import datetime
import io
import pandas as pd
import requests
import zipfile

from pathlib import Path

def download_credit_slices(d: datetime.date) -> None:
    for i in range(1, 400):
        url = f"https://kgc0418-tdw-data2-0.s3.amazonaws.com/slices/SLICE_CREDITS_{d:%Y_%m_%d}_{i}.zip"
        r = requests.get(url)
        if r.status_code != 200:
            continue
        with zipfile.ZipFile(io.BytesIO(r.content)) as z:
            z.extractall()

def download_cumulative_credit(d: datetime.date) -> None:
    url = f"https://kgc0418-tdw-data2-0.s3.amazonaws.com/slices/CUMULATIVE_CREDITS_{d:%Y_%m_%d}.zip"
    r = requests.get(url)
    if r.status_code != 200:
        return
    with zipfile.ZipFile(io.BytesIO(r.content)) as z:
        z.extractall(path="/home/serenitas/CorpCDOs/data/DTCC")

def load_option_data():
    base_dir = Path("/home/serenitas/CorpCDOs/data/DTCC/")
    df = pd.concat([
        pd.read_csv(f,
                    parse_dates=["EXECUTION_TIMESTAMP", "EFFECTIVE_DATE", "END_DATE"])
                                     for f in base_dir.glob("*.csv")])
    df = df[df.OPTION_FAMILY.notnull()]
    df = df.dropna(axis=1, how='all')
    del df["ASSET_CLASS"]
    del df["OPTION_FAMILY"]
    for col in ["INDICATION_OF_END_USER_EXCEPTION",
                "INDICATION_OF_OTHER_PRICE_AFFECTING_TERM",
                "BLOCK_TRADES_AND_LARGE_NOTIONAL_OFF-FACILITY_SWAPS"]:
        df[col] = df[col].map({"N": False, "Y": True})
    for col in ["ACTION", "CLEARED", "PRICE_NOTATION_TYPE", "OPTION_TYPE",
                "OPTION_CURRENCY", "INDICATION_OF_COLLATERALIZATION", "EXECUTION_VENUE",
                "DAY_COUNT_CONVENTION", "NOTIONAL_CURRENCY_1", "SETTLEMENT_CURRENCY"]:
        df[col] = df[col].astype("category")
    for col in ["OPTION_PREMIUM", "PRICE_NOTATION", "OPTION_STRIKE_PRICE"]:
        df[col] = df[col].str.replace(",", "").astype("float")
    df.UNDERLYING_ASSET_1 = df.UNDERLYING_ASSET_1.str.rsplit(":", n=1, expand=True)[1]
    for col in ["EFFECTIVE_DATE", "OPTION_EXPIRATION_DATE", "OPTION_LOCK_PERIOD"]:
        df[col+"_parsed"] = pd.to_datetime(df[col], errors="coerce")
    df.ORIGINAL_DISSEMINATION_ID = df.ORIGINAL_DISSEMINATION_ID.astype("Int64")
    return df

if __name__ == "__main__":
    pass
    # dr = pd.bdate_range("2018-01-01", "2019-02-11")
    # for d in dr:
    #     download_cumulative_credit(d)