Ganesh, S. et al. (2019). "Reinforcement learning for market making in a multi-agent dealer market." arXiv:1911.05892.

Falces Marin, J., Díaz Pardo de Vera, D. & López Gonzalo, E. (2022). "A reinforcement learning approach to improve the performance of the Avellaneda-Stoikov market-making algorithm." PLOS ONE 17(12). ("Alpha-AS")

Spooner, T. & Savani, R. (2020). "Robust market making via adversarial reinforcement learning." IJCAI 2020. (arXiv:2003.01820 — train against an adversary, inherit risk aversion.)

Cao, J., Šiška, D., Szpruch, L. & Treetanthiploet, T. (2024). "Logarithmic regret in the ergodic Avellaneda–Stoikov market making model." arXiv:2409.02025. (Provable online learning of AS's fill parameters.)

Hoque, M.R., Ferdaus, M.M. & Hassan, M.K. (2025). "Reinforcement learning in financial decision making: a systematic review." arXiv:2512.10913. (167 papers, 2017–2025 — the current RL-in-finance survey.)

LOB world models for training: Coletta, A. et al. (2022), "Learning to simulate realistic limit order book markets from data as a World Agent," ICAIF 2022; Nagy, P. et al. (2023), "Generative AI for end-to-end limit order book modelling," ICAIF 2023 (arXiv:2309.00638); Berti, L. et al. (2025), "TRADES: generating realistic market simulations with diffusion models," arXiv:2502.07071; JaxMARL-HFT (2025), arXiv:2511.02136 (GPU-scale multi-agent LOB RL).

Stoikov, S., Zhuang, E., Chen, H., Zhang, Q., Wang, S., Li, S. & Shan, C. (2024). "Market Making in Crypto." SSRN Working Paper 5066176, Cornell Financial Engineering Manhattan. (The Bar Portion signal, the 4–5× monthly-vol spread rule, triple-barrier risk overlay, and the live Hummingbot A/B on SOL/DOGE/GALA perpetuals.)

López de Prado, M. (2018). Advances in Financial Machine Learning. Wiley. (Origin of the triple-barrier method, Chapter 16.)

Grid trading, honestly: "Dynamic grid trading strategy: from zero expectation to market outperformance" (2025), arXiv:2506.11921. (Proves the plain grid's expected return is zero absent drift or vol edge.)

Perpetual futures & funding: He, S., Manela, A., Ross, O. & von Wachter, V., "Fundamentals of perpetual futures," arXiv:2212.06888; Ackerer, D. et al. (2026), "Perpetual futures pricing," Mathematical Finance.

Tooling: hftbacktest (github.com/nkaz001/hftbacktest — tick-level, queue-aware backtests with a worked GLFT tutorial) · Optuna (optuna.org — parameter search).

Dalen, S. (2025). "Toward Black-Scholes for prediction markets: a unified kernel and market-maker's handbook." arXiv:2510.15205.

Dubach, P.D. (2026). "The anatomy of a decentralized prediction market: microstructure evidence from the Polymarket order book." arXiv:2604.24366. (30B book events; what the public feed does and doesn't tell you.)

Croxson, K. & Reade, J.J. (2014). "Information and efficiency: goal arrival in soccer betting." The Economic Journal 124(575), 62–91. (In-play prices absorb scoring events near-instantly.)

O'Malley, A. J. (2008). "Probability formulas and statistical analysis in tennis." Journal of Quantitative Analysis in Sports 4(2); and Barnett, T. & Clarke, S. (2005). "Combining player statistics to predict outcomes of tennis matches." IMA Journal of Management Mathematics 16(2).

Courtsiding evidence: study of 141 Grand Slam matches (Roland Garros/Wimbledon 2009–2014, Betfair in-play data) documenting ~3.56% cumulative abnormal returns within the 5-second post-set window; see also Betfair's published in-play delay policies (betangel.com/betfair-inplay-delay).

SEC & CFTC (2010). "Findings regarding the market events of May 6, 2010." sec.gov/files/marketevents-report.pdf.

Christie, W. & Schultz, P. (1994). "Why do NASDAQ market makers avoid odd-eighth quotes?" Journal of Finance 49(5).

Congressional Research Service (2024). "Payment for order flow." Report IF12594.

SEC. Regulation NMS: Rules 610, 611, 612 (2005, as amended; 2024 tick/fee-cap amendments delayed to Nov 2027; Rule 611 rescission proposed June 2026); 2024 dealer-definition amendments (Rules 3a5-4, 3a44-2 — vacated by N.D. Texas Nov 2024, appeal withdrawn Feb 2025).

Kalshi: docs.kalshi.com (API: Create/Amend/Batch Orders, Queue Positions, WebSocket) · kalshi.com/fee-schedule.

Polymarket: docs.polymarket.com (CLOB, CTF, fees, liquidity rewards) · help.polymarket.com (maker rebates) · docs.uma.xyz (optimistic oracle).

Hyperliquid: hyperliquid.gitbook.io/hyperliquid-docs (fees, funding, HLP, protocol vaults).

Binance: binance.com/en/fee (spot/futures VIP schedules) · Binance Market Maker Program pages.

Interactive Brokers: ibkr.com fee & rebate pass-through documentation · Alpaca: docs.alpaca.markets.

Tooling: nautilustrader.io (event-driven backtest/live engine; L2-delta replay, queue-aware fills, Polymarket/BinaryOption adapter) · hummingbot.org (V2 controllers pmm_simple / pmm_dynamic / xemm_multiple_levels; legacy avellaneda_market_making, cross_exchange_market_making; paper mode).

Sacra (2026). Kalshi revenue/volume estimates ($22.9B volume 2025; sports ≈89% of fee revenue). TechCrunch (May 2026). Kalshi $1B Series F at $22B valuation.

JELLY incident (March 2025): Hyperliquid post-mortems and contemporaneous reporting (loss figures vary $10.5–15.3M across sources; treat as indicative).