Here's the commit message based on the diff:

Add quantitative finance module and examples

Implements a new quantitative finance module with key financial calculations:
- Time value of money (PV/FV)
- Investment performance metrics
- Risk measures (Sharpe ratio, Beta)
- Black-Scholes options pricing

Includes Jupyter notebook with examples and comprehensive README 
documentation. Module provides essential tools for financial analysis 
and trading calculations.

Fixes Tanu-N-Prabhu#36

Author: minimalProviderAgentMarket

Quantitative_Finance/Quantitative_Finance_Examples.ipynb

@@ -0,0 +1,183 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Quantitative Finance with Python\n",
+    "\n",
+    "This notebook demonstrates the usage of various quantitative finance functions implemented in `financial_utils.py`. We'll cover:\n",
+    "\n",
+    "1. Time Value of Money Calculations\n",
+    "2. Investment Performance Metrics\n",
+    "3. Risk Measures\n",
+    "4. Options Pricing\n",
+    "\n",
+    "Fixes #36"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "source": [
+    "from financial_utils import *\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Time Value of Money\n",
+    "\n",
+    "Let's explore how money changes value over time using Present Value (PV) and Future Value (FV) calculations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "source": [
+    "# Example: Calculate present value of $10,000 received in 5 years with 5% interest rate\n",
+    "future_amount = 10000\n",
+    "rate = 0.05\n",
+    "years = 5\n",
+    "\n",
+    "pv = present_value(future_amount, rate, years)\n",
+    "print(f\"Present value of ${future_amount:,} in {years} years at {rate:.1%} interest: ${pv:,.2f}\")\n",
+    "\n",
+    "# Verify with future value calculation\n",
+    "fv = future_value(pv, rate, years)\n",
+    "print(f\"Future value verification: ${fv:,.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Investment Performance Analysis\n",
+    "\n",
+    "Let's analyze a series of stock prices and calculate returns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "source": [
+    "# Sample stock price data\n",
+    "stock_prices = [100, 102, 99, 105, 103, 106, 109, 108, 110, 112]\n",
+    "\n",
+    "# Calculate returns\n",
+    "returns = calculate_returns(stock_prices)\n",
+    "\n",
+    "# Plot the prices and returns\n",
+    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))\n",
+    "\n",
+    "ax1.plot(stock_prices, marker='o')\n",
+    "ax1.set_title('Stock Price Over Time')\n",
+    "ax1.set_ylabel('Price ($)')\n",
+    "\n",
+    "ax2.bar(range(len(returns)), [r * 100 for r in returns])\n",
+    "ax2.set_title('Daily Returns')\n",
+    "ax2.set_ylabel('Return (%)')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Risk Analysis\n",
+    "\n",
+    "Let's calculate some risk metrics including the Sharpe Ratio and Beta."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "source": [
+    "# Calculate Sharpe ratio\n",
+    "risk_free_rate = 0.02  # 2% annual risk-free rate\n",
+    "sharpe = sharpe_ratio(returns, risk_free_rate)\n",
+    "print(f\"Sharpe Ratio: {sharpe:.2f}\")\n",
+    "\n",
+    "# Calculate Beta using simulated market returns\n",
+    "market_returns = [0.01, -0.005, 0.02, 0.015, -0.01, 0.02, 0.01, -0.005, 0.015]\n",
+    "beta_value = beta(returns, market_returns)\n",
+    "print(f\"Beta: {beta_value:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Options Pricing\n",
+    "\n",
+    "Let's explore option pricing using the Black-Scholes model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "source": [
+    "# Parameters for Black-Scholes\n",
+    "S = 100  # Current stock price\n",
+    "K = 100  # Strike price\n",
+    "T = 1.0  # Time to expiration (1 year)\n",
+    "r = 0.05  # Risk-free rate (5%)\n",
+    "sigma = 0.2  # Volatility (20%)\n",
+    "\n",
+    "# Calculate call and put option prices\n",
+    "call_price = black_scholes(S, K, T, r, sigma, 'call')\n",
+    "put_price = black_scholes(S, K, T, r, sigma, 'put')\n",
+    "\n",
+    "print(f\"Call Option Price: ${call_price:.2f}\")\n",
+    "print(f\"Put Option Price: ${put_price:.2f}\")\n",
+    "\n",
+    "# Plot option prices for different strike prices\n",
+    "strikes = np.linspace(80, 120, 41)\n",
+    "call_prices = [black_scholes(S, k, T, r, sigma, 'call') for k in strikes]\n",
+    "put_prices = [black_scholes(S, k, T, r, sigma, 'put') for k in strikes]\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(strikes, call_prices, label='Call Option')\n",
+    "plt.plot(strikes, put_prices, label='Put Option')\n",
+    "plt.axvline(x=S, color='gray', linestyle='--', alpha=0.5)\n",
+    "plt.xlabel('Strike Price')\n",
+    "plt.ylabel('Option Price')\n",
+    "plt.title('Option Prices vs Strike Price')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

Quantitative_Finance/README.md

@@ -0,0 +1,76 @@
+# Quantitative Finance Module
+
+This module provides essential tools and utilities for quantitative finance calculations and analysis. It implements various financial formulas and models commonly used in financial analysis and trading.
+
+Fixes #36
+
+## Contents
+
+1. `financial_utils.py` - Core financial calculation utilities
+2. `Quantitative_Finance_Examples.ipynb` - Interactive examples and tutorials
+
+## Features
+
+- Time Value of Money Calculations
+  - Present Value (PV)
+  - Future Value (FV)
+
+- Investment Performance Metrics
+  - Return Calculations
+  - Sharpe Ratio
+
+- Risk Measures
+  - Beta Calculation
+  - Volatility Analysis
+
+- Options Pricing
+  - Black-Scholes Model
+  - Call and Put Option Pricing
+
+## Usage
+
+```python
+from financial_utils import *
+
+# Calculate present value
+pv = present_value(future_value=1000, rate=0.05, periods=5)
+
+# Calculate investment returns
+returns = calculate_returns([100, 102, 99, 105, 103])
+
+# Calculate Sharpe ratio
+sharpe = sharpe_ratio(returns, risk_free_rate=0.02)
+
+# Price options using Black-Scholes
+call_price = black_scholes(S=100, K=100, T=1, r=0.05, sigma=0.2, option_type='call')
+```
+
+## Requirements
+
+- Python 3.8+
+- NumPy
+- Matplotlib (for visualizations in the notebook)
+
+## Getting Started
+
+1. Install the required dependencies:
+```bash
+pip install numpy matplotlib jupyter
+```
+
+2. Open the Jupyter notebook for interactive examples:
+```bash
+jupyter notebook Quantitative_Finance_Examples.ipynb
+```
+
+## Contributing
+
+Feel free to contribute by:
+- Adding new financial models and calculations
+- Improving existing implementations
+- Adding more examples and use cases
+- Enhancing documentation
+
+## License
+
+This project is open source and available under the MIT License.

Quantitative_Finance/__pycache__/financial_utils.cpython-312.pyc

@@ -0,0 +1,132 @@
+"""
+Quantitative Finance Utilities
+This module provides essential functions for financial calculations and analysis.
+"""
+
+import numpy as np
+import math
+
+def present_value(future_value: float, rate: float, periods: int) -> float:
+    """
+    Calculate the present value of a future sum of money.
+    
+    Args:
+        future_value: The future sum of money
+        rate: The interest rate (as a decimal)
+        periods: Number of time periods
+    
+    Returns:
+        The present value
+    """
+    return future_value / (1 + rate) ** periods
+
+def future_value(present_value: float, rate: float, periods: int) -> float:
+    """
+    Calculate the future value of a current sum of money.
+    
+    Args:
+        present_value: The current sum of money
+        rate: The interest rate (as a decimal)
+        periods: Number of time periods
+    
+    Returns:
+        The future value
+    """
+    return present_value * (1 + rate) ** periods
+
+def calculate_returns(prices: list[float]) -> list[float]:
+    """
+    Calculate the periodic returns from a series of prices.
+    
+    Args:
+        prices: List of asset prices
+    
+    Returns:
+        List of periodic returns
+    """
+    returns = []
+    for i in range(1, len(prices)):
+        periodic_return = (prices[i] - prices[i-1]) / prices[i-1]
+        returns.append(periodic_return)
+    return returns
+
+def sharpe_ratio(returns: list[float], risk_free_rate: float) -> float:
+    """
+    Calculate the Sharpe ratio for a series of returns.
+    
+    Args:
+        returns: List of periodic returns
+        risk_free_rate: The risk-free rate (as a decimal)
+    
+    Returns:
+        The Sharpe ratio
+    """
+    returns_array = np.array(returns)
+    excess_returns = returns_array - risk_free_rate
+    return np.mean(excess_returns) / np.std(excess_returns) if len(returns) > 1 else 0
+
+def beta(asset_returns: list[float], market_returns: list[float]) -> float:
+    """
+    Calculate the beta of an asset relative to the market.
+    
+    Args:
+        asset_returns: List of asset returns
+        market_returns: List of market returns
+    
+    Returns:
+        The beta value
+    """
+    if len(asset_returns) != len(market_returns):
+        raise ValueError("Asset and market returns must have the same length")
+    
+    covariance = np.cov(asset_returns, market_returns)[0][1]
+    market_variance = np.var(market_returns)
+    return covariance / market_variance if market_variance != 0 else 0
+
+def black_scholes(S: float, K: float, T: float, r: float, sigma: float, option_type: str = 'call') -> float:
+    """
+    Calculate option price using Black-Scholes formula.
+    
+    Args:
+        S: Current stock price
+        K: Strike price
+        T: Time to expiration (in years)
+        r: Risk-free interest rate
+        sigma: Volatility of the underlying asset
+        option_type: Type of option ('call' or 'put')
+    
+    Returns:
+        Option price
+    """
+    d1 = (math.log(S/K) + (r + sigma**2/2)*T) / (sigma*math.sqrt(T))
+    d2 = d1 - sigma*math.sqrt(T)
+    
+    if option_type.lower() == 'call':
+        return S*norm_cdf(d1) - K*math.exp(-r*T)*norm_cdf(d2)
+    else:  # put option
+        return K*math.exp(-r*T)*norm_cdf(-d2) - S*norm_cdf(-d1)
+
+def norm_cdf(x: float) -> float:
+    """
+    Calculate the cumulative distribution function of the standard normal distribution.
+    
+    Args:
+        x: Input value
+    
+    Returns:
+        Probability
+    """
+    return (1 + math.erf(x/math.sqrt(2))) / 2
+
+# Example usage:
+if __name__ == "__main__":
+    # Present Value example
+    print(f"Present value of $1000 in 5 years at 5% interest: ${present_value(1000, 0.05, 5):.2f}")
+    
+    # Returns calculation example
+    stock_prices = [100, 102, 99, 105, 103]
+    returns = calculate_returns(stock_prices)
+    print(f"Stock returns: {[f'{r:.2%}' for r in returns]}")
+    
+    # Sharpe ratio example
+    print(f"Sharpe ratio: {sharpe_ratio(returns, 0.02):.2f}")