Articles

What is Algotrading:

Pattern trading or algorithmic trading is the use of a trading system to make decisions about the transactions we do in the financial market. Algorithmic trading is done using advanced mathematical formulas and different algorithms, where human intervention is minimized and decisions are made quickly. One of the features of the trading pattern is that the system can identify and examine all the profitable opportunities in the market and suggest it to the trader.

In pattern trading, computer programs and software are used to increase the speed of trading, and in a short time, a lot of data can be checked based on various predetermined criteria. Algorithms used for trading patterns can be defined based on different principles. Therefore, traders can use different algorithms and after that the trading is done automatically by the system itself.

In fact, in algo trading, we use a computer program that is connected to the trading system so that the program actually trades and buys and sells stocks, futures, Forex, CFD and cryptocurrency markets, etc. for us. These types of programs generally use several steps to complete the transaction. First it gets the available data. Then they analyze and calculate these data, then analyze and check the conditions using tools and finally execute the appropriate order for the transaction.

Also, in AlgoTara's data analysis structure, artificial neural networks and deep data analysis are used exclusively for financial markets. Artificial neural networks (Artificial Neural Networks - ANN) or, in simpler words, neural networks, are modern computing systems and methods for machine learning, displaying knowledge, and finally applying the obtained knowledge to predict the output responses of complex systems. Using the knowledge of computer programming, a data structure can be designed that acts like a neuron. Then, by creating a network of these interconnected artificial neurons, he created a training algorithm for the network and applied this algorithm to the network.

These networks have shown very high efficiency for estimation and approximation. The range of application of these mathematical models derived from the functioning of the human brain is very wide, and as a few examples, we can mention the use of this mathematical tool in the processing of biological, telecommunication and electronic signals to help in astronomy and astronautics.

Deep learning is a subfield of machine learning that uses multiple layers of linear transformations to process sensory signals such as audio and images. In this method, the machine divides each complex concept into simpler concepts, and with the continuation of this process, it reaches the basic concepts for which it is able to make decisions, and thus there is no need for full human supervision to determine the necessary information for the machine at any moment.