AI-powered stock analysis & quantitative research
Machine learning models, backtesting frameworks, and data-driven trading strategies — from research to deployment.
VectorBT Review 2026: Best Python Backtester?
VectorBT is a fast Python backtesting library for algorithmic trading, pairing vectorized NumPy arrays with Numba and Rust so you can sweep thousands of strategies in seconds.
Kelly Criterion Position Sizing: A Practical Python Workflow
Kelly criterion position sizing in Python lets you compute the optimal capital fraction per trade from expected returns and volatility, so you can build disciplined, growth-oriented allocation rules.
AI Data Center Power Analysis: Utility Stock Supercycle
AI data center power demand utility stocks 2026 — learn to find earnings tailwinds via PPA backlogs and valuation screens to start trading the theme today.
Fed Holds, Big Tech Splits: Weekly Market Pulse
The Federal Reserve held rates in a split vote while mega-cap earnings diverged sharply. Learn what the data means for your systematic trading strategies.
AI Capex vs GDP: Analyzing the Macro Divergence
AI capex and GDP are diverging in 2026, splitting the market into two regimes. Learn how to position your quant strategy for this structural divergence so you can trade with confidence.
Dealer Gamma, Sentiment Collapse, and the 7,500 Pin: Reading Late-July 2026 Market Structure
SPX is pinned at 7,500 by positive dealer gamma while retail sentiment collapses and institutions stay net long. The 7,477 gamma flip is the tripwire. A quant framework for monitoring the regime change.
Building an Unusual Options Activity Detection System with Python
A code-first methodology for detecting unusual options activity (UOA) as a trading signal — volume/OI ratios, put/call thresholds, ML ensemble features, and the pitfalls that kill most UOA strategies.