Buy 1 tesla share a day and TP at 10k backtesting 3 years

tesla_daily_buy.py

import pandas as pd

# Load the data from the CSV file
data = pd.read_csv("TSLA_3_years_data.csv")

# Initialize variables
total_shares = 0
cumulative_cost = 0.0
total_profit = 0.0
data['Yesterday_Close'] = data['Close'].shift(1)
data['Daily_Change_Percent'] = ((data['Close'] - data['Yesterday_Close']) / data['Yesterday_Close']) * 100
#print(data.tail(20))
# Iterate through each row in the dataframe
for _, row in data.iterrows():
    open_price = row['Open']  # Assuming 'Open' is the name of the column with opening prices
    #if row['Daily_Change_Percent'] < 0.00:
    total_shares += 1  # Buy 1 share
    cumulative_cost += open_price  # Add to the total cost    

    close = row['Close']
    profit = row['Close']*total_shares - cumulative_cost
    #(0.1*cumulative_cost)
    if profit > 10000:
        date = row['Date']
        print(f"Total Shares sold: {total_shares}",f"Profit: ${profit:.2f}",f"Date: {date}")
        total_shares = 0
        cumulative_cost = 0.0
        total_profit += profit
    
# Calculate the average purchase price
average_price = cumulative_cost / total_shares

open_p_l = close*total_shares - cumulative_cost

print(f"Total Shares owned: {total_shares}")
print(f"Average Purchase Price: ${average_price:.2f}")
print(f"Total Realised Profit: ${total_profit:.2f}")
print(f"Open P/L: ${open_p_l:.2f}")