Explainers and definitions for financial data and trading concepts
A practical guide to interpreting ARIMA price forecasts — what mid, lower, and upper bounds mean, how confidence intervals grow with forecast horizon, and how to use them in trading decisions.
After building a neural network for stock price forecasting and watching it overfit, we moved to ARIMA. Here's why statistical models outperform deep learning on noisy financial time series.
Learn how EIA crude oil inventories, natural gas storage, and production data drive energy prices, and how to interpret weekly inventory reports for trading signals.
Learn how the ECB Composite Indicator of Systemic Stress measures financial system health across money markets, bonds, equities, FX, and financial intermediaries.
Learn how open-source intelligence (OSINT) methods help investors monitor geopolitical risk, supply chain disruptions, and market-moving events before they hit mainstream news.
Understand the Crypto Fear and Greed Index, how it's calculated, what each level signals, and how contrarian traders use extreme readings for timing decisions.
Learn how prediction markets aggregate real-money bets into event probabilities, and how investors use them to quantify geopolitical, economic, and political risk.
Understand the Treasury yield curve, what different curve shapes signal about the economy, and why yield curve inversions have historically preceded recessions.
Learn how the CFTC Commitments of Traders report reveals institutional positioning in futures markets and how traders use COT data for directional signals.
How institutional investors evaluate and integrate alternative data APIs into trading dashboards, risk systems, and research platforms. Covers API requirements, integration patterns, and vendor selection.
Learn why confidence intervals matter more than point estimates in stock predictions. How to interpret prediction ranges, use uncertainty for position sizing, and avoid the trap of false precision.
Learn what stock prediction models are, how they work, and the different approaches—from technical analysis to machine learning. Understand the strengths and limitations of each method.