Python For Finance By Dr. Yves J. Hilpisch – Free Download Course
The workshop also illustrates how to achieve “hardware-bound” input-output operations with Python/NumPy and libraries such as PyTables.
PS: If you want, you can also download the full Certificate In Python For Finance course (CPF) by Dr. Yves J. Hilpisch here for free.
✅ About This Course:
✅ Course Author: Yves J. Hilpisch
✅ Official Course Price: £299
✅ Free For Our VIP Members? : Yes
✅ Download Links : Mega & Google Drive
✅ Updatable? : Yes, all future updates included.
✅ Sales Page : You can check at the bottom of this page.
🏆 Here’s What You Get & Learn With This Course:
* How to best start using Python, related tool and libraries for Quant Finance
* How to model and store data efficiently with Python
* How to implement compact and performant financial algorithms
* How to visualize financial data with Python
* How to manage and analyze financial time series data
* How to implement performant I/O operations
* How to increase the performance of financial Python code
Lecture 1. Python and Tools
(RUNNING TIME: 1 Hour 22 Minutes)
The first lecture shows how to efficiently set-up a Python and develeopment environment for Python Quant Finance. It also introduces into IPython, and in particular into the Notebook version which allows interactive, browser-based financial analytics with Python
Lecture 2. Introductory Financial Use Cases
(RUNNING TIME: 1 Hour 50 Minutes)
This lecture immediately dives into three canonical use cases: calculating and plotting implied volatilities, implementing performant Monte Carlo simulations, backtesting a trend based trading strategy. These use cases illustrate the benefits of the major Python libraries (NumPy, pandas), explained in detail in later lectures.
Lecture 3. Data Types/Structures and Visualization
(RUNNING TIME: 1 Hour 16 Minutes & 31 Minutes)
This lecture is all about data modeling and storage with Python and the visualization of data. It introduces the basic data types and structures in Python, shows how to make use of NumPy’s array capabilities and how to write vectorized numerical code with Python/NumPy.
Lecture 4. Financial Time Series
(RUNNING TIME: 1 Hour 23 Minutes)
This lecture is about the use of the pandas library for the management and analysis of financial time series. It shows examples implementing simple and advanced analytics as well as time series visualization. It also shows how to work with High Frequency data.
Lecture 5. Input-Output Operations
(RUNNING TIME: 1 Hour 20 Minutes)
Financial analytics and financial application development mainly rests on the efficient and performant management and movement of (large, big) data. This lecture illustrates how to make sure that data reading and writing (to HDDs, SSDs) takes place at the maximum speed that any given hardware component allows. Examples also illustrate how to make use of compression techniques in such a context.
Lecture 6. Performance Libraries
(RUNNING TIME: 1 Hour 30 Minutes)
The Python ecosystem has to offer a number of powerful performance libraries. For example, using the Numba dynamic compling library allows to compile Python byte code at call-time to machine code by using the LLVM infrastructure. The resulting compiled functions are directly callable from Python. Similarly, using the Multiprocessing module of Python makes parallelization of Python function executions a simple and efficient task.
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You can find more info on the sales page here.
