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Advanced Python for Financial Technologies

PYTH-134 (3 Days)

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Python for Finance Training Overview

Maximize returns. Visualize your portfolio. Execute your latest Killer Trading Algorithm. All of these and more are easily within reach by harnessing the open-source power of Python, (see e.g. this blog discussing programming languages in the financial industry).

This Advanced Python course, Python for Finance Training, teaches you how to apply Python to a diverse range of financial technology applications, including acquiring data from popular financial data providers, as well as cleaning, exploring, and visualizing the resulting datasets. Attendees learn how to approach the implementation of algorithmic models and how to construct rich and insightful models, with an emphasis on ethics, compliance, and security. 

Location and Pricing

Most Accelebrate courses are delivered as private, customized, on-site training at our clients' locations worldwide for groups of 3 or more attendees and are custom tailored to their specific needs. Please visit our client list to see organizations for whom we have delivered private in-house training. These courses can also be delivered as live, private online classes for groups that are geographically dispersed or wish to save on the instructor's or students' travel expenses. To receive a customized proposal and price quote for private training at your site or online, please contact us.

In addition, some courses are available as live, online classes for individuals. See a schedule of online courses.

Python for Finance Training Objectives

  • Automatically extract financial data from common data providers
  • Know how to clean, aggregate, and manipulate financial data effectively
  • Conduct elementary time series analysis
  • Understand stochastic processes and common noise models
  • Construct models for inference and forecasting, such as ARIMA and linear and logistic regression
  • Generate powerful visualizations, such as candlestick charts
  • Extract financial data by scraping websites
  • Understand the fundamentals of supervised and unsupervised machine learning models as applied to finance
  • Apply Recurrent Neural Nets (RNNs) and Long Short-Term Memory Units (LSTMs) to financial time series and understand their limitations
  • Understand the principles behind Blockchain technology  

Python for Finance Training Outline

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Introduction
Crunching the Numbers: Numerical Python With NumPy
Introduction to the n-d-array
NumPy operations
Broadcasting
Missing data in NumPy (masked array)
NumPy structured arrays
Improving performance through vectorization
Random number generation
Introduction to Monte-Carlo methods
General approaches to implementing mathematical algorithms
Acquiring and Manipulating Financial Data With Pandas and Pandas-Datareader
Series versus DataFrames
Overview of data types in pandas
Pandas I/O tools: CSV/Excel/SQL
Pandas I/O tools: Pandas-datareader
Subsetting DataFrames
Creating and deleting variables
Discretization of continuous data
Scaling and standardizing data
Identifying duplicates
Dummy coding
Exploratory Data Analysis and Advanced Pandas Methods
Uni- and multivariate statistical summaries and detecting outliers
Group-wise calculations using pandas
Pivot tables
Long to wide and back: pivoting, stacking and melting
Python visualization: Matplotlib and seaborn
Pandas visualization: histograms, bar and box plots
Pandas visualization: Scatter plots and pie charts
Group-by plotting
Pandas plot formatting
mpl-finance and candlestick charts
Merging DataFrames
Pandas string methods
Implementing regular expressions in pandas
Handling missing data in pandas
Elementary Time Series Analysis
Date/time formats in Python and pandas
Running/rolling aggregates
Resampling
Stochastic Processes
Overview of noise models
Stationarity
Random walks and martingales
Brownian motion
Diffusion models
The Black-Scholes model—and its limitations
Time Series Forecasting
De-trending and seasonality
Interpolation and extrapolation
Auto-Regressive Integrated Moving Average (ARIMA) models
Measuring Impact: Testing For Group Differences
Null hypothesis testing and p-values
Group comparisons (p-values, t-tests, ANOVA, Chi-square tests)
Correlation
Progressing, With Regression Models
Linear regression
Logistic regression
Regression on count outcomes (Poisson processes)
Conclusion
Optional: Machine Learning Fundamentals for Finance with scikit-learn
Requirements: NumPy, pandas. Time required: 4 hours
Machine learning approaches to multivariate statistics
Machine Learning theory
Data pre-processing
Supervised versus Unsupervised learning
Unsupervised learning: clustering
Dimensionality reduction
Supervised learning: regression
Supervised learning: classification
Optional: Recurrent Neural Nets and LSTMs with PyTorch
Requirements: NumPy, pandas, Machine Learning fundamentals. Time required: 4 hours
Introduction to PyTorch
Regression in PyTorch
Artificial Neural Networks
RNNs/LSTMs with PyTorch
Optional: Scraping By: Obtaining Financial Data from Publicly Accessible Websites
Requirements: Base Python. Time required: 2 hours
Parsing HTML/CSS with BeautifulSoup
Establishing a Connection
Building a Web Scraper
Advanced Scraping: Building a Web Spider with Scrapy
Optional: Blockchain technologies
Requirements: Basic Python, NumPy (useful, but not mandatory). Time required: 4 hours.
The Ingredients For a Blockchain
The Hash Function
Advanced Functions
Constructing Your Own Blockchain
Shortcomings of current blockchain technologies
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Lecture percentage

50%

Lecture/Demo

Lab percentage

50%

Lab

Course Number:

PYTH-134

Duration:

3 Days

Prerequisites:

All Python For Finance training students should already be somewhat familiar with fundamental Python syntax and concepts.

Training Materials:

All students receive comprehensive courseware.

Software Requirements:

  • Any Windows, Linux, or Mac OS X operating system
  • An installation of Python 3.x (Anaconda installation recommended)
  • An IDE with Python support (Jupyter notebook, Spyder or PyCharm Community Edition)

Contact Us:

Accelebrate’s training classes are available for private groups of 3 or more people at your site or online anywhere worldwide.

Don't settle for a "one size fits all" public class! Have Accelebrate deliver exactly the training you want, privately at your site or online, for less than the cost of a public class.

For pricing and to learn more, please contact us.

Contact Us

Toll-free in US/Canada:
877 849 1850
International:
+1 678 648 3113

Toll-free in US/Canada:
866 566 1228
International:
+1 404 420 2491

925B Peachtree Street, NE
PMB 378
Atlanta, GA 30309-3918
USA

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