This tutorial covers the five core steps of AI quantitative trading from scratch: data collection & cleaning, strategy design, backtesting, out-of-sample (OOS) validation, and deployment & monitoring. It explains three key advantages of AI over manual trading - emotional stability, processing speed, and consistency - and shares key techniques to avoid the backtest-to-live trading gap.
AI trading bots face five major security threats: supply chain attacks, API key leaks, Prompt Injection, model poisoning, and exchange API vulnerabilities. This article breaks down each attack vector from an engineering perspective and provides actionable defense strategies and security checklists to help developers build truly secure automated trading systems.
The main difference between AI Agent and traditional trading bots is decision-making: traditional bots execute preset rules, while AI Agent can independently analyze market data and make decisions. Your choice depends on your experience level and strategy complexity, and the future trend is combining both.
The Bollinger Bands strategy shines in backtests but fails in live trading. Research shows it achieves 58%-65% win rate in ranging markets but only 33% in bull trends with -28% max drawdown. The problem lies in BB’s mean reversion assumption, but BTC trends can last for months. Adding ADX and bandwidth percentile for market state detection significantly improves strategy performance.
Judy AI Lab releases their internal crypto screening engine CoinSifter as open source. Supports 6 technical indicators, multi-timeframe scanning, Web UI and Telegram push notifications. This article explains the reasons for open sourcing, core features, architecture design, and a 5-minute quick start guide.
The market divides into three regimes: trending, ranging, and high volatility. A single strategy can only be profitable in one regime. This article proposes Regime-Based Strategy Routing, combining trend following, BB Squeeze, MACD Divergence, and mean reversion strategies, automatically switching based on market regime and adjusting position size based on multi-strategy confirmation as a confidence分级.
This article is a deep-dive from JudyAI Lab — an AI engineering playbook series with 100+ published guides, 5,000+ weekly readers across 60+ countries, focused on the practical side of running AI agents, trading systems, and content pipelines in production. The Problem: Why Did Your Strategy Suddenly Start Losing? A strategy that looked great in backtesting starts losing consistently after going live. It’s not a bug — the market changed. ...
A paper trading strategy with 87.5% apparent win rate fails statistical validation—Z-score yields p=0.24, no significant difference from coin flipping. Using Bayesian adjustment and Overfitting Index (OFI) with 33 real trades to establish a strategy validation logic that avoids the small-sample high-win-rate trap.
A profitable strategy suddenly stops working? It might not be the strategy — the market regime changed. Here’s how we detect market states using ADX, BB Width, and ATR, and automatically switch strategies.
Most traders spend 90% of their time finding signals and 10% thinking about position size. But math shows: the same strategy with different position sizing can produce results that differ by 10x.