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Tag: after-work

pythonpaperoptimal-executionlimit-order-bookafter-work

Introducing Optimal Execution Strategies in Limit Order Books with General Shape Functions for after-work quants

Alfonsi, Fruth & Schied (2007) extend LOB execution to general book shapes with resilient discrete-time block trades. Plain-language intro. Not investment advice.

pythonpapermarket-making

limit-order-book

after-work

Introducing Market Making under a Weakly Consistent Limit Order Book Model for after-work quants

Law & Viens (2019) study market making under a weakly consistent limit-order-book model—more book-aware than mid-only toys. Plain-language intro. Not investment advice.

pythonpaperlimit-order-bookmicrostructureafter-work

Introducing Limit Order Books (Gould et al. survey) for after-work quants

Gould et al. (2013) survey limit order books: mechanics, stylised facts, and modelling strands. Plain-language map for after-work Python learners. Not investment advice.

pythonpapermicrostructuremarket-impactafter-work

Introducing How Markets Slowly Digest Changes in Supply and Demand for after-work quants

Bouchaud, Farmer & Lillo (2008) explain why markets digest supply and demand slowly: long-memory order flow and propagator-style impact. Plain-language intro. Not investment advice.

pythonpapermarket-makingmicrostructureafter-work

Introducing High-frequency Market-making with Inventory Constraints and Directional Bets for after-work quants

Fodra & Labadie (2012) study high-frequency market-making with inventory constraints and optional directional views—quotes that respect risk limits. Plain-language intro. Not investment advice.

pythonpapermarket-makinglimit-order-bookafter-work

Introducing High-frequency trading in a limit order book for after-work quants

Avellaneda & Stoikov (2006) give the canonical inventory-based market-making recipe: reservation price plus intensity-calibrated bid/ask in a limit order book. Plain-language intro. Not investment advice.

pythonpaperexecutionreinforcement-learningafter-work

Introducing Double Deep Q-Learning for Optimal Execution for after-work quants

Ning, Lin & Jaimungal (2018) apply Double Deep Q-Learning to optimal execution—RL as a study tool for how to finish a parent order under market impact. Plain-language intro. Not investment advice.

pythonpapermicrostructurevolatilityafter-work

Introducing Dependent Microstructure Noise and Integrated Volatility Estimation from High-Frequency Data for after-work quants

Li, Laeven & Vellekoop (2018) study integrated volatility when microstructure noise is dependent—so naive high-frequency estimators misbehave. Plain-language intro. Not investment advice.

pythonpapermicrostructuredeep-learningafter-work

Introducing Deep Learning for Spatio-Temporal Modeling: Dynamic Traffic Flows and High Frequency Trading for after-work quants

Dixon, Polson & Sokolov (2018) use deep spatio-temporal nets on traffic flows and high-frequency trading—showing the same modeling habit across space and time. Plain-language intro. Not investment advice.

pythonpapermarket-makinginventory-riskafter-work

Introducing Dealing with the Inventory Risk: A Solution to the Market Making Problem for after-work quants

Guéant, Lehalle & Fernandez-Tapia (2011) turn the Avellaneda–Stoikov HJB into linear ODEs and give closed-form asymptotic quotes under inventory risk. Plain-language intro. Not investment advice.

pythonreplitcoding-agentsafter-workstart-using

Getting Started with Replit Agent

Sign up on Replit, prompt Agent for a tiny Python greet lab, read the files, run locally in the shell—keep scope tight after work.

pythontabninecoding-agentsafter-workstart-using

How to Start Using Tabnine

Install Tabnine in VS Code or JetBrains, sign in, complete one small Python change with review—evening-sized first session.

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