AI-powered stock analysis & quantitative research
Machine learning models, backtesting frameworks, and data-driven trading strategies — from research to deployment.
AI-Powered Stock Analysis Tools in 2026: TrendSpider, ChartingLens, and Koyfin Compared
A data-driven comparison of TrendSpider, ChartingLens, and Koyfin for quantitative stock analysis in 2026. Features, pricing, data quality, and real-world quant workflows.
Local Time Series Forecasting for Stocks: Running Google's TimesFM on Your Own Hardware
A practical tutorial on deploying Google's TimesFM 2.5 time-series foundation model locally for zero-shot stock price forecasting — from environment setup to multi-asset portfolio inference.
Sector Spotlight: Financials — Record Earnings, Rate-Sensitive Rotation, and AI-Driven Finance
Deep dive into the financial sector after Q2 2026's record earnings season. Goldman Sachs' best quarter in 157 years, JPMorgan +41% profit YoY, the rotation from tech to financials, and how AI is reshaping quant finance, payments, and asset management.
Weekly Market Pulse: July 13–17 — CPI Cools, Banks Break Records, Tech Selloff Deepens
June CPI posts largest monthly drop since April 2020, Goldman Sachs reports best quarter in 157-year history, IBM crashes 25% on earnings warning, TSMC raises CapEx to $60B, and the semiconductor selloff pushes SOX 20% below its record.
Macro Crosscurrents: Inflation Persistence, Central Bank Divergence, and the AI Investment Cycle
Global markets face resilient growth, persistent inflation risks, and uncertain monetary policy paths. A quantitative framework for navigating central bank divergence and the AI investment cycle in H2 2026.
TrendSpider Review 2026: AI Charting, Strategy Lab, and Automated Backtesting for Quants
TrendSpider combines automated pattern recognition, AI Strategy Lab (ML model training), Sidekick AI assistant, and no-code backtesting into one platform. Tested pricing, feature depth, data quality, and real workflow fit for quantitative analysts.
Hidden Markov Models for Market Regime Detection: A Complete Python Workflow
Markets switch between bull, bear, and range-bound regimes — but most strategies assume a single regime. Hidden Markov Models (HMMs) let you detect these states probabilistically and adapt your trading in real time. Here's how to build a regime-aware pipeline in Python with your existing quant data feed.