README.md

Custom Neuron Decision-Making and Visual Workflow Orchestration Quantitative

Examples

Models Example

Models Example

    let model = BlackScholesModel; // BinomialTreeModel OR BlackScholesModel GarchModel MonteCarloModel ...
    let params = OptionParameters {
        s: opts.s,
        k: opts.k,
        r: opts.r,
        sigma: opts.sigma,
        t: opts.t,
    };

    let call_price = model.call_price(&params);
    let put_price = model.put_price(&params);

Strategies Example

Strategies Example

fn test_dance() {
    let model = BlackScholesModel;
    let params1 = OptionParameters {
        s: 100.0,
        k: 90.0,
        r: 0.05,
        sigma: 0.2,
        t: 0.5,
    };
    let params2 = OptionParameters {
        s: 100.0,
        k: 100.0,
        r: 0.05,
        sigma: 0.2,
        t: 0.5,
    };
    let params3 = OptionParameters {
        s: 100.0,
        k: 110.0,
        r: 0.05,
        sigma: 0.2,
        t: 0.5
    };
    let dance = Dance::new(&model, params1, params2, params3);
    let price = dance.price();
    assert!(price > 0.0 && price < 100.0);
}

Quantitative Models

Binomial Tree Model

Used for option pricing by constructing a binomial tree to represent possible paths an asset's price could take over time. It is particularly useful for valuing American options, which can be exercised at any time before expiration.

Black-Scholes Model

Used model for pricing European options. It assumes that the price of the underlying asset follows a geometric Brownian motion with constant volatility and interest rate. The model provides a closed-form solution for option pricing.

Monte Carlo Model

Used to value options by simulating a large number of possible price paths for the underlying asset. It is particularly useful for valuing complex derivatives and options with path-dependent features, as it can accommodate various stochastic processes and payoff structures.

GARCH Model

Used for modeling financial time series data that exhibit volatility clustering. It extends the ARCH model by allowing past variances to influence current variances, providing a more flexible approach to volatility modeling.

GARCH/AGARCH Model More

Click More 100+ Model

Model Name	Description
AARCH	Handles asymmetric volatility in time series
DVEC-GARCH	Uses diagonal vector model to handle multivariate data volatility
GARJI	Combines GARCH model with jumps to capture sudden price changes
MS-GARCH	Combines Markov state switching with GARCH model
SPARCH	Handles smooth transitions in volatility
ADCC-GARCH	Handles asymmetric dynamic conditional correlation
EGARCH	Uses exponential function to handle asymmetric volatility
GDCC-GARCH	A generalized dynamic conditional correlation model
MV-GARCH	Handles multivariate data volatility
Spline-GARCH	Uses spline functions to model volatility
AGARCH	An adjusted GARCH model for better fit
EVT-GARCH	Incorporates extreme value theory into GARCH modeling
GED-GARCH	Uses Generalized Error Distribution for modeling
NAGARCH	Nonlinear asymmetric GARCH model
SQR-GARCH	Uses squared returns in GARCH model
ANN-ARCH	Uses artificial neural networks with ARCH model
F-ARCH	Fractionally integrated ARCH model
GJR-GARCH	Threshold GARCH model that captures leverage effect
NGARCH	Nonlinear GARCH model
STARCH	Smooth transition ARCH model
ANST-GARCH	Asymmetric nonlinear smooth transition GARCH model
FDCC-GARCH	Flexible dynamic conditional correlation GARCH model
GO-GARCH	Generalized orthogonal GARCH model
NL-GARCH	Nonlinear GARCH model
Stdev-ARCH	Standard deviation ARCH model
APARCH	Asymmetric power ARCH model
FGARCH	Flexible GARCH model
GQARCH	Quadratic GARCH model
NM-GARCH	Nonparametric GARCH model
STGARCH	Smooth transition GARCH model
ARCH-M	ARCH-in-mean model
FIAPARCH	Fractionally integrated asymmetric power ARCH model
GQTARCH	Generalized quadratic ARCH model
OGARCH	Orthogonal GARCH model
Structural GARCH	Models structural changes in volatility
ARCH-SM	Stochastic mean ARCH model
FIEGARCH	Fractionally integrated EGARCH model
HARCH	Hierarchical ARCH model
PARCH	Power ARCH model
Strong GARCH	Robust GARCH model
ATGARCH	Adaptive threshold GARCH model
FIGARCH	Fractionally integrated GARCH model
HGARCH	Heteroscedastic GARCH model
PC-GARCH	Principal component GARCH model
SWARCH	Switching ARCH model
Aug-GARCH	Augmented GARCH model
FIREGARCH	Fractionally integrated random effects GARCH model
HYGARCH	Hyperbolic GARCH model
PGARCH	Polynomial GARCH model
TGARCH	Threshold GARCH model
AVGARCH	Average GARCH model
Flex-GARCH	Flexible GARCH model
IGARCH	Integrated GARCH model
PNP-GARCH	Penalized nonparametric GARCH model
t-GARCH	Student-t GARCH model
B-GARCH	Bayesian GARCH model
GAARCH	Generalized asymmetric ARCH model
LARCH	Linear ARCH model
QARCH	Quadratic ARCH model
Tobit-GARCH	Tobit GARCH model
BEKK-GARCH	Baba, Engle, Kraft and Kroner GARCH model
GARCH-Delta	Delta GARCH model
Latent GARCH	Latent variable GARCH model
QTARCH	Quantile threshold ARCH model
TS-GARCH	Time series GARCH model
CCC-GARCH	Constant conditional correlation GARCH model
GARCH Diffusion	Diffusion GARCH model
Level GARCH	Level shift GARCH model
REGARCH	Robust and efficient GARCH model
UGARCH	Univariate GARCH model
Censored-GARCH	Censored GARCH model
GARCH-EAR	GARCH model with expected average returns
LGARCH	Logarithmic GARCH model
RGARCH	Robust GARCH model
VCC-GARCH	Varying coefficient correlation GARCH model
CGARCH	Component GARCH model
GARCH-Gamma	GARCH model with gamma distribution
LMGARCH	Log-mean GARCH model
Robust GARCH	Robust GARCH model
VGARCH	Vector GARCH model
COGARCH	Continuous-time GARCH model
GARCH-M	GARCH-in-mean model
Log-GARCH	Logarithmic GARCH model
Root GARCH	Root GARCH model
VSGARCH	Volatility spillover GARCH model
CorrARCH	Correlation ARCH model
GARCHS	Seasonal GARCH model
MAR-ARCH	Multivariate ARCH model
RS-GARCH	Regime switching GARCH model
Weak GARCH	Weak GARCH model
DAGARCH	Diagonal ARCH model
GARCHSK	GARCH model with skewness
MARCH	Moving average ARCH model
Robust DCC-GARCH	Robust dynamic conditional correlation GARCH model
ZARCH	Zero-inflated ARCH model
DCC-GARCH	Dynamic conditional correlation GARCH model
GARCH-t	GARCH model with t-distribution
Matrix EGARCH	Matrix exponential GARCH model
SGARCH	Seasonal GARCH model
Diag MGARCH	Diagonal multivariate GARCH model
GARCH-X	GARCH model with exogenous variables
MGARCH	Multivariate GARCH model
S-GARCH	Smooth GARCH model
DTARCH	Double threshold ARCH model
GARCHX	GARCH model with explanatory variables
Mixture GARCH	Mixture of GARCH models
Sign-GARCH	GARCH model with sign-dependent effects

Contributing

Contributions are welcome! Please open an issue or submit PR for any improvements or new features.