feat: TQQQ relative strength

common/market.py

@@ -18,7 +18,7 @@ def download_earnings_between(date_from, date_to):
         return {}
 
 
-def download_with_yf(ticker, period, interval):
+def download_ticker_with_interval(ticker, period, interval):
     try:
         opts = dict(
             tickers=ticker,

common/plotting.py

@@ -2,12 +2,12 @@
 import numpy as np
 import pandas as pd
 
-from common.market import download_with_yf
+from common.market import download_ticker_with_interval
 from common.subprocess_runner import open_file
 
 
 def plot_intraday(ticker, period="1d", interval="1m"):
-    data = download_with_yf(ticker, period=period, interval=interval)
+    data = download_ticker_with_interval(ticker, period=period, interval=interval)
     print(f"Plotting {ticker}")
     intraday = data.resample("30T").agg(
         {

tqqq-relative-strength.py

@@ -0,0 +1,183 @@
+#!/usr/bin/env python3
+"""
+Analyzes and visualizes the relative performance of TQQQ against QQQ holdings.
+
+Download QQQ holdings as a CSV File (Click on the Excel Download link)
+https://www.invesco.com/us/financial-products/etfs/holdings?audienceType=Investor&ticker=QQQ
+
+# Specify a custom CSV file for QQQ holdings
+python tqqq-relative-strength.py --qqq-csv path/to/qqq_holdings.csv
+"""
+from argparse import ArgumentParser, RawDescriptionHelpFormatter
+from datetime import datetime, timedelta
+from pathlib import Path
+
+import pandas as pd
+import seaborn as sns
+from matplotlib import pyplot as plt
+from matplotlib.dates import DateFormatter
+from stockstats import StockDataFrame
+
+from common.logger import setup_logging
+from common.market import download_ticker_data
+
+
+def parse_args():
+    parser = ArgumentParser(
+        description=__doc__, formatter_class=RawDescriptionHelpFormatter
+    )
+    parser.add_argument(
+        "--qqq-csv",
+        required=True,
+        help="Path to the CSV file containing QQQ holdings",
+    )
+    parser.add_argument(
+        "-v",
+        "--verbose",
+        action="count",
+        default=0,
+        dest="verbose",
+        help="Increase verbosity of logging output",
+    )
+    return parser.parse_args()
+
+
+def get_cached_data(cache_file: Path):
+    if cache_file.exists():
+        cache_modification_time = datetime.fromtimestamp(cache_file.stat().st_mtime)
+        if datetime.now() - cache_modification_time < timedelta(days=1):
+            return pd.read_csv(cache_file, index_col=0, parse_dates=True)
+    return None
+
+
+def save_to_cache(df: pd.DataFrame, cache_file: Path):
+    cache_file.parent.mkdir(parents=True, exist_ok=True)
+    df.to_csv(cache_file)
+
+
+def get_asset_data(symbol, start_date, end_date):
+    cache_dir = Path("output")
+    cache_file = cache_dir / f"{symbol}_{start_date}_{end_date}.csv"
+    cached_data = get_cached_data(cache_file)
+
+    if cached_data is not None:
+        print(f"Using cached data for {symbol}")
+        return StockDataFrame.retype(cached_data)
+
+    print(f"Downloading new data for {symbol}")
+    data = StockDataFrame.retype(
+        download_ticker_data(symbol, start=start_date, end=end_date)
+    )
+    save_to_cache(data, cache_file)
+    return data
+
+
+def get_qqq_holdings(csv_file_path):
+    df = pd.read_csv(csv_file_path)
+    return df["Holding Ticker"].tolist()
+
+
+def main():
+    focused_stock = "TQQQ"
+    qqq_holdings = get_qqq_holdings(args.qqq_csv)
+    stocks = [focused_stock] + qqq_holdings
+    start_date = "2010-01-01"
+    end_date = "2024-01-01"
+    df = pd.DataFrame()
+    for stock in stocks:
+        data = get_asset_data(stock, start_date, end_date)
+        df[stock] = data["close"]
+
+    df_pct = df.pct_change()
+    df_cum_pct = (1 + df_pct).cumprod() - 1
+
+    fig, ax = plt.subplots(figsize=(16, 8))
+
+    # Set up the plot style
+    sns.set_style("whitegrid")
+    sns.set_palette("cool")
+
+    focused_color = "#1E90FF"
+    other_color = "gray"
+
+    # Plot other stocks
+    for stock in qqq_holdings:
+        ax.plot(
+            df_cum_pct.index,
+            df_cum_pct[stock] * 100,
+            label=stock if stock == qqq_holdings[0] else "",
+            color=other_color,
+            alpha=0.5,
+            linewidth=1,
+        )
+
+    # Plot focused stock
+    ax.plot(
+        df_cum_pct.index,
+        df_cum_pct[focused_stock] * 100,
+        linewidth=2,
+        label=focused_stock,
+        color=focused_color,
+    )
+
+    # Styling
+    ax.set_title(
+        f"Cumulative Stock Performance: {start_date} to {end_date}",
+        fontsize=20,
+        fontweight="bold",
+        pad=20,
+    )
+    ax.set_xlabel("Date", fontsize=14, labelpad=10)
+    ax.set_ylabel("Cumulative Percentage Change", fontsize=14, labelpad=10)
+
+    # Format x-axis
+    ax.xaxis.set_major_formatter(DateFormatter("%b %Y"))
+    plt.xticks(rotation=45)
+
+    # Format y-axis as percentage
+    ax.yaxis.set_major_formatter(
+        plt.FuncFormatter(lambda y, _: "{:.0%}".format(y / 100))
+    )
+
+    # Add horizontal line at 0%
+    ax.axhline(y=0, color="red", linestyle="--", linewidth=1, alpha=0.5)
+
+    # Customize grid
+    ax.grid(True, linestyle=":", alpha=0.2)
+
+    # Remove top and right spines
+    ax.spines["top"].set_visible(False)
+    ax.spines["right"].set_visible(False)
+
+    # Add annotations
+    focused_stock_final_value = df_cum_pct[focused_stock].iloc[-1] * 100
+    stocks_above_focused = 0
+    for stock in stocks:
+        final_value = df_cum_pct[stock].iloc[-1] * 100
+        if stock == focused_stock or final_value > focused_stock_final_value:
+            color = focused_color if stock == focused_stock else other_color
+            ax.annotate(
+                f"{stock}: {final_value:.2f}%",
+                xy=(df_cum_pct.index[-1], final_value),
+                xytext=(10, 10),
+                textcoords="offset points",
+                color=color,
+                fontweight="bold",
+                fontsize=10,
+                arrowprops=dict(arrowstyle="->", color=color),
+            )
+            if stock != focused_stock and final_value > focused_stock_final_value:
+                stocks_above_focused += 1
+
+    # Print the number of stocks with pct change greater than the focused stock
+    print(
+        f"Number of stocks with percentage change greater than {focused_stock}: {stocks_above_focused}"
+    )
+    plt.tight_layout()
+    plt.show()
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    setup_logging(args.verbose)
+    main()