update docs

docs/source/api_reference.rst

@@ -11,4 +11,5 @@ This section provides detailed documentation for TorchQuant APIs.
    torchquantlib.calibration
    torchquantlib.utils
 
+
 For more information on specific modules and functions, please refer to the individual module pages.

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

@@ -0,0 +1,136 @@
+Bond Pricer
+===========
+
+.. automodule:: torchquantlib.core.asset_pricing.bond.bond_pricer
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+This module provides implementations for various bond pricing models.
+
+Zero Coupon Bond
+----------------
+
+.. autofunction:: torchquantlib.core.asset_pricing.bond.bond_pricer.zero_coupon_bond
+
+Usage Example
+^^^^^^^^^^^^^
+
+.. code-block:: python
+
+   import torch
+   from torchquantlib.core.asset_pricing.bond.bond_pricer import zero_coupon_bond
+
+   face_value = torch.tensor(1000.0)
+   rate = torch.tensor(0.05)
+   maturity = torch.tensor(5.0)
+
+   price = zero_coupon_bond(face_value, rate, maturity)
+   print(f"Zero Coupon Bond Price: {price.item():.2f}")
+
+Coupon Bond
+-----------
+
+.. autofunction:: torchquantlib.core.asset_pricing.bond.bond_pricer.coupon_bond
+
+Usage Example
+^^^^^^^^^^^^^
+
+.. code-block:: python
+
+   from torchquantlib.core.asset_pricing.bond.bond_pricer import coupon_bond
+
+   face_value = torch.tensor(1000.0)
+   coupon_rate = torch.tensor(0.06)
+   rate = torch.tensor(0.05)
+   periods = torch.tensor(10)
+
+   price = coupon_bond(face_value, coupon_rate, rate, periods)
+   print(f"Coupon Bond Price: {price.item():.2f}")
+
+Callable Bond
+-------------
+
+.. autofunction:: torchquantlib.core.asset_pricing.bond.bond_pricer.callable_bond
+
+Usage Example
+^^^^^^^^^^^^^
+
+.. code-block:: python
+
+   from torchquantlib.core.asset_pricing.bond.bond_pricer import callable_bond
+
+   face_value = torch.tensor(1000.0)
+   coupon_rate = torch.tensor(0.06)
+   rate = torch.tensor(0.05)
+   periods = torch.tensor(10)
+   call_price = torch.tensor(1050.0)
+   call_period = torch.tensor(5)
+
+   price = callable_bond(face_value, coupon_rate, rate, periods, call_price, call_period)
+   print(f"Callable Bond Price: {price.item():.2f}")
+
+Putable Bond
+------------
+
+.. autofunction:: torchquantlib.core.asset_pricing.bond.bond_pricer.putable_bond
+
+Usage Example
+^^^^^^^^^^^^^
+
+.. code-block:: python
+
+   from torchquantlib.core.asset_pricing.bond.bond_pricer import putable_bond
+
+   face_value = torch.tensor(1000.0)
+   coupon_rate = torch.tensor(0.06)
+   rate = torch.tensor(0.05)
+   periods = torch.tensor(10)
+   put_price = torch.tensor(950.0)
+   put_period = torch.tensor(5)
+
+   price = putable_bond(face_value, coupon_rate, rate, periods, put_price, put_period)
+   print(f"Putable Bond Price: {price.item():.2f}")
+
+Convertible Bond
+----------------
+
+.. autofunction:: torchquantlib.core.asset_pricing.bond.bond_pricer.convertible_bond
+
+Usage Example
+^^^^^^^^^^^^^
+
+.. code-block:: python
+
+   from torchquantlib.core.asset_pricing.bond.bond_pricer import convertible_bond
+
+   face_value = torch.tensor(1000.0)
+   coupon_rate = torch.tensor(0.06)
+   rate = torch.tensor(0.05)
+   periods = torch.tensor(10)
+   conversion_ratio = torch.tensor(20)
+   conversion_price = torch.tensor(55.0)
+
+   price = convertible_bond(face_value, coupon_rate, rate, periods, conversion_ratio, conversion_price)
+   print(f"Convertible Bond Price: {price.item():.2f}")
+
+Stochastic Rate Bond
+--------------------
+
+.. autofunction:: torchquantlib.core.asset_pricing.bond.bond_pricer.stochastic_rate_bond
+
+Usage Example
+^^^^^^^^^^^^^
+
+.. code-block:: python
+
+   import torch
+   from torchquantlib.core.asset_pricing.bond.bond_pricer import stochastic_rate_bond
+
+   face_value = torch.tensor(1000.0)
+   coupon_rate = torch.tensor(0.06)
+   rate = torch.tensor([0.05, 0.052, 0.054, 0.056, 0.058])
+   periods = torch.tensor(5)
+
+   price = stochastic_rate_bond(face_value, coupon_rate, rate, periods)
+   print(f"Stochastic Rate Bond Price: {price.item():.2f}")

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

@@ -0,0 +1,47 @@
+Fixed Income Forward
+====================
+
+.. automodule:: torchquantlib.core.asset_pricing.bond.fixed_income_forward
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+This module provides implementation for pricing fixed income forward contracts.
+
+Fixed Income Forward
+--------------------
+
+.. autofunction:: torchquantlib.core.asset_pricing.bond.fixed_income_forward.fixed_income_forward
+
+Usage Example
+^^^^^^^^^^^^^
+
+.. code-block:: python
+
+   import torch
+   from torchquantlib.core.asset_pricing.bond.fixed_income_forward import fixed_income_forward
+
+   face_value = torch.tensor(1000.0)
+   rate = torch.tensor(0.05)
+   time_to_maturity = torch.tensor(2.0)
+   forward_rate = torch.tensor(0.06)
+
+   forward_price = fixed_income_forward(face_value, rate, time_to_maturity, forward_rate)
+   print(f"Fixed Income Forward Price: {forward_price.item():.2f}")
+
+Formula
+^^^^^^^
+
+The forward price of a fixed income security is calculated using the following formula:
+
+.. math::
+
+   Forward Price = Face Value * e^{(Forward Rate - Current Rate) * Time to Maturity}
+
+Where:
+   - Face Value is the notional amount of the fixed income security
+   - Forward Rate is the interest rate agreed upon for the forward contract
+   - Current Rate is the current market interest rate
+   - Time to Maturity is the time until the forward contract expires (in years)
+
+Note that this formula assumes continuous compounding. For discrete compounding, a different formula would be required.

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

@@ -1,19 +1,18 @@
-torchquantlib.core.asset\_pricing.bond package
+Bond
 ==============================================
 
 
+.. toctree::
+   :maxdepth: 4
 
-torchquantlib.core.asset\_pricing.bond.bond\_pricer module
-----------------------------------------------------------
-
+   torchquantlib.core.asset_pricing.bond.bond_pricer
+   torchquantlib.core.asset_pricing.bond.fixed_income_forward
+   
 .. 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:

docs/source/torchquantlib.core.asset_pricing.money.future_pricer.rst

@@ -0,0 +1,58 @@
+Future Pricer
+=============
+
+.. automodule:: torchquantlib.core.asset_pricing.money.future_pricer
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+This module provides implementation for pricing futures contracts.
+
+
+.. autofunction:: torchquantlib.core.asset_pricing.money.future_pricer.future_pricer
+
+Usage Example
+^^^^^^^^^^^^^
+
+.. code-block:: python
+
+   import torch
+   from torchquantlib.core.asset_pricing.money.future_pricer import future_pricer
+
+   spot = torch.tensor(100.0)
+   rate = torch.tensor(0.05)
+   expiry = torch.tensor(1.0)
+
+   futures_price = future_pricer(spot, rate, expiry)
+   print(f"Futures Price: {futures_price.item():.2f}")
+
+Formula
+^^^^^^^
+
+The theoretical price of a futures contract is calculated using the following formula:
+
+.. math::
+
+   Futures Price = Spot Price * e^{(risk-free rate * time to expiry)}
+
+Where:
+   - Spot Price is the current market price of the underlying asset
+   - risk-free rate is the annualized risk-free interest rate
+   - time to expiry is the time until the futures contract expires (in years)
+
+Note:
+   - This model assumes perfect markets with no transaction costs or taxes.
+   - For commodities or dividend-paying stocks, additional factors would need to be considered in the pricing model.
+   - The formula uses continuous compounding.
+
+Limitations
+^^^^^^^^^^^
+
+The future pricer implemented in this module has some limitations:
+
+1. It assumes continuous compounding, which may not always reflect real-world scenarios.
+2. It does not account for dividends, which can affect the futures price for stock index futures.
+3. It does not consider storage costs or convenience yields, which are important factors for commodity futures.
+4. The model assumes perfect markets without transaction costs, taxes, or other frictions.
+
+For more complex scenarios or specific types of futures contracts, you may need to modify the pricing model or use more advanced techniques.

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

@@ -1,9 +1,11 @@
-torchquantlib.core.asset\_pricing.money package
+Money Market
 ===============================================
 
+.. toctree::
+   :maxdepth: 4
+
+   torchquantlib.core.asset_pricing.money.future_pricer
 
-torchquantlib.core.asset\_pricing.money.future\_pricer module
--------------------------------------------------------------
 
 .. automodule:: torchquantlib.core.asset_pricing.money.future_pricer
    :members: