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import datetime as dt
from zoneinfo import ZoneInfo
import databento as db
import matplotlib.pyplot as plt
import pandas as pd
from matplotlib.patches import Patch
from matplotlib.ticker import MaxNLocator

def get_volume_data(
    client: db.Historical,
    dataset: str,
    symbol: str,
    date: dt.date,
) -> pd.DataFrame:
    """Get volume data for all options for a given symbol."""
    volume_df = client.timeseries.get_range(
        dataset=dataset,
        symbols=f"{symbol}.OPT",
        schema="ohlcv-1d",
        stype_in="parent",
        start=date,
    ).to_df()

    return volume_df.groupby("symbol")["volume"].sum().reset_index()


def get_oi_data(
    client: db.Historical,
    dataset: str,
    symbol: str,
    date: dt.date,
) -> pd.DataFrame:
    """Get open interest data for all options for a given symbol."""
    # Start-of-day open interest is published before the RTH open at 9:30 ET
    end_time = dt.time(9, 30, 0, tzinfo=ZoneInfo("America/New_York"))
    stats_df = client.timeseries.get_range(
        dataset=dataset,
        symbols=f"{symbol}.OPT",
        schema="statistics",
        stype_in="parent",
        start=date,
        end=dt.datetime.combine(date, end_time),
    ).to_df()

    stats_df = stats_df[stats_df["stat_type"] == db.StatType.OPEN_INTEREST]
    stats_df = stats_df.drop_duplicates("symbol", keep="last")
    stats_df["open_interest"] = stats_df[stats_df["stat_type"] == db.StatType.OPEN_INTEREST][
        "quantity"
    ]

    return stats_df[["open_interest", "symbol"]]


def get_definition_data(
    client: db.Historical,
    dataset: str,
    symbol: str,
    date: dt.date,
) -> pd.DataFrame:
    """Get definition data for all options for a given symbol."""
    def_df = client.timeseries.get_range(
        dataset=dataset,
        symbols=f"{symbol}.OPT",
        schema="definition",
        stype_in="parent",
        start=date,
    ).to_df()

    def_df["days_to_expiration"] = (def_df["expiration"] - def_df.index.normalize()).dt.days
    def_df["expiration"] = def_df["expiration"].dt.normalize().dt.date

    return def_df[
        ["symbol", "strike_price", "instrument_class", "expiration", "days_to_expiration"]
    ]


def plot_oi_by_strike(
    df: pd.DataFrame,
    expiration_date: dt.date,
) -> None:
    df = df[df["expiration"] == expiration_date]
    trade_date = df["trade_date"].iloc[0]
    days_to_expiration = df["days_to_expiration"].iloc[0]

    df_strikes = df.groupby(["strike_price", "instrument_class"])["open_interest"].sum().unstack()

    # Plot bar chart
    _, ax = plt.subplots(figsize=(14, 6))
    df_strikes.plot(
        ax=ax,
        kind="bar",
        xlabel="Strike price",
        ylabel="Open interest",
    )

    # Adjust X ticks and labels
    ax.xaxis.set_major_locator(MaxNLocator(15))
    ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha="right")

    # Set title and legend
    ax.set_title(f"{symbol} open interest \n {trade_date} ({days_to_expiration} DTE)")
    ax.legend(handles=[Patch(facecolor="C0", label="Call"), Patch(facecolor="C1", label="Put")])

    plt.tight_layout()
    plt.show()


# Set parameters
dataset = "OPRA.PILLAR"
symbol = "NVDA"
start = dt.date(2025, 8, 19)

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

# Get strike price, expiration, and instrument class in the definition schema
def_df = get_definition_data(client, dataset, symbol, start)

# Volume from the OHLCV-1d schema
volume_df = get_volume_data(client, dataset, symbol, start)

# Start-of-day open interest is published in the morning in the statistics schema
stats_df = get_oi_data(client, dataset, symbol, start)

# Merge different schemas together
df = def_df.merge(volume_df, on="symbol", how="left")
df = df.merge(stats_df, on="symbol", how="left")

df["trade_date"] = start
df["volume"] = df["volume"].fillna(0).astype(int)
df["open_interest"] = df["open_interest"].fillna(0).astype(int)

# Print out top 10 contracts by open interest
print(df.nlargest(10, "open_interest").to_string(index=False))

# Plot open interest by expiration date
plot_oi_by_strike(df, dt.date(2025, 8, 22))

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               symbol  strike_price instrument_class expiration  days_to_expiration  volume  open_interest trade_date
NVDA  250919C00160000         160.0                C 2025-09-19                  31   52206         173864 2025-08-19
NVDA  260618P00075000          75.0                P 2026-06-18                 303       1         109796 2025-08-19
NVDA  250919C00170000         170.0                C 2025-09-19                  31    4636         106760 2025-08-19
NVDA  250822C00185000         185.0                C 2025-08-22                   3  158486         102111 2025-08-19
NVDA  250919C00185000         185.0                C 2025-09-19                  31   61860         101843 2025-08-19
NVDA  250919C00190000         190.0                C 2025-09-19                  31   59766          92379 2025-08-19
NVDA  250822C00190000         190.0                C 2025-08-22                   3   46489          91666 2025-08-19
NVDA  250919C00165000         165.0                C 2025-09-19                  31     759          90851 2025-08-19
NVDA  250919C00175000         175.0                C 2025-09-19                  31    5862          85548 2025-08-19
NVDA  260116C00100000         100.0                C 2026-01-16                 150     161          85414 2025-08-19