Recently I have spent some time to investigate the options to access the history of news articles via an API. I was mainly interested in APIs which can be accessed free of charge. Here is the list of the most useful providers: Guardian – Easy API – Acceptable Rate Limits – Access to over 1,900,000 pieces of content – Free for non-commercial usage New York Times – Provides API to search and separate API to Read more…
I am planning to use the DL4J Doc2Vec implementation for a sentiment analysis. However, I don’t want to start with an empty network but the staring point should be a pre-trained network: The initial trining should be done with the Sentiment140 dataset which can be found at https://www.kaggle.com/kazanova/sentiment140. It contains 1,600,000 tweets extracted using the twitter api. In this Gist I describe how to train and save a DL4J Doc2Vec. The serialized model is available Read more…
The basic goal of a ‘sentiment analysis’ is to classify a given text into positive, negative or neutral. SentiWordNet is a lexical resource for opinion mining. It assigns sentiments to each synset of WordNet which makes it possible to “calculate” an overall sentiment for a text. A SentiWordNet implementation can be found in DL4J in the deeplearning4j-nlp-uima artifact. This demo has been implemented in Scala using a Jupyter BeakerX Notebook.
In this demo I will show how to predict if the closing price of Apple, General Electric and Samsung Electronics is moving up or down. We do this with the help of a Random Forest Classifier. I tried to replicate the result from a research paper authored by Luckyson Khaidem, Snehanshu Saha, Sudeepa Roy Dey which is describing the following concept: Data Preprocessing exponentially smoothing Features Relative Strength Index Stochastic Oscillator Williams %R Moving Average Convergence Read more…
In this blog we show how to forecast the closing price of AAPL using a LSTM RRN network. We use the open, closing, high and low rates and the volume of the current day as input in order to predict the subsequent closing price. This demo has been implemented in Scala using Jupyter with the BeakerX kernel using the following libraries – Investor – DL4j The details can be found in the following Gist.
In recent years most banks are pushing their customers to invest in Funds with the argument that this is more profitable than any other option. As always it is valid to question this statement. As a compelling argument an index chart is presented which shows indeed that the stock market was growing! I tried to run some simulations using the data form my Investor framework to validate this statement: Here is the link to my Gist Read more…
A Long short-term memory (LSTM) network is a special type of a recurrent neural network (RNN). It remembers values over arbitrary time intervals which makes it suited to be used for forecasting of time series. I was struggling with the DL4J implementation of the LSTM to be used to forecast stock data and I was not sure where the issue was: was the data not suited for forecasting ?. did I have a bug in my Iterator which was Read more…
I thought it might be cool to have a stock price generator which is generating a steady sinus output, so that it can be used for the testing of trading strategies. A sinus generator function generally needs the following input amplitude omega phase This is oscillating between -amplitude and +amplitude so we also need to add the following additional parameter to raise the center on the y axis offset These parameter can also be determined Read more…
In this Blog we demonstrate how Investor can be used together with DL4j and Spark’s MLlib. In general the idea is to use the stock history data in order to predict an increase or decrease of the stock value with the help of Machine Learning. We will demonstrate how we can use Indicators to define the machine learning model and how to evaluate the models and finally to check the result using the Investor framework. Here Read more…
BeakerX is available with Version 1.2.0 now: So it is time to convert my static resources with the dynamic tables and charts which are made available with the help of Widgets. Heres is the link to my gist which shows the use of these Widgets in Scala with the Investor framework!