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data_analysis.py

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+import pandas as pd
+import yfinance as yf
+import matplotlib.pyplot as plt
+
+def print_stock_performance():
+    data = pd.read_csv('/Users/ethansmith/Documents/GitHub/investments/investment_data.csv')
+
+   # Convert Date and Time into a single datetime column and sort
+    data['DateTime'] = pd.to_datetime(data['Date'] + ' ' + data['Time'], dayfirst=True)
+    data.sort_values(by='DateTime', inplace=True)
+
+    # Remove '$' sign and convert to float
+    data['Price per Share USD'] = data['Price per Share USD'].replace('[\$,]', '', regex=True).astype(float)
+
+    # Dictionary to track each stock's performance
+    stock_performance = {}
+    SHARE_THRESHOLD = 1e-8  # Threshold for considering shares count as negligible
+
+
+    for index, row in data.iterrows():
+        ticker = row['Ticker Symbol']
+        shares = float(row['No. of Shares'])
+        transaction_type = row['Transaction Type']
+        transaction_value = shares * row['Price per Share USD']
+
+        if ticker not in stock_performance:
+            stock_performance[ticker] = {'shares': 0, 'invested': 0}
+
+        if transaction_type == 'BUY':
+            stock_performance[ticker]['shares'] += shares
+            stock_performance[ticker]['invested'] += transaction_value
+        elif transaction_type == 'SELL':
+            stock_performance[ticker]['shares'] -= shares
+            stock_performance[ticker]['invested'] -= transaction_value
+
+    # Display gains/losses and current holdings
+    for ticker, info in stock_performance.items():
+        # Adjust for small or negative share counts
+        if abs(info['shares']) < SHARE_THRESHOLD:
+            info['shares'] = 0
+
+        if info['shares'] == 0:
+            result = "Loss" if info['invested'] > 0 else "Gain"
+            print(f"{ticker} - {result}: ${abs(info['invested']):.2f}")
+        else:
+            print(f"{ticker} - Current Holdings: {info['shares']} shares, Invested: ${info['invested']:.2f}")
+
+    return stock_performance
+
+# Example usage
+##print_current_holdings()
+#valuation_changes = print_stock_performance()
+#for change in valuation_changes:
+#    print(change)
+
+
+
+
+
+import pandas as pd
+
+def analyze_stock_performance(csv_path):
+    data = pd.read_csv(csv_path)
+
+    # Convert Date and Time into a single datetime column and sort
+    data['DateTime'] = pd.to_datetime(data['Date'] + ' ' + data['Time'], dayfirst=True)
+    data.sort_values(by='DateTime', inplace=True)
+    data['Price per Share USD'] = data['Price per Share USD'].replace('[\$,]', '', regex=True).astype(float)
+
+    stock_performance = {}
+    portfolio_value_over_time = []
+
+    for index, row in data.iterrows():
+        ticker = row['Ticker Symbol']
+        shares = float(row['No. of Shares'])
+        transaction_type = row['Transaction Type']
+        transaction_value = shares * row['Price per Share USD']
+
+        if ticker not in stock_performance:
+            stock_performance[ticker] = {'shares': 0, 'invested': 0}
+
+        if transaction_type == 'BUY':
+            stock_performance[ticker]['shares'] += shares
+            stock_performance[ticker]['invested'] += transaction_value
+        elif transaction_type == 'SELL':
+            stock_performance[ticker]['shares'] -= shares
+            stock_performance[ticker]['invested'] -= transaction_value
+
+        # Calculate the total portfolio value at this point in time
+        total_value = sum(stock['invested'] for stock in stock_performance.values())
+        portfolio_value_over_time.append({'DateTime': row['DateTime'], 'TotalValue': total_value})
+
+    # Create a DataFrame for the portfolio value over time
+    portfolio_value_df = pd.DataFrame(portfolio_value_over_time)
+    return portfolio_value_df
+
+def plot_portfolio_value_over_time(performance_data):
+    plt.figure(figsize=(10, 6))
+    # Convert 'DateTime' and 'TotalValue' to lists or numpy arrays for proper handling
+    plt.plot(performance_data['DateTime'].tolist(), performance_data['TotalValue'].tolist())
+    plt.title('Portfolio Value Over Time')
+    plt.xlabel('Date')
+    plt.ylabel('Total Portfolio Value')
+    plt.grid(True)
+    plt.show()
+
+# Example usage
+csv_path = '/Users/ethansmith/Documents/GitHub/investments/investment_data.csv'
+##performance_data = analyze_stock_performance(csv_path)
+
+# Example usage
+##plot_portfolio_value_over_time(performance_data)
+
+
+def plot_stock_performance(stock_symbol, csv_path):
+    # Load your transaction data
+    data = pd.read_csv(csv_path)
+    data['DateTime'] = pd.to_datetime(data['Date'] + ' ' + data['Time'], dayfirst=True)
+    data.sort_values(by='DateTime', inplace=True)
+
+    # Filter data for the specific stock and calculate cumulative shares
+    stock_data = data[data['Ticker Symbol'] == stock_symbol].copy()
+    stock_data['Cumulative Shares'] = stock_data.apply(lambda x: x['No. of Shares'] if x['Transaction Type'] == 'BUY' else -x['No. of Shares'], axis=1).cumsum()
+
+    # Fetch historical market data for the stock
+    start_date = stock_data['DateTime'].min()
+    end_date = pd.to_datetime('today')
+    market_data = yf.download(stock_symbol, start=start_date, end=end_date)
+
+    # Merge market data with your transaction data
+    merged_data = pd.merge_asof(market_data, stock_data, left_index=True, right_on='DateTime')
+    merged_data['Holding Value'] = merged_data['Cumulative Shares'] * merged_data['Close']
+
+    # Plot the holding value over time
+    plt.figure(figsize=(12, 6))
+    plt.plot(merged_data['DateTime'].values, merged_data['Holding Value'].values)
+    plt.title(f'Holding Value Over Time: {stock_symbol}')
+    plt.xlabel('Date')
+    plt.ylabel('Holding Value')
+    plt.grid(True)
+    plt.show()
+
+##plot_stock_performance('VUAG.L', '/Users/ethansmith/Documents/GitHub/investments/investment_data.csv')
+
+
+def plot_total_portfolio_value(csv_path):
+    data = pd.read_csv(csv_path)
+    data['DateTime'] = pd.to_datetime(data['Date'] + ' ' + data['Time'], dayfirst=True)
+    data.sort_values(by='DateTime', inplace=True)
+    data['Price per Share USD'] = data['Price per Share USD'].replace('[\$,]', '', regex=True).astype(float)
+
+    unique_stocks = data['Ticker Symbol'].unique()
+    portfolio_values = []
+
+    for stock in unique_stocks:
+        stock_data = data[data['Ticker Symbol'] == stock].copy()
+        stock_data['Cumulative Shares'] = stock_data.apply(
+            lambda x: x['No. of Shares'] if x['Transaction Type'] == 'BUY' else -x['No. of Shares'], 
+            axis=1
+        ).cumsum()
+
+        try:
+            historical_data = yf.download(stock, start=stock_data['DateTime'].min(), end=pd.to_datetime('today'))
+            merged_data = pd.merge_asof(historical_data, stock_data, left_index=True, right_on='DateTime')
+            merged_data['Holding Value'] = merged_data['Cumulative Shares'] * merged_data['Close']
+            portfolio_values.append(merged_data[['DateTime', 'Holding Value']])
+        except Exception as e:
+            print(f"Failed to download data for {stock}: {e}")
+            continue  # Skip this stock
+
+    total_portfolio = pd.concat(portfolio_values).groupby('DateTime').sum().reset_index()
+
+    total_portfolio_cleaned = remove_outliers(total_portfolio, 'Holding Value', num_std=3)
+
+    # Plotting the total portfolio value over time
+    plt.figure(figsize=(12, 6))
+    plt.plot(total_portfolio['DateTime'].values, total_portfolio['Holding Value'].values)  # Use .values for proper indexing
+    plt.title('Total Portfolio Value Over Time')
+    plt.xlabel('Date')
+    plt.ylabel('Total Portfolio Value')
+    plt.grid(True)
+    plt.show()
+
+def remove_outliers(df, column_name, num_std=3):
+    """ Remove outliers from a dataframe by column, using n standard deviations """
+    mean_val = df[column_name].mean()
+    std_val = df[column_name].std()
+
+    # Define the upper and lower limit for detecting outliers
+    upper_limit = mean_val + (num_std * std_val)
+    lower_limit = mean_val - (num_std * std_val)
+
+    # Filter out outliers
+    filtered_df = df[(df[column_name] <= upper_limit) & (df[column_name] >= lower_limit)]
+    return filtered_df
+
+
+plot_total_portfolio_value(csv_path)
+
+
+
+
+# create stock dictionary for each stock in csv
+def create_stock_dictionaries(csv_path):
+    data = pd.read_csv(csv_path)
+    data['DateTime'] = pd.to_datetime(data['Date'] + ' ' + data['Time'], dayfirst=True)
+    data.sort_values(by='DateTime', inplace=True)
+
+    stocks_dict = {}
+    for index, row in data.iterrows():
+        ticker = row['Ticker Symbol']
+        shares = float(row['No. of Shares'])  # Ensuring shares are treated as float
+        price_per_share = float(row['Price per Share USD'].replace('$', ''))  # Handling potential $ sign
+
+        if ticker not in stocks_dict:
+            stocks_dict[ticker] = {'cumulative_shares': 0, 'total_cost': 0, 'transactions': []}
+
+        transaction = {'type': row['Transaction Type'], 'shares': shares, 'price_per_share': price_per_share}
+        stocks_dict[ticker]['transactions'].append(transaction)
+
+        if transaction['type'] == 'BUY':
+            stocks_dict[ticker]['cumulative_shares'] += shares
+            stocks_dict[ticker]['total_cost'] += shares * price_per_share
+        elif transaction['type'] == 'SELL':
+            # Assuming you sell shares at the same average price, if available
+            avg_price = stocks_dict[ticker]['total_cost'] / stocks_dict[ticker]['cumulative_shares'] if stocks_dict[ticker]['cumulative_shares'] > 0 else 0
+            stocks_dict[ticker]['cumulative_shares'] -= shares
+            stocks_dict[ticker]['total_cost'] -= shares * avg_price
+
+        if stocks_dict[ticker]['cumulative_shares'] > 0:
+            stocks_dict[ticker]['average_price_per_share'] = stocks_dict[ticker]['total_cost'] / stocks_dict[ticker]['cumulative_shares']
+        else:
+            stocks_dict[ticker]['average_price_per_share'] = 0
+
+    return stocks_dict
+
+# calculating the gain/loss for each stock
+def calculate_gain_loss(stocks_dict):
+    for ticker, stock_info in stocks_dict.items():
+        # Fetch current price for the stock
+        current_price = yf.Ticker(ticker).history(period="1d")['Close'].iloc[-1]
+
+        # Calculate current value of held shares
+        current_value = stock_info['cumulative_shares'] * current_price
+
+        # Calculate total invested amount
+        # Assuming 'total_cost' is already updated in stock_info during transaction processing
+        total_invested = stock_info['total_cost']
+
+        # Calculate gain/loss
+        gain_loss = current_value - total_invested
+
+        # Update the stocks_dict with current value and gain/loss
+        stock_info['current_value'] = current_value
+        stock_info['gain_loss'] = gain_loss
+
+    return stocks_dict