TradFi Quant vs Crypto Quant: A Full Comparison Across Data, Microstructure, Counterparty, and Compliance

TradFi-native and crypto-native quants routinely misread each other — tradfi people see crypto as unregulated, crypto natives see tradfi as fossilised. This post takes no side and compares both systems across data, microstructure, counterparty, market-maker ecology, and compliance, ending with a strategy-migration cheat sheet.

Advanced Funding Rate Arbitrage in 2026: Perp vs Spot, Cross-Exchange Hedging, and Dynamic Rebalancing in Depth

Funding-rate arbitrage is still one of the most reliable income sources in crypto quant in 2026, but the number of beginners losing half their PnL to execution is also at an all-time high. This post skips the basics and walks four advanced structures with capital usage, risk, and realistic annualised returns.

Writing Your Own Crypto Quant Strategy in Python — How Many Hours Does It Actually Take?

How long does it take a Pythonista to go from "I'll start tonight" to a first version of a crypto strategy that can run on a small live account? This piece breaks the path into six phases — data ingestion, prototype, backtest, robustness, paper trade, minimum live — with realistic hour budgets and the traps that eat time.

AI-Driven Crypto Quant in 2026: LLM Signals, RL Execution, Agent Loops, and What Actually Works

By 2026 AI in crypto quant has settled into three sharply separated layers — LLMs for event signals, RL for execution optimization, and agents for closed-loop decisions. This post gives an honest snapshot of what each layer can actually do, where it still breaks, and what running this stack really costs.

The Most Common Crypto Backtesting Pitfalls and How to Actually Avoid Them in 2026

Backtesting tooling has matured by 2026, but most beautiful equity curves still die from the same three sins: look-ahead bias, liquidity distortion, and miscounted fees and funding. This post dissects each pitfall with concrete symptoms, detection methods, fixes, and an executable self-audit checklist.

Open Source Crypto Arbitrage Bots Worth Comparing in 2026

Paid arb bots get the loudest marketing, but the ones that actually run long-term are mostly open source. This piece compares five open source crypto arbitrage bots across architecture, strategy coverage, real barrier and community activity, and tells you which one fits which type of user.

Does DCA Actually Work? Crypto Dollar-Cost Averaging by the Numbers

Ten years of $100 monthly Bitcoin DCA was worth roughly $350,000 by 2024. This piece breaks down the math vs lump sum, the historical performance across cycles, and which assets DCA actually rewards.

Backtesting Crypto Strategies: Data Sources, Common Traps, and Validation

A pretty equity curve is often a portrait of overfitting. This piece covers what backtests are really for, where to get data, the classic traps, and how to validate a strategy without fooling yourself.

Grid Trading Strategy: A Crypto Quant Beginner's Guide

Grid trading doesn't predict direction—it picks up scraps inside a sideways range. Here's how the mechanics work, the arithmetic vs geometric split, the parameters that matter, the markets it suits, and the trap that wipes it out.

How Do Beginners Get Started With Crypto Quant Trading? Core Concepts Explained

Quantitative trading replaces gut feel with code. This guide starts from a clean definition, compares it to manual trading, breaks down common strategies, covers data and backtests, and offers a pre-live checklist.

What Is Quantitative Trading? Strategies, Bots, and Risks in the Crypto Market

Quantitative trading uses programs and data to execute strategies automatically. This guide explains common strategies, how trading bots work, and the risks ordinary people should watch out for.