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Here is the course outline
COURSE NAME – Cryptocurrency and Algo Trading Infrastructure with Python: A Practical tutorial for Python developers
Sub-title – Discover the secrets behind trading infrastructure components, high speed in-memory NOSQL database options, cryptocurrency exchanges and simple technical indicators, and more. And use the same techniques in your Python code.
AUTHOR NAME – Bryan Downing
OVERVIEW – As Bitcoin ruled the financial news cycle in recent times, many have had a peak interest in cyrpto currency along with systematic trading. There are many advanced trading research techniques including machine learning, AI, or quant. This course was created for the ‘newbie’ who has a basic understand in popular programming languages but easy to learn such as Python. A focus of technical analysis was chosen since it is popular among retail traders. It is also seems to offer more predictable results as opposed to harder to learn concepts such as quant or machine learning. In essence, a simpler with more effective techniques were purposely chosen to get someone with basic programming knowledge (eg. Python) to get ramped up faster! It is very hopeful any student will become more confident in their capabilities to complete this without the unnecessary complexities that usually hold back their success.
TARGET AUDIENCE – Python programmers (including data scientists etc.) who want to understand algo techniques and apply them into a primitive automated trading system. Pro or Retail traders who have an interest in learning the basics of Python and systematic trading.
Infrastructure modern component implementation fo an algo trading system
Basic coverage of crypto currency Python packages and crypto currency exchanges
Technical analysis library package use for strategy idea development
Videos and sample Python source code scripts are made available at time of presentation. Note that scripts are meant to to be small and simple for easiest consumption. Advanced techniques are stripped out to ensure completion of this course. This can be used as a launching to pad to understand the components of a primitive algo trading system In short, it is devised in a way to get you up and running as quickly as possible.
WHAT WILL YOU LEARN
- The major components in any trading system
- Broker and asset class options
How crypto currency (e.g. Bitcoin) has becomes a hotbed of profit potential
Strategy coding basics to help in analyzing using popular techncial analysis techniques
All software mentioned is open and source with the exception of ChartDirector which is used for visualization.
ABOUT THE AUTHOR –
SUMMARY OF CONTENT
Crypto Idea strategy generation using forward looking data as similar to financial institutions who analyze and trade popular alt coins including Bitcoin
Pretty trading charts with ChartDirector
- Using TA-Lib like package popular technical analysis for strategy development
SECTION 1 – Programming Language Overview
SECTION DESCRIPTION – Covers pros and cons of each language you may come across on your journey with algo/automated trading. I also ist items you will need to consider when you want do live trading as well using correct concepts of High Frequency Trading (HFT) I cover 5 languages including:
- C and C++
SECTION 2 – Python tools and methodology
SECTION DESCRIPTION – Covers all elements of Python 2 vs 3 including a high level of ‘HelloWorld’ example. Also covers from a newbie’s POV on non essential Jypter and Machine Learning packages.
- Python 2 vs 3
- PIP the package manager
- Python install options on operating systems
- Tools like popular editors/IDEs e.g. Sublime
- Simple Debugging techniques
- Very high level concepts of popular machine learning frameworks and Juptyr use cases
SECTION 3 – Options to Capture Market Data
SECTION DESCRIPTION –Different options for market data including free vs paid
- IEX and IQFeed
- LMAX and Interactive Brokers broker overview options
- Dukascopy Forex.CFD broker Duka Python Package
- Intro to JForex platform to create CSV using Java example
SECTION 4 – How to create Pandas dataframe to manipulate data
SECTION DESCRIPTION – Simple Python script that does the following
- Read a comma separated value file (CSV)
- Apply statistical calculation and manipulate rows
- Export data to CSV or Excel format
SECTION 5 – Demo technical analysis indicator functions
SECTION DESCRIPTION – As the original TA-LIB can be complicated to build yet along with the buggy TA-Lib Python package, there was another alternative that came up. A much simpler approach with the popular TA-LIB shows a simpler demo of running various indicators. These examples include:
- Bollinger Band
- Exponential Moving Average
- Standard Deviation
- Relative Strength Indicator
SECTION DURATION – 30 minutes
SECTION DESCRIPTION –NOSQL Database of choice
- Why this is used
- Open source and benchmarks
- Compared against popular open source HFT project
- Server and client edition
- Python code demos
SECTION DESCRIPTION –Options to cover to fund via bank credit card or use an anonymous credit card. Note that these option can be removed, invalid, or change without notification.
- Different credit card option sources
- Why banks block funding crypto currency via credit card
- Round about way to fund anonymously via credit
SECTION DESCRIPTION –Over of Python package working examples with no registration needed for popular exchanges
- Intro to CoinMarket.com to depend on volume
- Reliable exchanges at time of recording
- Advantages of Pyrhon option vs web programming languages
- Examples of CCXT Python Package
Crypto Currency trading idea with forward looking data for your strategy
SECTION DESCRIPTION –This is a set of ideas where you can use forward looking data to see how institutions gauge Bitcoin. These include:.
- NVT Ratio
- Futures and Options market
- COT reports
- Use of CoinMarketCap.com
SECTION DESCRIPTION –This is an overall demo of features and how to install commercial ChartDirector for Python.
- How to install on Mac or Linux/Unix like systems
- Advantages over other open source Python packages like Matplotlib or Seaborn
- Benefits of ChartDirector
- Hints of Developing Interactive Self Standalone chart with potential live charting
- Note that charts are generated via operating platform console terminal
This will vary on a product or Python package basiss. This example is relatively basic.
Minimum Hardware Requirements
For successful completion of this course, students will require computer systems with at least the following:
- OS: Any
- Processor: Minimal
- Memory: 2 GB but may need 4+ GB if you to choose to install VirtualBox with Ubuntu Linux
- Storage: Minimal
Recommended Hardware Requirements
For an optimal experience with hands-on labs and other practical activities, we recommend the following configuration:
- OS: In order of preference of MacOS, Linux (e.g. Ubuntu), and Windows
- Processor: Any
- Memory: 2 GB but may need more 4 GB if you to choose install VirtualBox with Ubuntu Linux
- Storage: 12 GB
- Operating system: See above
- Browser: Any
- Python 2.7 but prefer 3.x.
Links and installation are provided in video module as required.
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