Artificial Intelligence (AI) and
Machine Learning (ML)

Capital markets dynamics are changing rapidly, with participation of more retail traders and investors and stock markets exists since decades, by now we have enough historical data of stocks, economy, inflation, geo politics situations, commodities, pandemic, etc. We believe in need to look how Artificial Intelligence and Machine Learning affects stock markets with by making dynamic algo trading strategies. Also, AI, ML and data science are radically transforming the world. It is already acknowledged that data is the new oil.

Artificial Intelligence for Algo Trading

With uncertain events, dynamicity of global markets tracking historical and live data, estimate the future market scenarios with great precision, AI-based algorithms are instrumental in optimizing people’s decision-making processes. Traders can take timely moves and optimize their gains based on these projections. We all know that human emotions may impact trading, a significant roadblock to achieving peak performance. Algorithms and computer programmes make choices faster than people without regard for extraneous considerations like emotions. Algo trading isn’t new, and we have been in the picture since the 80s, but lately, there has been a full-fledged use of AI for Algo trading.

There are different examples of Algo Trading strategies used in Artificial Intelligence
Trade Execution Algorithms

We divide the trade into smaller orders to reduce the impact on the stock price. Trade execution is a common practice.

Strategy Implementation Algorithms

We place trades based on indications generated by real-time market data.

Stealth / Gaming Algorithms

Price changes are caused by large transactions or other algorithmic tactics exploited by stealth or gaming algorithms.

Use of AI in Algorithmic Trading
High-Frequency Trading

High-Frequency Trading (HFT) is a type of algorithmic trading in which a huge order is executed in a fraction of a second. Humans are incapable of carrying out many commands in such a short period. Traders use algorithms and computers to automate orders because reading the market trend and placing bids manually takes a long time.

Helps in Finding Data Patterns

One of the most important goals for Artificial Learning is to use a large amount of previous data to forecast the future correctly. Traders frequently notice time and space-constrained localized trends and consider manipulating them for a higher return. Artificial Intelligence algorithms aid in discovering patterns that may be used in conjunction with traders’ intuition and expertise to make appropriate judgments.

Use of Chatbots

Chatbots converse with traders and provide them with a history of financial statements and other relevant data. Chatbots give the brokers helpful information, including real-time quotes, account statements, FAQs (Frequently Asked Questions), and price alerts. When Artificial Intelligence systems drive these chatbots, they outperform humans.

Machine Learning Models for Algo Trading
Time Series Model (Stock Price Prediction)

A time series is a set of measurements taken progressively in time over a period of time. Time series analysis, in its broadest sense, is inferring what happened to a set of data points in the past and attempting to forecast what will happen in the future. Time series analysis aims to comprehend the past while also forecasting the future. The ability to depict how variables change over time distinguishes time series data from other types of data. In precise sense, time is an important variable since it reveals how the data changes through time as well as the final outcomes.

Linear Regression / Polynomial Regression Model (Dynamic Support & Resistance for Stocks)

In order to determine a single relationship, linear regression examines two independent variables. This relates to the price and time variables in chart analysis. The ups and downs of price printed horizontally from day to day, minute to minute, or week to week, depending on the evaluated time frame, are recognised by investors and traders who employ charts. In Polynomial regression, the link between the independent variable x and the dependent variable y is described as an nth degree polynomial in x. The fitting of a nonlinear relationship between the value of x and the conditional mean of y is described by polynomial regression, abbreviated E(y|x).

Neural Networks (Prediction of Uncertainty, Volatility and Price for Stocks)

In the field of computer science, neural networks are cutting-edge. They're basically programmable algorithms that strive to replicate some characteristics of the human brain. The neurons in most neural networks are activated using mathematical functions. In arithmetic, a function is a relationship between a set of inputs and a set of outputs where each input corresponds to an output.

Pattern Recognition

Trading pattern recognition is the process of identifying patterns in the pricing of traded instruments. One advantage is that they have no bias on either the long or short side, which makes them extremely handy for CFD traders. Keep in mind that if you are consistently biassed to the long side of the market, you may be losing out on some of the pattern's most appealing qualities.

Our Research

Artificial Intelligence (AI) & Machine Learning (ML) based algorithmic trading strategies. Dynamic risk reward trades & dynamic risk management within each trading strategy.

Pattern based trading strategies (rising wedge, falling wedge patterns) to identify dynamic support and resistance.

Modelling pair trading strategies for forex, indices and stocks. Price predictive models, linear regression trading models.