Why Python?
About 15% of Investment Banking Jobs surveyed now require or prefer candidates to know Python. By the end of the year, that number will be 25%. By the end of 2026, it will be over 60%.
Identified as a "critical core skill requirement" by JP Morgan
Portfolio Optimization
Credit Risk Reporting
DCF Automation
Optimize client portfolios by integrating Python with Bloomberg for data extraction to then clean and process the data. Apply machine learning algorithms for optimal allocation, and stress-test portfolios under varying market conditions. Results are exported to Power BI for high-level visualization and reporting to stakeholders. Python integration into this workflow saves 20-25 hours per analysis and improves allocation accuracy by 30-40%.
Extract financial and bond data from Bloomberg and automatically calculate key financial ratios like debt-to-equity and interest coverage. Machine learning models predict default probabilities. The models are validated for accuracy and risk scores and probability metrics are exported to Excel for presentation. Save approximately 15 hours while improving the accuracy of your forecasts by 25% with this Python integration.
Create DCF models by integrating Python with Bloomberg for data extraction and Excel for input adjustments. Python calculates cash flows, present value, and terminal values while automating sensitivity and scenario analsyes. Results are exported to Excel for granular review and Power BI for dynamic visualizations. By automating these processes, the workflow saves 15-20 hours per model and improves valuation accuracy by 25-30%.
Your Investment is Risk Free.
We offer a 100%, no questions asked, 30 day money-back guarantee to completely de-risk your investment. Enroll now, and if for any reason you aren't completely satisfied, send us an email and you'll get a full refund for up to one month after enrollment.
Unsure if Python, or PyFi is right for you?
Hear from PyFi students. Be Certain in your decision.
Meet Zach Washam
EX Wells Fargo
PyFi Founder & Head of Instruction
While learning Python as an investment banker, Zach made an interesting observation: Python programming had a lot in common with the Excel models he made at work. By thinking of Python like Excel, Zach quickly learned the coding language and invented Wells Fargo Securities' first machine learning algorithm for investment banking and capital markets.
After submitting two algorithms for patent protection and winning Wells Fargo's 2018 "Local Sphere Innovation Award," Zach left investment banking to launch PyFi.
Now, Zach’s courses have been delivered to over ten thousand finance professionals and top banks around the world.
Learn the same algorithms that were used to advise the following companies:
*Slight changes to the code made to protect IP
PyFi's Unique Approach
Why banks like JP Morgan use PyFi
Immersion
The best way to learn a language is to immerse yourself in the environment where that language is spoken. The same is true for computer programming languages. Get setup in your environment
Relativity
Coding is just a series of logical operations. You're already an expert here because you write logical operations everyday in Excel. By teaching through Excel analogus, you'll learn faster and retain more of what you learn.
Application
We focus on finance concepts because that's what you need to undersand as a finance professional. You'll learn the most relevant techniques and libraries, write your first algorithm, and walk away with the same algorithms used to advise Fortune 500 companies like Tiffanies, McDonalds, Macys, Gap, and more.
Objectives
Learn how to apply Real-World Algorithms in finance.
Python Fundamentals
Applied Machine Learning
Become familiar and comfortable with coding envrionments
- Use Jupyter Notebook to write and execute code
- Perform calculations
- Generate outputs with static text and dynamic values
- Create and manipulate variables
Identify common types of Python objects
- Create and manipulate important data structures
- Lists, Tuples, Sets, and Dictionaries
Use python arguments
- Create custom functions
- Repeat tasks through iterable objects using for loops
- Incorporate conditional logic using if statements
Become familiar with Python libraries starting with NumPy
- Create and manipulate NumPy arrays, a new type of Python object
- Perform special NumPy array math and aggregation functions
- Leverage NumPy's useful randomization tools
Become familiar with the Pandas library
- Create and manipulate Pandas DataFrames
- Create and manipulate Pandas Series
- Filter data using boolean masks
- Segment and summarize data by class with groupbys
Identify overfit regression models
Master the skill of distinguishing overfit models that promise more than they deliver. By understanding the nuances of overfitting, you will be equipped to refine your models to ensure they perform reliably in real-world financial scenarios, enhancing their predictability and your confidence in their forecasts.
Compare different Machine Learning models
Dive deep into the world of regularized regression and decision tree ensemble algorithms. This course highlights the strengths and limitations of each, guiding you to make informed decisions on which algorithm to deploy for your financial modeling tasks, ensuring optimal balance and performance.
Explain the confusion matrix and its relation to the ROC curve
Gain clarity on evaluating model performance with an in-depth understanding of the confusion matrix and its critical role in financial predictions. Learn how it intertwines with the ROC curve to give you a comprehensive view of your model's accuracy, allowing you to enhance predictive models with precision.
Construct training data sets, testing data sets, and model pipelines
Learn to construct robust training and testing datasets and streamline your model development process with efficient pipelines. This skill set is crucial for developing models that are not only accurate but also scalable and adaptable to the fast-paced changes in the finance industry.
Perform advanced data cleaning, exploration, and visualization
Elevate your data preparation skills with advanced techniques in cleaning, exploring, and visualizing financial datasets. These foundational steps are key to uncovering insights and trends that drive successful machine learning models, setting the stage for impactful financial analysis and predictions.
Engineer features based on conditional relationships between existing features
Unlock the potential of your financial datasets by learning how to engineer features that reveal deeper insights. Understand the conditional relationships between features to create new, powerful predictors that enhance the performance of your machine learning models in predicting market movements and trends.
Build and finalize a machine learning classifier, and so much more…
Bring your financial prediction models across the finish line by mastering the art of building and finalizing machine learning classifiers. This course empowers you to not only construct but also fine-tune classifiers that stand up to the rigors of the finance world, ensuring your models are both accurate and resilient under market pressures.
Curriculum
Sample Lessons Below Each Tab
Both Python Fundamentals and Applied Machine Learning are approximately 3 hours in length each. Your enrollment comes with lifetime access and support within the course via a comment section under each video lesson.
Python Fundamentals
Applied Machine Learning
Introduction to Python
Setting Up Your Coding Environment:
We will walk you through setting up your coding environment step-by-step, including downloading the Anaconda distribution system and getting familiar with Jupyter Notebooks.
Performing Calculations:
You will learn how to use Python to perform basic mathematical calculations.
Generating Outputs with Static Text and Dynamic Values:
You will learn to incorporate text into your Python code and generate outputs with dynamic values.
- Module Files Zip: PF 01 Jupyter Notebook
- PF 01.01 Course Introduction
- PF 01.02 Downloading Anaconda (Updated 11.07.23)
- PF 01.03 Introducing Jupyter Notebook
- PF 01.04 Calculations
- PF 01.05 Exercise
- PF 01.05 Solution
- PF 01.06 Mathematical Operators
- PF 01.07 Text Outputs
- PF 01.08 Dynamic Outputs
- PF 01.09 Exercises
- PF 01.09 Solution
- PF 01.10 Variables
- PF 01.11 Exercise
- PF 01.11 Solution
- PF 01.12 Review
Python Objects
Python Objects:
Python is an object-oriented programming language, which means that Python code is organized around objects, or pieces of data that interact with each other based on instructions written in the code.
Data Structures:
In this section, you will learn about data structures - types of Python objects that help you to organize other objects. Examples include lists, tuples, sets, and dictionaries.
- Module Files Zip: Jupyter Notebook
- PF 02.01 Python Object Types
- PF 02.02 Exercise
- PF 02.02 Solution
- PF 02.03 Lists
- PF 02.04 Accessing List Objects
- PF 02.05 Exercise
- PF 02.05 Solution
- PF 02.06 Changing List Objects
- PF 02.07 More List Functions
- PF 02.08 Exercise
- PF 02.08 Solution
- PF 02.09 Tuples
- PF 02.10 Sets
- PF 02.11 Using Sets to Remove Duplicates
- PF 02.12 Set Operations
- PF 02.13 Exercise
- PF 02.13 Solution
- PF 02.14 Dictionaries
- PF 02.15 Accessing Dictionary Items
- PF 02.16 Exercise
- PF 02.16 Solution
- PF 02.17 Dictionary Functions
- PF 02.18 Exercise
- PF 02.18 Solution
- PF 02.19 Review
Custom Functions
Creating Custom Functions:
In addition to Python's built-in functions (similar to Excel's built-in functions), you can create custom functions that allow you to package and reuse your own custom code.
Repeating Tasks through Iterable Objects:
Learn how to automate tedious tasks by repeating actions through iterable objects using variables and For Loops.
Incorporating Conditional Logic:
Write more complex and useful applications that evaluate and respond to conditions that you define.
- PF.03 Module Files: Jupyter Notebook
- PF 03.01 Creating Custom Functions
- PF 03.02 Exercise
- PF 03.02 Solution
- PF 03.03 Adding Arguments
- PF 03.04 For Loops
- PF 03.05 Exercise
- PF 03.05 Solution
- PF 03.06 Filing a List For Loop
- PF 03.07 Exercise
- PF 03.07 Solution
- PF 03.08 Conditional Logic
- PF 03.09 Exercise
- PF 03.09 Solution
- PF 03.10 Review
Numpy
Importing Open-Source Packages:
Instead of building everything on your own, learn to leverage the work of others by importing third-party packages.
NumPy Arrays:
Get familiar with creating and manipulating a powerful new type of Python object: The NumPy Array.
Powerful Statistical Tools:
Use NumPy's powerful statistical functions to quickly and easily analyze large quantities of data.
- PF 04. Module Files: Juptyer Notebook
- PF 04.01 Importing Libraries
- PF 04.02 NumPy
- PF 04.03 NumPy Arrays
- PF 04.04 Multidimensional Arrays
- PF 04.05 Exercise
- PF 04.05 Solution
- PF 04.06 Reshape and Transpose
- PF 04.07 Selecting Objects
- PF 04.08 Exercise
- PF 04.08 Solution
- PF 04.09 Array Calculations
- PF 04.10 Array Functions
- PF 04.11 Exercise
- PF 04.11 Solution
- PF 04.12 The Random Module
- PF 04.13 Setting a Random Seed
- PF 04.14 Random Sampling with .choice()
- PF 04.15 Generating Sequences
- PF 04.16 Exercise PF 04.16 Solution
- PF 04.17 Review
Pandas
Two New Python Objects:
Pandas provides you with two new Python objects, the DataFrame and the Series, which you can use to import and manipulate data from your Excel files.
Filtering Data with Boolean Masks:
Use the Boolean object type to create indicator variables, test conditions, and filter your data.
Segmenting with Groupby:
Segment and summarize your data across categories using the useful .groupby() function.
- Module Zip Files: Juptyer Notebook, Stock Data
- PF 05.01 Pandas
- PF 05.02 Importing Data
- PF 05.03 Import From .csv
- PF 05.04 Get to Know Your Dataframe
- PF 05.05 Exercise
- PF 05.05 Solution
- PF 05.06 Summary Statistics
- PF 05.07 Exercise
- PF 05.07 Solution
- PF 05.08 Series
- PF 05.09 Series Functions
- PF 05.10 Feature Engineering
- PF 05.11 Exercise
- PF 05.11 Solution
- PF 05.12 Boolean Masks
- PF 05.13 Indicator Variables
- PF 05.14 Exercise
- PF 05.14 Solution
- PF 05.15 Segmenting with .groupby()
- PF 05.16 Exercise
- PF 05.16 Solution
- PF 05.17 Review
Certification Exam
Verify Your Learning:
Put your new Python skills to the test to see what you have learned and ensure retention.
Boost Your Resume:
Add an impressive certification to your resume to demonstrate your new skills to employers.
Complete the Prerequisite to Applied Machine Learning:
After completing Python Fundamentals, you will have the necessary prerequisite knowledge to dive into more advanced topics like Applied Machine Learning.
- Jupyter Notebook (Exam Exercises)
Data Cleaning & Exploration
Your machine learning models can only be as good as your training data. In this lesson, you will learn how to find and correct errors in your datasets using the data visualization packages Matplotlib and Seaborn.
- Module Zip Files : Jupyter Notebook, Stock Data v2
- ML 01.01 The ML Process
- ML 01.02 Matplotlib and Seaborn
- ML 01.03 A Few Quick Notes
- ML 01.04 Exercise
- ML 01.04 Solution
- ML 01.05 .countplot()
- ML 01.06 Exercise
- ML 01.06 Solution
- ML 01.07 Replace & Sparse Classes
- ML 01.08 Exercise
- ML 01.08 Solution
- ML 01.09 Exercise
- ML 01.09 Solution
- ML 01.10 Spotting Outlier
- ML 01.11 Exercise
- ML 01.11 Solution
- ML 01.12 Exercise
- ML 01.12 Solution
- ML 01.13 Exercise
- ML 01.13 Solution
- ML 01.14 Exercise
- ML 01.14 Solution
- ML 01.15 Exercise
- ML 01.15 Solution
- ML 01.16 NaN Object
- ML 01.17 Exercise
- ML 01.17 Solution
- ML 01.18 Dropping Null Values
- ML 01.19 Exercise
- ML 01.19 Solution
- ML 01.20 Box Plots
- ML 01.21 Exercise
- ML 01.21 Solution
- ML 01.22 Saving Your Dataframe
- ML 01.23 Review
Regression Algorithms
Learn about regression problems and 5 machine learning algorithms used to solve them. Those models are Lasso, Ridge, and Elastic Net, which fall under the category of Regularized Regression Models. The other models you will learn are Random Forest and Gradient Boosting which fall under the category of Decision Tree Ensemble Models. You will also learn about overfitting and the tools at your disposal to prevent it.
- ML 01.02 What are Regression Algorithms
- ML 02.02 Real Relationships and Overfitting
- ML 02.03 Regularizations
- ML 02.04 Decision Tree Ensemble Methods
Liquidity Regressor Case Study
Build your first machine learning model from start to finish. The model you will construct is an altered version of a real machine learning algorithm used by top investment banks to advise companies such as: HP, McDonalds, Nordstrom, and many more.
- Module ZipFiles: Liquidity_client.csv, Liqudiity_data.csv, ML 03 Jupyter Notebook
- ML 03.01 Case Study Overview
- ML 03.02 Exercise
- ML 03.02 Solution
- ML 03.03 Metadata
- ML 03.04 Exercise
- ML 03.04 Solution
- ML 03.05 Splitting Your Data
- ML 03.06 Exercise
- ML 03.06 Solution
- ML 03.07 train_test_split()
- ML 03.08 Unpacking Lists
- ML 03.09 Exercise
- ML 03.09 Solution
- ML 03.10 Progress Checkpoint
- ML 03.11 Model Pipelines
- ML 03.12 Exercise
- ML 03.12 Solution
- ML 03.13 Progress Checkpoint
- ML 03.14 Hyperparameter Tuning
- ML 03.15 Exercise
- ML 03.15 Solution
- ML 03.16 Exercise
- ML 03.16 Solution
- ML 03.17 Aggregating Hyperparameter Grids
- ML 03.18 Progress Checkpoints
- ML 03.19 Cross Validation
- ML 03.20 Creating Untrained Models
- ML 03.21 Exercise
- ML 03.21 Solution
- ML 03.22 Training and Tuning Models
- ML 03.23 Exercise
- ML 03.23 Solution
- ML 03.24 Model Evaluation
- ML 03.25 Exercise
- ML 03.25 Solution
- ML 03.26 Progress Checkpoint
- ML 03.27 Visualizing Model Predictions
- ML 03.28 Exercise
- ML 03.28 Solution
- ML 03.29 Using Your Model
- ML 03.30 Review
Classification Algorithms
Learn about classification problems and some machine learning algorithms used to solve them.
By the end of this lesson, you will be familiar with
▪ Characteristics of Binary Classification Problems
▪ Regularized Logistic Regression Models
▪ Decision Tree Ensemble Classification Models
- ML 04.01 Binary Classification
- ML 04.02 Logistic Regression
- ML 04.03 Decision Tree Classifier
Investor Classifier Case Study Pt I
Explore the data for your second machine learning model. The model you will construct is an altered version of a real machine learning algorithm used by top investment banks to predict investor behavior when raising capital for large corporate clients.
- ML 05 Module Zip File: Investor_data.csv, Jupyter Notebook
- ML 05.01 Case Study Overview
- ML 05.02 Exercise
- ML 05.02 Solution
- ML 05.03 Metadata
- ML 05.04 Exercise
- ML 05.04 Solution
- ML 05.05 One Error
- ML 05.06 Exercise
- ML 05.06 Solution
- ML 05.07 Countplot of Investors
- ML 05.08 Exercise
- ML 05.08 Solution
- ML 05.09 Exploring Relationships
- ML 05.10 Exercise
- ML 05.10 Solution
- ML 05.11 Reviewing Your Results
- ML 05.12 Feature Engineering
- ML 05.13 Exercise
- ML 05.14 Solution
- ML 05.14 Reviewing Tier Change
- ML 05.15 Controlling for Demotions
- ML 05.16 Exercise
- ML 05.16 Solution
- ML 05.17 Analyzing Goldman Sachs
- ML 05.18 Exercise
- ML 05.18 Solution
- ML 05.19 Implot()
- ML 05.20 Exercise
- ML 05.20 Solution
- ML 05.21 Review
Investor Classifier Cast Study Pt II
In this lesson, you will complete your second machine learning model. The model you will construct is an altered version of a real machine learning algorithm used by top investment banks to predict investor behavior when raising capital for large corporate clients.
- ML 06 Module Zip File: Investor_data2.csv, Jupyter Notebook
- ML 06.01 Import Packages and Data
- ML 06.02 Exercise
- ML 06.02 Solution
- ML 06.03 Dummy Variables ML 06.04 Exercise
- ML 06.04 Solution
- ML 06.05 Remove Redundant Target
- ML 06.06 Splitting Data
- ML 06.07 Exercise
- ML 06.07 Solution
- ML 06.08 Model Pipeline
- ML 06.09 Exercise
- ML 06.09 Solution
- ML 06.10 Validating Pipelines
- ML 06.11 Hyperparameters
- ML 06.12 Exercise ML 06.12 Solution
- ML 06.13 Validating Hyperparameter Grids
- ML 06.14 Cross Validation
- ML 06.15 Exercise
- ML 06.15 Solution
- ML 06.16 Fitting Untrainer Models
- ML 06.17 Exercise
- ML 06.17 Solution
- ML 06.18 AUROC
- ML 06.19 Confusion Matrix
- ML 06.20 Perfect AUROC
- ML 06.21 Calculating AUROC
- ML 06.22 Exercise
- ML 06.22 Solution
- ML 06.23 Review
Professional Certification Exam
Verify Your Learning: Put your new Machine Learning skills to the test to see what you have learned and ensure retention.
Boost Your Resume: Add an impressive certification to your resume to demonstrate your new skills to employers.
- Jupyter Notebook (Exam Exercises)
Completion Certificates
Prove your knowledge by completing PyFi's rigorous certification exams.
This program is perfect for...
*These banks have used PyFi training programs to introduce their employees to Python programming and its application in the world of finance.
How Can We Help You?
What makes PyFi's system unique?
PyFi's training is custom-tailored for finance professionals with minimal to no coding experience, and time constraints
We teach Python by building off your existing Excel skill set and focus on concepts relevant to you. We're not building rocket ships, we're teaching you how to work through finance workflows and develop systems for efficiency and scale.
With instructor support, and the most relevant concepts, PyFi is the market's best choice for someone in finance, who has decided to learn Python.
How will learning Python help my finance career?
Python is customizable and works with your current tech stack, and your next positions tech stack.
Because its free to use, and the most popular coding language in finance, the long tail effects of learning Python are only going to compound the return on your investment.
In the immediate term, you can plug into your current workflows involving Excel, BI tools, data providers, etc., to eliminate redundant steps while also improving the accuracy of your financial models.
With strong version control, you can build repeatable process into your team workflows that make not just you, but your whole group more efficient and really shining as a critical strategic leader
Imagine increasing team productivity by 30, or 50%, and doubling your models predictive power.
What does driving those results, for your firm, do for your career
Whats included in the PyFi Certification System?
The program includes two self study courses, Python Fundamentals, and Applied Machine Learning. You will also receive access to The Machine Learning Edge Blueprint, Vol I and Vol II which are the PDF versions of each course.
In the course you'll receive access to two working algorithms, "Investor Classifier" and "Liquidity Regressor" which you'll be able to use to run your data through and immediately access superior analytics when compared to Excel's legacy functions
The program also includes instructor support.
At the end of each course, you'll be invited to take a rigorous certification exam to test your knowledge and ensure retention.
How long does it take?
Both Python Fundamentals and Applied Machine Learning are approximately 3 hours in length each. Your enrollment comes with lifetime access and support within the course via a comment section under each video lesson.
What if I change my mind?
PyFi offers a no questions asked, 30 day money-back guarantee. If you aren't completely satisfied with PyFi's Certification System, send us an email and we will refund your money up to one month after you enroll.
How can we afford to do this? Because fewer than 1% of our customers ask for refunds. PyFi provides the best Python training on the market for time-poor seasoned finance professionals, and we know you are going to love it.
Do I need to work from a specific OS?
No. You can work through PyFi's training content from any OS.
Have any questions? We’re ready to help!
