AI-powered stock analysis & quantitative research
Machine learning models, backtesting frameworks, and data-driven trading strategies — from research to deployment.
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.
Sector Spotlight: Energy — $600B Crash to Comeback in the Hormuz Crisis
Deep dive into the US energy sector after Q2's 13% drawdown and July's Iran-driven reversal. Price data, capital discipline, $41B buyback bonanza, and how AI-driven trading strategies can capture oil volatility in the Hormuz crisis.
Weekly Market Pulse: July 6–10
SK Hynix scores the largest foreign IPO in US history, Iran tensions spike oil as ceasefire collapses, Dow closes above 53K for the first time, Fed minutes reveal rate split, and S&P 500 rallies to within 0.6% of all-time highs.
Fiscal.ai (formerly FinChat) Review: AI-Powered Equity Research Terminal
Fiscal.ai combines S&P Global Market Intelligence data with conversational AI for institutional-grade equity research. We tested the free tier, Pro, and Max — pricing, data quality, and workflow fit for quantitative analysts.
Pairs Trading with Cointegration: A Complete Statistical Arbitrage Workflow in Python
Pairs trading is the most accessible form of statistical arbitrage — no direction calls, no market timing, just relative value. Here's how to find cointegrated pairs, build a mean-reversion signal, and backtest it end-to-end in Python.