{"id":18887281,"url":"https://github.com/liberxue/cqf","last_synced_at":"2025-07-08T12:07:53.932Z","repository":{"id":249529986,"uuid":"831730402","full_name":"Liberxue/cqf","owner":"Liberxue","description":"Custom Neuron Decision-Making and Visual Workflow Orchestration Quantitative","archived":false,"fork":false,"pushed_at":"2025-04-10T14:37:25.000Z","size":1110,"stargazers_count":17,"open_issues_count":4,"forks_count":5,"subscribers_count":1,"default_branch":"dev","last_synced_at":"2025-04-14T22:53:06.605Z","etag":null,"topics":["binomial-options-pricing-model","blackscholes","bopm","burn","cqf","delta","gamma","garch-model","greeks-calculatio","heston-model","machine-learning-algorithms","monte-carlo-simulation","neurons","quantitative-trading","rho","rust","theta","vega"],"latest_commit_sha":null,"homepage":"","language":"Rust","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Liberxue.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":"SECURITY.md","support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2024-07-21T13:11:23.000Z","updated_at":"2025-02-28T14:51:06.000Z","dependencies_parsed_at":"2024-09-09T20:34:53.756Z","dependency_job_id":"b57e9fd0-b64d-4d1d-aa98-9e1a75001c9a","html_url":"https://github.com/Liberxue/cqf","commit_stats":null,"previous_names":["liberxue/cqf"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Liberxue/cqf","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Liberxue%2Fcqf","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Liberxue%2Fcqf/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Liberxue%2Fcqf/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Liberxue%2Fcqf/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Liberxue","download_url":"https://codeload.github.com/Liberxue/cqf/tar.gz/refs/heads/dev","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Liberxue%2Fcqf/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":264267069,"owners_count":23581929,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["binomial-options-pricing-model","blackscholes","bopm","burn","cqf","delta","gamma","garch-model","greeks-calculatio","heston-model","machine-learning-algorithms","monte-carlo-simulation","neurons","quantitative-trading","rho","rust","theta","vega"],"created_at":"2024-11-08T07:36:08.136Z","updated_at":"2025-07-08T12:07:53.909Z","avatar_url":"https://github.com/Liberxue.png","language":"Rust","funding_links":[],"categories":[],"sub_categories":[],"readme":"**Custom Neuron Decision-Making and Visual Workflow Orchestration Quantitative**\n\n \n[![](https://img.shields.io/badge/Rust-1.79.0+-blue)](https://releases.rs/docs/1.79.0)   \n\n## CLI DEMO\n[\u003cimg src=\"./ui/cqf.gif\" width=\"200%\"/\u003e](./ui/cqf.gif)\n\n\n## Examples\n\n\u003cdetails\u003e\n  \u003csummary\u003e Models Example   \u003c/summary\u003e\n  \n#### Models Example \n  \n```rust\n    let model = BlackScholesModel; // BinomialTreeModel OR BlackScholesModel GarchModel MonteCarloModel ...\n    let params = OptionParameters {\n        s: opts.s,\n        k: opts.k,\n        r: opts.r,\n        sigma: opts.sigma,\n        t: opts.t,\n    };\n\n    let call_price = model.call_price(\u0026params);\n    let put_price = model.put_price(\u0026params);\n```\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n  \u003csummary\u003e Strategies Example  \u003c/summary\u003e\n  \n#### [Strategies Example](core/src/tests)\n```rust\nfn test_dance() {\n    let model = BlackScholesModel;\n    let params1 = OptionParameters {\n        s: 100.0,\n        k: 90.0,\n        r: 0.05,\n        sigma: 0.2,\n        t: 0.5,\n    };\n    let params2 = OptionParameters {\n        s: 100.0,\n        k: 100.0,\n        r: 0.05,\n        sigma: 0.2,\n        t: 0.5,\n    };\n    let params3 = OptionParameters {\n        s: 100.0,\n        k: 110.0,\n        r: 0.05,\n        sigma: 0.2,\n        t: 0.5\n    };\n    let dance = Dance::new(\u0026model, params1, params2, params3);\n    let price = dance.price();\n    assert!(price \u003e 0.0 \u0026\u0026 price \u003c 100.0);\n}\n```\n\u003c/details\u003e\n\n\n## Quantitative Models\n\n### [Binomial Tree Model](core/src/models/binomial_tree.rs)\n**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.\n\n\n### [Black-Scholes Model](core/src/models/black_scholes.rs)\n**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.\n\n### [Monte Carlo Model](core/src/models/monte_carlo.rs)\n**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.\n\n\n### [GARCH Model](core/src/models/garch.rs)\n\n**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.\n\n###  GARCH/AGARCH Model More  \n\u003cdetails\u003e\n  \u003csummary\u003e Click More 100+ Model  \u003c/summary\u003e\n  \n  | **Model Name**    | **Description**                                           |\n  |-------------------|-----------------------------------------------------------|\n  | AARCH             | Handles asymmetric volatility in time series              |\n  | DVEC-GARCH        | Uses diagonal vector model to handle multivariate data volatility |\n  | GARJI             | Combines GARCH model with jumps to capture sudden price changes |\n  | MS-GARCH          | Combines Markov state switching with GARCH model          |\n  | SPARCH            | Handles smooth transitions in volatility                  |\n  | ADCC-GARCH        | Handles asymmetric dynamic conditional correlation        |\n  | EGARCH            | Uses exponential function to handle asymmetric volatility |\n  | GDCC-GARCH        | A generalized dynamic conditional correlation model       |\n  | MV-GARCH          | Handles multivariate data volatility                      |\n  | Spline-GARCH      | Uses spline functions to model volatility                 |\n  | AGARCH            | An adjusted GARCH model for better fit                    |\n  | EVT-GARCH         | Incorporates extreme value theory into GARCH modeling     |\n  | GED-GARCH         | Uses Generalized Error Distribution for modeling          |\n  | NAGARCH           | Nonlinear asymmetric GARCH model                          |\n  | SQR-GARCH         | Uses squared returns in GARCH model                       |\n  | ANN-ARCH          | Uses artificial neural networks with ARCH model           |\n  | F-ARCH            | Fractionally integrated ARCH model                        |\n  | GJR-GARCH         | Threshold GARCH model that captures leverage effect       |\n  | NGARCH            | Nonlinear GARCH model                                     |\n  | STARCH            | Smooth transition ARCH model                              |\n  | ANST-GARCH        | Asymmetric nonlinear smooth transition GARCH model        |\n  | FDCC-GARCH        | Flexible dynamic conditional correlation GARCH model      |\n  | GO-GARCH          | Generalized orthogonal GARCH model                        |\n  | NL-GARCH          | Nonlinear GARCH model                                     |\n  | Stdev-ARCH        | Standard deviation ARCH model                             |\n  | APARCH            | Asymmetric power ARCH model                               |\n  | FGARCH            | Flexible GARCH model                                      |\n  | GQARCH            | Quadratic GARCH model                                     |\n  | NM-GARCH          | Nonparametric GARCH model                                 |\n  | STGARCH           | Smooth transition GARCH model                             |\n  | ARCH-M            | ARCH-in-mean model                                        |\n  | FIAPARCH          | Fractionally integrated asymmetric power ARCH model       |\n  | GQTARCH           | Generalized quadratic ARCH model                          |\n  | OGARCH            | Orthogonal GARCH model                                    |\n  | Structural GARCH  | Models structural changes in volatility                   |\n  | ARCH-SM           | Stochastic mean ARCH model                                |\n  | FIEGARCH          | Fractionally integrated EGARCH model                      |\n  | HARCH             | Hierarchical ARCH model                                   |\n  | PARCH             | Power ARCH model                                          |\n  | Strong GARCH      | Robust GARCH model                                        |\n  | ATGARCH           | Adaptive threshold GARCH model                            |\n  | FIGARCH           | Fractionally integrated GARCH model                       |\n  | HGARCH            | Heteroscedastic GARCH model                               |\n  | PC-GARCH          | Principal component GARCH model                           |\n  | SWARCH            | Switching ARCH model                                      |\n  | Aug-GARCH         | Augmented GARCH model                                     |\n  | FIREGARCH         | Fractionally integrated random effects GARCH model        |\n  | HYGARCH           | Hyperbolic GARCH model                                    |\n  | PGARCH            | Polynomial GARCH model                                    |\n  | TGARCH            | Threshold GARCH model                                     |\n  | AVGARCH           | Average GARCH model                                       |\n  | Flex-GARCH        | Flexible GARCH model                                      |\n  | IGARCH            | Integrated GARCH model                                    |\n  | PNP-GARCH         | Penalized nonparametric GARCH model                       |\n  | t-GARCH           | Student-t GARCH model                                     |\n  | B-GARCH           | Bayesian GARCH model                                      |\n  | GAARCH            | Generalized asymmetric ARCH model                         |\n  | LARCH             | Linear ARCH model                                         |\n  | QARCH             | Quadratic ARCH model                                      |\n  | Tobit-GARCH       | Tobit GARCH model                                         |\n  | BEKK-GARCH        | Baba, Engle, Kraft and Kroner GARCH model                 |\n  | GARCH-Delta       | Delta GARCH model                                         |\n  | Latent GARCH      | Latent variable GARCH model                               |\n  | QTARCH            | Quantile threshold ARCH model                             |\n  | TS-GARCH          | Time series GARCH model                                   |\n  | CCC-GARCH         | Constant conditional correlation GARCH model              |\n  | GARCH Diffusion   | Diffusion GARCH model                                     |\n  | Level GARCH       | Level shift GARCH model                                   |\n  | REGARCH           | Robust and efficient GARCH model                          |\n  | UGARCH            | Univariate GARCH model                                    |\n  | Censored-GARCH    | Censored GARCH model                                      |\n  | GARCH-EAR         | GARCH model with expected average returns                 |\n  | LGARCH            | Logarithmic GARCH model                                   |\n  | RGARCH            | Robust GARCH model                                        |\n  | VCC-GARCH         | Varying coefficient correlation GARCH model               |\n  | CGARCH            | Component GARCH model                                     |\n  | GARCH-Gamma       | GARCH model with gamma distribution                       |\n  | LMGARCH           | Log-mean GARCH model                                      |\n  | Robust GARCH      | Robust GARCH model                                        |\n  | VGARCH            | Vector GARCH model                                        |\n  | COGARCH           | Continuous-time GARCH model                               |\n  | GARCH-M           | GARCH-in-mean model                                       |\n  | Log-GARCH         | Logarithmic GARCH model                                   |\n  | Root GARCH        | Root GARCH model                                          |\n  | VSGARCH           | Volatility spillover GARCH model                          |\n  | CorrARCH          | Correlation ARCH model                                    |\n  | GARCHS            | Seasonal GARCH model                                      |\n  | MAR-ARCH          | Multivariate ARCH model                                   |\n  | RS-GARCH          | Regime switching GARCH model                              |\n  | Weak GARCH        | Weak GARCH model                                          |\n  | DAGARCH           | Diagonal ARCH model                                       |\n  | GARCHSK           | GARCH model with skewness                                 |\n  | MARCH             | Moving average ARCH model                                 |\n  | Robust DCC-GARCH  | Robust dynamic conditional correlation GARCH model        |\n  | ZARCH             | Zero-inflated ARCH model                                  |\n  | DCC-GARCH         | Dynamic conditional correlation GARCH model               |\n  | GARCH-t           | GARCH model with t-distribution                           |\n  | Matrix EGARCH     | Matrix exponential GARCH model                            |\n  | SGARCH            | Seasonal GARCH model                                      |\n  | Diag MGARCH       | Diagonal multivariate GARCH model                         |\n  | GARCH-X           | GARCH model with exogenous variables                      |\n  | MGARCH            | Multivariate GARCH model                                  |\n  | S-GARCH           | Smooth GARCH model                                        |\n  | DTARCH            | Double threshold ARCH model                               |\n  | GARCHX            | GARCH model with explanatory variables                    |\n  | Mixture GARCH     | Mixture of GARCH models                                   |\n  | Sign-GARCH        | GARCH model with sign-dependent effects                   |\n\u003c/details\u003e\n\n# Contributing\n\nContributions 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.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fliberxue%2Fcqf","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fliberxue%2Fcqf","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fliberxue%2Fcqf/lists"}