I Know First Stock Forecast Algorithm
Stock Forecast Algorithm
The system is a predictive stock forecast algorithm that is based on Artificial Intelligence (AI), Machine Learning (ML), and incorporating elements of Artificial Neural Networks and Genetic Algorithms.
That means: The Algorithm creates relationships between the different financial assets and is able to modify, delete and create new relationships. Based on the relationships and the latest market data, the algorithm calculates the forecasts. Since the Algorithm learns from its previous forcasts and is continously adapting the relationships, it is able to adapt quickly to changing market situations.
I Know First Market Prediction System models and predicts the flow of money between the markets. It separates the predictable part from stochastic (random) noise. It then creates a model that projects the future trajectory of the given market in the multidimensional space of other markets.
The system outputs the predicted trend as a number, positive or negative, along with the wave chart that predicts how the waves will overlap the trend. This helps the trader to decide which direction to trade, at what point to enter the trade, and when to exit.
The model is 100% empirical, meaning it is based on historical data and not on any human derived assumptions. The human factor is only involved in building the mathematical framework and initially presenting to the system the “starting set” of inputs and outputs.
From that point onwards the computer algorithms take over; they constantly propose “theories” and test them automatically on years of daily market data, then validate them on the most recent data, which prevents over-fitting. Some inputs are being “rejected”, meaning they don’t improve the model. Then another input could be substituted.
This bootstrapping system is self learning, and thus live. The resulting formula is constantly evolving, as new daily data is added and as a better machine-proposed “theory” is found.
Some stocks are members of several separate modules. Thus multiple predictions can be obtained, based on different data sets. Also each module consists of a number of sub-modules, each giving an independent prediction. If sub-modules give contradictory predictions, this should be a warning sign. Six different filters are also employed to refine the predictions.