AI can help in predicting cryptocurrency value

Since 2008, Cryptocurrencies have walked a long way to make its impact on the market. From zero to $400bn, the cryptocurrencies market has touched the sky in a decade. Started with only Bitcoin, there are now more than 3,000 cryptocurrencies in the market.

So was it an easy way to get this market value? Clearly no! As Bitcoin has seen the days of value $20,000 to less than a cent too. You can’t predict the market value of cryptocurrencies. There are many individuals who have made a lot of money in the cryptocurrency market but some has lost a lot of money too. Artificial Intelligence can help you know when the market will go down or up in cryptocurrencies term. It will help investors to invest in them at right time. If you are your looking for technology equipment then you can choose to find some of best Amazon Coupon Code via IndiaShoppers.in.

Let’s check how:

Use of sentiment analysis to predict the value

There are many predictors who predict the value of cryptocurrencies. But, it isn’t easy to predict it. Funny thing about the crypto market is that it is not like the stock marketing. In this market, there are no such factors called cash flow or available assets to predict the value. In this case, investors depend on sentiments.

Recently developer Teju Tadi talked about sentiment analysis. As the market value of cryptocurrency depends on the crowd, Teju said that the sentiment analysis can be done through news headlines, Reddit posts, and tweets. These things can give you the indications where the cryptocurrency value will head- either rock bottom or skyrocket.

Moreover, Teju is working on analyzing the sentiment out of these tweets, Reddit posts and more to develop an AI cryptocurrency trading bot using recursive neural tensor networks (RNTNs).

Recursive Neural Tensor Networks

Recursive Neural Tensor Networks checks out the semantic compositions of text which further determine the sentiment from just little set of information like tweets and Reddit posts.

By parsing the data into a binary tree, all the words get their own vector representations and are represented as leaves. The approach used is bottom up where these vectors act as the parameters to serve as the inputs to a softmax classifier. Then, vectors are classified into five different classes and given a score.

As per Teju, “When similarities are encoded between two words, the two vectors move across to the next root. A score and class are outputted. A score represents the positivity or negativity of a parse while the class encodes the structure in current parses. The first leaf group receives the parse and then the second leaf receives the next word. The score of the parse with all three words are outputted and it moves on to the next root group.”

Moreover, he added “The recursion process continues until all inputs are used up, with every single word included. In practical applications RNTN’s end up being more complex than this. Rather than using the immediate next word in a sentence for the next leaf group, an RNTN would try all the next words and eventually checks vectors that represent entire sub-parses. Performing this at every step of the recursive process, the RNTN can analyze every possible score of the syntactic parse.”

Technology Support

Working in Intel AI Academy, Teju used the Intel AI DevCloud to run RNTNs and play with the Twitter data to get the results. Intel AI DevCloud runs on Intel Xeon Scalable processors and is loaded with frameworks and tools to ease the work in the field of machine learning and deep learning. These frameworks and tools include neon framework, Intel Optimization for Theano, Intel Optimization for TensorFlow, Intel Optimization for Caffe, Intel Distribution for Python including NumPy, SciPy, and scikit-learn, and the Keras library. If you are looking for best antivirus for your system then you can try to make online shopping with available Flipkart Offers online today.

An Opportunity For Developers

Teju talked about his business, Mycointrac which is based on market intelligence in the niche of cryptocurrencies. He said, “Once the product is fully developed, I plan to utilize the data provided by it as one of the factors to make key investment decisions for my new cryptocurrency hedge fund, Sentience Investments L.P., which has been operational since January first. The plan is to develop trading strategies based on a number of high-frequency, machine-learning techniques, as well as deep learning and sentiment analysis.”

Teju seems very confident on how RNTNs can help in other opportunities too like arbitrage where assets are bought and sold at a time in different markets and the profit is cutout by checking the difference between the two market prices.

The financial sector also seeks developers to utilize AI for the market’s upcoming challenging like always high returns on market investments.