Stock Fusion Algorithms

Market Forecasting Algorithms
StockFusion Studio
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
residuals
X
Y
X
residuals
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
ppp
SMA
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

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Stock Fusion Algorithms

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