Multi-Exchange Crypto Strategy Backtesting
A research framework for comparing crypto strategy assumptions across Binance, OKX, Bybit, and Coinbase-style markets.
Topic
Crypto Quant
AI-assisted crypto market research, funding rates, exchange data, on-chain signals, and 24/7 risk controls.
5 notes Current articles in this track.
02 Track number in the iTapGo Quant research library.
Education All examples are for learning and research only.
Risk-first Claims are framed with limitations and validation concerns.
AI Quant Trading in Crypto Markets: A Practical Introduction
Crypto markets are open 24/7, fragmented across exchanges, and shaped by funding rates, liquidity shocks, and on-chain behavior.
Funding Rates as AI Features in Crypto Quant Research
Perpetual futures funding rates can reveal leverage pressure, but they require careful timing, normalization, and regime analysis.
Backtesting Crypto Strategies in a 24/7 Market
Crypto backtests need explicit rules for time zones, session boundaries, exchange outages, liquidity, and weekend volatility.
Using On-Chain Data in AI Quant Signals
On-chain data can enrich crypto research, but wallet flows, exchange balances, and protocol metrics are noisy and easy to misinterpret.