Upload metrics_calculations.py

metrics_calculations.py

@@ -0,0 +1,145 @@
+from dataframes import bank_statement_df, investment_statement_df, debts_df, user_info_df
+from dataframes import data
+import pandas as pd
+import numpy as np
+import streamlit as st
+
+users = pd.unique(user_info_df['UserId'])
+
+def monthly_budget():
+    monthly_budget_dict = {}  
+
+    for user_id in users:
+        user_bank_data = bank_statement_df[bank_statement_df['UserId'] == user_id]
+        monthly_budget_series = user_bank_data.groupby('description')['debit'].sum()
+        monthly_budget_dict[user_id] = monthly_budget_series
+    monthly_budget_df = pd.DataFrame(monthly_budget_dict)
+    return monthly_budget_df
+
+def monthly_expenses():
+    bank_statement_df['date'] = pd.to_datetime(bank_statement_df['date'], format='%d/%m/%Y')
+    bank_statement_df['Month'] = bank_statement_df['date'].dt.to_period("M")
+    monthly_expenses_df = bank_statement_df.groupby(['UserId', 'Month'])['debit'].sum().reset_index()
+    monthly_expenses_df.rename(columns={'debit': 'MonthlyExpenses'}, inplace=True)
+    return monthly_expenses_df
+
+def savings_rate():
+    bank_statement_df['date'] = pd.to_datetime(bank_statement_df['date'])
+    monthly_data = bank_statement_df.groupby(['UserId', bank_statement_df['date'].dt.to_period("M")]).agg(
+        {'debit': 'sum', 'credit': 'sum'}).reset_index()
+    monthly_data.rename(columns={'debit': 'MonthlyExpenses', 'credit': 'MonthlyIncome'}, inplace=True)
+    monthly_data['SavingsRate'] = np.where(monthly_data['MonthlyIncome'] > 0,
+                                        ((monthly_data['MonthlyIncome'] - monthly_data['MonthlyExpenses']) /
+                                            monthly_data['MonthlyIncome']) * 100, 0)
+    return monthly_data
+
+def debt_overview():
+    users_debt_overviews = {}
+
+    for user_id in users:
+        user_debts_data = debts_df[debts_df['UserId'] == user_id]
+        user_debt_overview = user_debts_data.groupby('debtType')['amount'].sum().reset_index()
+        users_debt_overviews[user_id] = user_debt_overview
+    return users_debt_overviews
+
+def cash_flow():
+    cash_flows = {} 
+
+    for user_id in users:
+        user_bank_data = bank_statement_df[bank_statement_df['UserId'] == user_id].copy()
+        user_bank_data['date'] = pd.to_datetime(user_bank_data['date'])
+        monthly_expenses = user_bank_data.groupby(user_bank_data['date'].dt.to_period("M"))['debit'].sum()
+        monthly_expenses.index = monthly_expenses.index.to_timestamp()
+        
+        monthly_income = user_bank_data.groupby(user_bank_data['date'].dt.to_period("M"))['credit'].sum()
+        monthly_income.index = monthly_income.index.to_timestamp()
+        
+        cash_flow = monthly_income - monthly_expenses
+        cash_flows[user_id] = cash_flow
+
+    # Store the cash flows in a single dataframe with a user ID column
+    cash_flow_df = pd.concat([df.reset_index().assign(UserId=user_id) for user_id, df in cash_flows.items()], ignore_index=True)
+    # Rename the '0' column to 'CashFlow'
+    cash_flow_df.rename(columns={0: 'CashFlow'}, inplace=True)
+    return cash_flow_df   
+
+def performance_chart():
+    investment_statement = investment_statement_df.copy()
+
+    investment_statement['date'] = pd.to_datetime(investment_statement['date'], format='%d/%m/%Y')
+
+    investment_statement.sort_values(by=['UserId', 'date'], inplace=True)
+
+    # Calculate daily balance
+    investment_statement['daily_balance'] = investment_statement.groupby('UserId')['dividends'].cumsum()
+    investment_statement['daily_balance'].fillna(0, inplace=True)
+
+    return investment_statement
+
+def portfolio_value():
+    portfolio_values = {}
+    for user_id, user_investment_df in investment_statement_df.groupby("UserId"):
+        portfolio_value = user_investment_df.groupby("date")["balance"].sum()
+        portfolio_values[user_id] = portfolio_value
+
+    portfolio_df = pd.concat([df.reset_index().assign(UserId=user_id) for user_id, df in portfolio_values.items()], ignore_index=True)
+    return portfolio_df
+
+def asset_allocation():
+    user_asset_allocations = []
+    for user_id in investment_statement_df['UserId'].unique():
+        user_df = investment_statement_df[investment_statement_df['UserId'] == user_id]
+        asset_allocation = user_df.groupby('description')['buy'].sum().reset_index()
+        asset_allocation.rename(columns={'description': 'Asset', 'buy': 'Allocation'}, inplace=True)
+        asset_allocation['UserId'] = user_id
+        user_asset_allocations.append(asset_allocation)
+
+    all_user_asset_allocations = pd.concat(user_asset_allocations, ignore_index=True)
+    all_user_asset_allocations = all_user_asset_allocations[all_user_asset_allocations['Allocation'] > 0]
+    return all_user_asset_allocations
+
+def dividend_income():
+    investment_statement_df['date'] = pd.to_datetime(investment_statement_df['date'], format='%d/%m/%Y')
+    dividend_income = investment_statement_df.groupby(['UserId', investment_statement_df['date'].dt.to_period("M")])['dividends'].sum()
+    return dividend_income
+
+
+def proj_retirement_savings():
+    projected_savings_data = []
+    portfolio_values = {} 
+    for user_data in data:
+        user_id = user_data["user_id"]
+        current_age = user_data["currentAge"]
+        retirement_age = user_data["retirementAge"]
+        life_expectancy = user_data["lifeExpectancy"]
+        savings_rate = user_data["savingsRate"]
+        investment_returns = user_data["investmentReturns"]
+
+
+        user_investment_df = investment_statement_df[investment_statement_df['UserId'] == user_id]
+        portfolio_value = user_investment_df.groupby("date")["balance"].sum()
+        
+        portfolio_values[user_id] = portfolio_value
+
+    for user_id, portfolio_value in portfolio_values.items():
+        projected_savings = portfolio_value.iloc[-1]
+        
+        years_until_retirement = retirement_age - current_age
+
+        for year in range(years_until_retirement, life_expectancy - current_age + 1):
+            annual_savings = projected_savings * savings_rate
+            projected_savings += annual_savings * (1 + investment_returns)
+            
+            projected_savings_data.append({
+                "UserId": user_id,
+                "Year": current_age + year,
+                "ProjectedSavings": projected_savings
+            })
+
+    projected_savings_df = pd.DataFrame(projected_savings_data)
+    pd.set_option('display.float_format', '{:.2f}'.format)
+
+    return projected_savings_df
+
+
+