I’m working on a Python script that uses the pandas library to manipulate data. However, I’m encountering an error that I can’t seem to resolve. Here’s the relevant part of my code:

import pandas as pd

# Create a dataframe to store the capital over time
capital_df = pd.DataFrame(columns=['time', 'capital'])

# Some code here...

# Append the current capital to the dataframe
capital_df = capital_df.append({'time': df.index[i], 'capital': capital}, ignore_index=True)

When I run this code, I get the following error:

Exception has occurred: AttributeError
'DataFrame' object has no attribute 'append'

This is strange because append is a standard method for pandas DataFrame objects. I’ve checked and capital_df is indeed a DataFrame when append is called.

Here’s what I’ve tried so far to resolve this issue:

1. Checked for any variable or function named capital_df that might be overwriting the DataFrame capital_df.
2. Updated my pandas library using pip.
3. Checked my pandas version to make sure it’s updated.
4. Restarted my Python environment.
5. Uninstalled and reinstalled pandas.

Despite these efforts, the issue persists.

Any help would be greatly appreciated. Thank you!

My full codes are attached below:

import pandas as pd
import matplotlib.pyplot as plt
import time
import requests

# Define the stock symbol and your API key
symbol = 'AAPL'
api_key = 'API'  

# Define the initial capital
initial_capital = 10000
capital = initial_capital

# Create a dataframe to store the capital over time
capital_df = pd.DataFrame(columns=['time', 'capital'])

while True:
    # Download minute-level data for the current trading day
    url = f'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol={symbol}&interval=1min&apikey={api_key}'
    response = requests.get(url)
    data = response.json()
    df = pd.DataFrame(data['Time Series (1min)']).T
    df = df.rename(columns={'1. open': 'Open', '2. high': 'High', '3. low': 'Low', '4. close': 'Close', '5. volume': 'Volume'})
    df = df.astype(float)

    # Calculate 20 day moving average and standard deviation
    df['20_day_mean'] = df['Close'].rolling(window=20).mean()
    df['20_day_std'] = df['Close'].rolling(window=20).std()

    # Create signals based on the z-score
    df['z_score'] = (df['Close'] - df['20_day_mean']) / df['20_day_std']
    df['signals'] = 0
    df.loc[df['z_score'] > 1, 'signals'] = -1
    df.loc[df['z_score'] < -1, 'signals'] = 1

    # Simulate trading
    for i in range(1, len(df)):
        # If the signal is 1, buy the stock
        if df['signals'].iloc[i] == 1:
            capital -= df['Close'].iloc[i]
        # If the signal is -1, sell the stock
        elif df['signals'].iloc[i] == -1:
            capital += df['Close'].iloc[i]
        # Append the current capital to the dataframe
        capital_df = capital_df.append({'time': df.index[i], 'capital': capital}, ignore_index=True)

    # Plot the capital over time
    plt.figure(figsize=(10, 5))
    plt.plot(capital_df['time'], capital_df['capital'])
    plt.title('Capital Over Time')
    plt.xlabel('Time')
    plt.ylabel('Capital')
    plt.show()

    print('Current earning:', capital - initial_capital)

    # Wait for 1 minute
    time.sleep(60)