Tag Archives: prediction

Google TensorFlow short term stock market prediction machine learning

Google TensorFlow short term stock prediction machine learning

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Description from this online tutorial from KDNuggets.com

In this post you will see an application of Convolutional Neural Networks to stock market prediction, using a combination of stock prices with sentiment analysis.

Check out this link

 

http://quantlabs.net/blog/2017/12/google-tensorflow-short-term-stock-prediction-machine-learning/

 

Or watch this video

In this post you will see an application of Convolutional Neural Networks to stock market
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Google TensorFlow short term stock prediction machine learning

 

Google TensorFlow short term stock prediction machine learning

Description from this online tutorial from KDNuggets.com

In this post you will see an application of Convolutional Neural Networks to stock market prediction, using a combination of stock prices with sentiment analysis.

https://www.kdnuggets.com/2017/12/tensorflow-short-term-stocks-prediction.html

NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!

Stock prediction with Machine Learning

Stock market prediction with Machine Learning

Best place to learn machine learning online. Tell him I sent you

Description:

Can we predict the price of Microsoft stock using Machine Learning? We’ll train the Random Forest, Linear Regression, and Perceptron models on many years of historical price data as well as sentiment from news headlines to find out!

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NOTE that this is for NON programmers/technical folks details from the READ ME Doc here: http://quantlabs.net/blog/2017/09/new-course-on-dukascopy-visual-jforex-for-non-programming-to-do-algo-forex-trading/
Dukascopy Visual JForex for NON programming to do algo forex trading
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Dukascopy Tools and API development for Algo forex trading Here is the 6 video descriptions of this video series. I am moving towards this retail forex broker so I learned this toolset and API. This is the results in this README file with video time lengths. http://quantlabs.net/blog/2017/09/new-course-on-dukascopy-tools-and-api-development-for-algo-forex-trading-2/
Dukascopy Tools and API development for Algo forex trading
Dukascopy Tools and API development for Algo forex trading
$47.00
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Visit our Brand NEW QuantLabs Store
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Stock market prediction with Machine Learning

Stock market prediction with Machine Learning

Best place to learn machine learning online

Description:

Can we predict the price of Microsoft stock using Machine Learning? We’ll train the Random Forest, Linear Regression, and Perceptron models on many years of historical price data as well as sentiment from news headlines to find out!

 

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Is US non Farm payroll useful a leading indicator for market direction and prediction

Is US non Farm payroll useful a leading indicator for market direction and prediction

This is one of many factor I use for US of A

https://www.quandl.com/SGE/USANFP-United-States-Non-Farm-Payrolls

Thanks from Sholom for sending

P.S. You can get this for free of FRED site

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Demo of Garch Prediction of Forex Data with older Matlab functions

Demo of Garch Prediction of Forex Data with older Matlab functions

Tutorial:

http://radio.feld.cvut.cz/matlab/toolbox/garch/chap1_23.html

Useful links from video:

http://www.mathworks.com/help/econ/garchfit.html

http://www.mathworks.com/help/econ/garchpred.html

Asymptotic meaning:

http://answers.yahoo.com/question/index?qid=20081030122228AAb0SRo

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Now it is time to attack that Matlab Curve Fitting toolbox for market direction with forecasting and prediction

Now it is time to attack that Matlab Curve Fitting toolbox for market direction with forecasting and prediction

This toolbox could be a surprise for a lot of people in trying to figure out how to

predict market direction with curve fitting. Is it as easy as I think? I will be posting

a bunch of videos for QuantLabs.net Premium Membership on this today.

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Today is a GARCH prediction day focusing on many models and strategies for R

Today is a GARCH prediction day focusing on many models and strategies for R. This will be recorded as well for my members. I will be holding a webinar for them as well so get  . I will reveal what it will include once I schedule it.
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Improve your trading strategies with R code for prediction, pair trading, cointegration, back testing, etc

My Algorithm, Modelling, and Strategy Development courses just keep getting bigger and better. In the next few weeks, I’ll be posting R source code to teach you exactly how to:

1. Trade using a GARCH volatility forecast

2. Model for VAR, simulation/estimation, statistical tests, benchmarks, cointegration with the Engle & Granger Two-Step Procedure, and autoregressive moving average models

3. Conduct proper time series analysis with simple component analysis, linear filtering, decomposition, regression analysis, exponential smoothing and prediction, autocorrelation, and parameter estimation and prediction with ARIMA models

4. Carry out pair trading with plotting spreads, Dickey–Fuller and Phillips-Perron tests, estimated parameters for back testing, valid trade signals, and properly back tested performances.

If you’re interested in any of the above (and there’s more coming), then …

Get in the action now! Start learning how to accurately predict market moving events like volatility and pricing trends.

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Get even more benefits including our HFT and Algo Development courses, software tool kits, and more!

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Good trading,

Bryan

P.S. Remember: R is totally free as it’s open source. You don’t need expensive proprietary software packages to work with it. Volatility forecasting, pairs trading, cointegration, estimating, simulation, and much more. It’s all coming ASAP!

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Is regression a good method for prediction when a variable is dependent on multiple attributes each having seasonal variations

Is regression a good method for prediction when a variable is dependent on multiple attributes each having seasonal variations

 

For simple or multiple regression, you can’t directly conduct regression method for your data, since one of the assumption for simple/multiple regression is independent variable should not have multicolinearity among them. Maybe you can use robust regrssion or use time series method (ARIMA) that can address your data.

 

It is quite common to perform seasonal adjustment of data where there is a strong seasonal trend, then use the seasonally-adjusted data in further analyses. Many textbooks and websites describe techniques for doing such adjustments. With daily numbers you must take into account both day of the week AND season, and this can get a little tricky. For example December 2011 was odd because by the last pre-Christmas Saturday, which is usually a big shopping day, many of us had already finished buying gifts since Christmas fell on a Sunday. Anyway, look up how to do such adjustments, and you can use the adjusted data as the inputs for linear regression.

 

But in my data there is no collinearity and whereever it exists i can reduce those attributes during dimensionality reduction process

 

 

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