A convolutional neural network can read candlestick history not as folklore but as structured time–price geometry, extracting probabilistic signals from raw OHLCV data while remaining constrained by the hard limits of noise, regime change, and calibration.

Interactive models: opportunity cost, real rates, and DXY vs gold price. Gamified sliders.

The February 2026 update to the Probabilistic Equity Valuator recalibrates its DCF engine with forward-weighted consensus growth, sector-adaptive terminal rates, continuous WACC premiums, VIX-responsive risk pricing, and a multiples cross-check — cutting bias by 22% and generating +21.7pp of alpha on undervalued picks.

Testing Whether the Modern Economy Entered a New Regime Through Structural Breaks in Productivity, Income Distribution, Debt Expansion, and Monetary Order

When Bitcoin shed $4,000 in two hours last January weekend, it appeared as sudden as lightning—but the storm had been gathering for weeks in the mathematics of Death Crosses, institutional outflows, and long-term holder distribution. This is the story of how technical indicators, on-chain data, and derivatives leverage created a predictable pressure system that was waiting to ignite.

A Framework for Making Smarter Decisions in Financial Markets

A framework for equity valuation that replaces point estimates with probability distributions. Cash flows are modeled on their natural positive support via log-increments, the economic constraint $r > g_{\text{term}}$ is enforced in sampling rather than assumed, and dependence between growth and discount rates is captured through a heavy-tailed copula. Nowcasting layers—robust anomaly detection and calibrated sentiment—update short-horizon priors without duplicating price information. The result is a valuation output that preserves uncertainty through to decision statistics: exceedance probabilities, expected mispricing, and tail risk measures that support risk-constrained portfolio allocation.

If this article has been sought, it’s reasonable to assume a vexing situation is afoot. You have been presented with a dataset—a term which here describes a vast and bewildering ledger of figures and facts—and have been assigned the lamentable task of prediction. You seek to discern what is to come from what has been, a truly precarious endeavor, and you have likely learned that relying on a single method of divination is a path to spectacular error ...

This design proposal presents a comprehensive framework for enhancing the Relative Strength Index (RSI) by incorporating macroeconomic liquidity conditions and market sentiment. The traditional RSI, while widely used, suffers from well-documented limitations including false signals during trending markets and fixed thresholds that fail to adapt to changing market conditions

A step-by-step mathematical explanation of typed mean-field-type games (MFTGs), reinforcement learning integration, risk measures, and forecasting under uncertainty for decentralized markets.