VolatilityX
  • Overview
    • Challenges
      • Information Asymmetry
      • Behavioral Biases
      • The Need for 24/7 Monitoring
  • Opportunity
    • Generational Wealth Transfer
    • Emergence of AI Agents
  • VolatilityX
    • Democratizing Access
    • AI Agents Elevate the Game
  • Architecture
    • Data Ingestion Layer
    • Data Processing and Transformation
    • Anomaly Detection Engine
    • Multi-Agent System Architecture
    • Information Dissemination
  • Agents
    • Agent Ecosystem
    • Stocks Agent
      • Data Sources
      • Twitter Agent
      • Simplifying News: Educate & Transform (Q1 2025)
      • Building Our Own Research Reports (Q2 2025)
    • Crypto (Q1 2025)
    • Commodities (Soon)
    • Bonds (Soon)
  • Tokenomics
    • Tokenomics & Utility
  • Roadmap
  • Conclusion
Powered by GitBook
On this page
  1. Architecture

Data Ingestion Layer

Sources of Data

A. Real-Time Price Feeds

  • US Stock Prices: We subscribe to live feeds or APIs (e.g., from IEX, Polygon.io, or other market data providers) that supply ticks, aggregated quotes, or candlestick bars in intervals (1-second, 1-minute, etc.).

  • Crypto: Similar real-time APIs from prominent exchanges (Binance, Coinbase, Kraken). Crypto is 24/7, so the pipeline must handle non-stop data flow.

  • Commodities: Feeds for metals (gold, silver), energy (crude oil, natural gas), and agricultural commodities (corn, soybeans). These might come from futures exchanges like CME.

B. Options Data We ingest option chains for stocks and ETFs, along with Greeks (Delta, Gamma, Theta, Vega). Since each underlying asset can have multiple expiration dates and strike prices, this data set can balloon in size. Efficiency and intelligent caching are critical.

C. Analyst Research

  • Equity Analysts: We gather target prices, upgrade/downgrade reports, and consensus from research platforms or aggregators (e.g., Refinitiv, FactSet, or direct broker RSS feeds).

  • Crypto Whitepapers: For major coins or token offerings, we archive PDF whitepapers or developer roadmaps.

D. News

  • Financial News Outlets: Bloomberg, CNBC, Reuters, major newspapers.

  • Press Releases: Company announcements, SEC filings (10-K, 10-Q, 8-K) for equities.

  • Crypto Project Updates: Official Medium posts, GitHub commit logs, or ecosystem announcements.

E. Company Data

  • Fundamentals: Annual and quarterly filings, balance sheets, income statements, and presentations.

  • Earnings Call Transcripts: NLP-ready text for sentiment analysis.

F. Reddit Chatter, Social Forums

  • Subreddits (e.g., r/WallStreetBets, r/CryptoCurrency) and other Discord or Telegram channels. This often requires advanced text-scraping and language processing, plus rate-limit respect.

2.2 Ingestion Architecture

We typically implement a streaming architecture using technologies such as Apache Kafka, RabbitMQ, or AWS Kinesis (depending on the deployment environment). Each data feed is either:

  • Real-time (pushing updates to our system as they occur), or

  • Polled in intervals (e.g., scraping Reddit every 30 seconds or 1 minute, subject to terms of service).

Raw data is tagged with metadata: timestamps, source IDs, asset tickers, etc. We store them initially in a Data Lake (S3, HDFS, or equivalent) for long-term archiving, and simultaneously push them into a real-time queue for immediate consumption by downstream processors.

Key Considerations

  1. Latency: Real-time price feeds must propagate quickly (within milliseconds to seconds) to ensure the Agents or analytics modules see fresh data.

  2. Fault Tolerance: We use replication in Kafka clusters or multi-availability-zone setups to mitigate single-point failures.

  3. Data Quality: Each feed may have different structures, missing fields, or spurious values. We use a schema registry and cleaning routines to unify them.

PreviousArchitectureNextData Processing and Transformation

Last updated 4 months ago