Topic 1: MongoDB Vector Search Use Cases
- ๐ฆ Kronos Research (Taipei)
- ๐ณ Trades billions of dollars in cryptocurrency
- ๐ Analyzes and improves algorithmic models
- ๐ Quantitative research for high-frequency cryptocurrency trading (HFT)
- ๐ค Computer programs to transact high volumes of orders in seconds
- ๐ Analyzes multiple markets and executes orders
- ๐ Derivatives trading
- ๐ค Machine learning/AI models trained on large volumes of proprietary market data
- ๐ Identifies profitable and repeatable market phenomena
- ๐ก๏ธ Extensive operations suite to control risk and prevent trading errors
- ๐ฌ Ensures correct behavior even during severe market turbulence
Prediction Intelligence: ๐ฎ
- ๐ข Data centers as close as possible to actual exchanges to limit latency
- ๐ฉ๏ธ Crypto exchanges are natively in the cloud, allowing high-frequency traders to be physically located close to the exchanges
Data Format Flexibility: ๐
- ๐๏ธ Data are not structurally rigid, like market data (bid and ask prices, trades)
- ๐ค Bots might have 20 configurations or key-value pairs, while others have only 6
- ๐พ Efficiently store data and analyze how configurations change over time, and how data is updated and selected
Atlas Data Federation: ๐
- ๐ Charts: Data visualization, easy to create and share
- ๐ For specific strategies and simulation results
- ๐ Visualize the different relationships
- ๐ Adjust the dials for trading bots
Highlight: ๐ก
- '๐ค 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: ๐
- ๐ป Handles data analytics, scalability, and distributed processing
- โก๏ธ Accelerates insights by delivering real-time intelligence
- ๐๏ธ Manages the data lifecycle from ingestion to transactions to retirement
- ๐ซ Eliminates data duplication
- โฑ๏ธ Optimized for real-time processing
- ๐ Flexible model deployment and model monitoring (drift detection)
- ๐ Integrated Python environment
MongoDB Machine Learning Use Cases: ๐
- ๐ซ Fraud prevention
- ๐ง Predictive maintenance - patterns to predict and prevent failures
- ๐ฏ Real-time recommendation engines
- ๐ญ Process optimization - minimizing costs
ACID-Compliant Transactions in MongoDB: ๐น
Challenges Solved:
- ๐ Separate queries to retrieve live and archival data across systems, and merging the results - a pain for developers.
- ๐ Maintaining transactional data integrity between different parties, requiring all-or-nothing execution for multi-document transactions.
ACID-Compliant Examples:
- ๐ณ Bank - Transfer of funds between accounts, payment processing, trading platforms, updating the "System of Record" and real-time dashboards.
- ๐ฅ Healthcare - Ensuring patient records are updated accurately and up-to-date, preventing data anomalies.
- ๐ช Inventory Management - Orders are atomic, payment transactions are secure and accurate, updating available inventory.
Cost-Saving Feature: ๐ฐ
Online Archive:
- ๐๏ธ Optimize costs while keeping data accessible
- ๐ Custom rules to automatically archive infrequently accessed data to cloud object storage
- ๐ 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
Danny Chan, specialty of FSI and Serverless
Kenny Chan, specialty of FSI and Machine Learning
Top comments (0)