TradingMachine Machine Learning for Python (Pandas, PyBrain, TA-Lib) - Matlab, R project and Python | futures trading

Go Back

> Futures Trading, News, Charts and Platforms > Platforms and Indicators > Matlab, R project and Python

TradingMachine Machine Learning for Python (Pandas, PyBrain, TA-Lib)
Started:December 8th, 2013 (08:26 PM) by Big Mike Views / Replies:2,364 / 1
Last Reply:December 8th, 2013 (08:26 PM) Attachments:0

Welcome to

Welcome, Guest!

This forum was established to help traders (especially futures traders) by openly sharing indicators, strategies, methods, trading journals and discussing the psychology of trading.

We are fundamentally different than most other trading forums:
  • We work extremely hard to keep things positive on our forums.
  • We do not tolerate rude behavior, trolling, or vendor advertising in posts.
  • We firmly believe in openness and encourage sharing. The holy grail is within you, it is not something tangible you can download.
  • We expect our members to participate and become a part of the community. Help yourself by helping others.

You'll need to register in order to view the content of the threads and start contributing to our community. It's free and simple, and we will never resell your private information.

-- Big Mike

Thread Tools Search this Thread

TradingMachine Machine Learning for Python (Pandas, PyBrain, TA-Lib)

Old December 8th, 2013, 08:26 PM   #1 (permalink)
Site Administrator
Manta, Ecuador
Futures Experience: Advanced
Platform: My own custom solution
Favorite Futures: E-mini ES S&P 500
Big Mike's Avatar
Posts: 45,432 since Jun 2009
Thanks: 28,836 given, 79,558 received

TradingMachine Machine Learning for Python (Pandas, PyBrain, TA-Lib)


TradingMachine is intend to bring optimization and machine techniques into finance algorithmic trading.

Discussion and Help



* Optimization on strategy parameters.
* PyBrain Integration (Reinforcement Learning, Neural Network ...).
* TA-Lib Integration (Most common technical analysis available.)
* Pandas (High speed time series data analysis)


# You will first need to install TALib. ta-lib is a python wrapper for that. Please refer [TA-Lib](TA-Lib: Technical Analysis Library - Products)

TradingMachine can be installed via pip

pip install numpy
pip install matplotlib
pip install pandas
pip install ta-lib
pip install tradingmachine

If there are problems installing the dependencies, please consider install scipy stack.
For Windows, the [Enthought Python Distribution](
includes most of the necessary dependencies.
On OSX, the [Scipy Superpack](fonnesbeck/ScipySuperpack @ GitHub) works very well.
Other platforms, the [Scipy Stack](Python Extension Packages for Windows - Christoph Gohlke) has binary available to install.

After installation, you will need to create a configuration file in home directory named ".tmconfig.ini".

2 HistoricalDataPath = /Users/chen/Repository/historicaldata

Configuration file is intended to point to the historical data folder.
A copy of historical data can be downloaded from: [historicalata](


* Python (>= 3.3.1)
* numpy (>= 1.7.1)
* pandas (>= 0.11.0)
* pytz
* ta-lib


For other questions, please contact Chen Huang <>.


Due to time constraints, please do not PM me if your question can be resolved or answered on the forum.

Need help?
1) Stop changing things. No new indicators, charts, or methods. Be consistent with what is in front of you first.
2) Start a journal and post to it daily with the trades you made to show your strengths and weaknesses.
3) Set goals for yourself to reach daily. Make them about how you trade, not how much money you make.
4) Accept responsibility for your actions. Stop looking elsewhere to explain away poor performance.
5) Where to start as a trader? Watch this webinar and read this thread for hundreds of questions and answers.
Help using the forum? Watch this video to learn general tips on using the site.

If you want
to support our community, become an Elite Member.

Reply With Quote
The following 2 users say Thank You to Big Mike for this post:

Old December 8th, 2013, 08:26 PM   #2 (permalink)
Quick Summary
Quick Summary Post

Quick Summary is created and edited by users like you... Add FAQ's, Links and other Relevant Information by clicking the edit button in the lower right hand corner of this message.


Reply > Futures Trading, News, Charts and Platforms > Platforms and Indicators > Matlab, R project and Python > TradingMachine Machine Learning for Python (Pandas, PyBrain, TA-Lib)

Thread Tools Search this Thread
Search this Thread:

Advanced Search

Upcoming Webinars and Events (4:30PM ET unless noted)

An Afternoon with FIO trader bobwest

Elite only

NinjaTrader 8: Programming Profitable Trading Edges w/Scott Hodson

Elite only

Anthony Drager: Executing on Intermarket Correlations & Order Flow, Part 2

Elite only

Adam Grimes: Five critically important keys to professional trading

Elite only

Machine Learning Concepts w/FIO member NJAMC

Elite only

MarketDelta Cloud Platform: Announcing new mobile features

Dec 1

NinjaTrader 8: Features and Enhancements

Dec 6

Similar Threads
Thread Thread Starter Forum Replies Last Post
Machine Learning/AI discussion (Generic) NJAMC The Elite Circle 70 August 22nd, 2016 01:35 PM
Webinar: Intro to Machine Learning by NJAMC Big Mike Elite Automated Trading 53 December 20th, 2015 06:23 PM
AUTOPOST: NJAMC Machine Learning Autoposts NJAMC Elite Trading Journals 52 December 27th, 2013 11:08 PM
Gekko Quant, good R blog on trading, machine learning Big Mike Matlab, R project and Python 3 December 8th, 2013 04:09 AM
R Machine Learning Big Mike Matlab, R project and Python 3 December 5th, 2013 12:46 AM

All times are GMT -4. The time now is 04:00 AM.

Copyright © 2016 by All information is for educational use only and is not investment advice.
There is a substantial risk of loss in trading commodity futures, stocks, options and foreign exchange products. Past performance is not indicative of future results.
no new posts

Page generated 2016-10-23 in 0.06 seconds with 19 queries on phoenix via your IP