import numpy as np # Given current stock price current_price = 163.02 # Simulate future stock prices num_samples = 1000 # You can adjust this number based on your needs growth_rate_samples = np.random.lognormal(mean=-0.8404, sigma=2.5, size=num_samples) future_prices = current_price * np.exp(growth_rate_samples) # Print or visualize the simulated future prices print(future_prices) i cant get an answer , im keeping errors
import numpy as np
# Given current stock price
current_price = 163.02
# Simulate future stock prices
num_samples = 1000 # You can adjust this number based on your needs
growth_rate_samples = np.random.lognormal(mean=-0.8404, sigma=2.5, size=num_samples)
future_prices = current_price * np.exp(growth_rate_samples)
# Print or visualize the simulated future prices
print(future_prices)
i cant get an answer , im keeping errors
1. Import the necessary library:
- Import the NumPy library to work with arrays and mathematical functions.
2. Define the Current Stock Price:
- Set the 'current_price' variable to the current stock price for which you want to simulate future prices.
3. Specify Parameters for the Lognormal Distribution:
- Determine the parameters (mean and standard deviation) for the underlying normal distribution.
- Calculate the mean of the normal distribution as 'mean_of_normal' using the current price and the volatility (sigma) of the lognormal distribution.
- Set the standard deviation of the normal distribution as 'std_dev_of_normal'.
4. Simulate Future Stock Prices:
- Specify the number of samples you want to generate as 'num_samples'.
- Generate random samples from a normal distribution with the calculated mean and standard deviation using 'np.random.normal'.
- Exponentiate these samples to convert them into lognormally distributed values.
5. Scale Future Prices:
- Scale the simulated lognormal values by the 'current_price' to obtain the future stock prices.
6. Print or Visualize the Results:
- Print the array of simulated future stock prices.
End of Algorithm.
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