Python Numpy Mini Projects:Stock Price Analyzer

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               This project analyses the stock prices. It has following functionalities.

Code Implementation:

              This program starts by creating stock by using NumPy array.

  • The basic statistics include Average price, maximum, minimum prices and standard deviations.
  • Next, Daily price changes are calculated using np.diff(prices).
  • Daily returns price is calculated and the data is rounded to 2 digit.
  • Stock Volatility is calculated and displayed.
  • 7 day moving window is calculated along with average value.
  • Best and worst days are displayed according to the returns value.
  • It shows how a simple 30,000 initial value gives you profit.

Code:

import numpy as np

# Sample stock closing prices

prices = np.array([80, 100, 108, 120, 208, 110, 125, 311, 117])

print("Stock Prices:")

print(prices)

#Basic statistics

print("The basic statistics of the data:")

print("Average Price:", np.mean(prices))

print("Maximum Price:", np.max(prices))

print("Minimum Price:", np.min(prices))

print("Standard Deviation:", np.std(prices))

#Daily Price changes

daily_change = np.diff(prices)

print("Daily Changes:")

print(daily_change)

#Daily Returns

returns = (prices[1:] - prices[:-1]) / prices[:-1] * 100

print("Daily Returns (%):")

print(np.round(returns, 2))

# Average Daily Return

avg_return = np.mean(returns)

print("Average Daily Return (%):", round(avg_return,2))

#Stock Volatility

volatility = np.std(returns)

print("Volatility (%):", round(volatility,2))

# 7-Day Moving Average

window = 7

moving_avg = np.convolve(

    prices,

    np.ones(window)/window,

    mode='valid'

)

print("7-Day Moving Average:")

print(np.round(moving_avg,2))

#Best and Worst Day

best_day = np.argmax(returns)

worst_day = np.argmin(returns)

print("Best Return Day:", best_day + 1)

print("Worst Return Day:", worst_day + 1)

print("Best Return:", round(returns[best_day],2), "%")

print("Worst Return:", round(returns[worst_day],2), "%")

#Growth of ₹30,000 Investment

initial_investment = 30000

shares = initial_investment / prices[0]

final_value = shares * prices[-1]

profit = final_value - initial_investment

print("Final Value: ₹", round(final_value,2))

print("Profit: ₹", round(profit,2))

Output:

Stock Prices:

[ 80 100 108 120 208 110 125 311 117]

The basic statistics of the data:

Average Price: 142.11111111111111

Maximum Price: 311

Minimum Price: 80

Standard Deviation: 68.39662831918032

Daily Changes:

[  20    8   12   88  -98   15  186 -194]

Daily Returns (%):

[ 25.     8.    11.11  73.33 -47.12  13.64 148.8  -62.38]

Average Daily Return (%): 21.3

Volatility (%): 62.25

7-Day Moving Average:

[121.57 154.57 157.  ]

Best Return Day: 7

Worst Return Day: 8

Best Return: 148.8 %

Worst Return: -62.38 %

Final Value: ₹ 43875.0

Profit: ₹ 13875.0

This is the way of implementing Stock Price Analyzer in NumPy using Python. Hope, this code is useful. Keep Coding!!!

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