Trading Markets – Programming in Python For Traders

Chris and I are a generation apart yet our journey is the same.

Amibroker and Excel have been good to me and my clients for years. TradeStation was good to Chris for years. But we both realized in order to keep up with the professional quant firms, we needed to move to an open source professionally used language.

That language, as so many major quant firms have found, is Python.

Here is Why Many of The Top Quant Firms Use Python and Why You Should Too

There are many reasons why Python has become the go-to programming language for these multi-billion dollar fund companies. This includes…

1. It’s easy to learn – You can learn how to program Python in under 10 hours. In 10 hours, you will be able to do in-depth research, code more complicated trading strategies, and analyze your backtested results better and faster than ever before!
2. It’s faster than most languages. You will spend less time writing your code which leads you to having more time analyzing your results and improving your strategies.
3. It’s Open Source – This means you’ll have access to the same trading code and tools created by many of the best researchers, programmers and traders in the world.
4. Python has the best libraries for data analyses and quantitative trading. This means again you will be using the same tools as professional quant trading desks and hedge fund managers do. This can’t be said for other languages like TradeStation and Amibroker.

What You’ll Learn In Programming in Python For Traders?

• Week One – You’ll gain the foundation in order to do your backtesting, research and signal generation.
  This foundation will lay the groundwork for you to scale into the upcoming weeks.
  Your homework will include learning how to do technical analysis calculations in Python including moving averages, RSI, and the other major technical indicators used by professionals.
• Week Two – You’re going to be backtesting in Python!
  You’ll be writing code in Python and testing strategies and signals to find market edges. For example, you’ll be writing code using a 2, 3, or 4 period RSI on various levels, such as RSI below 30, RSI below 20, etc.
  By the end of week 2, you’ll be able to test various market conditions (for example overbought and oversold conditions) and calculate the historical edges that exist in those conditions.
• Week Three – You’ll be writing full fledged trading strategies. This includes allocating capital to trades, adding risk management tools, and analyzing portfolio returns.
  At the end of Week 3, you will be able to run more advanced backtests of your trading ideas and strategies.
• Week Four – In Week 4 you’ll be analyzing your backtests. This includes analyzing your cumulative returns, analyzing your risk (drawdowns, volatility, etc.), analyzing correlations through time, and a deep dive into analyzing individual signals in order for you to see when and how to best optimize your trading strategies.
• Week Five – In Week 5 you’ll be writing more advanced backtests. This includes creating signal list generation and managing a portfolio of multiple securities. You’ll also learn advanced concepts on position sizing in order for you to optimize the edges you are finding in your strategies.
  By the end of this course you will have the ability to find your own market edges, build your backtest, and do a deep analysis of the test results.

More courses from the same author: Trading Markets