Technology Development

We develop tactical investment ideas and use cutting-edge technology to assist firms in putting them into action.

We collaborate with business and enterprise operations lines to ensure that analytical models meet both internal and regulatory needs. Financial Inference Technology specializes in artificial intelligence (AI) and machine learning (ML)-based trade execution and systems. These AI trading systems automate backtesting, trade execution, manage drawdowns and manage gain/loss limits.  

Evaluate crypto, equity, and commodities market fluctuations to increase the profitability of your financial institution.

Obtain automated buy/sell/stop points functionality. We can also assist with developing automated interfaces for trading systems to provide fast and efficient trade executions 24 hours a day, seven days a week. FIT Solutions USA also provides expertise to analyze financial data sets.

This includes using a variety of alternative data (both structured and unstructured) to create financial and quantitative projections.

Trading with Predictive Analytics (Sentiment Based)

With the use of sentiment analysis, AI can forecast the moves of other traders as well as the direction of stocks based on the study of news headlines, social media comments, and other platforms.

Machine Learning and Model Execution and Analysis with an AI Trading System

Regardless of the program or system employed, there are various phases to developing data-driven investment strategies. First, datasets are merged, standardized, outliers are removed, and factors having economic importance are created.

Second, analytical tools can then be used to determine how effectively these characteristics explain stock price movement and whether they have long-term worth. Finally, utilizing rules-based procedures or more sophisticated statistical based optimizations, these signals can be transformed into portfolios.

Machine learning excels in detecting patterns in data. We may utilize it to improve our typical data-driven investment strategies by looking for and exploiting patterns in our factors, for example. This enables us to create models that explain market performance in terms of a range of variables. To be successful in machine learning, the more mundane elements must be automated. Quants must be equipped with advanced tools that enable them to address these issues efficiently.

Machine learning and other expert systems are being used by a growing number of capital markets organizations to create algorithmic trading systems that learn from data rather than relying on rules-based systems.

AI is revolutionizing the trading desk because of data scientists' hiring, developments in cloud computing, and the availability of open-source frameworks for training machine learning models. The top institutions have already implemented self-learning algorithms for equities trading.

Because machine learning discovers patterns and behaviors in past data and learns from it, it is a natural progression from algorithmic trading. Programmers and quant strategists build these algorithms; however, these if/then rules-based algorithms do not learn on their own and must be updated.

The big difference with machine learning is that you hand it over to the computer to automatically discover the best trading patterns and update the algorithms automatically with no human intervention. ML systems allow us to measure events in the environment and incorporate new data from the market into the decision calculation rather than hard-coding rules all the time.

The most typical application of AI approaches is in algo execution scheduling, where urgency levels are calibrated to reduce market impact and optimize opportunistic pricing points.

Big data can be utilized to learn how better to plan algo kid slices than traditional algo code. In addition, AI is being used to predict price and volume and spread capture optimization looks to be working, especially in markets with bigger spreads.

Like the human brain, artificial neural networks accept data inputs and deliver data outputs across multiple nodes. These are already being developed across many industries.  Those nodes feature additional layers of variables and coefficients that update as new data arrives.

The future of AI/ML appears to be bright. Imagine having a swat team of brilliant engineers and data scientists with access to Amazon AWS's virtually infinite processing capacity and being able to construct AI-based trading platforms that automatically adjust their variables.