In the development of the fully autonomous machine learning predictive trading system, I have collaborated with some of the smartest quants and algorithm developers, that combine predictive modeling, quantitative analysis, and platforms such as MATLAB and Python to create powerful trading tools that analyze market data and take into account hundreds of market factors to identify lucrative trading opportunities pertaining to what the market is doing in real-time rather than use lagging information.

Predictive modeling uses historical data to predict future events. Typically, historical data is used to build a mathematical model that captures important trends. That predictive model is then used on current data to predict what will happen next, or to suggest actions to take for optimal outcomes.
Quantitative analysis aims to understand or predict behavior or events through the use of mathematical measurements and calculations, statistical modeling and research. The primary advantage of quantitative analysis is that it involves studying precise, definitive values that can easily be compared with each other.
 
MATLAB and Python are two of the most useful software platforms ever created and widely used by many quants, algo developers and other classes of traders and funds. Being able to run millions of data points in a matter of seconds or less makes these software platforms the ultimate developer’s tool. Whatever you can imagine and conceive, you can build in these platforms.
 
All original theories and strategy ideas are modeled and tested in one or both of these platforms. If it works, then it is converted to a usable indicator and made available to trade. If it doesn’t work, it is either modified and retested or completely scrapped.
Predictive modeling is superior to human intuition in selecting the best indicators and combining them into a prediction. There have been over 150 academic studies comparing human experts to statistical models attesting to this fact.

Predictive modeling is a process used in predictive analytics to create a statistical model of future behavior. Predictive analytics is the area of data mining concerned with forecasting probabilities and trends. Predictive modeling in trading is a modeling process wherein the probability of an outcome is predicted using a set of predictor variables.

 

Predictive models can be built for different assets like stocks, futures, currencies, commodities etc. Predictive modeling is extensively used by trading firms to devise strategies and trade. At Empirical we trade to exploit micro-trends in currencies, gold, silver, oil, and equity indexes, with a multi-time frame, diversified portfolio approach.