NumPy is a powerful library in Python that provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays. In financial analysis, NumPy is often used for tasks such as data manipulation, mathematical operations, and statistical analysis. Here are some ways NumPy can be useful in financial analysis
- import numpy as np# Create a NumPy arrayprices = np.array([100, 105, 110, 95, 120])# Calculate returnsreturns = np.diff(prices) / prices[:-1]
- Mathematical Operations:import numpy as np# Calculate mean and standard deviationprices = np.array([100, 105, 110, 95, 120])mean_price = np.mean(prices)std_dev = np.std(prices)
- Statistical Analysis:import numpy as np# Calculate mean and standard deviationreturns = np.array([0.02, -0.01, 0.03, -0.02, 0.01])mean_return = np.mean(returns)std_dev_return = np.std(returns)
- Time Value of Money (TVM) Calculations:import numpy as np# Calculate present valuecash_flows = np.array([-100, 50, 75, 30, 20])discount_rate = 0.05present_value = np.npv(discount_rate, cash_flows)
- Random Number Generation:import numpy as np# Generate random numbersrandom_numbers = np.random.normal(0, 1, 1000) # 1000 samples from a normal distribution with mean 0 and standard deviation 1
