Automating the Management of Hundreds of accounts

If you manage multiple accounts through to purchase T-bills, T-notes, T-bonds, TIPS, FRNs, or C of I certificates then you know how antiquated and difficult it can be to manage all of your accounts at the United States Treasury. I’ve written a Python program to aggregate all of these accounts into a single summary Excel document. This program can be expanded to buy and sell securities. However, in its published state is simply aggregates all the data and returns it to a CSV file.

The program is written in Python and uses the Selenium WebDriver to interact with the website. The program is designed to work with multiple accounts, and it reads the account information from a CSV file. It also has a feature to bypass the one-time password by logging in to your email and extracting the OTP. The program works by logging in to each account, checking the account details, and updating the account information as needed. It currently returns the following data:

Automating the login to each account

To do this, the program reads a CSV file containing all of your account numbers and other relevant information, such as LLC names and purchase amounts. It then loops through each account, retrieves the necessary information from the website using Selenium, and saves the data into a new column of the CSV file.

Here’s an example of the code that performs this task:

import pandas as pd
from treasury_direct import process_account

if __name__ == '__main__':
    df = pd.read_csv('accounts.csv')
    url = ""

    for index, row in df.iterrows():
        account_number = row['Treasury Direct']

        # Check if the row is already complete
        if not pd.isna(row['LLC Name']) and not pd.isna(row['Original Purchase Amount']) \
                and not pd.isna(row['Current Value']) and not pd.isna(row['Issue Date']) \
                and not pd.isna(row['Interest Rate']):

        success = process_account(account_number, df, index, url)

        if success:

    df.to_csv('accounts.csv', index=False)

Automatically retrieving one-time passcodes (OTPs) from emails

To log into each account, the program needs to retrieve OTPs from emails sent by It uses the Gmail API to search for unread emails from containing the subject line “One Time Passcode” and retrieves the OTP from the email body. Once it has the OTP, the program enters it into the appropriate field on the login page.

Here’s an example of the code that retrieves OTPs:

from gmail import get_otp

# Perform the one-time password process here
# Enter the OTP in the input field
# Continuously try to get OTP from the email until it's received
otp = None
while otp is None:
    otp = get_otp()
    if otp is None:
        # Sleep for 10 seconds before trying again
otp_input = driver.find_element_by_name("otp")

Finding the OTP in the email

            # Get the first unread email
            message = messages[0]
            msg = service.users().messages().get(userId='me', id=message['id'], format='full').execute()
            msg_str = base64.urlsafe_b64decode(msg['payload']['body']['data']).decode()
            otp = msg_str.splitlines()[6].split()[0]
            if otp:
                one_time_passcode = otp
                return one_time_passcode
                print("No One Time Passcode found in the email.")
                return None

Moving OTP emails to trash

To avoid cluttering your inbox, the program moves OTP emails to the trash folder after retrieving the OTPs. It uses the Gmail API to search for emails from containing the subject line “One Time Passcode” and moves them to the trash folder.

Here’s an example of the code that moves OTP emails to trash:

def move_otp_emails_to_trash():
        creds = get_credentials()
        service = build('gmail', 'v1', credentials=creds)
        results = service.users().messages().list(userId='me',
                                                  q=' subject:"One Time Passcode"').execute()
        messages = results.get('messages', [])
        if not messages:
            print('No messages found.')
            for message in messages:
                service.users().messages().trash(userId='me', id=message['id']).execute()
                print(f"Moved message with ID {message['id']} to trash.")
    except HttpError as error:
        print(f'An error occurred: {error}')

Extracting Account Information

        # Locate the elements containing the desired information
        llc_and_account_number = driver.find_element_by_xpath('//div[@id="accountnumber"]').text
        original_purchase_amount = driver.find_element_by_xpath('//p[contains(text(), "Series I current holdings total amount")]/span').text
        current_value = driver.find_element_by_xpath('//p[contains(text(), "Series I current holdings current value")]/span').text
        issue_date = driver.find_element_by_xpath('(//tr[contains(@class, "altrow")]/td)[3]').text
        interest_rate = driver.find_element_by_xpath('//td[contains(text(), "%")]').text

        # Separate the LLC name and account number
        llc_name, account_number = llc_and_account_number.split(':', 1)
        llc_name = llc_name.strip().replace("LLC Name: ", "")
        account_number = account_number.strip()

        # Print the extracted information
        print(f"LLC Name: {llc_name}")
        print(f"Account Number: {account_number}")
        print(f"Original Purchase Amount: {original_purchase_amount}")
        print(f"Current Value: {current_value}")
        print(f"Issue Date: {issue_date}")
        print(f"Interest Rate: {interest_rate}")

        # Save the extracted information as new columns for the current row
        df.loc[index, 'LLC Name'] = llc_name
        df.loc[index, 'Original Purchase Amount'] = original_purchase_amount
        df.loc[index, 'Current Value'] = current_value
        df.loc[index, 'Issue Date'] = issue_date
        df.loc[index, 'Interest Rate'] = interest_rate
    except NoSuchElementException:
        print(f"Failed to extract ibond information for account {account_number}. Moving to the next account.")

        bank_name = driver.find_element_by_xpath('//tr[@class="altrow1"][1]/td[3]/strong').text
        routing_number = driver.find_element_by_xpath('//tr[@class="altrow1"][2]/td[3]/strong').text
        account_number = driver.find_element_by_xpath('//tr[@class="altrow1"][3]/td[3]/strong').text
        names_on_account = driver.find_element_by_xpath('//tr[@class="altrow1"][4]/td[3]/strong').text
        account_type = driver.find_element_by_xpath('//tr[@class="altrow1"][5]/td[3]/strong').text
        return_code = driver.find_element_by_xpath('//tr[@class="altrow1"][6]/td[3]/strong').text

        # Print the extracted information
        print("Bank Name:", bank_name)
        print("Routing Number:", routing_number)
        print("Account Number:", account_number)
        print("Name(s) on Account:", names_on_account)
        print("Account Type:", account_type)
        print("Return Code:", return_code)

        # Save the extracted information as new columns for the current row
        df.loc[index, 'Bank Name'] = bank_name
        df.loc[index, 'Routing Number'] = routing_number
        df.loc[index, 'Account Number'] = account_number
        df.loc[index, 'Name(s) on Account'] = names_on_account
        df.loc[index, 'Account Type'] = account_type
        df.loc[index, 'Return Code'] = return_code

    except NoSuchElementException:
        print(f"Failed to extract information for account {account_number}. Moving to the next account.")

Full Code:

Coming soon.

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