New STuff

Author: usrbinomarbash

.idea/.gitignore

@@ -0,0 +1,160 @@
+import yfinance as yf
+import numpy as np
+import pandas as pd
+from sklearn.cluster import KMeans
+
+tickers = ['AAPL', 'MSFT', 'GOOGL', 'AMZN', 'META', 'TSLA', 'NFLX', 'NVDA'] 
+
+stock_dt = yf.download(tickers, start="2023-09-01", end="2024-09-01", group_by='ticker') 
+stock_prices = []
+for ticker in tickers: 
+    closing_prices = stock_dt[ticker]['Close'].values[:200]   
+    stock_prices.append(closing_prices) 
+stock_prices_numpyy = np.array(stock_prices) 
+print(stock_prices_numpyy.shape)
+
+
+# Date
+stock_dt.index = pd.to_datetime(stock_dt.index)
+
+# Monthly averages, max and mins
+def calculate_monthly_stats():
+    monthly_stats = {}
+    for ticker in tickers:
+        # Group data in month and calc mean max min
+        monthly_data = stock_dt[ticker]['Close'].resample('M').agg(['mean', 'max', 'min'])
+        monthly_stats[ticker] = monthly_data
+    return monthly_stats
+
+
+#Task3 daily price change
+def calculate_the_daily_share_price_change():
+    daily_returns={}
+    for ticker in tickers:
+        daily_returns[ticker]=stock_dt[ticker]['Close'].pct_change().dropna()
+    return daily_returns
+
+daily_returns=calculate_the_daily_share_price_change()
+
+
+#Task3 highest share price change
+def highest_single_day_changes():
+    highest_increase={}
+    highest_decrease={}
+
+    for ticker in tickers:
+        highest_decrease[ticker]=daily_returns[ticker].idxmin(), daily_returns[ticker].min()
+        highest_increase[ticker]=daily_returns[ticker].idxmax(), daily_returns[ticker].max()
+
+    return highest_decrease, highest_increase
+
+highest_decrease,highest_increase=highest_single_day_changes()
+print("The highest single day price increases: ")
+
+for ticker, (date, change) in highest_increase.items():
+    print(f"{ticker} {date} with increase of {change:.2%}")
+
+print("\nHighest single day decrease ")
+
+for ticker,(date, change) in highest_decrease.items():
+    print(f"{ticker}:{date} with decrease of {change:.2%}")
+ 
+volatility = {ticker: np.std(daily_returns[ticker]) for ticker in daily_returns.keys()}
+most_volatile_element_ticker=max(volatility,key=volatility.get)
+
+print(f"\nMost Volatile stock: {most_volatile_element_ticker} with volatility {volatility[most_volatile_element_ticker]:.4f}")
+
+#Task 4
+def cluster_stocks(number_clusters=3):
+    the_returned_matrix=np.array([daily_returns[ticker] for ticker in tickers]) 
+    kmeans_algorithm=KMeans(n_clusters=number_clusters, random_state=42)
+    kmeans_algorithm.fit(the_returned_matrix)
+    return kmeans_algorithm.labels_
+
+the_stock_clusters=cluster_stocks(number_clusters=3)
+
+print("Stock Clusters")
+
+for ticker,cluster in zip(tickers, the_stock_clusters):
+    print(f"{ticker} is within the cluster {cluster}")
+
+monthly_stats = calculate_monthly_stats()
+for ticker, stats in monthly_stats.items():
+    print(f"{ticker} Monthly Stats:\n", stats)
+
+# Find how many days Tesla stock was above $500
+def days_above_500(ticker, threshold=500):
+    above_threshold = stock_dt[ticker]['Close'][stock_dt[ticker]['Close'] > threshold].count()
+    return above_threshold
+
+tesla_above_500 = days_above_500('TSLA', 500)
+print(f"The tesla share price was above $500 on {tesla_above_500} days.")
+
+def find_top_5_days(ticker):
+    top5days = stock_dt[ticker]['Close'].nlargest(5)
+    return top5days
+
+for ticker in tickers:
+    top_5 = find_top_5_days(ticker)
+    print(f"Top 5 days for {ticker}:\n", top_5)
+
+
+
+def high_and_low():
+    highest_price = stock_dt.xs('Close', level=1, axis=1).idxmax(axis=1)
+    lowest_price = stock_dt.xs('Close', level=1, axis=1).idxmin(axis=1)
+    return highest_price, lowest_price
+
+highest_price, lowest_price = high_and_low()
+print(f"Company with highest prices on each day:\n{highest_price}")
+print(f"Company with lowest prices on each day:\n{lowest_price}")
+
+#Task 4
+company_idx = 2
+google_prices = stock_prices_numpyy[company_idx, :]
+
+
+#Task 5 finding the mean and std and generate 200 values
+the_mean = np.mean(google_prices)
+standard_deviation = np.std(google_prices)
+new_stock_prices = np.random.normal(the_mean, standard_deviation, 200)
+new_stock_prices= new_stock_prices.reshape(1,-1)
+updated_stock_share_price = np.concatenate((stock_prices_numpyy, new_stock_prices))
+print("Updated stock share price shape: ",updated_stock_share_price.shape)
+
+
+#Task 6  META
+company_idx = 4
+meta_share_price = stock_prices_numpyy[company_idx, :]
+
+meta_share_price_changes = np.diff(meta_share_price)/meta_share_price[:-1]
+
+## mean 
+mean_percent_diff = np.mean(meta_share_price_changes)
+standard_percent_change = np.std(meta_share_price_changes)
+
+#daily percentage
+number_of_days=30
+forecast_percent_difference= np.random.normal(mean_percent_diff, standard_percent_change, number_of_days)
+
+
+forecasted_prices = [meta_share_price[-1]]
+
+for standard_percent_change in forecast_percent_difference:
+    upcoming_price= forecasted_prices[-1]*(1+standard_percent_change)
+    forecasted_prices.append(upcoming_price)
+
+forecasted_prices = np.array(forecasted_prices[1:])
+
+print("The predicted price of Meta is for the next 30 days: ")
+print(forecasted_prices)
+
+predicted_days_in_time = pd.date_range(start=stock_dt.index[-1],periods=number_of_days, freq='D')
+predicted_data_frame = pd.DataFrame({'Date': predicted_days_in_time, 'Predicted Share Price': forecasted_prices})
+
+
+print(predicted_data_frame)
+
+
+#saved file
+np.savetxt("file.txt",stock_prices_numpyy)

.idea/Python.iml

@@ -0,0 +1,19 @@
+pos=0
+neg=0
+odd=0
+even=0
+while(True):
+    num=int(input("Please enter a number"))
+    if(num != 0):
+        if(num>0):
+            pos+=1
+        elif(num<0):
+            neg+=1
+        if(num%2==0):
+            even+=1
+        elif(num%2!=0):
+            odd+=1
+    else:
+        break
+print(f"Positive Number: {pos}, Negative Number: {neg}, Odd Number: {odd}, Even Number: {even}")
+

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,22 @@
+for i in range(1,11, 1):
+                #range([start,stop),step)
+    print(i)
+
+def factorial(i):
+    if(i<1):
+        return "Factorial cannot be negative"
+    if(i==1 or i==0):
+        return 1
+    else:
+        return i*factorial(i-1)
+print(factorial(6))
+
+
+def squarenumb(nums):
+    squares = []
+    for num in nums :
+        squares.append(num*num)
+    return squares
+
+
+print(squarenumb([5,6,8,8,9]))

.idea/misc.xml

@@ -0,0 +1,61 @@
+import math
+#function
+def factorial(num):
+    if(num == 1):
+        return 1
+    else:
+        x = num * (num-1)
+        return x
+print(factorial(4))
+def factorial2(num2):
+    for i in range(1, num2):
+        num2 = num2*i
+    return num2
+
+mylist1=[1,2,3]
+def calc_average(mylist1):
+    for i in mylist1:
+        sum56=sum(mylist1)
+        average = sum56/len(mylist1)
+        return average
+print(calc_average(mylist1))
+
+def fahrenheit(celsius):
+    f= (9/5*celsius)+32
+    return f
+
+def add_two_number(num1=2, num2=3):
+    return num1+num2
+
+print(add_two_number(3))
+print(add_two_number(2,7))
+print(add_two_number())
+
+#glo
+num33=8
+#local scope
+def local_global_scope():
+    global num7
+    num7 = 8
+    num1=3
+    return num1
+
+local_global_scope()
+
+def print_stars():  #down
+    num = int(input("Enter a number"))
+    for i in range(1, num+1):
+        for j in range(i):
+            print("*",end=' ')
+        print()
+    return ""
+
+def print_stars_up():  #down
+    num = int(input("Enter a number"))
+    for i in range(num,0,-1):
+        for j in range(i):
+            print("*",end=' ')
+        print()
+    return ""
+print(print_stars())
+print(print_stars_up())