Starting automated trading, what are most common setups?

[Joebone]

I have enjoyed this read!

Bump to encourage more of this

 

[VincentvanG]

Hi everyone,

I have been learning how to do automated trading for a few months. Still doing paper trading for testing.

I will be doing scalping, day trading, closing position at the end of the day. My current focus is Emini S&P 500 future, may add other future later.

Here is my setup:

I am using Interactive Broker (IB), I am using R for backtesting specific/customised strategy, otherwise I am using Amibroker for backtesting to quickly check if an algorithm is profitable.

My current data feed is IB history and order book, collecting this using IB Java API running 24x7 on my Ubuntu box (will change to IB Controler running on AWS later)

I am using Java/Scala to derive signals from time series data, based on my IB datafeed above.

In summary,
Purpose: scalping Emini future
Datafeed: IB
Data Collection/Data Transformation: Java/Scala
Backtesting: R, Amibroker
Strategy Discovery (automatically discover profitable strategy) : R, Python (using machine learning)
My background: I work as a developer and a machine learning researcher.

What is your setup? Any better data feed? my budget is about $100/month.
Any recommendation to improve my setup? I am really new in this automated trading field. My personal blog is http://Trading.Computer

Thank you.

Trading.Computer[/QUOTE

Johanmul,
Thank you for your advice.

Here my honest self-assessment of my skill set:
Machine Learning: intermediate to expert
Algorithm: expert
Programming: intermediate to expert
Data Processing: expert
Trading/markets: basic, I believe I need to learn a lot in trading/markets, there is no way I can apply my other skill set effectively without knowing the trading/markets knowledge in depth.

Many years ago, someone who claim that he is a machine learning (ML) expert, told me that he does not to understand the domain, in this example fraud, to apply machine learning. He failed, as the features that he built --as inputs of ML algorithm-- are too generic, not using any domain knowledge.

In depth understanding the domain --in this use case-- trading/markets is paramount to the overall success. Machine learning, algorithm, programming, data processing are just supporting tools.

First, need to understand the trading/markets domain in depth, then formulate the problem to be solved (that is asking the right questions), finally applying the right tools correctly to solve the problem (the answers).

My setup is just my iphone, a basic pc and free alerts from my broker.
My skill assessment:
Machine Learning: None
Algorithm: basic
Programming: None. Use builders
Data processing: basic

I pay nothing for data feeds. Just use broker.
Yet I am profitable 11/12 months in the year.

 

[2weels]

now working on transforming raw time series data in higher level features.

Would you be willing to say a little more about what features you are deriving from the data, and where you get ideas for features?

Thanks

 

[Joebone]

My setup is just my iphone, a basic pc and free alerts from my broker.
My skill assessment:
Machine Learning: None
Algorithm: basic
Programming: None. Use builders
Data processing: basic

I pay nothing for data feeds. Just use broker.
Yet I am profitable 11/12 months in the year.

what broker?

 

[ValeryN]

It's an old post, but I'm sure there will be enough people curious about how things played out for tradingcomputer. Would you mind sharing your journey over the last couple of years?