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nasdaq.py
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nasdaq.py
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import os
import requests
import pandas as pd
import datetime
import numpy as np
from utils import *
import json
from deribit import crypto_put_call_delta_volatility
# ETF 期权数据
NASDAQ_ETF_OPTION_DICT = {
'GLD': 'SPDR追踪金价ETF',
'SLV': '白银ETF-iShares',
'TLT': 'iShares 20+ Year Treasury Bond ETF',
'USO': '美国石油指数基金',
'XLE': '能源ETF-SPDR (主要是石油行业)',
'XOP': '油气开采',
'XLY': 'SPDR可选消费品ETF',
'QQQ': '纳斯达克100ETF-ProShares',
'SPY': '标普500ETF-SPDR',
'DIA': 'SPDR追踪道指ETF',
'IWM': '罗素2000指数ETF-iShares',
'KWEB': 'KraneShares中概互联网指数ETF',
'LQD': 'iShares iBoxx投资级公司债',
'HYG': 'iShares iBoxx高收益公司债',
'EEM': 'iShares MSCI Emerging Markets ETF',
'FXI': 'iShares China Large-Cap ETF, 富时25中国',
'VXX': '短期期货恐慌指数ETF',
}
# ETF 历史价格数据
NASDAQ_ETF_DICT = {
'XLE': '能源ETF-SPDR (主要是石油行业)',
'XOP': '油气开采',
'USO': '美国石油指数基金',
'QQQ': '纳斯达克100ETF-ProShares',
'SPY': '标普500ETF-SPDR',
'DIA': 'SPDR追踪道指ETF',
'IWM': '罗素2000指数ETF-iShares',
'KWEB': 'KraneShares中概互联网指数ETF',
'VXX': '短期期货恐慌指数ETF',
'BIL': 'SPDR Bloomberg 1-3 Month T-Bill ETF',
'SHY': 'iShares 1-3 Year Treasury Bond ETF',
'IEF': 'iShares 7-10 Year Treasury Bond ETF',
'TLT': 'iShares 20+ Year Treasury Bond ETF',
'GOVT': 'iShares U.S. Treasury Bond ETF',
'LQD': 'iShares iBoxx投资级公司债',
'HYG': 'iShares iBoxx高收益公司债',
'TIP': '通胀债券指数ETF',
'GLD': 'SPDR追踪金价ETF',
'SLV': '白银ETF-iShares',
'EEM': 'iShares MSCI Emerging Markets ETF',
# 'VWO': 'Vanguard FTSE Emerging Markets ETF',
'VNM': '越南',
'INDA': '印度',
'EWJ': 'iShares MSCI Japan ETF',
'FXI': 'iShares China Large-Cap ETF, 富时25中国',
'XBI': 'SPDR生物科技ETF',
'XLY': 'SPDR可选消费品ETF',
'XLP': 'SPDR必需消费品ETF',
'XLF': 'SPDR金融ETF',
'XLV': 'SPDR医疗保健ETF',
'XLI': 'SPDR工业ETF',
'XLB': 'SPDR原材料ETF',
'XLC': 'SPDR通信服务ETF',
'XLK': 'SPDR科技ETF',
'XLU': 'SPDR公用事业ETF',
'XLRE': 'SPDR房地产ETF',
'SOXX': 'iShares Semiconductor ETF',
'SMH': 'VanEck Semiconductor ETF',
'EMB': 'iShares J.P. Morgan USD Emerging Markets Bond ETF',
'EMLC': 'VanEck J.P. Morgan EM Local Currency Bond ETF, 新兴市场本币主权债ETF',
# BTC
'ARKB': 'ARK 21Shares Bitcoin ETF',
'BITB': 'Bitwise Bitcoin ETP Trust',
'FBTC': 'Fidelity Wise Origin Bitcoin Trust',
'EZBC': 'Franklin Bitcoin ETF',
'GBTC': 'Grayscale Bitcoin Trust',
'DEFI': 'Hashdex Bitcoin ETF',
'BTCO': 'Invesco Galaxy Bitcoin ETF',
'IBIT': 'iShares Bitcoin Trust',
'BRRR': 'Valkyrie Bitcoin Fund',
'HODL': 'VanEck Bitcoin Trust',
'BTCW': 'WisdomTree Bitcoin Trust',
}
headers = {
"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8",
"Accept-Encoding": "gzip, deflate, br",
"Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
"Connection": "keep-alive",
"Host": "api.nasdaq.com",
'Sec-Fetch-Dest': 'document',
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/101.0.4951.67 Safari/537.36",
}
def keep_str(s):
return s
def parse_last_trade(se, variety, last_trade):
tmp = last_trade.split('$')[1].split('(')
price = tmp[0].strip(' ')
tmp = tmp[1].split('AS OF ')[1].split(' ET')[0].split(', ')
md = tmp[0].split(' ')
month = int(month_dict[md[0]])
day = int(md[1])
yhm = tmp[1].split(' ')
if len(yhm) == 3:
year = int(yhm[0])
hour = int(yhm[1].split(':')[0])
minute = int(yhm[1].split(':')[1])
if yhm[2] == 'PM':
if hour != 12:
hour = hour + 12
else:
URL = 'https://api.nasdaq.com/api/quote/{}/info?assetclass=etf'
url = URL.format(variety)
while (1):
try:
r = se.get(url, verify=False, headers=headers)
z = json.loads(r.content)
if (z['data']['marketStatus']) == 'Closed':
t = z['data']['primaryData']['lastTradeTimestamp']
tmp = t.split(', ')
month = int(month_dict[tmp[0].split(' ')[0].upper()])
day = int(tmp[0].split(' ')[1])
year = int(tmp[1])
hour = 16
minute = 0
if (z['data']['marketStatus']) == 'Pre-Market':
try:
t = z['data']['secondaryData']['lastTradeTimestamp']
if 'Closed at ' in t:
t = t.replace('Closed at ', '')
tmp = t.split(', ')
month = int(month_dict[tmp[0].split(' ')[0].upper()])
day = int(tmp[0].split(' ')[1])
tmp = tmp[1].split(' ')
year = int(tmp[0])
hour = int(tmp[1].split(':')[0]) + 12 # PM
minute = 0
except:
t = z['data']['primaryData']['lastTradeTimestamp']
tmp = t.split(', ')
month = int(month_dict[tmp[0].split(' ')[0].upper()])
day = int(tmp[0].split(' ')[1])
year = int(tmp[1])
hour = 16
minute = 0
break
except:
print('ERROR')
time.sleep(5)
pass
time_dt = datetime.datetime(year=year, month=month, day=day, hour=hour, minute=minute, second=0)
time_str = time_dt.strftime('%Y-%m-%d %H:%M:%S')
return price, time_dt, time_str
def update_nasdaq_etf_option_data(variety):
# example
# URL = 'https://api.nasdaq.com/api/quote/XLE/option-chain?assetclass=etf&limit=60&offset=60&fromdate=2023-08-11&todate=2023-08-25&excode=oprac&callput=callput&money=at&type=all'
PRICE_URL = 'https://api.nasdaq.com/api/quote/{}/option-chain?assetclass=etf&fromdate={}&todate={}&excode=oprac&callput=callput&money=at&type=all'
GREEKS_URL = 'https://api.nasdaq.com/api/quote/{}/option-chain/greeks?assetclass=etf&date={}'
se = requests.session()
start_time_dt = datetime.datetime.now()
# +3 month
if start_time_dt.month <= 9:
year = start_time_dt.year
month = start_time_dt.month+3
day = calendar.monthrange(year, month)[-1]
end_time_dt = datetime.datetime(year=year, month=month, day=day)
else:
year = start_time_dt.year+1
month = start_time_dt.month-9
day = calendar.monthrange(year, month)[-1]
end_time_dt = datetime.datetime(year=year, month=month, day=day)
start_time = start_time_dt.strftime('%Y-%m-%d')
end_time = end_time_dt.strftime('%Y-%m-%d')
url = PRICE_URL.format(variety, start_time, end_time)
expiry_dict = {}
cookies = None
while (1):
try:
if cookies is None:
r = se.get(url, verify=False, headers=headers, timeout=15)
else:
r = se.get(url, verify=False, cookies=cookies, headers=headers, timeout=15)
z = json.loads(r.content)
if cookies is None:
cookies = r.cookies
last_trade = z['data']['lastTrade']
print(last_trade)
price, time_dt, time_str = parse_last_trade(se, variety, last_trade)
print(price, time_dt, time_str)
hour = time_dt.hour
minute = time_dt.minute
datas = z['data']['table']['rows']
for i in range(len(datas)):
data = datas[i]
s = data['expirygroup']
if s != '':
continue
s = data['drillDownURL'].split('/')
s = s[len(s)-1][6:12]
# key example XLE230825
key = variety + s
if not(key in expiry_dict):
expiry_dict[key] = {}
expiry_dict[key+'volume'] = 0
# [option price last, volume, oi]
expiry_dict[key]['C'+data['strike']] = [data['c_Last'], data['c_Volume'], data['c_Openinterest'], None, None, None, None, None, None]
expiry_dict[key]['P'+data['strike']] = [data['p_Last'], data['p_Volume'], data['p_Openinterest'], None, None, None, None, None, None]
if data['c_Volume'] == '--':
pass
else:
expiry_dict[key+'volume'] = expiry_dict[key+'volume'] + float(data['c_Volume'])
if data['p_Volume'] == '--':
pass
else:
expiry_dict[key+'volume'] = expiry_dict[key+'volume'] + float(data['p_Volume'])
print('PRICE ' + key)
break
except Exception as e:
print(e, 'NASDAQ PRICE URL ERROR ' + variety)
time.sleep(15)
# PRICE
path = os.path.join(option_price_dir, 'nasdaq', variety+'.csv')
df = pd.DataFrame(columns=['time', 'price'], data=[[time_str, price]])
if os.path.exists(path):
old_df = pd.read_csv(path)
old_df = pd.concat([old_df, df], axis=0)
old_df.drop_duplicates(subset=['time'], keep='last', inplace=True) # last
old_df['time'] = pd.to_datetime(old_df['time'], format='%Y-%m-%d %H:%M:%S')
old_df.sort_values(by='time', axis=0, ascending=True, inplace=True)
old_df['time'] = old_df['time'].apply(lambda x:datetime.datetime.strftime(x,'%Y-%m-%d %H:%M:%S'))
old_df.to_csv(path, encoding='utf-8', index=False)
else:
df.to_csv(path, encoding='utf-8', index=False)
# GREEKS
if (hour == 16 and minute == 0):
for key in expiry_dict:
if 'volume' in key:
continue
t = key.replace(variety, '')
t = '20'+t
t_str = pd.to_datetime(t, format='%Y%m%d').strftime('%Y-%m-%d')
url = GREEKS_URL.format(variety, t_str)
while (1):
try:
r = se.get(url, verify=False, headers=headers, timeout=15)
z = json.loads(r.content, parse_float=keep_str)
datas = z['data']['table']['rows']
for i in range(len(datas)):
data = datas[i]
strike = data['strike']
d = expiry_dict[key]
if ('C'+strike) in d:
d['C'+strike][3] = data['cDelta']
d['C'+strike][4] = data['cIV']
d['C'+strike][5] = data['cGamma']
d['C'+strike][6] = data['cRho']
d['C'+strike][7] = data['cTheta']
d['C'+strike][8] = data['cVega']
if ('P'+strike) in d:
d['P'+strike][3] = data['pDelta']
d['P'+strike][4] = data['pIV']
d['P'+strike][5] = data['pGamma']
d['P'+strike][6] = data['pRho']
d['P'+strike][7] = data['pTheta']
d['P'+strike][8] = data['pVega']
print('GREEKS ' + key)
break
except Exception as e:
print(e, 'NASDAQ GREEKS URL ERROR ' + variety)
time.sleep(15)
inst_ids_list = []
volume_list = []
col3_add = ['close', 'volume', 'oi', 'delta', 'imp_vol', 'gamma', 'rho', 'theta', 'vega']
for key in expiry_dict:
if 'volume' in key:
continue
inst_ids_list.append(key)
volume_list.append(expiry_dict[key+'volume'])
d = expiry_dict[key]
col1 = ['time']
col2 = ['time']
col3 = ['time']
datas = [time_str]
for h in d:
opt_type = h[0]
strike = h[1:]
col1 += [opt_type for _ in range(len(col3_add))]
col2 += [strike for _ in range(len(col3_add))]
col3 += col3_add
datas += d[h]
df = pd.DataFrame(columns=[col1, col2, col3], data=[datas])
df.replace('--', '', inplace=True)
path = os.path.join(option_price_dir, 'nasdaq', key+'.csv')
if os.path.exists(path):
old_df = pd.read_csv(path, header=[0,1,2])
old_df = pd.concat([old_df, df], axis=0)
old_df.drop_duplicates(subset=[('time','time','time')], keep='last', inplace=True) # last
old_df.loc[:, pd.IndexSlice['time','time','time']] = old_df.loc[:, pd.IndexSlice['time','time','time']].apply(lambda x:pd.to_datetime(x, format='%Y-%m-%d %H:%M:%S'))
old_df.sort_values(by = ('time','time','time'), inplace=True)
old_df.loc[:, pd.IndexSlice['time','time','time']] = old_df.loc[:, pd.IndexSlice['time','time','time']].apply(lambda x:datetime.datetime.strftime(x,'%Y-%m-%d %H:%M:%S'))
old_df.to_csv(path, encoding='utf-8', index=False)
else:
df.to_csv(path, encoding='utf-8', index=False)
# INFO
inst_ids_list = np.array(inst_ids_list, dtype=str)
volume_list = np.array(volume_list, dtype=float)
sort = np.argsort(volume_list)[::-1]
inst_ids = ''
for i in range(len(volume_list)):
inst_ids += inst_ids_list[sort[i]]
inst_ids += ','
info_df = pd.DataFrame(columns=['time', 'inst_ids'], data=[[time_str, inst_ids]])
path = os.path.join(option_price_dir, 'nasdaq', variety+'_info'+'.csv')
if not(os.path.exists(path)):
info_df.to_csv(path, encoding='utf-8', index=False)
else:
old_info_df = pd.read_csv(path)
old_info_df = pd.concat([old_info_df, info_df], axis=0)
old_info_df.drop_duplicates(subset='time', keep='last', inplace=True)
old_info_df['time'] = old_info_df['time'].apply(lambda x:pd.to_datetime(x, format='%Y-%m-%d %H:%M:%S'))
old_info_df.sort_values(by = 'time', inplace=True)
old_info_df['time'] = old_info_df['time'].apply(lambda x:datetime.datetime.strftime(x,'%Y-%m-%d %H:%M:%S'))
old_info_df.to_csv(path, encoding='utf-8', index=False)
def update_nasdaq_etf_option_info_detail(variety):
print(variety + ' info detail')
info_path = os.path.join(option_price_dir, 'nasdaq', variety+'_info'+'.csv')
info_df = pd.read_csv(info_path)
info_t = pd.DatetimeIndex(pd.to_datetime(info_df['time'], format='%Y-%m-%d %H:%M:%S'))
earlist_time = '2023-07-22 00:00:00'
path = os.path.join(option_price_dir, 'nasdaq', variety+'_info_detail'+'.csv')
last_line_time = get_last_line_time(path, variety+'_info_detail', earlist_time, 19, '%Y-%m-%d %H:%M:%S')
last_line_time_dt = pd.to_datetime(last_line_time, format='%Y-%m-%d %H:%M:%S')
if (last_line_time == earlist_time):
start_idx = 0
else:
start_idx = np.where(info_t == last_line_time_dt)[0][0] + 1
etf_path = os.path.join(option_price_dir, 'nasdaq', variety+'.csv')
etf_df = pd.read_csv(etf_path)
etf_t = pd.DatetimeIndex(pd.to_datetime(etf_df['time'], format='%Y-%m-%d %H:%M:%S'))
etf_price = np.array(etf_df['price'], dtype=float)
opt_dict = {}
COL = ['inst_id', 'total_put_volume', 'total_call_volume', 'total_put_oi', 'total_call_oi',
'atm_put_iv', 'atm_call_iv', '25d_put_iv', '25d_call_iv',
'10d_put_iv', '10d_call_iv', '5d_put_iv', '5d_call_iv',
'put_volume_max1', 'put_volume_max1_strike', 'put_volume_max2', 'put_volume_max2_strike',
'put_volume_max3', 'put_volume_max3_strike', 'put_volume_max4', 'put_volume_max4_strike',
'put_volume_max5', 'put_volume_max5_strike',
'call_volume_max1', 'call_volume_max1_strike', 'call_volume_max2', 'call_volume_max2_strike',
'call_volume_max3', 'call_volume_max3_strike', 'call_volume_max4', 'call_volume_max4_strike',
'call_volume_max5', 'call_volume_max5_strike',
'put_oi_max1', 'put_oi_max1_strike', 'put_oi_max2', 'put_oi_max2_strike',
'put_oi_max3', 'put_oi_max3_strike', 'put_oi_max4', 'put_oi_max4_strike',
'put_oi_max5', 'put_oi_max5_strike',
'call_oi_max1', 'call_oi_max1_strike', 'call_oi_max2', 'call_oi_max2_strike',
'call_oi_max3', 'call_oi_max3_strike', 'call_oi_max4', 'call_oi_max4_strike',
'call_oi_max5', 'call_oi_max5_strike',]
df = pd.DataFrame()
for i in range(start_idx, len(info_t)):
t = info_t[i]
inst_ids = info_df.loc[i, 'inst_ids'].split(',')
inst_ids.remove('')
n = 0
cols = ['time', 'total_put_volume', 'total_call_volume', 'total_put_oi', 'total_call_oi']
for inst_id in inst_ids:
n = n + 1
cn = 'c'+str(n)
cols += [c+'_'+cn for c in COL]
if not(inst_id in opt_dict):
opt_path = os.path.join(option_price_dir, 'nasdaq', inst_id+'.csv')
opt_df = pd.read_csv(opt_path, header=[0,1,2])
opt_t = pd.DatetimeIndex(pd.to_datetime(opt_df['time']['time']['time'], format='%Y-%m-%d %H:%M:%S'))
strike = get_full_strike_price(opt_df)
opt_dict[inst_id] = [opt_t, opt_df, strike]
_df = pd.DataFrame(columns=cols)
_df.loc[i, 'time'] = t.strftime('%Y-%m-%d %H:%M:%S')
_df.loc[i, 'total_put_volume'] = 0
_df.loc[i, 'total_call_volume'] = 0
_df.loc[i, 'total_put_oi'] = 0
_df.loc[i, 'total_call_oi'] = 0
n = 0
for inst_id in inst_ids:
n = n + 1
cn = 'c'+str(n)
temp_t = opt_dict[inst_id][0]
try:
w = np.where(temp_t == t)[0][0]
except:
continue
temp_df = opt_dict[inst_id][1].loc[w,:]
strike = opt_dict[inst_id][2]
put_volume = temp_df.loc[pd.IndexSlice['P', :, 'volume']].sum()
call_volume = temp_df.loc[pd.IndexSlice['C', :, 'volume']].sum()
_df.loc[i, 'total_put_volume_'+cn] = put_volume
_df.loc[i, 'total_call_volume_'+cn] = call_volume
_df.loc[i, 'total_put_volume'] = _df.loc[i, 'total_put_volume'] + put_volume
_df.loc[i, 'total_call_volume'] = _df.loc[i, 'total_call_volume'] + call_volume
put_oi = temp_df.loc[pd.IndexSlice['P', :, 'oi']].sum()
call_oi = temp_df.loc[pd.IndexSlice['C', :, 'oi']].sum()
_df.loc[i, 'total_put_oi_'+cn] = put_oi
_df.loc[i, 'total_call_oi_'+cn] = call_oi
_df.loc[i, 'total_put_oi'] = _df.loc[i, 'total_put_oi'] + put_oi
_df.loc[i, 'total_call_oi'] = _df.loc[i, 'total_call_oi'] + call_oi
## put volume sort
tmp = temp_df.loc[pd.IndexSlice['P', :, 'volume']]
P_volume = tmp.replace(np.nan, -1.0)
idx = P_volume.index
P_volume = np.array(P_volume)
sort = np.argsort(P_volume)
P_volume = P_volume[sort]
idx = idx[sort]
if len(idx) >= 5:
_df.loc[i, 'put_volume_max1_'+cn] = P_volume[-1]
_df.loc[i, 'put_volume_max1_strike_'+cn] = idx[-1]
_df.loc[i, 'put_volume_max2_'+cn] = P_volume[-2]
_df.loc[i, 'put_volume_max2_strike_'+cn] = idx[-2]
_df.loc[i, 'put_volume_max3_'+cn] = P_volume[-3]
_df.loc[i, 'put_volume_max3_strike_'+cn] = idx[-3]
_df.loc[i, 'put_volume_max4_'+cn] = P_volume[-4]
_df.loc[i, 'put_volume_max4_strike_'+cn] = idx[-4]
_df.loc[i, 'put_volume_max5_'+cn] = P_volume[-5]
_df.loc[i, 'put_volume_max5_strike_'+cn] = idx[-5]
## call volume sort
tmp = temp_df.loc[pd.IndexSlice['C', :, 'volume']]
C_volume = tmp.replace(np.nan, -1.0)
idx = C_volume.index
C_volume = np.array(C_volume)
sort = np.argsort(C_volume)
C_volume = C_volume[sort]
idx = idx[sort]
if len(idx) >= 5:
_df.loc[i, 'call_volume_max1_'+cn] = C_volume[-1]
_df.loc[i, 'call_volume_max1_strike_'+cn] = idx[-1]
_df.loc[i, 'call_volume_max2_'+cn] = C_volume[-2]
_df.loc[i, 'call_volume_max2_strike_'+cn] = idx[-2]
_df.loc[i, 'call_volume_max3_'+cn] = C_volume[-3]
_df.loc[i, 'call_volume_max3_strike_'+cn] = idx[-3]
_df.loc[i, 'call_volume_max4_'+cn] = C_volume[-4]
_df.loc[i, 'call_volume_max4_strike_'+cn] = idx[-4]
_df.loc[i, 'call_volume_max5_'+cn] = C_volume[-5]
_df.loc[i, 'call_volume_max5_strike_'+cn] = idx[-5]
## put oi sort
tmp = temp_df.loc[pd.IndexSlice['P', :, 'oi']]
P_oi = tmp.replace(np.nan, -1.0)
idx = P_oi.index
P_oi = np.array(P_oi)
sort = np.argsort(P_oi)
P_oi = P_oi[sort]
idx = idx[sort]
if len(idx) >= 5:
_df.loc[i, 'put_oi_max1_'+cn] = P_oi[-1]
_df.loc[i, 'put_oi_max1_strike_'+cn] = idx[-1]
_df.loc[i, 'put_oi_max2_'+cn] = P_oi[-2]
_df.loc[i, 'put_oi_max2_strike_'+cn] = idx[-2]
_df.loc[i, 'put_oi_max3_'+cn] = P_oi[-3]
_df.loc[i, 'put_oi_max3_strike_'+cn] = idx[-3]
_df.loc[i, 'put_oi_max4_'+cn] = P_oi[-4]
_df.loc[i, 'put_oi_max4_strike_'+cn] = idx[-4]
_df.loc[i, 'put_oi_max5_'+cn] = P_oi[-5]
_df.loc[i, 'put_oi_max5_strike_'+cn] = idx[-5]
## call oi sort
tmp = temp_df.loc[pd.IndexSlice['C', :, 'oi']]
C_oi = tmp.replace(np.nan, -1.0)
idx = C_oi.index
C_oi = np.array(C_oi)
sort = np.argsort(C_oi)
C_oi = C_oi[sort]
idx = idx[sort]
# print(idx)
# print(C_oi)
if len(idx) >= 5:
_df.loc[i, 'call_oi_max1_'+cn] = C_oi[-1]
_df.loc[i, 'call_oi_max1_strike_'+cn] = idx[-1]
_df.loc[i, 'call_oi_max2_'+cn] = C_oi[-2]
_df.loc[i, 'call_oi_max2_strike_'+cn] = idx[-2]
_df.loc[i, 'call_oi_max3_'+cn] = C_oi[-3]
_df.loc[i, 'call_oi_max3_strike_'+cn] = idx[-3]
_df.loc[i, 'call_oi_max4_'+cn] = C_oi[-4]
_df.loc[i, 'call_oi_max4_strike_'+cn] = idx[-4]
_df.loc[i, 'call_oi_max5_'+cn] = C_oi[-5]
_df.loc[i, 'call_oi_max5_strike_'+cn] = idx[-5]
w = np.where(etf_t == t)[0][0]
price = etf_price[w]
_df.loc[i, 'inst_id_'+cn] = inst_id
# if (np.isnan(temp_df.loc[pd.IndexSlice['P', :, 'delta']].tolist()[0])):
# _df.loc[i, '25d_put_iv_'+cn] = np.nan
# _df.loc[i, '25d_call_iv_'+cn] = np.nan
# _df.loc[i, 'atm_put_iv_'+cn] = np.nan
# _df.loc[i, 'atm_call_iv_'+cn] = np.nan
# _df.loc[i, '10d_put_iv_'+cn] = np.nan
# _df.loc[i, '10d_call_iv_'+cn] = np.nan
# _df.loc[i, '5d_put_iv_'+cn] = np.nan
# _df.loc[i, '5d_call_iv_'+cn] = np.nan
# else:
ret = crypto_put_call_delta_volatility(temp_df, 0.25, price, strike, strike)
_df.loc[i, '25d_put_iv_'+cn] = ret[1]
_df.loc[i, '25d_call_iv_'+cn] = ret[3]
_df.loc[i, 'atm_put_iv_'+cn] = ret[4]
_df.loc[i, 'atm_call_iv_'+cn] = ret[5]
ret = crypto_put_call_delta_volatility(temp_df, 0.1, price, strike, strike)
_df.loc[i, '10d_put_iv_'+cn] = ret[1]
_df.loc[i, '10d_call_iv_'+cn] = ret[3]
ret = crypto_put_call_delta_volatility(temp_df, 0.05, price, strike, strike)
_df.loc[i, '5d_put_iv_'+cn] = ret[1]
_df.loc[i, '5d_call_iv_'+cn] = ret[3]
df = pd.concat([df, _df], axis=0)
if (len(df) > 0):
path = os.path.join(option_price_dir, 'nasdaq', variety+'_info_detail'+'.csv')
if os.path.exists(path):
old_df = pd.read_csv(path)
old_df = pd.concat([old_df, df], axis=0)
old_df.drop_duplicates(subset=['time'], keep='last', inplace=True)
old_df['time'] = old_df['time'].apply(lambda x:pd.to_datetime(x, format='%Y-%m-%d %H:%M:%S'))
old_df.sort_values(by = 'time', inplace=True)
old_df['time'] = old_df['time'].apply(lambda x:datetime.datetime.strftime(x,'%Y-%m-%d %H:%M:%S'))
old_df.to_csv(path, encoding='utf-8', index=False)
else:
df.to_csv(path, encoding='utf-8', index=False)
def plot_nasdaq_option_data(t, df, data_dict, etf_t, etf_price, variety, cn):
contract = data_dict[cn][-1]
put_oi_max1 = np.array(df['put_oi_max1_'+cn], dtype=float)
put_oi_max2 = np.array(df['put_oi_max2_'+cn], dtype=float)
put_oi_max3 = np.array(df['put_oi_max3_'+cn], dtype=float)
put_oi_max4 = np.array(df['put_oi_max4_'+cn], dtype=float)
put_oi_max5 = np.array(df['put_oi_max5_'+cn], dtype=float)
call_oi_max1 = np.array(df['call_oi_max1_'+cn], dtype=float)
call_oi_max2 = np.array(df['call_oi_max2_'+cn], dtype=float)
call_oi_max3 = np.array(df['call_oi_max3_'+cn], dtype=float)
call_oi_max4 = np.array(df['call_oi_max4_'+cn], dtype=float)
call_oi_max5 = np.array(df['call_oi_max5_'+cn], dtype=float)
put_oi_max1_strike = np.array(df['put_oi_max1_strike_'+cn], dtype=float)
put_oi_max2_strike = np.array(df['put_oi_max2_strike_'+cn], dtype=float)
put_oi_max3_strike = np.array(df['put_oi_max3_strike_'+cn], dtype=float)
put_oi_max4_strike = np.array(df['put_oi_max4_strike_'+cn], dtype=float)
put_oi_max5_strike = np.array(df['put_oi_max5_strike_'+cn], dtype=float)
call_oi_max1_strike = np.array(df['call_oi_max1_strike_'+cn], dtype=float)
call_oi_max2_strike = np.array(df['call_oi_max2_strike_'+cn], dtype=float)
call_oi_max3_strike = np.array(df['call_oi_max3_strike_'+cn], dtype=float)
call_oi_max4_strike = np.array(df['call_oi_max4_strike_'+cn], dtype=float)
call_oi_max5_strike = np.array(df['call_oi_max5_strike_'+cn], dtype=float)
put_oi_max = np.vstack((put_oi_max1, put_oi_max2, put_oi_max3, put_oi_max4, put_oi_max5))
put_oi_max_strike = np.vstack((put_oi_max1_strike, put_oi_max2_strike, put_oi_max3_strike, put_oi_max4_strike, put_oi_max5_strike))
sort = np.argsort(put_oi_max_strike, axis=0)
put_oi_max_strike = np.take_along_axis(put_oi_max_strike, sort, axis=0)
put_oi_max = np.take_along_axis(put_oi_max, sort, axis=0)
put_oi_avg_strike = np.sum(put_oi_max[2:5, :]*put_oi_max_strike[2:5, :], axis=0) / np.sum(put_oi_max[2:5, :], axis=0)
call_oi_max = np.vstack((call_oi_max1, call_oi_max2, call_oi_max3, call_oi_max4, call_oi_max5))
call_oi_max_strike = np.vstack((call_oi_max1_strike, call_oi_max2_strike, call_oi_max3_strike, call_oi_max4_strike, call_oi_max5_strike))
sort = np.argsort(call_oi_max_strike, axis=0)
call_oi_max_strike = np.take_along_axis(call_oi_max_strike, sort, axis=0)
call_oi_max = np.take_along_axis(call_oi_max, sort, axis=0)
call_oi_avg_strike = np.sum(call_oi_max[0:3, :]*call_oi_max_strike[0:3, :], axis=0) / np.sum(call_oi_max[0:3, :], axis=0)
put_oi_max = np.array(df['put_oi_max1_'+cn], dtype=float)
w = np.where(put_oi_max > 0)[0]
datas = [
[[[t,data_dict['5d_put_iv_'+cn],cn+' '+contract+' 5d_put_iv','color=darkgreen'],
[t,data_dict['5d_call_iv_'+cn],cn+' '+contract+' 5d_call_iv','color=red'],
],
[],''],
[[[t,data_dict['10d_put_iv_'+cn],cn+' '+contract+' 10d_put_iv','color=darkgreen'],
[t,data_dict['10d_call_iv_'+cn],cn+' '+contract+' 10d_call_iv','color=red'],
],
[],''],
[[
[t,data_dict['5d_put_iv_'+cn]-data_dict['5d_call_iv_'+cn],cn+' '+contract+' 5d_skew',''],
[t,data_dict['10d_put_iv_'+cn]-data_dict['10d_call_iv_'+cn],cn+' '+contract+' 10d_skew',''],
[t,data_dict['25d_put_iv_'+cn]-data_dict['25d_call_iv_'+cn],cn+' '+contract+' 25d_skew',''],
[t,data_dict['atm_put_iv_'+cn]-data_dict['atm_call_iv_'+cn],cn+' '+contract+' atm_skew',''],
],
[],''],
[[[etf_t,etf_price,variety,'color=black, width=4'],
[t[w],call_oi_max_strike[0,:][w],'call 持仓量一','color=red'],
[t[w],call_oi_max_strike[1,:][w],'call 持仓量二','color=orange'],
[t[w],call_oi_avg_strike[w],'加权平均','color=darkgray'],
[t[w],put_oi_max_strike[4,:][w],'put 持仓量一','color=darkgreen'],
[t[w],put_oi_max_strike[3,:][w],'put 持仓量二','color=purple'],
[t[w],put_oi_avg_strike[w],'加权平均','color=darkgray'],],[],''],
[[[t,data_dict['25d_put_iv_'+cn],cn+' '+contract+' 25d_put_iv','color=darkgreen'],
[t,data_dict['25d_call_iv_'+cn],cn+' '+contract+' 25d_call_iv','color=red'],
],
[],''],
[[[t,data_dict['atm_put_iv_'+cn],cn+' '+contract+' atm_put_iv','color=darkgreen'],
[t,data_dict['atm_call_iv_'+cn],cn+' '+contract+' atm_call_iv','color=red'],
],
[],''],
]
plot_many_figure(datas, max_height=900)
def get_nasdaq_lastday_option_strike_volume_oi(exchange, variety, inst_id):
path = os.path.join(option_price_dir, exchange, inst_id+'.csv')
df = pd.read_csv(path, header=[0,1,2])
t = pd.DatetimeIndex(pd.to_datetime(df['time']['time']['time'], format='%Y-%m-%d %H:%M:%S'))
L = len(t)
if (L < 2):
print('L < 2')
return
col = df.columns.tolist()
res = [(col[i][1]) for i in range(len(col)) if col[i][0] == 'P']
strikes_str = []
for i in res:
if i not in strikes_str:
strikes_str.append(i)
strike = []
put_oi = []
call_oi = []
i = L-2
while (i >= 0):
hour = t[i].hour
if hour == 16:
t1 = t[i]
break
i = i - 1
for strike_str in strikes_str:
strike.append(float(strike_str))
put_oi.append(df.loc[i, pd.IndexSlice['P', strike_str, 'oi']])
call_oi.append(df.loc[i, pd.IndexSlice['C', strike_str, 'oi']])
strike = np.array(strike, dtype=float)
put_oi = np.array(put_oi, dtype=float)
call_oi = np.array(call_oi, dtype=float)
path = os.path.join(option_price_dir, exchange, variety+'.csv')
if not os.path.exists(path):
return
price_df = pd.read_csv(path)
t = pd.DatetimeIndex(pd.to_datetime(price_df['time'], format='%Y-%m-%d %H:%M:%S'))
idx = np.where(t == t1)[0]
if len(idx) > 0:
price = price_df.loc[idx, 'price']
else:
price = None
return strike, put_oi, call_oi, price
def plot_nasdaq_intraday_option_strike_volume_oi(exchange, variety):
exchange = 'nasdaq'
path = os.path.join(option_price_dir, exchange, variety+'_info'+'.csv')
if not os.path.exists(path):
return
df = pd.read_csv(path)
inst_ids = df.loc[len(df)-1, 'inst_ids'].split(',')
# dom1, dom2
for inst_id in [inst_ids[0], inst_ids[1], inst_ids[2]]:
path = os.path.join(option_price_dir, exchange, inst_id+'.csv')
df = pd.read_csv(path, header=[0,1,2])
t = pd.DatetimeIndex(pd.to_datetime(df['time']['time']['time'], format='%Y-%m-%d %H:%M:%S'))
L = len(t)
if (L < 2):
print('L < 2')
return
col = df.columns.tolist()
res = [(col[i][1]) for i in range(len(col)) if col[i][0] == 'P']
strikes_str = []
for i in res:
if i not in strikes_str:
strikes_str.append(i)
strike = []
put_oi = []
call_oi = []
put_vol = []
call_vol = []
for strike_str in strikes_str:
strike.append(float(strike_str))
put_oi.append(df.loc[L-1, pd.IndexSlice['P', strike_str, 'oi']])
call_oi.append(df.loc[L-1, pd.IndexSlice['C', strike_str, 'oi']])
put_vol.append(df.loc[L-1, pd.IndexSlice['P', strike_str, 'volume']])
call_vol.append(df.loc[L-1, pd.IndexSlice['C', strike_str, 'volume']])
strike = np.array(strike, dtype=float)
put_oi = np.array(put_oi, dtype=float)
call_oi = np.array(call_oi, dtype=float)
put_vol = np.array(put_vol, dtype=float)
call_vol = np.array(call_vol, dtype=float)
path = os.path.join(option_price_dir, exchange, variety+'.csv')
if not os.path.exists(path):
return
price_df = pd.read_csv(path)
etf_price = price_df.loc[len(price_df)-1, 'price']
strike_last_day, put_oi_last_day, call_oi_last_day, etf_price_last_day = get_nasdaq_lastday_option_strike_volume_oi(exchange, variety, inst_id)
put_oi_ld_change = put_oi.copy()
call_oi_ld_change = call_oi.copy()
for i in range(len(strike)):
for k in range(len(strike_last_day)):
if strike[i] == strike_last_day[k]:
put_oi_ld_change[i] = put_oi_ld_change[i] - put_oi_last_day[k]
call_oi_ld_change[i] = call_oi_ld_change[i] - call_oi_last_day[k]
# print(strike)
# exit()
strike_sort = np.sort(strike)
gap = float((strike_sort[len(strike_sort)//2]-strike_sort[len(strike_sort)//2-1]) / 5)
if gap == 0:
gap = 1
fig1 = figure(frame_width=1300, frame_height=150)
fig1.quad(left=strike-gap, right=strike, bottom=0, top=put_oi, fill_color='darkgreen')
fig1.quad(left=strike, right=strike+gap, bottom=0, top=call_oi, fill_color='red')
fig1.line(x=[etf_price, etf_price], y=[0, np.nanmax(call_oi)], line_width=1, line_color='black', legend_label=inst_id + ' oi')
fig1.legend.location='top_left'
fig2 = figure(frame_width=1300, frame_height=160, x_range=fig1.x_range)
fig2.quad(left=strike-gap, right=strike, bottom=0, top=put_vol, fill_color='darkgreen')
fig2.quad(left=strike, right=strike+gap, bottom=0, top=call_vol, fill_color='red')
fig2.line(x=[etf_price_last_day, etf_price_last_day], y=[0, np.nanmax(call_vol)], line_width=1, line_color='black', legend_label=inst_id + ' 1d volume')
fig2.legend.location='top_left'
fig2.background_fill_color = "lightgray"
fig3 = figure(frame_width=1300, frame_height=160, x_range=fig1.x_range)
fig3.quad(left=strike-gap, right=strike, bottom=0, top=put_oi_ld_change, fill_color='darkgreen')
fig3.quad(left=strike, right=strike+gap, bottom=0, top=call_oi_ld_change, fill_color='red')
fig3.line(x=[etf_price_last_day, etf_price_last_day], y=[np.nanmin(call_oi_ld_change), np.nanmax(call_oi_ld_change)], line_width=1, line_color='black', legend_label=inst_id + ' 1d oi change')
fig3.legend.location='top_left'
show(column(fig1,fig2,fig3))
def plot_nasdaq_option_datas(variety):
path = os.path.join(option_price_dir, 'nasdaq', variety+'_info_detail'+'.csv')
df = pd.read_csv(path)
t = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d %H:%M:%S'))
cols = df.columns.tolist()
cols.remove('time')
data_dict = {}
for col in cols:
if 'inst_id' in col:
data_dict[col[8:]] = np.array(df[col], dtype=str)
else:
data_dict[col] = np.array(df[col], dtype=float)
# if 'oi' in col:
# tmp = data_dict[col]
# tmp[tmp == 0] = np.nan
# data_dict[col] = tmp
etf_path = os.path.join(option_price_dir, 'nasdaq', variety+'.csv')
etf_df = pd.read_csv(etf_path)
etf_t = pd.DatetimeIndex(pd.to_datetime(etf_df['time'], format='%Y-%m-%d %H:%M:%S'))
etf_price = np.array(etf_df['price'], dtype=float)
# plot_nasdaq_option_data(t, df, data_dict, etf_t, etf_price, variety, 'c1')
# plot_nasdaq_option_data(t, df, data_dict, etf_t, etf_price, variety, 'c2')
# plot_nasdaq_option_data(t, df, data_dict, etf_t, etf_price, variety, 'c3')
# plot_nasdaq_option_data(t, df, data_dict, etf_t, etf_price, variety, 'c4')
# plot_nasdaq_option_data(t, df, data_dict, etf_t, etf_price, variety, 'c5')
# plot_nasdaq_option_data(t, df, data_dict, etf_t, etf_price, variety, 'c6')
contract_c1 = data_dict['c1'][-1]
contract_c2 = data_dict['c2'][-1]
contract_c3 = data_dict['c3'][-1]
contract_c4 = data_dict['c4'][-1]
contract_c5 = data_dict['c5'][-1]
contract_c6 = data_dict['c6'][-1]
datas = [
[[[t,data_dict['total_call_volume_c1'],'c1 '+contract_c1+' total_call_volume','color=red'],
[t,data_dict['total_put_volume_c1'],'c1 '+contract_c1+' total_put_volume','color=darkgreen'],
],
[[t,data_dict['total_put_volume_c1']-data_dict['total_call_volume_c1'],'c1 '+contract_c1+' total_put_volume - total_call_volume','style=vbar'],],''],
[[[t,data_dict['total_call_volume_c2'],'c2 '+contract_c2+' total_call_volume','color=red'],
[t,data_dict['total_put_volume_c2'],'c2 '+contract_c2+' total_put_volume','color=darkgreen'],
],
[[t,data_dict['total_put_volume_c2']-data_dict['total_call_volume_c2'],'c2 '+contract_c2+' total_put_volume - total_call_volume','style=vbar'],],''],
[[[t,data_dict['total_call_volume_c3'],'c3 '+contract_c3+' total_call_volume','color=red'],
[t,data_dict['total_put_volume_c3'],'c3 '+contract_c3+' total_put_volume','color=darkgreen'],
],
[[t,data_dict['total_put_volume_c3']-data_dict['total_call_volume_c3'],'c3 '+contract_c3+' total_put_volume - total_call_volume','style=vbar'],],''],
[[[etf_t,etf_price,variety,'color=black']],
[[t,data_dict['total_put_oi']/data_dict['total_call_oi'],' total_put_oi / total_call_oi',''],],''],
[[[t,data_dict['total_call_volume'],'total_call_volume','color=red'],
[t,data_dict['total_put_volume'],'total_put_volume','color=darkgreen'],
],
[[t,data_dict['total_put_volume']-data_dict['total_call_volume'],'total_put_volume - total_call_volume','style=vbar'],],''],
[[[t,data_dict['total_call_oi'],'total_call_oi','color=red'],
[t,data_dict['total_put_oi'],'total_put_oi','color=darkgreen'],
],
[[t,data_dict['total_put_oi']-data_dict['total_call_oi'],' total_put_oi - total_call_oi','style=vbar'],],''],
[[[t,data_dict['25d_call_iv_c1'],'c1 '+contract_c1+' 25d_call_iv','color=red'],
[t,data_dict['25d_put_iv_c1'],'c1 '+contract_c1+' 25d_put_iv','color=darkgreen'],
],
[[t,data_dict['25d_put_iv_c1']-data_dict['25d_call_iv_c1'],'c1 '+contract_c1+' 25d_put_iv - 25d_call_iv','style=vbar'],],''],
[[[t,data_dict['25d_call_iv_c2'],'c2 '+contract_c2+' 25d_call_iv','color=red'],
[t,data_dict['25d_put_iv_c2'],'c2 '+contract_c2+' 25d_put_iv','color=darkgreen'],
],
[[t,data_dict['25d_put_iv_c2']-data_dict['25d_call_iv_c2'],'c2 '+contract_c2+' 25d_put_iv - 25d_call_iv','style=vbar'],],''],
[[[t,data_dict['total_call_volume_c4'],'c4 '+contract_c4+' total_call_volume','color=red'],
[t,data_dict['total_put_volume_c4'],'c4 '+contract_c4+' total_put_volume','color=darkgreen'],
],
[[t,data_dict['total_put_volume_c4']-data_dict['total_call_volume_c4'],'c4 '+contract_c4+' total_put_volume - total_call_volume','style=vbar'],],''],
# [[[t,data_dict['total_call_volume_c5'],'c5 '+contract_c5+' total_call_volume','color=red'],
# [t,data_dict['total_put_volume_c5'],'c5 '+contract_c5+' total_put_volume','color=darkgreen'],
# ],
# [[t,data_dict['total_put_volume_c5']-data_dict['total_call_volume_c5'],'c5 '+contract_c5+' total_put_volume - total_call_volume','style=vbar'],],''],
# [[[t,data_dict['total_call_volume_c6'],'c6 '+contract_c6+' total_call_volume','color=red'],
# [t,data_dict['total_put_volume_c6'],'c6 '+contract_c6+' total_put_volume','color=darkgreen'],
# ],
# [[t,data_dict['total_put_volume_c6']-data_dict['total_call_volume_c6'],'c6 '+contract_c6+' total_put_volume - total_call_volume','style=vbar'],],''],
]
plot_many_figure(datas, max_height=1200)
# datas = [
# [[[t,data_dict['total_call_oi_1m'],'1month '+contract_1m+' total_call_oi','color=red'],
# [t,data_dict['total_put_oi_1m'],'1month '+contract_1m+' total_put_oi','color=darkgreen'],
# ],
# [[t,data_dict['total_put_oi_1m']-data_dict['total_call_oi_1m'],'1month '+contract_1m+' total_put_oi - total_call_oi','style=vbar'],],''],
# [[[t,data_dict['total_call_oi_1q'],'1quarter '+contract_1q+' total_call_oi','color=red'],
# [t,data_dict['total_put_oi_1q'],'1quarter '+contract_1q+' total_put_oi','color=darkgreen'],
# ],
# [[t,data_dict['total_put_oi_1q']-data_dict['total_call_oi_1q'],'1quarter '+contract_1q+' total_put_oi - total_call_oi','style=vbar'],],''],
# [[[t,data_dict['total_call_oi'],'total_call_oi','color=red'],
# [t,data_dict['total_put_oi'],'total_put_oi','color=darkgreen'],
# ],
# [[t,data_dict['total_put_oi']-data_dict['total_call_oi'],' total_put_oi - total_call_oi','style=vbar'],],''],
# [[[etf_t,etf_price,variety,'color=black']],[],''],
# [[[t,data_dict['total_put_volume']/data_dict['total_call_volume'],' total_put_volume / total_call_volume lhs',''],
# ],
# [[t,data_dict['total_put_oi']/data_dict['total_call_oi'],' total_put_oi / total_call_oi rhs',''],],''],
# ]
# plot_many_figure(datas, max_height=1000)
def update_all_nasdaq_etf_option_data():
for variety in NASDAQ_ETF_OPTION_DICT:
print(variety)
update_nasdaq_etf_option_data(variety)
for variety in NASDAQ_ETF_OPTION_DICT:
update_nasdaq_etf_option_info_detail(variety)
def compute_total_dividend(etf_path, dividend_path):
etf_df = pd.read_csv(etf_path)
etf_t = pd.DatetimeIndex(pd.to_datetime(etf_df['time'], format='%Y-%m-%d'))
plus = np.zeros(len(etf_t), dtype=float)
if os.path.exists(dividend_path):
try:
div_df = pd.read_csv(dividend_path)
div_t = pd.DatetimeIndex(pd.to_datetime(div_df['exOrEffDate'], format='%Y-%m-%d'))
amount = np.array(div_df['amount'], dtype=float)
div_t, amount = get_period_data(div_t, amount, start=etf_t[0].strftime('%Y-%m-%d'), end=etf_t[-1].strftime('%Y-%m-%d'))
for i in range(len(div_t)):
w = np.where(etf_t >= div_t[i])[0]
plus[w] += amount[i]
except:
pass
etf_df['div_plus'] = plus
etf_df.to_csv(etf_path, encoding='utf-8', index=False)
def convert_time_format(x):
try:
y = pd.to_datetime(x, format='%m/%d/%Y')
z = datetime.datetime.strftime(y,'%Y-%m-%d')
except:
z = None
return z
def update_nasdaq_etf_dividend(variety, se=None):
if se is None:
se = requests.session()
NASDAQ_ETF_DIVIDEND_URL = 'https://api.nasdaq.com/api/quote/{}/dividends?assetclass=etf'
url = NASDAQ_ETF_DIVIDEND_URL.format(variety)
print(variety + ' DIVIDEND')
path = os.path.join(option_price_dir, 'nasdaq', variety+'_dividend'+'.csv')
while (1):
try:
r = se.get(url, verify=False, headers=headers)
z = json.loads(r.content)
df = pd.DataFrame(z['data']['dividends']['rows'])
if (len(df) > 0):
df.drop(['declarationDate'], axis=1, inplace=True)
df.replace('\$', '', regex=True, inplace=True)
df['exOrEffDate'] = df['exOrEffDate'].apply(lambda x:pd.to_datetime(x, format='%m/%d/%Y'))
df.sort_values(by = 'exOrEffDate', inplace=True)
df['exOrEffDate'] = df['exOrEffDate'].apply(lambda x:datetime.datetime.strftime(x,'%Y-%m-%d'))
df['recordDate'] = df['recordDate'].apply(convert_time_format)
df['paymentDate'] = df['paymentDate'].apply(convert_time_format)
df.to_csv(path, encoding='utf-8', index=False)
break
except Exception as e:
print(e, ' NASDAQ ETF DIVIDEND')