Cashflow tracker with AI

Instruction: You are an expert in personal financial management and processing csv file for finance data and create a monthly cashflow report for it including the budget categorization, bills, save similar as the one in Mint by intuit or Simplify by Quicken. The transactions delimited by ###. When writing the list, put them in date order so I can double check. If there are multiple entries with the same date as key, add -1, -2, ... for more entries as the key. Do not double calculate any entry.

Do not calculate anything, just return a json format for the categories. here is a sample format
{"Income": 
{"Payroll Deposit":{date: amount}, "Accounts Payable": {date: amount}, ... },
{"Savings":
{Customer Transfer Dr:{date: amount} ,...},
{"Expenses": 
{"Miscellaneous Express":{date: amount}, "WITHDRAWAL" :{date: amount}}
}

Use this for breaking down the list:

•	Income: any positive cash flow except from Customer Transfer Cr. MB-TRANSFER. This includes my full-time job income (Payroll Deposit), other deposits (DEPOSIT and Accounts Payable), and positive transfers from my savings account (Customer Transfer Cr. MB-TRANSFER).
•	Savings: any transaction with Customer Transfer Cr. MB-TRANSFER. consider the sign
•	Expenses: any negative cash flow that is not saving This includes my credit card payments (Miscellaneous Payment), withdrawals (WITHDRAWAL), and bill payments (Bill Payment: MB-QUESTRADE). 

Create each month separately: January, then February, etc.

###
2/27/2023,80.00,-,DEPOSIT ,FREE INTERAC E-TRANSFER
1/3/2023,-12.45,-,Miscellaneous Payment ,Credit card

###

cashflow = []
incomes_plot = {}
expenses_plot = {}
savings_plot = {}

for month in data:
    # Income
    incomes = data[month]["Income"]
    income_values = list(incomes.values())
    income_values = [sum(i.values()) for i in income_values]
    income_category = list(incomes.keys())
    total_incomes = sum(income_values)
    print(income_category)

    for i in income_category:
        if i not in incomes_plot:
            incomes_plot[i] = [0] * len(data)
        incomes_plot[i][list(data).index(month)] = income_values[income_category.index(i)]

    # Expenses
    expenses = data[month]["Expenses"]
    expenses_values = list(expenses.values())
    expenses_values = [sum(i.values()) for i in expenses_values]
    expense_category = list(expenses.keys())
    total_expenses = sum(expenses_values)

    for i in expense_category:
        if i not in expenses_plot:
            expenses_plot[i] = [0] * len(data)
        expenses_plot[i][list(data).index(month)] = expenses_values[expense_category.index(i)]

    # Savings
    savings = data[month]["Savings"]
    savings_values = list(savings.values())
    savings_values = [sum(i.values()) for i in savings_values]
    savings_category = list(savings.keys())
    total_savings = sum(savings_values)

    for i in savings_category:
        if i not in savings_plot:
            savings_plot[i] = [0] * len(data)
        savings_plot[i][list(data).index(month)] = savings_values[savings_category.index(i)]

    cashflow.append(total_incomes + total_expenses + total_savings)

# Cashflow over time
print(cashflow)
plt.figure(figsize=(10, 5))
plt.plot(list(data), cashflow, label='Cashflow')
plt.plot(list(data), [sum(cashflow[:i]) for i in range(1, len(cashflow)+1)], label='Cumulative Cashflow')
plt.xlabel('Month')
plt.legend(loc='best')

# Move investment transfers out of Expenses into a separate series
invest = expenses_plot.pop('Bill Payment')

# Income breakdown
plt.figure(figsize=(10, 5))
plt.stackplot(list(data), incomes_plot.values(), labels=incomes_plot.keys())
plt.legend(loc='upper left')
plt.title('Income')

# Expense breakdown
plt.figure(figsize=(10, 5))
plt.stackplot(list(data), expenses_plot.values(), labels=expenses_plot.keys())
plt.legend(loc='best')
plt.title('Expenses')

# Savings + investments
plt.figure(figsize=(10, 5))
plt.stackplot(list(data), savings_plot.values(), labels=savings_plot.keys())
plt.plot(list(data), [-i for i in invest], label='Invested')
plt.title('Savings')
plt.legend(loc='best')

plt.show()