Stock-Monitoring-App

myenv/main.py

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+import sys
+sys.path.append('/path/to/yfinance') # I had trouble importing yfinance at first.
+import yfinance as yf # Library where I can fetch stocks data.
+import requests # To interact with web services like IFTTT.
+import time # Provide functions related to time and delays.
+
+# The event name I set up in my IFTTT applet.
+ifttt_event_name = 'stock_price_drop'
+
+# My IFTTT webhook key.
+ifttt_webhook_key = 'hThx0WApHyDPrZzRnUlxAv0QMkIUfB55haBlJWd5kIC'
+
+# The ticker symbols of the stocks I am monitoring, I can add or remove as needed.
+stocks = ['TSLA', 'AAPL', 'MSFT', 'GOOGL', 'NKE']
+
+# This is a dictionary that stores the previous prices of the stocks.
+previous_prices = {}
+
+'''This function fetches the current stock price. I used the parameter stock, so I can get individual stock prices. 
+I have to index with:.iloc, if not an alert appears, warning that method of indexing may change in a future Pandas version. It suggests using the iloc method to ensure it is compatible with future versions. '''
+def get_stock_price(stock):
+    stock_data = yf.Ticker(stock)
+    price = stock_data.history(period='1d')['Close'].iloc[0] # I had to add .iloc here (explained above).
+    return price
+
+# Similar to above, this time I want price for the last 7 days, I used the mean function to get the average.
+def calculate_7_day_average(stock):
+    stock_data = yf.Ticker(stock)
+    historical_data = stock_data.history(period='7d')
+    return historical_data['Close'].mean()
+
+# Check if prices fall by £0.25 or more and notify me using the IFTTT applet, round prices to 2 decimal places.
+def send_price_drop_notification(stock, current_price):
+    notification = {'value1': f'{stock} stock price fell by at least £0.25. Current price: £{current_price:.2f}'}
+    url = f'https://maker.ifttt.com/trigger/{ifttt_event_name}/with/key/{ifttt_webhook_key}'
+    response = requests.post(url, json=notification)
+    print(f'Notification sent: {notification}')
+    print('£' * 50)
+
+# Similar to above but will notify me when prices are below the seven day average.
+def send_average_drop_notification(stock, current_price, seven_day_average):
+    notification = {'value2': f'{stock} below 7 day average. 7 day average: {seven_day_average:.2f} Current price: £{current_price:.2f}'}
+    url = f'https://maker.ifttt.com/trigger/{ifttt_event_name}/with/key/{ifttt_webhook_key}'
+    response = requests.post(url, json=notification)
+    print(f'Notification sent: {notification}')
+    print('*' * 50)
+
+'''By creating a while loop I can run my code every 60 seconds as long as the conditions inside this loop are true. '''
+while True:
+    for stock in stocks:
+        # Stored my fetch price function in a variable.
+        current_price = get_stock_price(stock)
+        print(f'{stock}: {current_price:.2f}')
+
+        # Stored my 7 day average function in a variable.
+        seven_day_average = calculate_7_day_average(stock)
+        print(f'7-day Average for {stock}: {seven_day_average:.2f}')
+
+        # Check if both conditions are met, then notify me.
+        if stock in previous_prices and current_price <= previous_prices[stock] - 0.25:
+            send_price_drop_notification(stock, current_price)
+
+        # Update the previous price in the dictionary to the current price.
+        previous_prices[stock] = current_price
+
+        # Check if the current price is less than 7 day average and notify me.
+        if current_price < seven_day_average:
+            send_average_drop_notification(stock, current_price, seven_day_average)
+
+    print('-' * 50)
+    time.sleep(60)

myenv/unit_test.py

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+import unittest
+
+# Import the function you want to test
+from main import calculate_7_day_average
+
+class TestCalculate7DayAverage(unittest.TestCase):
+    def test_calculate_7_day_average(self):
+        # Test case 1: Test case with seven figures
+        test_stock_data_1 = [100.0, 110.0, 95.0, 105.0, 112.0, 108.0, 98.0]
+        expected_average_1 = 104.57  # Rounded to two decimal places
+
+        result_1 = calculate_7_day_average(test_stock_data_1)
+        self.assertAlmostEqual(result_1, expected_average_1, places=2)
+
+        # Test case 2: Test case with eight figures
+        test_stock_data_2 = [50.0, 60.0, 55.0, 70.0, 65.0, 62.0, 58.0, 50.0]
+        expected_average_2 = 60.00  # Rounded to two decimal places
+
+        result_2 = calculate_7_day_average(test_stock_data_2)
+        self.assertAlmostEqual(result_2, expected_average_2, places=2)
+
+if __name__ == '__main__':
+    unittest.main()
+

unit_test.py

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+import unittest
+
+# Import the function you want to test
+from main import calculate_7_day_average
+
+class TestCalculate7DayAverage(unittest.TestCase):
+    def test_calculate_7_day_average(self):
+        # Test case 1: Test case with seven figures
+        test_stock_data_1 = [100.0, 110.0, 95.0, 105.0, 112.0, 108.0, 98.0]
+        expected_average_1 = 104.57  # Rounded to two decimal places
+
+        result_1 = calculate_7_day_average(test_stock_data_1)
+        self.assertAlmostEqual(result_1, expected_average_1, places=2)
+
+        # Test case 2: Test case with eight figures
+        test_stock_data_2 = [50.0, 60.0, 55.0, 70.0, 65.0, 62.0, 58.0, 50.0]
+        expected_average_2 = 60.00  # Rounded to two decimal places
+
+        result_2 = calculate_7_day_average(test_stock_data_2)
+        self.assertAlmostEqual(result_2, expected_average_2, places=2)
+
+if __name__ == '__main__':
+    unittest.main()
+