tqqq-alternative-path.py

import matplotlib.pyplot as plt

from common.market import download_ticker_data

# Define the custom function to adjust the data for dividends and splits, if necessary
def adjust_data(data):
    # Assuming the function 'download_ticker_data' returns adjusted close prices
    # If not, add your adjustment logic here
    data["Adj Close"] = data["Close"]
    return data


# Download historical data for QQQ between 2000 and 2010
qqq_data = download_ticker_data("QQQ", "2000-01-01", "2010-12-31")
qqq_data = adjust_data(qqq_data)

# Download historical data for TQQQ between 2010 and 2020
tqqq_data = download_ticker_data("TQQQ", "2010-01-01", "2020-12-31")
tqqq_data = adjust_data(tqqq_data)

# Calculate daily returns for QQQ
qqq_data["Returns"] = qqq_data["Adj Close"].pct_change()

# Determine the length of the shorter series
min_length = min(len(qqq_data), len(tqqq_data))

# Truncate the longer series to match the length of the shorter one
if len(qqq_data) > min_length:
    qqq_data = qqq_data.iloc[-min_length:]  # Take the last 'min_length' entries
elif len(tqqq_data) > min_length:
    tqqq_data = tqqq_data.iloc[-min_length:]  # Take the last 'min_length' entries

# Initialize the new TQQQ prices with the first close price from the truncated data
new_tqqq_prices = [tqqq_data["Adj Close"].iloc[0]]

# Apply the QQQ returns to the TQQQ price series from the truncated data
for i in range(1, len(qqq_data)):
    new_price = new_tqqq_prices[-1] * (1 + qqq_data["Returns"].iloc[i])
    new_tqqq_prices.append(new_price)

# Add the new prices to the truncated TQQQ DataFrame
tqqq_data = tqqq_data.iloc[
    -min_length:
]  # Ensuring tqqq_data is truncated to min_length
tqqq_data["Modeled Price"] = new_tqqq_prices

# Plot the modeled prices
plt.figure(figsize=(14, 7))  # Set the figure size
plt.plot(
    tqqq_data.index,
    tqqq_data["Modeled Price"],
    label="Modeled TQQQ Price",
    linestyle="--",
)

# Title and labels
plt.title("Modeled TQQQ Prices")
plt.xlabel("Date")
plt.ylabel("Price")

# Legend
plt.legend()

# Display the plot
plt.show()

# Save the modeled prices to a CSV file (optional)
tqqq_data.to_csv("modeled_tqqq_prices.csv")