TFA Curriculum for Python Programming for Finance Program
- Pankaj Maheshwari
- Jan 1, 2024
- 15 min read
Updated: Dec 12, 2025
The Python Programming for Finance Program is a comprehensive, hands-on curriculum designed to transform finance professionals into proficient quantitative developers capable of building production-grade financial models, automated analytics systems, and high-performance computing applications used in institutional trading, risk management, and investment research. This rigorous program establishes both fundamental programming competency and advanced technical skills, progressing systematically from Python basics through object-oriented architecture, data analytics, process automation, parallel computing, and scientific computing with mathematical and statistical libraries essential for quantitative finance.
Built around the Anaconda ecosystem and Jupyter Notebook development environment—industry standards for data science and quantitative analysis—the curriculum emphasizes practical implementation through progressive hands-on exercises that mirror real-world quantitative development workflows. Participants develop proficiency not merely in Python syntax, but in professional software engineering practices, including code organization, error handling, performance optimization, and maintainable architecture required for collaborative development in institutional quantitative teams.
Foundation: Core Programming Skills (Modules 1-2)
The program begins with Python fundamentals, establishing mastery of core data types (strings, integers, floats), variables, operators, and Python's essential built-in data structures—lists for time-series manipulation, tuples for immutable reference data, sets for unique identifier management, and dictionaries for mapping securities to attributes and organizing hierarchical financial data. Participants progress to control flow statements including conditional logic for trading rules and portfolio rebalancing triggers, loop constructs for iterating through portfolios and processing time-series data, and elegant comprehensions for data transformation. Critical emphasis is placed on exception handling using TRY-EXCEPT-FINALLY patterns, custom exception classes for financial applications, and comprehensive error logging—developing the defensive programming mindset essential for building resilient systems that gracefully handle data quality issues, API failures, and unexpected market conditions.
Architecture: Professional Development Practices (Module 3)
Object-oriented programming establishes the architectural foundation for building scalable, maintainable quantitative libraries. Participants learn to model financial entities as classes—instruments (Bond, Option, Swap), portfolios, pricing models, and risk analytics—implementing inheritance hierarchies that promote code reuse, polymorphism for flexible pricing engines handling diverse products, encapsulation for protecting object state and ensuring data integrity, and abstraction through interfaces that enforce architectural standards. Advanced coverage includes magic methods and operator overloading for natural financial syntax, design patterns relevant to quantitative finance (Strategy, Factory, Observer patterns), and SOLID principles that guide professional library development at financial institutions.
Data Engineering: Analytics, Automation, and Performance (Module 4)
The data analytics and automation module integrates three critical capabilities that distinguish production quantitative developers. Pandas proficiency enables sophisticated financial time-series analysis, portfolio data manipulation, and analytics workflows, including data ingestion from multiple sources (CSV, Excel, JSON, SQL databases), transformation operations (filtering, grouping, merging, pivoting), and calculations for return attribution, risk metrics, and performance analysis. NumPy establishes numerical computing foundations with vectorized array operations that eliminate slow Python loops, achieving 10-100x speedups critical for real-time risk systems and large-scale portfolio calculations.
Process automation addresses operational efficiency through scheduled workflows, report generation (PDF creation for risk dashboards), web scraping for market data extraction, and API integration for real-time data feeds from financial data providers. Participants build automated pipelines for daily portfolio valuation, end-of-day risk reporting, and continuous market monitoring—mirroring 24/7 operational workflows at global institutions. High-performance computing coverage includes multiprocessing for distributing Monte Carlo simulations and VaR calculations across CPU cores, threading for concurrent API requests and I/O operations, Numba JIT compilation for achieving C-like performance in numerical loops, and Dask for processing datasets beyond memory limits—essential techniques for institutional-scale computations involving thousands of securities and millions of scenarios.
Mathematical Computing: Scientific Libraries for Quantitative Finance (Module 5)
The scientific computing module integrates NumPy and SciPy ecosystems that power institutional quantitative research and analytics. Advanced NumPy coverage addresses linear algebra operations fundamental to portfolio optimization (matrix multiplication, inversion, eigenvalue decomposition, Cholesky factorization), vectorization paradigms for efficient array-oriented computation, and random number generation for Monte Carlo methods with correlation structures and variance reduction techniques. SciPy capabilities span probability distribution objects for parametric VaR and maximum likelihood estimation, optimization routines for portfolio construction and model calibration, numerical integration for derivatives pricing, and interpolation methods for yield curve and volatility surface construction.
Throughout this module, participants implement complete quantitative applications synthesizing mathematical concepts with computational libraries: mean-variance portfolio optimization using NumPy linear algebra and SciPy constrained minimization, Monte Carlo option pricing engines with vectorized payoff calculations, parametric VaR models with distribution fitting and backtesting, correlation matrix analysis using eigenvalue decomposition for risk factor identification, and maximum likelihood parameter estimation with goodness-of-fit testing. Emphasis is placed on computational efficiency, numerical stability, code profiling for performance optimization, and best practices for scientific computing that meet institutional standards for accuracy and robustness.
Integrated Learning Approach
The Python Programming for Finance Program employs a progressive, project-based pedagogy where each module builds upon previous foundations while introducing increasingly sophisticated applications. Early modules establish syntax and programming logic through financial examples (portfolio filtering, return calculations, trade execution rules); intermediate modules develop architectural patterns and data engineering capabilities through larger systems (pricing model libraries, automated risk reporting pipelines); and advanced modules synthesize all skills into complete quantitative applications (Monte Carlo engines, portfolio optimizers, VaR models) that demonstrate production-ready capabilities.
Module 1.1: Python Fundamentals and Data Structures (6.75 hrs)
This foundational module establishes essential Python programming skills required for quantitative finance applications, focusing on core data types, variables, operators, and built-in data structures that form the backbone of financial modeling, data analysis, and algorithmic implementation. Participants begin with Anaconda Navigator and Jupyter Notebook setup—the industry-standard development environment for data science and quantitative finance—before progressing through systematic coverage of Python's fundamental building blocks.
Learning Outcomes:
Introduction to Python Data Types and Variables (1.5 hrs)
Perform Operations in Python (1.25 hrs)
Basics and Applications of Python Lists, Tuples, Sets, Dictionaries (4 hrs)
The module provides a comprehensive treatment of Python's core data types, including strings, integers, floats, and booleans, emphasizing their behavior in financial contexts such as price calculations, precision handling in monetary values, and proper data type selection for performance-critical applications. Participants learn variable declaration, assignment mechanics, naming conventions aligned with professional coding standards (PEP 8), and type casting operations essential for data transformation pipelines. Hands-on exercises address common pitfalls in numerical operations, floating-point arithmetic precision, and string manipulation for financial data parsing—building the attention to detail required in production quantitative systems.
The curriculum advances to Python's essential operators, including arithmetic, comparison, logical, and assignment operators, with practical applications in financial calculations, conditional logic for trading rules, and boolean expressions for data filtering. Participants develop fluency in operator precedence, expression evaluation, and efficient computation patterns used in vectorized financial analytics.
A central focus addresses Python's built-in data structures—lists, tuples, sets, and dictionaries—which serve as the primary containers for organizing financial data, portfolio positions, market datasets, and model parameters. Participants master list operations for time-series data manipulation, tuple immutability for fixed reference data, set operations for unique identifier management and portfolio intersection/union calculations, and dictionary structures for mapping securities to prices, storing multi-dimensional risk metrics, and building hierarchical data representations. Advanced coverage includes nested dictionaries for complex data hierarchies such as multi-level portfolio structures, instrument attributes, and market data organization patterns used in institutional systems.
Through progressive hands-on exercises, participants build practical skills in data structure selection, indexing and slicing operations, iteration patterns, and performance considerations—establishing the programming foundation required for subsequent modules covering data analytics with Pandas, numerical computing with NumPy, and financial model implementation.
Module 1.2: Control Flow Statements and Exception Handling (11.5 hrs)
This module develops essential programming logic and robust error management capabilities critical for building production-grade quantitative finance applications. Participants master control flow mechanisms that enable conditional execution, iterative processing, and elegant data transformations—foundational skills for implementing trading algorithms, portfolio rebalancing logic, risk calculations, and automated data processing workflows used in institutional finance systems.
Learning Outcomes:
Conditional Statements – IF, ELIF, ELSE, Nested Conditional Statements (2.2 hrs)
Loop Statements – FOR, WHILE, and Nested Loop Statements, Controls (2.25 hrs)
Comprehensions – List, Set, Dictionary (1.5 hrs)
Understanding Errors and Exception Handling Basics – TRY, EXCEPT, FINALLY (2 hrs)
Raising Exceptions – RAISE, Custom Exceptions, and Logging Errors (1.2 hrs)
With Statement and Context Managers (1.2 hrs)
The module begins with conditional statements including IF, ELIF, and ELSE constructs, progressing to nested conditional logic for complex decision trees. Participants apply these structures to practical financial scenarios such as implementing trade execution rules based on market conditions, portfolio rebalancing triggers based on drift thresholds, credit rating classifications, option payoff calculations, and multi-criteria investment screening logic. Emphasis is placed on writing clear, maintainable conditional logic that accurately captures business rules while avoiding common pitfalls such as unreachable code branches and logical errors in complex conditions.
The curriculum advances to loop statements—FOR and WHILE loops—essential for processing time-series data, iterating through portfolios, performing Monte Carlo simulations, and implementing recursive calculations in bond mathematics and derivatives pricing. Participants learn loop control mechanisms, including BREAK, CONTINUE, and PASS statements, understanding when to use each for optimal code flow. Advanced coverage includes nested loops for multi-dimensional data processing, such as analyzing correlation matrices, processing multi-asset portfolios, and conducting grid-based parameter optimization in model calibration exercises.
A key focus addresses Python comprehensions—list, set, and dictionary comprehensions—which provide elegant, performant alternatives to traditional loops for data transformation and filtering operations. Participants master comprehension syntax for tasks such as filtering securities by criteria, transforming price data, calculating returns across portfolios, extracting options by strike ranges, and building lookup dictionaries from market data. This functional programming approach enhances code readability and execution speed, aligning with professional quantitative development practices.
The module dedicates substantial coverage to exception handling, a critical capability for building resilient financial applications that gracefully manage data quality issues, API failures, calculation errors, and unexpected market conditions. Participants learn the TRY-EXCEPT-FINALLY pattern for catching and handling exceptions, understanding Python's exception hierarchy, and how to catch specific versus general exceptions appropriately. Advanced topics include raising custom exceptions with RAISE statements, creating domain-specific exception classes for financial applications (e.g., InsufficientDataException, CalibrationFailureException), implementing comprehensive error logging for debugging and audit trails, and using context managers with WITH statements for reliable resource management in file operations and database connections.
Through practical exercises, participants build robust data processing pipelines that validate inputs, handle missing market data, manage API timeouts, log errors with actionable diagnostics, and implement fallback mechanisms—developing the defensive programming mindset essential for production quantitative systems.
Module 1.3: Object-Oriented Programming Concepts for Advanced Programming
This module introduces object-oriented programming (OOP) paradigms essential for building scalable, maintainable quantitative finance applications and model libraries. Participants develop expertise in designing and implementing class-based architectures that encapsulate financial instruments, portfolio structures, pricing models, and risk analytics—moving beyond procedural scripting to professional software engineering practices used in institutional quantitative development teams and production trading systems.
Learning Outcomes:
Introduction to Basic Concepts– Classes, Objects, Attributes, and Methods
Inheritance, Polymorphism, Encapsulation, and Abstraction
Magic Methods and Operator Overloading
The module begins with fundamental OOP concepts, including classes, objects, attributes, and methods, establishing the blueprint-instance relationship central to object-oriented design. Participants learn to model financial entities as classes—such as Bond, Option, Portfolio, and RiskModel classes—defining their properties (attributes like notional, maturity, strike price, positions) and behaviors (methods like calculate_price, compute_greeks, aggregate_risk). Through hands-on implementation, learners develop intuition for when to use class-level versus instance-level attributes, understand the role of constructors (init methods) for object initialization with market data or instrument parameters, and master the self parameter for accessing object state within methods.
The curriculum advances to the four pillars of OOP: inheritance, polymorphism, encapsulation, and abstraction. Participants implement inheritance hierarchies where specialized instrument classes (EquityOption, InterestRateSwap, TreasuryBond) inherit common functionality from base classes (Derivative, FixedIncomeInstrument, Security), promoting code reuse and maintaining consistent interfaces across instrument types. Polymorphism is explored through method overriding, enabling different instrument classes to provide specialized implementations of common methods like calculate_pv or compute_sensitivity while maintaining uniform calling conventions—critical for building flexible pricing engines that handle diverse product types through common interfaces.
Encapsulation principles teach participants to protect internal object state using private and protected attributes (naming conventions with leading underscores), exposing controlled access through property decorators and getter/setter methods. This approach ensures data integrity in financial objects, validates inputs (e.g., ensuring positive notionals, valid date ranges), and maintains invariants critical for model correctness. Abstraction is addressed through abstract base classes (ABC module) that define contracts for derived classes—for instance, requiring all instrument classes to implement specific pricing or risk methods—enforcing architectural standards across model libraries.
The module culminates in advanced OOP techniques, including magic methods (dunder methods) and operator overloading, enabling natural syntax for financial calculations. Participants implement repr and str methods for readable object representations in debugging and logging, eq and lt for comparing instruments by various criteria, add and mul for portfolio aggregation and position scaling, and len and getitem for making portfolio objects behave like collections. These techniques enable elegant, Pythonic code such as portfolio1 + portfolio2 for combining positions or option * 100 for scaling notional—enhancing code readability while maintaining type safety.
Through progressive project work, participants build increasingly sophisticated class hierarchies such as a complete derivatives pricing framework with a base Derivative class, specialized option types (European, American, Asian), pricing engine classes using different models (Black-Scholes, Monte Carlo, Binomial Tree), and portfolio classes that aggregate positions and compute risk metrics.
Module 1.4: Data Analytics, Process Automation, and Multi-Core Processing
This comprehensive module transforms participants into proficient quantitative developers capable of building production-grade data pipelines, automated analytics workflows, and high-performance computing applications essential for institutional finance operations. The module integrates three critical domains: data analytics using Pandas and NumPy for financial dataset manipulation, process automation for operational efficiency and real-time market data integration, and parallel processing techniques for computationally intensive risk calculations and Monte Carlo simulations used at scale in investment banks and asset managers.
Learning Outcomes:
Introduction to Pandas – Series and DataFrame Objects
Reading and Writing Data – CSV, Excel, JSON, and SQL Databases
Data Inspection and Exploration
Data Operations and Transformation
Introduction to NumPy Arrays
NumPy Operations – Element-Wise Operations and Broadcasting
Mathematical Functions – Exponential, Logarithmic, Trigonometric Functions
Automation of Repetitive Tasks Using Python
PDF Generation
Web Scraping Integrated Market Data – BeautifulSoup and Requests
API Integration Integrated Fetching Real-Time Market Data
Scheduling Scripts
Introduction to Parallel Processing – Multiprocessing vs Multithreading
Multiprocessing Module – Pool and Process Classes
Parallel Execution
Threading for I/O-Bound Operations
Numba for Just-In-Time (JIT) Compilation
Dask for Large-Scale Data Processing Beyond Memory Limits
Data Analytics with Pandas and NumPy
The module begins with Pandas, the industry-standard library for financial time-series analysis and tabular data manipulation. Participants master Series and DataFrame objects as fundamental structures for organizing market data, portfolio positions, and analytics results. Comprehensive coverage includes reading and writing data across multiple formats—CSV files for market data exports, Excel spreadsheets for reporting and integration with legacy systems, JSON for API responses, and SQL databases for enterprise data warehouses. Participants develop robust data inspection and exploration workflows, including summary statistics, missing data detection, data type validation, and quality checks critical for ensuring model input integrity.
Advanced Pandas operations cover data transformation techniques essential for financial analytics: filtering securities by criteria, sorting portfolios by risk metrics, grouping instruments by sector or rating for attribution analysis, merging market data with reference data using various join types, pivoting data for multi-dimensional analysis, and applying custom functions across time series for rolling calculations. Participants implement practical applications such as portfolio return attribution, factor exposure analysis, correlation matrices, and performance metrics calculation—building the data wrangling expertise required for quantitative research and risk reporting functions.
The NumPy component establishes foundations for numerical computing with n-dimensional arrays optimized for vectorized operations. Participants learn array creation, indexing, slicing, and broadcasting mechanics that enable efficient element-wise operations on large datasets without explicit loops. Coverage includes mathematical functions (exponential, logarithmic, trigonometric) applied to financial calculations such as continuously compounded returns, option pricing formulas, and yield calculations. This vectorization paradigm dramatically accelerates computations, reducing execution time from minutes to seconds for portfolio-scale analytics—a critical optimization for real-time risk systems.
Process Automation and Market Data Integration
The automation component addresses the operational imperative of reducing manual workflows and enabling real-time analytics. Participants learn to automate repetitive tasks, including report generation, data extraction, and transformation pipelines, file processing workflows, and scheduled analytics refreshes. Practical applications include PDF generation using ReportLab for client reports and risk dashboards, automated email distribution of analytics results, and file system operations for organizing market data archives and model outputs.
A key focus addresses web scraping and API integration for acquiring real-time and historical market data. Participants master the BeautifulSoup and Requests libraries for extracting data from financial websites, understanding HTML structure navigation, handling pagination, and implementing robust error handling for unreliable data sources. API integration coverage includes RESTful API consumption for fetching real-time equity prices, foreign exchange rates, interest rate data, and economic indicators from providers such as Alpha Vantage, Yahoo Finance, FRED, and Bloomberg-compatible endpoints. Participants implement authentication mechanisms, rate limit handling, response parsing, and data validation—building production-ready data acquisition systems.
Script scheduling techniques enable participants to automate recurring tasks using cron jobs on Linux systems, Task Scheduler on Windows, or Python-based scheduling libraries like schedule and APScheduler. Applications include daily portfolio valuation runs, end-of-day risk report generation, overnight model calibration jobs, and continuous monitoring systems that alert on market events or limit breaches—mirroring the automated workflows that enable 24/7 operations at global financial institutions.
Parallel Processing and High-Performance Computing
The final component addresses computational performance optimization essential for large-scale risk analytics, Monte Carlo simulations, and portfolio optimization. Participants learn to distinguish between CPU-bound tasks suited for multiprocessing and I/O-bound tasks appropriate for multithreading, understanding Python's Global Interpreter Lock (GIL) and its implications for parallel execution strategies.
The multiprocessing module coverage includes Pool and Process classes for distributing computations across CPU cores. Participants implement parallel Monte Carlo simulations for option pricing and VaR calculations, parallel portfolio optimization across multiple scenarios, and parallel sensitivity calculations (Greeks) for derivatives portfolios—achieving linear or near-linear speedup proportional to available cores. Practical exercises demonstrate workload partitioning, result aggregation, and memory management considerations for large-scale parallel computations.
Threading techniques address I/O-bound operations such as concurrent API requests for fetching multiple securities' market data, parallel database queries, and simultaneous file processing. Participants understand thread synchronization, race conditions, and when threading provides performance benefits versus introducing complexity without gains.
Advanced performance optimization introduces Numba for just-in-time (JIT) compilation of Python functions to machine code, enabling C-like performance for numerical loops without leaving Python. Participants apply Numba decorators to financial calculations such as option pricing loops, risk aggregation algorithms, and Monte Carlo path generation—often achieving 10-100x speedups with minimal code changes. The module concludes with Dask for big data processing beyond memory limits, enabling participants to handle datasets larger than available RAM through intelligent chunking, lazy evaluation, and distributed computing paradigms—critical for processing years of tick data or running large-scale backtests across thousands of securities.
Through integrated project work, participants build complete systems such as: (1) automated daily portfolio risk reporting with parallel VaR calculations and PDF generation, (2) real-time market data ingestion pipelines with API integration and scheduled refreshes, (3) high-performance Monte Carlo engines using multiprocessing and Numba optimization, and (4) large-scale historical analysis workflows using Dask for processing multi-year datasets. These projects develop the end-to-end capabilities required for quantitative development roles.
Module 1.5: Python Integrated Mathematics, Statistics, and Finance
This integrative module bridges computational Python skills with mathematical and statistical foundations essential for quantitative finance applications, establishing participants' capability to implement rigorous analytical models using industry-standard scientific computing libraries. The module focuses on NumPy and SciPy ecosystems that power institutional quantitative research, risk analytics, and derivatives pricing systems—enabling participants to translate mathematical theory into production-grade implementations for portfolio optimization, option pricing, risk measurement, and statistical modeling.
NumPy for Mathematical Computing and Linear Algebra
The module begins with advanced NumPy capabilities for linear algebra operations fundamental to quantitative finance. Participants master matrix and vector computations, including dot products for portfolio variance calculations, matrix multiplication for factor model applications, matrix inversion for solving systems of linear equations in regression and optimization, eigenvalue decomposition for principal component analysis (PCA) of yield curves and equity returns, and Cholesky decomposition for generating correlated random variables in Monte Carlo simulations. These operations form the computational backbone of Modern Portfolio Theory, multi-factor risk models, and covariance matrix estimation used throughout institutional asset management.
Vectorization techniques receive substantial emphasis as the paradigm shift from scalar loop-based computation to array-oriented operations. Participants learn to eliminate explicit Python loops by expressing calculations as NumPy array operations, achieving 10-100x performance improvements critical for real-time risk analytics and large-scale portfolio calculations. Applications include vectorized return calculations across thousands of securities, efficient Greeks computation for derivatives portfolios, and broadcast operations for scenario analysis across multi-dimensional arrays representing portfolios, scenarios, and time steps.
Random Number Generation and Probability Distributions
Random number generation capabilities establish the foundation for Monte Carlo methods pervasive in quantitative finance. Participants master NumPy's random module for generating samples from uniform, normal (Gaussian), lognormal, and other distributions essential for simulating asset price paths, modeling interest rate dynamics, and conducting scenario analysis. Applications include generating correlated asset returns using Cholesky decomposition of covariance matrices, simulating geometric Brownian motion for equity price paths, and implementing variance reduction techniques such as antithetic variates and control variates to enhance Monte Carlo convergence rates.
SciPy Ecosystem for Advanced Scientific Computing
The SciPy component introduces a comprehensive scientific computing ecosystem built atop NumPy, providing specialized modules for optimization, integration, interpolation, and statistical analysis. Participants explore SciPy's architecture and learn to navigate its extensive module structure for selecting appropriate tools for specific quantitative finance problems.
A central focus addresses probability distribution objects through the scipy.stats module, which provides unified interfaces to dozens of continuous and discrete distributions. Participants master working with distribution objects to compute probability density functions (PDF) for evaluating likelihood in maximum likelihood estimation, cumulative distribution functions (CDF) for calculating probabilities and Value-at-Risk, percent point functions (PPF/inverse CDF) for generating quantiles and confidence intervals, and random sampling methods for Monte Carlo simulations. Applications include fitting distributions to historical return data, conducting goodness-of-fit tests, calculating risk metrics at specified confidence levels, and implementing parametric VaR models using normal and Student's t-distributions.
Advanced topics cover essential SciPy modules for quantitative applications: scipy.optimize for portfolio optimization using constrained minimization (efficient frontier construction, risk parity), option implied volatility solving, and yield curve fitting through non-linear least squares; scipy.integrate for numerical integration in option pricing (Heston model, jump-diffusion models) and fixed-income analytics; scipy.interpolate for yield curve construction and volatility surface interpolation; and scipy.linalg for advanced linear algebra operations, including singular value decomposition (SVD) for dimensionality reduction and robust matrix computations.
Integration with Financial Applications
Throughout the module, participants implement complete quantitative finance applications that synthesize mathematical concepts with Python libraries. Projects include: (1) portfolio optimization using NumPy linear algebra for mean-variance calculations and SciPy optimization for constrained efficient frontier construction, (2) Monte Carlo option pricing engines using NumPy random number generation and vectorized payoff calculations with variance reduction techniques, (3) parametric VaR models using SciPy distribution fitting and quantile calculations with backtesting frameworks, (4) correlation matrix analysis using eigenvalue decomposition for risk factor identification and portfolio risk attribution, and (5) maximum likelihood estimation for distribution parameter fitting with goodness-of-fit testing using SciPy statistical functions.
The module emphasizes computational efficiency, numerical stability, and best practices for scientific computing in finance. Participants learn to avoid common pitfalls such as unnecessary copying of large arrays, inappropriate use of loops where vectorization applies, numerical instability in matrix operations, and inefficient random number generation patterns. Code profiling techniques identify performance bottlenecks, enabling participants to optimize critical paths in computational workflows—skills essential for developing production quantitative systems that process large portfolios and execute complex calculations within strict latency requirements.
By integrating mathematical rigor with computational implementation, this module equips participants with the scientific computing foundation required for advanced quantitative finance applications covered in subsequent subjects, including derivatives pricing, portfolio optimization, VaR modeling, and statistical risk analytics.
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