AI Quant
Machine learning workflows, feature engineering, model validation, and AI-assisted market research.
Research Library
All research notes
A structured library for AI quantitative trading education, with emphasis on reproducibility, validation, execution realism, and risk management.
Crypto Quant
AI-assisted crypto market research, funding rates, exchange data, on-chain signals, and 24/7 risk controls.
Strategy Research
Systematic strategy design with hypotheses, rules, validation windows, and failure tests.
Backtesting
Backtest construction, walk-forward evaluation, realistic costs, and data integrity checks.
Risk Management
Drawdown, sizing, execution risk, governance, and operational controls for trading systems.
Research Tools
Practical AI assistant workflows, documentation patterns, Python research habits, and review checklists.
Latest first
Complete article list
Articles are written as educational research notes, not signals or personalized investment recommendations.
Multi-Exchange Crypto Strategy Backtesting
A research framework for comparing crypto strategy assumptions across Binance, OKX, Bybit, and Coinbase-style markets.
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.
What Is AI Quantitative Trading?
A practical introduction to AI quantitative trading, how models fit into a research workflow, and where risk control matters most.
Why Backtest Profit Does Not Mean Live Profit
Backtests are useful research tools, but they can become dangerously persuasive when costs, slippage, data leakage, and overfitting are ignored.
A Research Plan for a Moving Average Crossover Strategy
How to turn a familiar moving average crossover idea into a disciplined research plan with rules, costs, metrics, and failure checks.
Maximum Drawdown: The Risk Metric Every Strategy Needs
Maximum drawdown explains the deepest peak-to-trough loss in a strategy and helps investors understand pain, recovery, and position sizing.
AI Feature Engineering for Market Data
A practical overview of features used in AI trading research, including price, volume, volatility, regime, and text-derived signals.
Walk-Forward Validation Explained
Walk-forward validation tests a strategy through repeated train-test windows and helps reduce the false confidence of a single backtest split.
Transaction Costs and Slippage Can Break a Strategy
A strategy with a small edge can disappear once commissions, spreads, market impact, and delayed execution are included.
How to Use ChatGPT in Trading Research Without Fooling Yourself
AI assistants can help with research notes, code review, and hypothesis generation, but they should not be treated as market oracles.
Overfitting in Quant Strategies
Overfitting happens when a strategy learns historical noise instead of a durable market behavior. Here is how to recognize and reduce it.
A Pre-Launch Risk Checklist for AI Trading Systems
Before an AI-assisted trading system goes live, review data, validation, execution, monitoring, and governance risks.