pip install pandas numpy matplotlib yfinance

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import yfinance as yf
from tabulate import tabulate
from datetime import datetime

# Define the stock symbol and the date range for our data
stock_symbol = 'TSLA'
start_date = '2024-01-01'
end_date = datetime.today().strftime('%Y-%m-%d')  # Sets end date to today's date
print(f"Double Moving Average Crossover Trading for: {stock_symbol}\nStart Date: {start_date}\nEnd Date: {end_date}")

df = yf.download(stock_symbol, start=start_date, end=end_date)

# Select the desired columns (first level of MultiIndex)
df.columns = df.columns.get_level_values(0)

# Keep only the columns you are interested in
df = df[['Open', 'Close', 'Volume', 'Low', 'High']]

# If the index already contains the dates, rename the index
df.index.name = 'Date'  # Ensure the index is named "Date"
    
# Resetting the index if necessary
df.reset_index(inplace=True)

# Ensure that the index is of type datetime
df['Date'] = pd.to_datetime(df['Date'])

# Set the 'Date' column as the index again (in case it's reset)
df.set_index('Date', inplace=True)

df.head()

# Plot the closing price
plt.figure(figsize=(12, 6))

plt.plot(df['Close'], label='Closing Price')

# Add title, labels, and legend
plt.title(f'{stock_symbol} Closing Price')
plt.xlabel('Date')
plt.ylabel('Price ($)')
plt.legend()

# Save the plot in 300dpi
plt.savefig(f'{stock_symbol}_stock_chart.png', dpi=300)

# Show the plot
plt.show()

# Calculate the short and long EMAs
SHORT_WINDOW = 10
LONG_WINDOW = 30
df['SHORT_WINDOW'] = df['Close'].ewm(span=SHORT_WINDOW, adjust=False).mean()
df['LONG_WINDOW'] = df['Close'].ewm(span=LONG_WINDOW, adjust=False).mean()

# Plot the Close Price and EMAs
plt.figure(figsize=(14, 7))

# Plot Close Price
plt.plot(df.index, df['Close'], label='Close Price', linewidth=2)

# Plot Short-term EMA
plt.plot(df.index, df['SHORT_WINDOW'], label=f'{SHORT_WINDOW}-day EMA', linestyle='--')

# Plot Long-term EMA
plt.plot(df.index, df['LONG_WINDOW'], label=f'{LONG_WINDOW}-day EMA', linestyle='--')

# Labels and title
plt.xlabel('Date')
plt.ylabel('Price ($)')
plt.title(f'{stock_symbol} Close Price with Short and Long EMAs')
plt.legend()

# Save plot in 300dpi
plt.savefig(f'{stock_symbol}_moving_averages.png', dpi=300)

plt.show()

# Generate buy and sell signals in a single column
df['Signal'] = np.where(
    (df['SHORT_WINDOW'] > df['LONG_WINDOW']) & (df['SHORT_WINDOW'].shift(1) <= df['LONG_WINDOW'].shift(1)), 1,  # Buy Signal
    np.where(
        (df['SHORT_WINDOW'] < df['LONG_WINDOW']) & (df['SHORT_WINDOW'].shift(1) >= df['LONG_WINDOW'].shift(1)), -1,  # Sell Signal
        0  # No Signal
    )
)

# Define the fee calculation function
def calculate_fee(amount: float) -> float:
    """Calculate the brokerage fee based on transaction amount."""
    fee = amount * 0.0025  # 0.25% of the transaction
    return max(fee, 0.01)   # Minimum fee of $0.01

# Starting capital
initial_cash = 100  # Example initial capital
cash = initial_cash
shares = 0
df['Portfolio Value'] = cash

# List to store transaction details for tabulation
transaction_details = []

for i, row in df.iterrows():
    if row['Signal'] == 1 and cash > 0:  # Buy condition
        # Calculate the fee for the buy transaction
        fee = calculate_fee(cash)
        
        # Calculate how many shares can be bought after deducting the fee
        shares_bought = (cash - fee) / row['Close']
        
        # Deduct the cash and fee for the purchase
        cash -= shares_bought * row['Close'] + fee
        
        # Add the bought shares to the portfolio
        shares += shares_bought
        
        # Record transaction details
        transaction_details.append([row.name, 'Buy', round(row['Close'], 2), round(fee, 2), round(cash + (shares * row['Close']), 2)])

    elif row['Signal'] == -1 and shares > 0:  # Sell condition
        # Calculate how much cash is earned from selling shares
        total_sale_amount = shares * row['Close']
        
        # Calculate the fee for the sell transaction
        fee = calculate_fee(total_sale_amount)
        
        # Cash earned after the fee is deducted
        cash_earned = total_sale_amount - fee
        
        # Update the cash balance after selling shares
        cash += cash_earned
        
        # All shares are sold
        shares = 0
        
        # Record transaction details
        transaction_details.append([row.name, 'Sell', round(row['Close'], 2), round(fee, 2), round(cash, 2)])

    # Update portfolio value
    df.at[i, 'Portfolio Value'] = cash + (shares * row['Close'])

# Summarize results using tabulate
print(tabulate(transaction_details, headers=["Date", "Action", "Price ($)", "Fee ($)", "Portfolio Value ($)"], tablefmt="pretty"))

# Final performance
final_value = cash + (shares * df.iloc[-1]['Close'])
profit = final_value - initial_cash
print(f"\nFinal Portfolio Value: ${final_value:.2f}")
print(f"Total Profit/Loss: ${profit:.2f}")

+---------------------------+--------+-----------+---------+---------------------+
|           Date            | Action | Price ($) | Fee ($) | Portfolio Value ($) |
+---------------------------+--------+-----------+---------+---------------------+
| 2024-05-01 00:00:00+00:00 |  Buy   |  179.99   |  0.25   |        99.75        |
| 2024-06-11 00:00:00+00:00 |  Sell  |  170.66   |  0.24   |        94.34        |
| 2024-06-12 00:00:00+00:00 |  Buy   |  177.29   |  0.24   |        94.11        |
| 2024-08-05 00:00:00+00:00 |  Sell  |  198.88   |  0.26   |        105.3        |
| 2024-09-05 00:00:00+00:00 |  Buy   |  230.17   |  0.26   |       105.04        |
| 2024-10-15 00:00:00+00:00 |  Sell  |  219.57   |  0.25   |        99.95        |
| 2024-10-25 00:00:00+00:00 |  Buy   |  269.19   |  0.25   |        99.7         |
+---------------------------+--------+-----------+---------+---------------------+

Initial Portfolio Vaue: $100.00
Final Portfolio Value: $121.67
Total Profit/Loss: $21.67