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Danny Chan for MongoDB Builders

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๐ŸŒ MongoDB in the Financial Industry: Vector Search and ACID-Compliant Transactions ๐Ÿ’ฐ

Topic 1: MongoDB Vector Search Use Cases



- ๐Ÿฆ Kronos Research (Taipei)

  • ๐Ÿ’ณ Trades billions of dollars in cryptocurrency
  • ๐Ÿ“Š Analyzes and improves algorithmic models
  • ๐Ÿš€ Quantitative research for high-frequency cryptocurrency trading (HFT)
  • ๐Ÿค– Computer programs to transact high volumes of orders in seconds
  • ๐ŸŒ Analyzes multiple markets and executes orders
  • ๐Ÿ”— Derivatives trading
  • ๐Ÿค– Machine learning/AI models trained on large volumes of proprietary market data
  • ๐Ÿ” Identifies profitable and repeatable market phenomena
  • ๐Ÿ›ก๏ธ Extensive operations suite to control risk and prevent trading errors
  • ๐Ÿ”ฌ Ensures correct behavior even during severe market turbulence



Prediction Intelligence: ๐Ÿ”ฎ

  • ๐Ÿข Data centers as close as possible to actual exchanges to limit latency
  • ๐ŸŒฉ๏ธ Crypto exchanges are natively in the cloud, allowing high-frequency traders to be physically located close to the exchanges


Data Format Flexibility: ๐Ÿ“‚

  • ๐Ÿ—ƒ๏ธ Data are not structurally rigid, like market data (bid and ask prices, trades)
  • ๐Ÿค– Bots might have 20 configurations or key-value pairs, while others have only 6
  • ๐Ÿ’พ Efficiently store data and analyze how configurations change over time, and how data is updated and selected


Atlas Data Federation: ๐Ÿ“Š

  • ๐Ÿ“Š Charts: Data visualization, easy to create and share
  • ๐Ÿ” For specific strategies and simulation results
  • ๐Ÿ” Visualize the different relationships
  • ๐Ÿ” Adjust the dials for trading bots


Highlight: ๐Ÿ’ก

  • '๐Ÿค” On a given day, what's the distribution of profit and loss results across the different configurations?'



Topic 2: MongoDB and Machine Learning



MongoDB Machine Learning Capabilities: ๐Ÿ“Š

  • ๐Ÿ’ป Handles data analytics, scalability, and distributed processing
  • โšก๏ธ Accelerates insights by delivering real-time intelligence
  • ๐Ÿ—ƒ๏ธ Manages the data lifecycle from ingestion to transactions to retirement
  • ๐Ÿšซ Eliminates data duplication
  • โฑ๏ธ Optimized for real-time processing
  • ๐Ÿ” Flexible model deployment and model monitoring (drift detection)
  • ๐Ÿ Integrated Python environment


MongoDB Machine Learning Use Cases: ๐Ÿš€

  • ๐Ÿšซ Fraud prevention
  • ๐Ÿ”ง Predictive maintenance - patterns to predict and prevent failures
  • ๐ŸŽฏ Real-time recommendation engines
  • ๐Ÿญ Process optimization - minimizing costs


ACID-Compliant Transactions in MongoDB: ๐Ÿ’น
Challenges Solved:

  1. ๐Ÿ” Separate queries to retrieve live and archival data across systems, and merging the results - a pain for developers.
  2. ๐Ÿ”’ Maintaining transactional data integrity between different parties, requiring all-or-nothing execution for multi-document transactions.


ACID-Compliant Examples:

  • ๐Ÿ’ณ Bank - Transfer of funds between accounts, payment processing, trading platforms, updating the "System of Record" and real-time dashboards.
  • ๐Ÿฅ Healthcare - Ensuring patient records are updated accurately and up-to-date, preventing data anomalies.
  • ๐Ÿช Inventory Management - Orders are atomic, payment transactions are secure and accurate, updating available inventory.


Cost-Saving Feature: ๐Ÿ’ฐ
Online Archive:

  • ๐Ÿ—‚๏ธ Optimize costs while keeping data accessible
  • ๐Ÿ“‚ Custom rules to automatically archive infrequently accessed data to cloud object storage
  • ๐Ÿ” Retain the ability to query archived data through a single endpoint



Reference:

https://www.mongodb.com/products/capabilities/transactions
ACID Transactions with MongoDB

https://www.mongodb.com/blog/post/simplifying-data-science-iguazio-mongodb
IoT & IIoT โ€” generating insights to identify patterns

https://www.mongodb.com/solutions/customer-case-studies/kronos
MongoDB Atlas Charts Enables Kronos to Trade Billions on Crypto Markets Every Day

https://www.mongodb.com/products/platform/atlas-online-archive
Online Archive. Tier your MongoDB Atlas data, query it in place.

https://www.mongodb.com/library/vector-search/vector-search-quick-start?lb-mode=overlay
Atlas Vector Search Quick Start

https://www.mongodb.com/developer/products/atlas/agent-fireworksai-mongodb-langchain/
Building an AI Agent With Memory Using MongoDB, Fireworks AI, and LangChain

https://www.mongodb.com/developer/products/mongodb/langchain-vector-search/
Introduction to LangChain and MongoDB Atlas Vector Search


Editor

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Danny Chan, specialty of FSI and Serverless

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Kenny Chan, specialty of FSI and Machine Learning

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