Topic 1: MongoDB Vector Search Use Cases
– 🏦 Kronos Research (Taipei)
💳 Trades billions of dollars in cryptocurrency
📊 Analyzes and improves algorithmic models
🚀 Quantitative research for high-frequency cryptocurrency trading (HFT)
🤖 Computer programs to transact high volumes of orders in seconds
🌍 Analyzes multiple markets and executes orders
🔗 Derivatives trading
🤖 Machine learning/AI models trained on large volumes of proprietary market data
🔍 Identifies profitable and repeatable market phenomena
🛡️ Extensive operations suite to control risk and prevent trading errors
🔬 Ensures correct behavior even during severe market turbulence
Prediction Intelligence: 🔮
🏢 Data centers as close as possible to actual exchanges to limit latency
🌩️ Crypto exchanges are natively in the cloud, allowing high-frequency traders to be physically located close to the exchanges
Data Format Flexibility: 📂
🗃️ Data are not structurally rigid, like market data (bid and ask prices, trades)
🤖 Bots might have 20 configurations or key-value pairs, while others have only 6
💾 Efficiently store data and analyze how configurations change over time, and how data is updated and selected
Atlas Data Federation: 📊
📊 Charts: Data visualization, easy to create and share
🔍 For specific strategies and simulation results
🔍 Visualize the different relationships
🔍 Adjust the dials for trading bots
Highlight: 💡
‘🤔 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: 📊
💻 Handles data analytics, scalability, and distributed processing
⚡️ Accelerates insights by delivering real-time intelligence
🗃️ Manages the data lifecycle from ingestion to transactions to retirement
🚫 Eliminates data duplication
⏱️ Optimized for real-time processing
🔍 Flexible model deployment and model monitoring (drift detection)
🐍 Integrated Python environment
MongoDB Machine Learning Use Cases: 🚀
🚫 Fraud prevention
🔧 Predictive maintenance – patterns to predict and prevent failures
🎯 Real-time recommendation engines
🏭 Process optimization – minimizing costs
ACID-Compliant Transactions in MongoDB: 💹
Challenges Solved:
🔍 Separate queries to retrieve live and archival data across systems, and merging the results – a pain for developers.
🔒 Maintaining transactional data integrity between different parties, requiring all-or-nothing execution for multi-document transactions.
ACID-Compliant Examples:
💳 Bank – Transfer of funds between accounts, payment processing, trading platforms, updating the “System of Record” and real-time dashboards.
🏥 Healthcare – Ensuring patient records are updated accurately and up-to-date, preventing data anomalies.
🏪 Inventory Management – Orders are atomic, payment transactions are secure and accurate, updating available inventory.
Cost-Saving Feature: 💰
Online Archive:
🗂️ Optimize costs while keeping data accessible
📂 Custom rules to automatically archive infrequently accessed data to cloud object storage
🔍 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 — 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
