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Tag: python

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.

pythonpapermicrostructure

market-impact

after-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.

pythonzedacpcoding-agentsafter-workstart-using

Getting Started with Zed ACP Agents

Install Zed, open a tiny Python lab, add one External Agent from the ACP Registry, run one reviewed thread—after-work friendly.

pythonpapertransaction-costsmarket-impactafter-work

Introducing Anomalous Price Impact and Square-Root Liquidity for after-work quants

Tóth, Bouchaud and coauthors (2011) explain the empirical square-root market-impact law via latent liquidity that vanishes near the price. Plain-language intro. Not investment advice.

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