Quantitative Trading

A step-by-step guide to AI quantitative trading covering five core building processes: data collection & cleaning, strategy design, backtesting, out-of-sample validation (OOS), and deployment & monitoring. Explains three key advantages of AI over manual trading - emotional stability, processing speed, and consistency - and shares essential techniques to bridge the gap between backtesting and live performance.

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.

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.

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. Our small-cap volume surge strategy (based on CEX volume spikes + technical confirmation) was designed as long-only. Simple logic: detect abnormal volume → confirm technically → go long. Backtests looked promising. But after deploying to Testnet, certain tokens kept losing: ...

The Numbers Lie Our paper trading system ran for a month, and the numbers looked beautiful on a spreadsheet: 87.5% win rate, 7 wins, 1 loss, 2 breaks even When most people see those numbers, their reaction is: “Awesome! Time to trade real money!” Our reaction was: “Hold on. Let the math speak first.” What is Z-Score? At its core, Z-score is asking one simple question: How much better are you than just flipping a coin? ...

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.

We developed a mean reversion strategy. Backtesting showed 3 out of 8 combinations hitting 100% win rate. Then we ran Out-of-Sample validation, and 100% crashed to 25%. Here’s what happened.