ATMU-large-trades-2024-03-21.py

import io
import requests
import pandas as pd

def trade_quote(symbol, expiration, date):

    url = "http://127.0.0.1:25510/v2/bulk_hist/option/trade_quote"
    
    querystring = { "root": symbol, "exp": expiration, "start_date": date, "end_date": date, "use_csv":"true" }

    headers = {"Accept": "application/json"}

    response = requests.get(url, headers=headers, params=querystring)
    
    df = pd.read_csv(io.StringIO(response.content.decode('utf-8')))    

    return df
# ----------------------------------------------------------------------
# Get all expirations for date

def get_all_expirations(date, symbol):

    url = "http://127.0.0.1:25510/v2/list/expirations"

    querystring = { "root": symbol }

    headers = {"Accept": "application/json"}

    response = requests.get(url, headers=headers, params=querystring)

    expirations = pd.DataFrame(response.json()['response'], columns=response.json()['header']['format'])
 
    expirations_ = expirations[expirations['date'] >= int(date)]

    return expirations_

# tmp = get_all_expirations('20240517', 'TSLA')

# tmp.head(10)
# ----------------------------------------------------------------------
# Get all trades for date, symbol.

def get_all_trades(date, symbol):

    print(f'Retrieving trades for {date} and {symbol}')

    expirations = get_all_expirations(date, symbol)

    dfs = []

    for expiration in expirations['date']:
        print(f'    Retrieving data for expiration {expiration}')
        dfs.append(trade_quote(symbol, str(expiration), date))

    result = pd.concat(dfs)

    return result

# ----------------------------------------------------------------------
# Get 'ATMU' trades for '20240321'

df = get_all_trades('20240321', 'ATMU')

df['premium'] = df['size'] * df['price'] * 100

columns_to_drop = ['No data for the specified timeframe and chain. Debug code 1', 'ext_condition1', 'ext_condition2', 'ext_condition3', 'ext_condition4', 'ask_condition', 'bid_condition', 'records_back', 'condition_flags', 'volume_type' ]

df = df.drop(columns=columns_to_drop)

df[df['premium'] > 10000]

output.txt

>>> df[df['premium'] > 10000]
     root  expiration   strike right   ms_of_day      sequence  condition     size  exchange  price  price_flags  ms_of_day2  bid_size  bid_exchange   bid  ask_size  ask_exchange    ask        date    premium
5    ATMU  20240419.0  25000.0     C  36663211.0  1.218486e+09      129.0   1006.0      11.0   3.50          3.0  36646965.0      87.0           4.0  3.30       3.0          47.0   4.00  20240321.0   352100.0
157  ATMU  20240419.0  30000.0     C  43123505.0  1.713976e+09      132.0   2000.0       4.0   0.64          1.0  43122241.0       4.0           7.0  0.35       1.0          73.0   0.70  20240321.0   128000.0
405  ATMU  20240419.0  30000.0     C  43543497.0  1.743029e+09       18.0    283.0       6.0   0.70          1.0  43543497.0      11.0           9.0  0.65       1.0          60.0   0.70  20240321.0    19810.0
545  ATMU  20240419.0  30000.0     P  43123505.0  1.713976e+09      132.0   2000.0       4.0   1.64          5.0  43123505.0     229.0           4.0  1.60       4.0          47.0   1.90  20240321.0   328000.0
0    ATMU  20240719.0  20000.0     C  42082996.0  1.646707e+09      130.0     24.0       6.0   9.47          7.0  42072693.0      20.0           9.0  6.90      30.0           4.0  10.90  20240321.0    22728.0
2    ATMU  20241018.0  30000.0     C  37863841.0  1.326942e+09      133.0  10000.0       7.0   2.65          1.0  37858866.0       3.0          11.0  1.80      23.0          11.0   4.30  20240321.0  2650000.0
5    ATMU  20241018.0  35000.0     C  37863842.0  1.326942e+09      133.0  10000.0       7.0   1.00          7.0  37863135.0       1.0           1.0  0.30      30.0           9.0   1.85  20240321.0  1000000.0