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Python for Financial Traders training course

Name: Python training course
Start: 2024-08-12 00:00
End: 2024-08-15 00:00
Location: Python for Financial Traders training course

Use Python, Pandas and statistical computing libraries to analyse & visualize data and gather actionable insights

"I hadn't integrated Pandas with Python before joining my company. So it's very useful to consolidate my understanding of such skill via this course. The Jupyter notebooks provided will be a valuable resource for revising the materials and are really well laid out."

JL, Data Analyst, March 2021

Highlights Details Audience Feedback Prices/Dates

Public Courses

Highlights

Explore the basics of the Python programming language
Earn the confidence to consume market data and extract features
Conduct real-world financial analysis in Python
Explore many of the key libraries and tools commonly used in the financial services industry
Acquire knowledge on how to perform transformations on data
Understand how to work with financial time series data
Apply Python to build pricing and simple predictive models
Visualise your data with professional looking charts and graphs
Perform technical analysis and back-testing of models
Use Python to consume financial data from spreadsheets
Perform technical analysis to choose symbols between data ranges
Use Python to build a trading strategy and to execute back-testing

A taster video for Python Remote Training

Course Details

Environment Set-up

• The Anaconda distribution as Python Data Science platform
• Overview on Python virtual environment set-up
• Running Python code in Jupyter notebook

Python core concepts

• Built-in data types in Python
• Working with strings, numbers, lists, tuples and dictionaries
• Control flow statements
• Conditional execution with if statements
• Conditional loops with where statements
• Looping over a sequence with for statements
• Defining and using custom functions
• Working with dates and times
• Accessing data on file (CSV, JSON, ...)

Python Data Science libraries

• Working with data in pandas

Working with table-like data in pandas
Creating Series and DataFrame objects
Loading data from file into DataFrame objects
Adding, removing and updating databases

Retrieving data in pandas

Querying data to extract specific rows and columns
Filtering data on specific conditions
Understanding pandas indexing

Data manipulation in pandas

Data transformation
Applying functions to transform individual values
Applying functions to aggregate values by rows and columns

Handling missing data in pandas

Identifying missing data points
Filtering out missing data
Filling missing data with given values, interpolation and other filling strategies

Data Analysis in pandas

Extracting summary statistics over DataFrame objects
Performing data aggregation queries (groupby() method)
Aggregating multiple columns in the same query
Exploratory analysis of new datasets

Data Visualisation in pandas

Plotting data from a Series or DataFrame object
Bar plots, line plots, scatter plots, histograms and other common charts
Basic customisation of charts

Working with multiple tables

Concatenation of multiple tables based on structure/schema
Join/merge operations with DataFrame objects based on values
Reindexing operations, dealing with duplicate labels in the index
Dealing with duplicate records
Renaming columns

Time Series with pandas

Working with date and time data types in pandas
Creating ranges of date/time data points
Indexing by time
Resampling: data aggregation over time
Moving window operations, e.g. moving average
Defining custom calendars, custom business days, custom holidays

Working with text data in DataFrames

Using the str attribute in pandas objects
String manipulation functions in pandas
Filtering data with string pattern matching

SQL databases

Connecting to SQL databases with SQLAlchemy
Loading data from SQL to pandas
Sending SQL queries to a database and retrieving the results in Python and pandas

NumPy

Working with multi-dimensional arrays with NumPy
Arithmetic operations with NumPy arrays
Vectorised operations with NumPy arrays
Stats and linear algebra with NumPy
Slicing and indexing NumPy arrays

Data Visualisation with matplotlib and plotly

Overview on the basic types of charts available with the Python libraries
Bar plots, line plots, histograms, scatter plots, pie charts
Customising the layout and format of a chart
Examples of static visualisation with matplotlib
Examples of interactive visualisation with plotly

Who should attend

This course is of benefit to Financial Traders, business analysts, quantitative analysts, sales and marketing, financial software developers and any professional seeking to perform data analysis.

Prior knowledge of Python is not required.

Feedback

4.8 out of 5 average

JL, Data Analyst, March 2021

Client feedback from Python training course:

Enquiries & Bookings

Live courses - face-to-face or remote

PUBLIC COURSES

18 Dec 2023 - 3 days

£2000 +VAT

29 Jan 2024 - 3 days

£2000 +VAT

11 Mar 2024 - 3 days

£2000 +VAT

CUSTOMISED COURSES

* Train entire team
* Tailored content
* Flexible duration

From £1200+VAT per day
depending on your
group size

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More about this course

Our three-day course is an introduction to data science and the Python programming language, exploring many of the key libraries and tools commonly used in the financial services industry.

Aimed at finance professionals, the course builds real solutions for data analysis using Pandas, visualisation using Matplotlib and Plot.ly, and n-dimensional matrix manipulation and linear algebra using Numpy.

The course is run by instructors with experience in applying Python in finance, therefore adding value with highly relevant Labs and models.

FAQs

CONTACT
+44 (0)20 8446 7555

[email protected]

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