Have a closer look into the Finamics structure and internal design.

ANN-TP system is structured to unify all possible data types to one common format. We build a system that takes into account as many factors as possible: technical indicators, chart patterns, correlations, fundamentals, trading activity of other traders, analysts and hedge-fund managers, news, tweets, articles and other data that may correlate with assets prices.

Finamics has a multi-layer structure that allows us to increase the level complexity in each of these layers in parallel.




We collect, process, store and analyze huge volumes of financial data that we receive from 40+ data sources. Each data source is a public or private site or API service that has information that we think may correlate with the prices of assets we predict or will predict.


Finamics ANN-TP is the code that describes the logic of how we interpret data from the data sources. It indicates what we should do when we receive a new data point from the data source – should we buy, should we sell, should we stay neutral.

Each strategy in the system generates a prediction, during its backtesting. These predictions are recorded in the database. Each prediction contains information about the direction (to buy or to sell), confidence level, duration and meet data of the market conditions in which this signal has happened.

We have strategies that are only based on quotes data. These strategies can be applied to any financial instrument and to any time-frame, since they are just a denormalization of historical series of quotes. Some strategies are based on the trading activity of other market player (traders, experts, hedge-fund managers). These strategies predict how successful a newly placed trade based on the past performance of the trader will be. Some strategies are based on tweets, news, and articles. These strategies use our own and open-sourced tools of syntax analysis to determine the sentiment level of the text.


Finamics ANN-TP is the program that takes as an input all predictions from all the strategies in the system and calculates weights for each strategy based on its performance. Then using this vector of weights, it calculates a cumulative prediction that is used to place buy and sell orders in the simulation environment.

We have many simulations with the different logic of weights distribution. Some of them use deep-learning neural nets, decision trees, gradient boosting, reinforcement learning, quality learning and many other machine learning models to properly calculate the weights. Our Core Team and Finamics Research Group experiment every day to find best performing predictive models. After all the results for all the simulations are calculated, the best performing simulation is selected and allowed to manage the funds.