Crypto

Introduction If you’ve ever placed a trade on a stock or crypto exchange, you’ve interacted—directly or indirectly—with an order book. The order book is the living core of modern electronic markets: it lists who wants to buy, who wants to sell, at what prices, and how much. Understanding its mechanics helps traders manage execution, helps developers build reliable trading systems, and helps researchers reason about price formation and liquidity. This article is a comprehensive, practitioner-friendly deep dive into order books. We’ll cover how they’re structured, how matching engines work, how to interpret book dynamics, execution tactics, data feeds and APIs, common pitfalls, and practical engineering patterns. We’ll also share concise Python snippets for building and analyzing a simplified limit order book. ...

Introduction Automated Market Makers (AMMs) are the liquidity engines powering most decentralized exchanges (DEXs). Instead of relying on traditional order books and human market makers, AMMs use deterministic formulas—called bonding curves—to continuously quote buy and sell prices for assets. This design unlocks 24/7 liquidity, permissionless market creation, and composability across decentralized finance (DeFi). Yet AMMs also introduce new mechanics and risks: slippage, impermanent loss, MEV (maximal extractable value), and complex design trade-offs. ...

Introduction If you want to learn algorithmic crypto trading without risking real money, paper trading is the safest, fastest way to start. In this guide, you’ll build a minimal, efficient paper trading loop in Python that runs on your laptop, uses real-time market data, and simulates orders with fees and slippage—no exchange account or API keys required. We’ll use public market data (via CCXT) and a small “paper broker” to track positions, PnL, and trades. ...