Handling multiple stock selections

import databento as db
import matplotlib.pyplot as plt
import pandas as pd

# Set parameters
dataset = "XNAS.ITCH"
symbols = ["MSFT", "AAPL", "TSLA", "AMZN"]
start = "2018-06"
end = "2025-04"

# Create a reference client
ref_client = db.Reference(key="$YOUR_API_KEY")

# Request adjustment factors data for the relevant time range
df_factors = ref_client.adjustment_factors.get_range(
    symbols=symbols,
    start=start,
    end=end,
    countries=["US"],
)

# Filter listings on Nasdaq/NGS (Global Select Market)
df_factors = df_factors[df_factors["operating_mic"] == "XNGS"]

# Filter factors with (A)pply status
df_factors = df_factors[df_factors["status"] == "A"]

# Filter option 1 (default) for all factors
df_factors = df_factors[(df_factors["option"] == 1)]

# Convert the ex_dates index to UTC timestamps
df_factors.index = pd.to_datetime(df_factors.index, utc=True)

# Create a historical client
hist_client = db.Historical(key="$YOUR_API_KEY")

# Request daily bars data to obtain close prices
data = hist_client.timeseries.get_range(
    dataset=dataset,
    schema="ohlcv-1d",
    symbols=symbols,
    start=start,
    end=end,
)
df_bars = data.to_df()


def apply_factors(df_adjusted, df_bars, factors_symbol, symbol):
    for ex_date, factor in zip(factors_symbol.index, factors_symbol["factor"]):
        mask = (df_adjusted.index < ex_date) & (df_bars["symbol"] == symbol)
        df_adjusted.loc[mask, "close_adjusted"] *= factor


# Apply the adjustment factors:
# Create a new dataframe for the adjusted prices
df_adjusted = pd.DataFrame(index=df_bars.index)
df_adjusted["symbol"] = df_bars["symbol"]
df_adjusted["close_adjusted"] = df_bars["close"].copy()

for symbol in symbols:
    # Filter the bars and factors for the current symbol
    bars_symbol = df_bars[df_bars["symbol"] == symbol]
    factors_symbol = df_factors[df_factors["symbol"] == symbol]

    # Apply the factors using the `apply_factors` function defined above
    apply_factors(df_adjusted, df_bars, factors_symbol, symbol)

# Plot the results
for symbol in symbols:
    df_symbol = df_adjusted[df_adjusted["symbol"] == symbol]
    plt.plot(df_symbol.index, df_symbol["close_adjusted"], label=f"{symbol} adjusted")

plt.legend()
plt.xlabel("Date (UTC)")
plt.ylabel("Daily Close Price (USD)")
plt.title("Adjusted Close Prices for Selected Stocks")
plt.show()