Financial Data Engineering Tamer Khraisha

Spis treści książki

• Foreword
• Preface
  • Who Should Read This Book?
  • Prerequisites
  • What to Expect from This Book
  • Book Resources and References
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• I. Foundations of Financial Data Engineering
• 1. Financial Data Engineering Clarified
  • Defining Financial Data Engineering
    • First of All, What Is Finance?
      • Finance as an economic function
      • Finance as a market
      • Finance as a research field
      • Finance as a technology
    • Defining Data Engineering
    • Defining Financial Data Engineering
  • Why Financial Data Engineering?
    • Volume, Variety, and Velocity of Financial Data
      • Volume
      • Velocity
      • Variety
    • Finance-Specific Data Requirements and Problems
    • Financial Machine Learning
      • Supervised learning
      • Unsupervised learning
      • Reinforcement learning
    • The Disruptive FinTech Landscape
    • Regulatory Requirements and Compliance
  • The Financial Data Engineer Role
    • Description of the Role
    • Where Do Financial Data Engineers Work?
      • FinTech
      • Commercial banks
      • Investment banks
      • Asset management firms
      • Hedge funds
      • Regulatory institutions
      • Financial data vendors
      • Security exchanges
      • Big tech firms
    • Responsibilities and Activities of a Financial Data Engineer
      • Starting with data
      • Scaling with data
      • Leading with data
    • Skills of a Financial Data Engineer
      • Financial domain knowledge
      • Technical data engineering skills
      • Business and soft skills
  • Summary
• 2. Financial Data Ecosystem
  • Sources of Financial Data
    • Public Financial Data
      • Regulatory disclosure requirements
      • Public institutional and governmental data
      • Public research data
      • Free stock market APIs
    • Security Exchanges
    • Commercial Data Vendors, Providers, and Distributors
      • Bloomberg
      • LSEG Eikon
      • FactSet
      • S&P Global Market Intelligence
      • Wharton Research Data Services
    • Survey Data
    • Alternative Data
    • Confidential and Proprietary Data
  • Structures of Financial Data
    • Time Series Data
    • Cross-Sectional Data
    • Panel Data
    • Matrix Data
    • Graph Data
      • Simple graphs
      • Directed graphs
      • Weighted graphs
      • Multipartite graphs
      • Temporal graphs
      • Multilayer graphs
    • Text Data
  • Types of Financial Data
    • Fundamental Data
    • Market Data
    • Transaction Data
      • Transaction specifications
      • Initiation date
      • Settlement date
      • Settlement method
      • Transaction parties
    • Analytics Data
    • Alternative Data
    • Reference Data
    • Entity Data
  • Benchmark Financial Datasets
    • Center for Research in Security Prices
    • Compustat Financials
    • Trade and Quote Database
    • Institutional Brokers Estimate System
    • IvyDB OptionMetrics
    • Trade Reporting and Compliance Engine
    • Orbis Global Database
    • SDC Platinum
    • Standard & Poors Dow Jones Indices
    • Alternative Datasets
      • BitSight Security Ratings
      • Global New Vehicle Registrations
      • Weather Source
      • Patent data
  • Summary
• 3. Financial Identification Systems
  • Financial Identifiers
    • Financial Identifier and Identification System Defined
    • The Need for Financial Identifiers
    • Who Creates Financial Identification Systems?
      • International Organization for Standardization (ISO)
      • National Numbering Agencies
      • Financial data vendors
      • Financial institutions
  • Desired Properties of a Financial Identifier
    • Uniqueness
    • Globality
    • Scalability
    • Completeness
    • Accessibility
    • Timeliness
    • Authenticity
    • Granularity
    • Permanence
    • Immutability
    • Security
  • Financial Identification Systems Landscape
    • International Securities Identification Number
    • Classification of Financial Instruments
    • Financial Instrument Short Name
    • Committee on Uniform Security Identification Procedures
    • Legal Entity Identifier
    • Transaction Identifiers
    • Stock Exchange Daily Official List
    • Ticker Symbols
    • Derivative Identifiers
      • Option symbol
      • CFI, UPI, and OTC ISIN
      • Alternative Instrument Identifier
    • Financial Instrument Global Identifier
    • FactSet Permanent Identifier
    • LSEG Permanent Identifier
    • Digital Asset Identifiers
    • Industry and Sector Identifiers
    • Bank Identifiers
  • Summary
• 4. Financial Entity Systems
  • Financial Entity Defined
  • Financial Named Entity Recognition
    • Named Entity Recognition Described
    • How Does Named Entity Recognition Work?
      • Data preprocessing
      • Entity extraction
      • Entity categorization
      • Entity disambiguation
      • Evaluation
    • Approaches to Named Entity Recognition
      • Lexicon/dictionary-based approach
      • Rule-based approach
      • Feature-engineering machine learning approach
      • Deep learning approach
      • Large language models
      • Wikification
      • Knowledge graphs
    • Named Entity Recognition Software Libraries
  • Financial Entity Resolution
    • Entity Resolution Described
    • The Importance of Entity Resolution in Finance
      • Multiple identifiers
      • Missing identifiers
      • Data aggregation and integration
      • Data deduplication
    • How Does Entity Resolution Work?
      • Data preprocessing
      • Indexing
      • Comparison
      • Classification
      • Evaluation
    • Approaches to Entity Resolution
      • Deterministic linkage
        • Link tables
        • Exact matching
        • Rule-based matching
      • Probabilistic linkage
      • Supervised machine learning approach
    • Entity Resolution Software Libraries
  • Summary
• 5. Financial Data Governance
  • Financial Data Governance
    • Financial Data Governance Defined
    • Financial Data Governance Justified
  • Data Quality
    • Dimension 1: Data Errors
    • Dimension 2: Data Outliers
    • Dimension 3: Data Biases
    • Dimension 4: Data Granularity
    • Dimension 5: Data Duplicates
    • Dimension 6: Data Availability and Completeness
    • Dimension 7: Data Timeliness
    • Dimension 8: Data Constraints
    • Dimension 9: Data Relevance
  • Data Integrity
    • Principle 1: Data Standards
    • Principle 2: Data Backups
    • Principle 3: Data Archiving
    • Principle 4: Data Aggregation
    • Principle 5: Data Lineage
    • Principle 6: Data Catalogs
    • Principle 7: Data Ownership
    • Principle 8: Data Contracts
    • Principle 9: Data Reconciliation
  • Data Security and Privacy
    • Data Privacy
    • Data Anonymization
      • Anonymization strategy
      • Anonymization techniques
    • Data Encryption
    • Access Control
  • Summary
• II. The Financial Data Engineering Lifecycle
• 6. Overview of the Financial Data Engineering Lifecycle
  • Financial Data Engineering Lifecycle Defined
  • Criteria for Building the Financial Data Engineering Stack
    • Criterion 1: Open Source Versus Commercial Software
    • Criterion 2: Ease of Use Versus Performance
    • Criterion 3: Cloud Versus On Premises
      • On premises
      • Cloud computing
    • Criterion 4: Public Versus Private Versus Hybrid Cloud
      • Public cloud
      • Private cloud
      • Hybrid cloud
    • Criterion 5: Single Versus Multi-Cloud
    • Criterion 6: Monolithic Versus Modular Codebase
      • Monolith architecture
      • Modular architecture
  • Summary
• 7. Data Ingestion Layer
  • Data Transmission and Arrival Processes
    • Data Transmission Protocols
      • Application layer
      • Transport layer
      • Network layer
      • Network access layer
    • Data Arrival Processes
      • Scheduled data arrival process
      • Event-driven data arrival process
      • Homogeneous data arrival process
      • Heterogeneous data arrival process
      • Single-item data arrival process
      • Bulk data arrival process
  • Data Ingestion Formats
    • General-Purpose Formats
    • Big Data Formats
    • In-Memory Formats
    • Standardized Financial Formats
      • Financial Information eXchange (FIX)
      • eXtensible Business Reporting Language (XBRL)
      • Financial products Markup Language (FpML)
      • Open Financial Exchange (OFX)
      • Universal Financial Industry Message Scheme (ISO 20022)
  • Data Ingestion Technologies
    • Financial APIs
    • Financial Data Feeds
    • Secure File Transfer
    • Cloud Access
    • Web Access
    • Specialized Financial Software
  • Data Ingestion Best Practices
    • Meet Business Requirements
    • Design for Change
    • Enforce Data Governance
    • Perform Benchmarking and Stress Testing
  • Summary
• 8. Data Storage Layer
  • Principles of Data Storage System Design
    • Principle 1: Business Requirements
    • Principle 2: Data Modeling
    • Principle 3: Transactional Guarantee
    • Principle 4: Consistency Tradeoffs
    • Principle 4: Scalability
    • Principle 5: Security
  • Data Storage Modeling
    • SQL Versus NoSQL
    • Primary Versus Secondary
    • Operational Versus Analytical
    • Native Versus Non-Native
    • Multi-Model Versus Polyglot Persistence
  • Data Storage Models
    • The Data Lake Model
      • Why data lakes?
      • Technological implementations of data lakes
      • Data modeling with data lakes
      • Data governance
      • Financial use cases of data lakes
    • The Relational Model
      • Why relational databases?
        • SQL standards
        • ACID transactions
        • Analytical querying
        • Schema enforcement
      • Data modeling with relational databases
        • Normalization
        • Constraints
        • Indexing of relational databases
      • Technological implementations of relational databases
      • Financial use cases of relational databases
    • The Document Model
      • Why document databases?
      • Data modeling with document databases
        • Document and collection structure
        • Denormalization
        • Indexing of document databases
      • Technological implementations of document databases
      • Financial use cases of document databases
    • The Time Series Model
      • Why time series databases?
      • Data modeling with time series
      • Technological implementations of time series databases
      • Financial use cases of time series databases
    • The Message Broker Model
      • Why message brokers?
      • Data modeling with message brokers
        • Topic modeling
        • Message schemas
      • Technological implementations of message brokers
      • Financial use cases of message brokers
    • The Graph Model
      • Why a graph model?
      • Data modeling with graph databases
      • Technological implementations of graph databases
      • Financial use cases of graph databases
    • The Warehouse Model
      • Why data warehouses?
      • Data modeling with data warehouses
      • Technological implementations of data warehousing
      • Financial use cases of data warehouses
    • The Blockchain Model
  • Summary
• 9. Data Transformation and Delivery Layer
  • Data Querying
    • Querying Patterns
      • Time series queries
      • Cross-section queries
      • Panel queries
      • Analytical queries
    • Query Optimization
      • Database-side query optimization
      • User-side query optimization
        • Scenario 1
        • Scenario 2
        • Scenario 3
        • Scenario 4
        • Scenario 5
        • Scenario 6
  • Data Transformation
    • Transformation Operations
      • Format conversion
      • Data cleaning
      • Data adjustments
      • Data standardization
      • Data filtering
      • Feature engineering
      • Advanced analytical computations
    • Transformation Patterns
      • Batch versus streaming transformations
      • Memory-based versus disk-based transformations
      • Full versus incremental data transformations
    • Computational Requirements
      • Computational performance
        • Computational speed
        • Throughput
        • Computational efficiency
        • Scalability
      • Computing environments
  • Data Delivery
    • Data Consumers
    • Delivery Mechanisms
  • Summary
• 10. The Monitoring Layer
  • Metrics, Events, Logs, and Traces
    • Metrics
    • Events
    • Logs
    • Traces
  • Data Quality Monitoring
  • Performance Monitoring
  • Cost Monitoring
  • Business and Analytical Monitoring
  • Data Observability
  • Summary
• 11. Financial Data Workflows
  • Workflow-Oriented Software Architectures
  • What Is a Data Workflow?
  • Workflow Management Systems
    • Flexibility
    • Configurability
    • Dependency Management
    • Coordination Patterns
    • Scalability
    • Integration
  • Types of Financial Data Workflows
    • Extract-Transform-Load Workflows
    • Stream Processing Workflows
    • Microservice Workflows
    • Machine Learning Workflows
  • Summary
• 12. Hands-On Projects
  • Prerequisites
  • Project 1: Designing a Bank Account Management System Database with PostgreSQL
    • Conceptual Model: Business Requirements
      • Entities
      • Relationships
      • Constraints
    • Logical Model: Entity Relationship Diagram
    • Physical Model: Data Definition and Manipulation Language
    • Project 1: Local Testing
    • Project 1: Clean Up
    • Project 1: Summary
  • Project 2: Designing a Financial Data ETL Workflow with Mage and Python
    • Project 2: Workflow Definition
    • Project 2: Database Design
    • Project 2: Local Testing
    • Project 2: Clean Up
    • Project 2: Summary
  • Project 3: Designing a Microservice Workflow with Netflix Conductor, PostgreSQL, and Python
    • Project 3: Workflow Definition
    • Project 3: Database Design
    • Project 3: Local Testing
    • Project 3: Clean Up
    • Project 3: Summary
  • Project 4: Designing a Financial Reference Data Store with OpenFIGI, PermID, and GLEIF APIs
    • Project 4: Prerequisites
    • Project 4: Local Testing
    • Project 4: Clean Up
    • Project 4: Summary
  • Conclusion
  • Follow Updates on These Projects
  • Report Issues or Ask Questions
• The Path Forward: Trends Shaping Financial Markets
  • Financial Integration
  • Digitalization of Financial Markets and Cloud Adoption
  • Financial Regulation
  • Financial Data Sharing and Marketplaces
  • Financial Standardization
  • Artificial Intelligence and Language Models
  • Architectures for Specific Business Domains
  • Data Collection
  • Speed and Efficiency
  • Tokenization, Blockchain, and Digital Currencies
  • What Can You Do Next?
• Afterword
• Index

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