How you can make an effective Equipment Getting to know Fx trading Method in Python: Start

An introduction to the construction of a profitable machine learning strategy. Covers the basics of classification algorithms, data preprocessing, and feature selection. Discussion of Python machine learning resources; including the Sentdex channel, and the Python Machine learning book. In the next video we will look at different data sources and how to clean the data.

*** Sentdex Channel ***

https://www.youtube.com/user/sentdex

FOREX Playlist:

*** Python Machine Learning – Sebastian Raschka ***

*** Python Resources ***

SciKit-Learn Documentation:
http://scikit-learn.org/stable/

*** FOREX Daily Trend Prediction Research Paper ***

http://www.wseas.us/e-library/conferences/2011/Penang/ACRE/ACRE-05.pdf

Here are some very useful websites if you would like to learn more about Neural Networks and Fuzzy Logic.

Learn Artificial Neural Networks is website dedicated to providing educational information about artificial intelligence technologies. They have two great articles explaining Neural Networks and Fuzzy Logic in details. There are also other articles related to AI.
http://www.learnartificialneuralnetworks.com/#Intro
http://www.learnartificialneuralnetworks.com/fuzzy-logic.html

In Akri Ltd, there is an article titled „From Logic to Fuzzy Logic“ which compares traditional logic and Fuzzy logic using real-world examples, showcasing how Fuzzy Logic has revolutionized the world.
http://www.akri.org/ai/flogic.htm

Holos is an Europe-based company which provides its clients with products and services that improve the information access, support decisions and generate knowledge. In its company website, Holos explains how it integrates Fuzzy Logic into decision-making activities.
http://www.holos.pt/en/web/guest/sistemas-de-suporte-a-decisao-baseados-em-logica-difusa

Here is an article explaining „A Fuzzy Logic based Trading System“
http://nisis.risk-technologies.com/events/symp2007/papers/BB25_p_kaymak.pdf

This article explains the importance of Neural Networks and Fuzzy Logic using diagrams to help understand.
http://www.cs.berkeley.edu/~zadeh/papers/Fuzzy%20Logic%2c%20Neural%20Networks%2c%20and%20Soft%20Computing-1994.pdf
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20 Antworten auf „How you can make an effective Equipment Getting to know Fx trading Method in Python: Start“

  1. If you are going through these videos be sure to use an older version of plotly. I rolled back to 3.3.0 in order to make sure the tests were the same as the examples. pip install plotly==3.3.0

  2. Great tutorial. Thanks for using the ideas from my paper. By the way, I have updated the algorithm and have the updated code in Matlab and now using more classifiers like XGboost and logistic regression.

  3. I've been trying to implement my scaloing strategy into code for a while. My eyes and my brain can recognize the charts and what's going to happen. Turning that into code is hard.

  4. Price action is a fundamental piece of algotrading, wether is an indicator, RSI, volume, to name a few, which the vast mayority do A KNOWLEDGE what price is Doing.
    Been trading for a while, and the only true indicator to predict price, is price itself.
    If you think about it, Bollinger bands, MACD, stochastics, Fourier, sine and what not, ARE ALL NOTHING MORE THEN MATHEMATICAL DERIVATIVES OF PRICE ACTION.

  5. How long did it take you to learn this? I don't have much of a coding background but for my model in graduate school I need a daily bias predictor, your original concept from the paper is daily. Thanks for showing us all the framework of building it

  6. Hi buddy, great playlist you got here, I am big ML fan in Financial Markets.
    I am video 2 and I get this error:

    Traceback (most recent call last):
    File "C:/Users/Jay Son/OneDrive/HIT400/MLforecasting/data processing.py", line 8, in <module>
    df.date = pd.to_datetime(df.date,format='%d.%m.%Y %H:%M%:S.%f')
    File "C:UsersJay SonOneDriveHIT400MLforecastingvenvlibsite-packagespandascoretoolsdatetimes.py", line 376, in to_datetime
    result = _assemble_from_unit_mappings(arg, errors=errors)
    File "C:UsersJay SonOneDriveHIT400MLforecastingvenvlibsite-packagespandascoretoolsdatetimes.py", line 446, in _assemble_from_unit_mappings
    unit = {k: f(k) for k in arg.keys()}
    File "C:UsersJay SonOneDriveHIT400MLforecastingvenvlibsite-packagespandascoretoolsdatetimes.py", line 446, in <dictcomp>
    unit = {k: f(k) for k in arg.keys()}
    File "C:UsersJay SonOneDriveHIT400MLforecastingvenvlibsite-packagespandascoretoolsdatetimes.py", line 441, in f
    if value.lower() in _unit_map:
    AttributeError: 'tuple' object has no attribute 'lower'

    Any help?

  7. I am Areej Baasher one of authors of the Research Paper "Forex Daily Trend Prediction using Machine Learning Techniques"
    which you used as a resource, you did a good job youtubing this tutorial.
    Where did you reach in applying the algorithm?

  8. I am Areej Baasher one of authors of the Research Paper "Forex Daily Trend Prediction using Machine Learning Techniques"
    which you used as a resource, you did a good job youtubing this tutorial.
    Where did you reach in applying the algorithm?

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