Stock Fusion Algorithms

Market Forecasting Algorithms

StockFusion Studio

www.stockfusion.net

http://www.stockfusion.net/

http://www.stockfusion.net/

Premium selection of algorithms

StockFusion Studio - Intelligent Trading Expert Adviser 2

• Self-optimizing ARIMA expert

• Finite Impulse Response Neural Network

• Finite State Markov Automation

• Stepwise Best Regression

• Square Root Regression

• Square Regression

• Logistic Regression

ARIMA for time series forecasting


ARIMA models are, in theory, the most

general class of models for forecasting a

time series which can be made to be

“stationary” by differencing.

An ARIMA model can be viewed as a “filter”

that tries to separate the signal from the

noise, and the signal is then extrapolated into

the future to obtain forecasts.

Example of ARIMA forecast


Self-optimizing ARIMA expert


• Full ARIMA(p,d,q) implementation

• Unlimited order of mixed modeling

• Conditional error estimates

• Chi-square statistics on residuals

• Expert inference for optimal parameters

• Automatic trend adjustments

• Prediction on multiple future horizons

FIR Neural Network


• Finite-Impulse-Response (FIR)

• Optimal selection of filter parameters

• Adaptive neural network training

• Temporal back-propagation algorithm

Finite State Markov Automation


• Market data flow exploration

• Dynamically construct Markov models

• Building state transition graph

• Predict future market states

Stepwise Best Regression


Stepwise Regression Algorithm


• Enter and remove predictors, in a stepwise

manner, until there is no justifiable reason

to enter or remove more.

• At each step, enter or remove a predictor

based on partial F-tests.

• Stop when no more predictors can be

justifiably entered or removed from the

stepwise model.

Linear Regression


Linear Regression Model


• Simple linear regression

• Least squares estimator

• Single explanatory variable

iii εβXαY

• Classics of technical analysis

• Useful as a reference for comparison with nonlinear estimates

Linear versus Nonlinear Fit


Linear fit does not give

random residuals Nonlinear fit gives

random residuals

X

resid

ua

ls

X

Y

X

resid

ua

ls

Y

X

Square Root Regression


• The square-root transformation

iii εXββY 110

• Used to

• overcome violations of the

homoscedasticity assumption

• fit a non-linear relationship

Square Root Transformation


Shape of original relationship

X

b1 > 0

b1 < 0

X

Y

Y

Y

Y

Relationship when transformed

i1i10i εXββY i1i10i εXββY

Quadratic Regression Model


where:

β0 = Y intercept

β1 = regression coefficient for linear effect of X on Y

β2 = regression coefficient for quadratic effect on Y

εi = random error in Y for observation i

Model form:

iiii εXβXββY 2

12110

Logistic Regression


Log Transformation


Original multiplicative model Transformed multiplicative model

The Multiplicative Model:

Original multiplicative model Transformed exponential model

The Exponential Model:

Forecast with average value


• Simple moving average predictor

• Predicted value equal to moving

average over previous values

• Useful as a reference for comparison

with more complex algorithms

n

pppSMA

nMMM )1(1

History Prophet


• Dummy predictor for strategy testing

• Predicts every point with its future value

• Imitates a “prophet” knowing the future

• Delivers 100% of profitable trades

• Explicitly uses forward info

• Not suitable for practical trading

• Analog of “Maximum Profit System”

Maximum Profit Simulation


Extensible algorithmic API


• Modular algorithmic server

• Extendable calculation engine

• Real-time C++ core framework

• Open standard development API

• Universal DLL interface

• Compatibility with development tools

• Multiple sample models

About StockFusion


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Stock Fusion Algorithms

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