Global Predictions Inc

Analyzes historical OHLCV (open, high, low, close, volume) data and technical indicators using ensemble machine learning models (likely LSTM, gradient boosting, or hybrid architectures) to generate forward-looking price predictions and trend direction probabilities. The system ingests aggregated market data, applies feature engineering for volatility, momentum, and mean-reversion signals, then outputs probabilistic forecasts with confidence intervals across multiple timeframes (daily, weekly, monthly).

Scans historical price and volume data across stocks, indices, commodities, and cryptocurrencies to identify statistical anomalies, unusual correlations, and recurring chart patterns (head-and-shoulders, triangles, breakouts) using unsupervised learning or rule-based pattern matching. The system flags deviations from normal trading behavior (e.g., volume spikes, volatility compression, correlation breakdowns) that may signal emerging opportunities or risks, outputting ranked alerts by statistical significance.

Aggregates and analyzes alternative data sources (social media mentions, news sentiment, options flow, insider transactions, or fund flows) to generate market sentiment scores and contrarian signals. The system applies NLP or rule-based scoring to quantify bullish/bearish sentiment, identifies when sentiment diverges from price action (e.g., extreme pessimism at market bottoms), and surfaces contrarian opportunities where crowd positioning may be crowded or extreme.

Analyzes a user's portfolio holdings to decompose risk across asset classes, sectors, and geographies, and identifies hidden correlations and concentration risks. The system ingests a portfolio snapshot (holdings, weights, or transaction history), calculates pairwise correlations between assets, performs factor analysis to identify common drivers of returns, and surfaces concentration risks (e.g., overweight to tech, currency exposure, or single-country risk) that may not be obvious from raw holdings.

Enables users to construct custom scenarios (e.g., interest rate hikes, earnings misses, sector rotation) and simulate their impact on portfolio returns, asset prices, or market indices. The system applies parametric or Monte Carlo simulation methods to model how changes in macro variables (rates, inflation, GDP growth) or micro variables (earnings, margins, valuations) propagate through asset prices, outputting probability distributions of outcomes and sensitivity rankings showing which variables matter most.

Ingests and normalizes market data (prices, volumes, spreads, order book depth) from multiple exchanges and data providers, handling format differences, latency variations, and data quality issues to present a unified, clean view. The system applies data validation rules to detect stale quotes, crossed markets, or obvious errors, and provides standardized OHLCV data, bid-ask spreads, and volume metrics across stocks, indices, commodities, and crypto in a consistent format.