Builtin forecasting expert works on unique collection of elaborate predictors, technical indicators, digital filters and statistical tests to achieve top forecasting accuracyand reliability in full automated unattended mode. Not offered in any other product.
Trading strategies
Forecasting algorithms
Trading performance criteria

Technical indicators

Free open API enables unlimited system extensibility both in data access and additional forecasting technologies which then are native consumed in system kernel expert reasoning. SOAP, DCOM and C++ bindings give easy direct integration with virtually any enterprise infrastructure including Java, NET, Delphi, MS Office, online portals and interactive web services.
Formulas
Below we provide the full table of algorithms available in ForeStock. Algorithms are grouped according to their packages. Packages are separate modules in common algorithmic space and are licensed individually. You can watch names of packages in License Manager. Licensing any package implies all algorithms contained inside it.
Forecasting algorithm  Description  Type 
ARIMA Expert  
ARIMA with expert model fit  Seasonal AutoRegressive Integrated Moving Average forecasting model with automatic expert inference on all model parameters.  Predictor 
Finite State Markov Automation  
Finite State Markov Automation  We dynamically construct Markov models that describe the characteristics of Market data flow. Such models are used to predict future market states.  Predictor 
Finite Impulse Response NN  
Finite impulse response neural network  The finite impulse response neural network is a neural network, where scalar weights are replaced with moving average filters. These filters compute a weighted average of past values presented to the network, as opposed to the feedforward network, which only computes a weighted “average” of the current value. These networks are trained using a variation on the backpropagation algorithm.  Predictor 
Advanced Regressions  
Forecast with average value  Classical moving average with period 20  Predictor 
Linear regression  Linear regression liney = at + bcalculated over 20 last points  Predictor 
Exponential Fit  Exponential regression curvey = e^{at + b}calculated over 20 last points  Predictor 
Logarithmic Fit  Logarithmic regressiony = log(at + b)calculated over 20 last points  Predictor 
Logistic Fit  Logistic regressiony = c / [1 + e^{(at + b)}]calculated over 20 last points  Predictor 
Square Fit  Parabolic regressiony = (at + b)^{2}calculated over 20 last points  Predictor 
Square Root Fit  Square root regressiony = (at + b)^{1/2}calculated over 20 last points  Predictor 
History Prophet  Emulates “ideal” predictor. Forecast is set to real next observed value, which ensures 100% forecasting accuracy on historical data. It is very useful to calibrate performance of trading strategies in “ideal” conditions. In no case, it should be used as predictor in real trading.  Predictor 
Naive Predictor  Forecast with the previous price. Dummy forecast to evaluate performance of other algorithms.  Predictor 