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Impl Garch model (#2)
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* #binomial tree Model init

* add binomial tree models docs && add unit test

* Refactor binomial tree model to use  enum for price initialization

* Impl Garch model && add Readme
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Liberxue authored Jul 28, 2024
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types: [opened, synchronize]
paths-ignore:
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readme = "README.md"
license = "Apache-2.0"


[dependencies]

201 changes: 194 additions & 7 deletions README.md
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<div align="center">
[![Rust Version](https://img.shields.io/badge/Rust-1.79.0+-blue)](https://releases.rs/docs/1.79.0)
---

**Custom Neuron Decision-Making and Visual Workflow Orchestration Quantitative**

<br/>
</div>

[![](https://img.shields.io/badge/Rust-1.79.0+-blue)](https://releases.rs/docs/1.79.0)

## Examples

<details>
<summary> Models Example </summary>

#### Models Example

```rust
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);
```

</details>

<details>
<summary> Strategies Example </summary>

#### [Strategies Example](core/src/tests)
```rust
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);
}
```
</details>


## Quantitative Models

### [Binomial Tree Model](core/src/models/binomial_tree.rs)
**U**sed 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](core/src/models/black_scholes.rs)
**U**sed 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](core/src/models/monte_carlo.rs)
**U**sed 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](core/src/models/garch.rs)

**U**sed 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
<details>
<summary> Click More 100+ Model </summary>

| **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 |
</details>

# Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or new features.
Contributions are welcome! [Please open an issue](https://github.com/Liberxue/cqf/issues/new) or [submit PR ] (https://github.com/Liberxue/cqf/pulls) for any improvements or new features.

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