Research Project • In Progress

Alpha Factory
Superhuman-Scale Intelligence

Multi-modal alpha generation from time series, news, social media, financial reports, candlestick charts, and earnings calls—processing data at scales impossible for human traders.

Vision

Human traders are limited by time, attention, and cognitive capacity. They can monitor a handful of assets, read dozens of news articles daily, and analyze a finite number of charts. But what if a system could process thousands of assets simultaneously, consuming millions of data points across multiple modalities in real-time?

Alpha Factory is our answer: a multi-modal AI system designed to operate at superhuman scale, extracting trading signals from diverse data sources that no human team could possibly synthesize.

The Problems We Solve

Severity: High | Medium | Low

Single-Modality Limitation

High

Traditional quant systems rely primarily on price/volume data, missing crucial signals from news, sentiment, and fundamental data.

Information Overload

Medium

The volume of available market data far exceeds human processing capacity. Critical signals are lost in the noise.

Slow Signal Discovery

Medium

Human researchers take months to discover and validate new alpha signals. Markets adapt faster than traditional research cycles.

Context Loss

High

Analyzing data sources in isolation misses the crucial interactions between price action, news sentiment, and fundamental changes.

Our Approach

Multi-Modal Data Input

📈

Time Series Data

Price, volume, order book dynamics, and microstructure patterns across multiple timeframes.

📰

News & Media

Real-time news articles, press releases, and breaking events with NLP-powered sentiment analysis.

💬

Social Sentiment

Twitter, Reddit, StockTwits, and social media signals aggregated and analyzed for market sentiment.

📊

Financial Reports

Structured data from earnings reports, balance sheets, and regulatory filings (10-K, 10-Q, 8-K).

📉

Visual Analysis

Candlestick charts and technical pattern recognition through computer vision.

🎙️

Audio Intelligence

Earnings call transcripts and audio analysis for tone, sentiment, and forward guidance.

Alpha Extraction Strategies

💼

Fundamental Analysis

Deep value investing principles: P/E ratios, DCF models, competitive moats, and intrinsic value.

🎭

Sentiment Synthesis

Multi-source sentiment aggregation from news, social media, and analyst reports.

🔢

Mathematical Functions

Statistical arbitrage, mean reversion, momentum strategies, and quantitative factor models.

📚

Expert Knowledge

Trading wisdom from research papers, investment books, and proven market strategies.

⏱️

Time-Series Indicators

Technical indicators: RSI, MACD, Bollinger Bands, moving averages, and custom signals.

🤝

Hybrid Agent Collaboration

Multiple AI agents debate, combine insights, and reach consensus on trading signals.

By combining multi-modal data inputs with diverse alpha extraction strategies, Alpha Factory discovers patterns and relationships that are invisible in single-source analysis.

For example: detecting early market reversals through simultaneous shifts in news sentiment, technical price patterns, and earnings call tone—signals that are weak individually but powerful when correlated across modalities.

Technical Highlights

Multi-Modal Data Input

Time Series Data ingestion

  • High-frequency tick data processing with microsecond precision
  • Multi-exchange aggregation across equities, crypto, and derivatives

News & Media processing pipeline

  • Real-time NLP analysis of financial news from 50+ sources
  • Entity recognition and event extraction for market-moving announcements

Social Sentiment aggregation

  • Twitter/Reddit sentiment tracking for 10,000+ tickers
  • Influence-weighted scoring to filter noise from valuable signals

Financial Reports parser

  • Automated extraction from 10-K, 10-Q, 8-K filings
  • Structured data generation from unstructured regulatory documents

Visual Analysis (Chart recognition)

  • Computer vision for candlestick pattern detection
  • Technical formation recognition (head & shoulders, triangles, etc.)

Audio Intelligence (Earnings calls)

  • Speech-to-text transcription with speaker diarization
  • Tone and sentiment analysis from CEO/CFO delivery patterns

Alpha Extraction Strategies

Fundamental Analysis engine

  • Automated DCF models with dynamic growth assumptions
  • Multi-factor valuation: P/E, P/B, EV/EBITDA, PEG ratios

Sentiment Synthesis system

  • Cross-source sentiment fusion (news + social + reports)
  • Temporal sentiment tracking to detect momentum shifts

Mathematical Functions library

  • Statistical arbitrage: pairs trading, mean reversion, cointegration
  • Momentum strategies: trend following, breakout detection

Expert Knowledge integration

  • Rule extraction from 100+ research papers and investment books
  • Backtested strategies from quant literature (Fama-French, momentum, value)

Time-Series Indicators framework

  • 50+ technical indicators: RSI, MACD, Bollinger Bands, Ichimoku
  • Custom indicator builder for proprietary signal development

Hybrid Agent Collaboration

  • Multiple AI agents debate and vote on trading signals
  • Consensus mechanisms to combine diverse perspectives and reduce false positives

Current Status

Alpha Factory is in active development. We're building both the multi-modal data infrastructure and the alpha extraction systems in parallel.

MULTI-MODAL DATA INPUT

Time Series Data ingestion70% • Q2 2026
News & Media processing pipeline65% • Q2 2026
Social Sentiment aggregation50% • Q3 2026
Financial Reports parser60% • Q2 2026
Visual Analysis (Chart recognition)40% • Q3 2026
Audio Intelligence (Earnings calls)30% • Q3 2026

ALPHA EXTRACTION STRATEGIES

Fundamental Analysis engine25% • Q3 2026
Sentiment Synthesis system30% • Q3 2026
Mathematical Functions library30% • Q3 2026
Expert Knowledge integration15% • Q4 2026
Time-Series Indicators framework25% • Q3 2026
Hybrid Agent Collaboration10% • Q4 2026

Future Direction

Our roadmap includes expanding to additional data modalities (satellite imagery, alternative data), developing adaptive models that evolve with market conditions, and creating a framework for continuous alpha discovery and validation.

Research Collaboration

We're particularly interested in collaborating with researchers working on multi-modal learning, attention mechanisms, and cross-domain transfer learning.

🧠

ML Researchers

Multi-modal learning and attention mechanisms

📊

Data Scientists

Alpha extraction and signal discovery

💼

Trading Firms

Test in production environments