add more documentation

docs/source/api_reference.rst

@@ -1,30 +1,14 @@
-api_reference.rst
-===============================
 API Reference
 =============
+
+This section provides detailed documentation for the TorchQuant API.
+
 .. toctree::
    :maxdepth: 2
 
    torchquantlib.core
-   torchquantlib.models
-   torchquantlib.utils
    torchquantlib.calibration
+   torchquantlib.risk
+   torchquantlib.utils
 
-torchquantlib.core.rst (Example for one module)
-===============================
-Core
-========
-
-.. automodule:: torchquantlib.core
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-Submodules
-----------
-
-.. toctree::
-   :maxdepth: 4
-
-   torchquantlib.core.asset_pricing
-   torchquantlib.core.risk
+For more information on specific modules and functions, please refer to the individual module pages.

docs/source/changelog.rst

@@ -0,0 +1,18 @@
+Changelog
+=========
+
+This page documents the version history and changes made to TorchQuant.
+
+Version 1.3.0 (Current Release)
+-------------------------------
+
+- Current stable release of TorchQuant
+- Four main modules: core, calibration, risk, and utils
+- Compatible with PyTorch 2.4.0
+
+For detailed changes in this version, please refer to the release notes.
+
+Previous Versions
+-----------------
+
+For information about previous versions, please check our GitHub repository.

docs/source/conf.py

@@ -1,44 +1,35 @@
 # Configuration file for the Sphinx documentation builder.
+#
+# For the full list of built-in configuration values, see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Project information -----------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
 
 import os
 import sys
 import sphinx_rtd_theme
 
-sys.path.insert(0, os.path.abspath('..'))
+sys.path.insert(0, os.path.abspath('../../torchquantlib'))
 
-# -- Project information -----------------------------------------------------
 project = 'TorchQuant'
 copyright = '2024, Jialue Chen'
 author = 'Jialue Chen'
 release = '1.3.0'
 
 # -- General configuration ---------------------------------------------------
-extensions = [
-    'sphinx.ext.autodoc',
-    'sphinx.ext.napoleon',
-    'sphinx.ext.viewcode',
-    'sphinx.ext.githubpages',
-]
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
+
+extensions = ['sphinx.ext.autodoc', 'sphinx.ext.viewcode', 'sphinx.ext.napoleon', 'sphinx.ext.mathjax', 'sphinx.ext.coverage', 'sphinx.ext.githubpages','sphinx.ext.intersphinx','sphinx.ext.autosummary']
 
 templates_path = ['_templates']
 exclude_patterns = []
 
+
+
 # -- Options for HTML output -------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
+
 html_theme = 'sphinx_rtd_theme'
-html_theme_path=[sphinx_rtd_theme.get_html_theme_path()]
+html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
 html_static_path = ['_static']
-
-# -- Extension configuration -------------------------------------------------
-autodoc_member_order = 'bysource'
-autodoc_typehints = 'description'
-napoleon_google_docstring = True
-napoleon_numpy_docstring = True
-napoleon_include_init_with_doc = False
-napoleon_include_private_with_doc = False
-napoleon_include_special_with_doc = True
-napoleon_use_admonition_for_examples = False
-napoleon_use_admonition_for_notes = False
-napoleon_use_admonition_for_references = False
-napoleon_use_ivar = False
-napoleon_use_param = True
-napoleon_use_rtype = True

docs/source/examples.rst

@@ -0,0 +1,30 @@
+Examples
+========
+
+This section provides examples of how to use TorchQuant for various quantitative finance tasks.
+
+Model Calibration
+-----------------
+
+Here's an example of calibrating a model using TorchQuant:
+
+.. code-block:: python
+
+   import torch
+   import torchquant as tq
+
+   # Load market data
+   market_data = tq.utils.load_market_data('path/to/data.csv')
+
+   # Create a model
+   model = tq.models.HestonModel()
+
+   # Create a calibrator
+   calibrator = tq.calibration.ModelCalibrator(model, market_data)
+
+   # Perform calibration
+   calibrated_params = calibrator.calibrate()
+
+   print("Calibrated parameters:", calibrated_params)
+
+More examples covering different aspects of TorchQuant will be added in future updates.

docs/source/index.rst

@@ -1,20 +1,23 @@
-===============================
-.. TorchQuant documentation master file
+TorchQuant documentation
+========================
 
-Welcome to TorchQuant's documentation!
-=============================================
+Welcome to TorchQuant's documentation. TorchQuant is a library for quantitative finance using PyTorch.
 
 .. toctree::
    :maxdepth: 2
    :caption: Contents:
 
    introduction
    installation
-   usage
+   quickstart
    api_reference
+   examples
+   contributing
+   changelog
 
 Indices and tables
 ==================
+
 * :ref:`genindex`
 * :ref:`modindex`
 * :ref:`search`

docs/source/installation.rst

@@ -1,65 +1,19 @@
 Installation
 ============
 
-This section covers the installation process for TorchQuant.
-
-Prerequisites
--------------
-
-Before installing TorchQuant, ensure you have the following:
-
-- Python 3.7 or higher
-- pip (Python package installer)
-
-Installing from PyPI
---------------------
-
-The easiest way to install TorchQuant is using pip:
+TorchQuant can be installed using pip, the Python package installer. To install TorchQuant, run the following command:
 
 .. code-block:: bash
 
-    pip install torchquantlib
-
-Installing from Source
-----------------------
-
-To install TorchQuant from source:
-
-1. Clone the repository:
-
-   .. code-block:: bash
-
-       git clone https://github.com/jialuechen/torchquant.git
-       cd torchquant
-
-2. Install the package:
+   pip install torchquantlib
 
-   .. code-block:: bash
-
-       pip install .
-
-Verifying the Installation
---------------------------
-
-To verify that TorchQuant has been installed correctly, you can run:
-
-.. code-block:: python
-
-    import torchquantlib
-    print(torchquantlib.__version__)
-
-This should print the version number of the installed package.
-
-Upgrading
----------
-
-To upgrade to the latest version of TorchQuant, use:
-
-.. code-block:: bash
+Requirements
+------------
 
-    pip install --upgrade torchquantlib
+TorchQuant requires:
 
-Troubleshooting
----------------
+- Python 3.7+
+- PyTorch 2.4.0+
+- NumPy
 
-If you encounter any issues during installation, please check our :doc:`troubleshooting` guide or open an issue on our GitHub repository.
+For GPU acceleration, ensure you have a CUDA-compatible GPU and the appropriate CUDA toolkit installed.

docs/source/modules.rst

@@ -0,0 +1,7 @@
+torchquantlib
+=============
+
+.. toctree::
+   :maxdepth: 4
+
+   torchquantlib

docs/source/quickstart.rst

@@ -0,0 +1,26 @@
+Quickstart Guide
+================
+
+This guide will help you get started with TorchQuant quickly.
+
+Basic Usage
+-----------
+
+Here's a simple example of using TorchQuant:
+
+.. code-block:: python
+
+   import torch
+   from torchquantlib.core.asset_pricing.option.black_scholes_merton import black_scholes_merton
+
+   spot = torch.tensor(100.0)
+   strike = torch.tensor(105.0)
+   expiry = torch.tensor(1.0)
+   volatility = torch.tensor(0.2)
+   rate = torch.tensor(0.05)
+   dividend = torch.tensor(0.02)
+
+  price = black_scholes_merton('call', 'european', spot, strike, expiry, volatility, rate, dividend)
+  print(f'Option Price: {price.item()}')
+
+This example demonstrates how to use the Black-Scholes model to price a European call option.

docs/source/torchquantlib.calibration.rst

@@ -0,0 +1,21 @@
+torchquantlib.calibration package
+=================================
+
+Submodules
+----------
+
+torchquantlib.calibration.model\_calibrator module
+--------------------------------------------------
+
+.. automodule:: torchquantlib.calibration.model_calibrator
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: torchquantlib.calibration
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/source/torchquantlib.core.asset_pricing.bond.rst

@@ -0,0 +1,29 @@
+torchquantlib.core.asset\_pricing.bond package
+==============================================
+
+Submodules
+----------
+
+torchquantlib.core.asset\_pricing.bond.bond\_pricer module
+----------------------------------------------------------
+
+.. automodule:: torchquantlib.core.asset_pricing.bond.bond_pricer
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+torchquantlib.core.asset\_pricing.bond.fixed\_income\_forward module
+--------------------------------------------------------------------
+
+.. automodule:: torchquantlib.core.asset_pricing.bond.fixed_income_forward
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: torchquantlib.core.asset_pricing.bond
+   :members:
+   :undoc-members:
+   :show-inheritance: