*These banks have used one or both of PyFi’s self-study courses to introduce their financiers to Python programming and its application in the world of finance.
Why is Python important in Sales & Trading?
Dominant companies like Amazon, Toyota, JP Morgan, Goldman Sachs, Blackstone, Blackrock, AQR, etc.are increasingly preferring or requiring their finance professionals to know Python. This cost effective and accessible tool gives users the level of control needed to remove or significantly reduce redundancy in their workflows while also increasing the accuracy of their predictive models giving one employee, the output of many.
Example 1
Example 2
Example 3
Python scripts integrate SAP Analytics Cloud with Tableau by leveraging the SAP Analytics Cloud OData API. Python fetches real-time financial data, cleans it, and sends it to Tableau for visualization. This integration reduces reporting cycle time by an estimated 40% while ensuring accurate and up-to-date dashboards.
Automating credit risk analysis begins by extracting financial and bond data from Bloomberg Terminal, which is exported into Python using API calls. Python performs feature engineering on the data, such as calculating key financial ratios and metrics like debt-to-equity and interest coverage. These features are then fed into machine learning models (e.g., using scikit-learn or XGBoost) to predict default probabilities. The trained models are validated in Python, ensuring robust and accurate outputs, before exporting the results, including risk scores and probability metrics, back into Excel for presentation. Additionally, Python-generated visualizations (e.g., risk distribution plots) can enhance the reports or feed into dashboards, providing actionable insights alongside the raw analysis. This integration eliminates manual calculations, enhances model accuracy, and streamlines the reporting process. This integration saves an estimated 15 hours per model while increasing accuracy by 25%
Automating credit risk analysis begins by extracting financial and bond data from Bloomberg Terminal, which is exported into Python using API calls. Python performs feature engineering on the data, such as calculating key financial ratios and metrics like debt-to-equity and interest coverage. These features are then fed into machine learning models (e.g., using scikit-learn or XGBoost) to predict default probabilities. The trained models are validated in Python, ensuring robust and accurate outputs, before exporting the results, including risk scores and probability metrics, back into Excel for presentation. Additionally, Python-generated visualizations (e.g., risk distribution plots) can enhance the reports or feed into dashboards, providing actionable insights alongside the raw analysis. This integration eliminates manual calculations, enhances model accuracy, and streamlines the reporting process. This integration saves an estimated 15 hours per model while increasing accuracy by 25%
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PyFi's Unique Approach
Why Top Banks In North America Use PyFi
Python Fundamentals
Applied Machine Learning
Orientation
Get setup in your coding environment and start writing your first lines of code in under 30 minutes. Setup your environment and quickly understand how to command basic functions that you’ll build on throughout the course.
Build on your existing foundation with excel analogs
By relating lessons and techniques to concepts finance professionals are already familiar with, PyFi courses deliver higher retention rates and a more efficient learning process. You are already a logical thinker and problem solver which is demonstrated by your work in Excel. PyFi builds off this foundation.
Practical application
The Python programming language is an immense topic to cover. There are many things you can learn and even more ways you can apply Python which are not relevant to you as a finance professional. PyFi teaches you what you need to know to be effective in your work without needing to pursue a formal computer science degree.
Apply proven solutions to your work
Because Python is open source, you’re able to apply working libraries, or frameworks, to your work with simple commands. Imagine downloading sophisticated financial models you could simply plug your data into. That’s exactly what’s possible with the Python programming language and PyFi gets you started by plugging the NumPy and Pandas library directly into your financial analysis.
Prepare Your Data
If you want a great machine learning algorithm, you need great training data. Correct errors, eliminate sparse classes, and remove unwanted observations to provide your algorithm premium fuel.
Build Your Pipelines
Model pipelines tell your algorithm how to process your data. Construct pipelines that standardize your data to a common scale, define competing model classes, and specify random states.
Train & Tune Your Algorithm
Optimizing your machine learning algorithm is like tuning a race car. Train competing models and tune your hyperparameters using cross-validation to maintain the integrity of your testing data.
Select the Winning Model
Your algorithm competes against itself to produce optimal results. After training and tuning your models, you can select the winning model and use it to make superior predictions.
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.
Learn the same algorithms that were used to advise the following companies:
*Slight changes to the code made to protect IP
Completion Certificates
After completing each course and passing the certification exam, students will be granted a PyFi Certification for that specific course. Use these certificates as a signal to employers that you have the technical skills to immediately add value to your team.
Meet Zach Washam,
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 thousands of finance students and professionals around the world.
*These banks have used one or both of PyFi’s self-study courses to introduce their financiers to Python programming and its application in the world of finance.
How Can We Help You?
What makes PyFi courses unique?
If you're looking to learn general Python this might not be the right class for you.
But if you'd rather understand how Python can be used in the world of finance, instead of building some irrelevant game, and if you'd like to learn the skills needed to build your own tools, then you're in the right place.
PyFi is built specifically for professionals in the world of finance and teaches you by relating concepts to excel so that you can learn faster and retain more of what you learn.
How long does it take to complete a course?
Both Python Fundamentals and Applied Machine Learning are approximately 3.5 hours in length each. A student can generally complete each course in 6-10 hours when working through all of the exercises. Your enrollment comes with lifetime access and support within the course via a comment section under each video lesson.
How will learning Python help my finance career?
Firms are in the business of making profit, whether thats a bank, a fund, a corporation, etc. They hire you based on your ability to provide disproportionate value to them and their goals, a value that exceeds the cost of your salary. As technology changes, the level of demand of employee output continues to rise. In a pool of many talented candidates, having a competitive advantage that can make you more efficient and effective in your workflows and can make your predictive models more accurate means the value you are able to provide to your firm will be significantly greater than your contemporaries. Python is a competitive advantage in finance. In fact, more and more positions are preferring or requiring this skill to even be considered for the role in the first place.
What if I don't like the course?
PyFi offers a no questions asked, 30 day money-back guarantee. If you aren't completely satisfied with the course, 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, and we know you are going to love it.
Do I need to work from a specific OS?
No. You can work through PyFi content from any OS.
Whats included in the Bundle Offer?
Both PyFi self study courses, Python Fundamentals, and Applied Machine Learning are included. You will also receive access to The Machine Learning Edge Blueprint, Vol I and Vol II which are the full text PDF versions of each course.
Do I need to understand investing and portfolios to take the courses?
The finance specific topics that are discussed in the courses can be picked up as you work through each topic.
Have any questions? We’re ready to help!
