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Custom Neuron Decision-Making and Visual Workflow Orchestration Quantitative

CLI DEMO

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
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

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.

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.

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.

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.