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deribit.py
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deribit.py
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import os
import requests
import pandas as pd
import datetime
import numpy as np
from utils import *
import json
import math as m
from position import *
# from cn_fut_opt import *
import calendar
# 分钟
period = 60
def get_deribit_file_name(ss):
if 'PERPETUAL' in ss:
return ss
month_dict = {'JAN':'01','FEB':'02','MAR':'03','APR':'04','MAY':'05','JUN':'06',
'JUL':'07','AUG':'08','SEP':'09','OCT':'10','NOV':'11','DEC':'12'}
s = ss.split('-')
if len(s[1]) == 6:
# 4AUG23
y = s[1][4:]
m = month_dict[s[1][1:4]]
d = '0' + s[1][0]
else:
# 25AUG23
y = s[1][5:]
m = month_dict[s[1][2:5]]
d = s[1][0:2]
name = s[0] + y + m + d
# name example: BTC230825
return name
def crypto_implied_volatility(S0, K, T, r, price, Otype):
e = 1e-3
x0 = 3 # 300%
sqrt_T = m.sqrt(T)
def newtons_method(S0, K, T, sqrt_T, r, price, Otype, x0, e):
k=0
delta = call_bsm(S0, K, r, T, sqrt_T, Otype, x0) - price
while delta > e:
k=k+1
if (k > 40):
return np.nan
_vega = vega(S0, K, r, T, sqrt_T, x0)
if (_vega == 0.0):
return np.nan
x0 = (x0 - (call_bsm(S0, K, r, T, sqrt_T, Otype, x0) - price)/_vega)
delta = abs(call_bsm(S0, K, r, T, sqrt_T, Otype, x0) - price)
return x0
iv = newtons_method(S0, K, T, sqrt_T, r, price, Otype, x0, e)
return iv
def calculate_crypto_greeks(S0, K, T, r, price, Otype):
if (np.isnan(S0) or np.isnan(K) or np.isnan(price) or S0==0.0):
return [0, 0]
if (Otype == 'C' and S0/K > 3):
return [0, 0]
if (Otype == 'P' and K/S0 > 3):
return [0, 0]
sqrt_T = m.sqrt(T)
# print(S0, K, T, r, price, Otype)
# imp_vol
iv = crypto_implied_volatility(S0, K, T, r, price, Otype)
d1 = ((m.log(S0/K)) + (r + (iv*iv)/2)*T)/(iv*sqrt_T)
# delta
if Otype == 'C':
delta = ss.norm.cdf(d1)
else:
delta = ss.norm.cdf(d1) - 1
return [round(iv,5), round(delta,5)]
def get_deribit_future_inst_id_data(fut_df, inst_id):
t1 = np.array(fut_df['time']['time'])
cs = ['c7','c6','c5','c4','c3','c2','c1']
k = 0
start = 0
end = 0
for i in range(len(cs)):
c = cs[i]
tmp = (fut_df[c]['inst_id'] == inst_id)
ii = tmp[tmp == True].index
if (len(ii) > 0):
if (k == 0):
start = ii[0]
end = ii[len(ii)-1]
close = np.array(fut_df.loc[ii, pd.IndexSlice[c, 'close']], dtype=float)
else:
close = np.concatenate((close, np.array(fut_df.loc[ii, pd.IndexSlice[c, 'close']], dtype=float)), axis=0)
end = ii[len(ii)-1]
k = k + 1
if (start == 0 and end == 0):
return None, None
t = t1[start:end+1]
# print(t)
# print(len(t), len(open))
return t, close
def update_deribit_option_data():
headers = {
"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8",
"Accept-Language": "zh-CN,zh;q=0.8,zh-TW;q=0.7,zh-HK;q=0.5,en-US;q=0.3,en;q=0.2",
"Accept-Encoding": "gzip, deflate, br",
"Cache-Control": "no-cache",
"Content-Type": "application/x-www-form-urlencoded; charset=UTF-8",
"Host": "www.deribit.com",
"Connection": "keep-alive",
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:106.0) Gecko/20100101 Firefox/106.0",
}
SUMMARY_URL = 'https://www.deribit.com/api/v2/public/get_book_summary_by_currency?currency={}&kind=option'
INST_URL = 'https://www.deribit.com/api/v2/public/get_instruments?currency={}&kind=option'
DATA_URL = 'https://www.deribit.com/api/v2/public/get_tradingview_chart_data?instrument_name={}&start_timestamp={}&end_timestamp={}&resolution={}'
# session = requests.session()
proxy = {'https':'127.0.0.1:8889'}
t = datetime.datetime.now()
if t.hour >= 16:
earlist_time_dt = datetime.datetime(year=t.year, month=t.month, day=t.day, hour=16, minute=0, second=0)
else:
earlist_time_dt = datetime.datetime(year=t.year, month=t.month, day=t.day, hour=0, minute=0, second=0)
earlist_time = earlist_time_dt.strftime('%Y-%m-%d %H:%M:%S')
for variety in ['BTC', 'ETH']:
# for variety in ['ETH']:
inst_url = INST_URL.format(variety)
se = requests.session()
while (1):
try:
r = se.get(inst_url, verify=False, headers=headers, proxies=proxy)
break
except Exception as e:
print(e)
time.sleep(5)
cookies = r.cookies
data_json = json.loads(r.content)
# print(data_json)
inst_df = pd.DataFrame(data_json["result"])
inst_df = inst_df[['instrument_name', 'expiration_timestamp']]
inst_dict = {}
for i in range(len(inst_df)):
s = inst_df.loc[i, 'instrument_name'].split("-")
ss = s[0] + '-' + s[1]
if not(ss in inst_dict):
inst_dict[ss] = [inst_df.loc[i, 'expiration_timestamp'], inst_df.loc[i, 'instrument_name']]
else:
inst_dict[ss].append(inst_df.loc[i, 'instrument_name'])
# BOOK SUMMARY
url = SUMMARY_URL.format(variety)
while(1):
try:
r = se.get(url, verify=False, headers=headers, cookies=cookies, proxies=proxy)
data_json = json.loads(r.content)
break
except:
print(variety, 'BOOK SUMMARY GET ERROR')
time.sleep(5)
# print(data_json)
summary_df = pd.DataFrame(data_json["result"])
summary_dict = {}
for i in range(len(summary_df)):
summary_dict[summary_df.loc[i, 'instrument_name']] = [summary_df.loc[i, 'volume'], summary_df.loc[i, 'open_interest']]
# BTC FUTURE PRICE
path = os.path.join(future_price_dir, 'deribit', variety+'.csv')
fut_df = pd.read_csv(path, header=[0,1])
info_df = pd.DataFrame()
for ss in inst_dict:
df = pd.DataFrame()
name = get_deribit_file_name(ss)
opt_path = os.path.join(option_price_dir, 'deribit', name+'.csv')
last_line_time = get_last_line_time(opt_path, name, '2023-07-22 00:00:00', 19, '%Y-%m-%d %H:%M:%S')
if last_line_time == '2023-07-22 00:00:00':
last_line_time = earlist_time
tmp = time.strptime(last_line_time, '%Y-%m-%d %H:%M:%S')
last_line_time_ts = int(time.mktime(tmp))*1000
tmp = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
tmp = time.strptime(tmp, '%Y-%m-%d %H:%M:%S')
current_time_ts = int(time.mktime(tmp))*1000
inst_ids = inst_dict[ss]
expiration_timestamp = inst_ids[0]
inst_ids.pop(0)
fut_t, fut_price = get_deribit_future_inst_id_data(fut_df, name)
if fut_t is None:
continue
for inst_id in inst_ids:
print(inst_id)
start_time_ts = last_line_time_ts + 100 # +0.1s
temp_df = pd.DataFrame()
while ((start_time_ts+1000*60*period) < current_time_ts):
if (current_time_ts - start_time_ts) > (1000*60*60*24*20): # 20d
end_time_ts = start_time_ts + 1000*60*60*24*20
else:
end_time_ts = current_time_ts
url = DATA_URL.format(inst_id, str(start_time_ts), str(end_time_ts), str(period))
while (1):
try:
r = se.get(url, verify=False, headers=headers, cookies=cookies, proxies=proxy)
break
except:
print('DERIBIT OPTION GET ERROR')
time.sleep(10)
year_milliseconds = float(365*24*60*60*1000)
data_json = json.loads(r.content)
temp_df1 = pd.DataFrame(data_json["result"])
temp_df1['time'] = temp_df1['ticks'].apply(lambda x:time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(float(x)/1000)))
temp_df1['oi'] = np.nan
# inst_id example : BTC-01AUG23-30000-C
K = float((inst_id.split('-'))[2])
strike = str(int(K))
opt_type = inst_id[-1]
rate = 0
for i in range(len(temp_df1)):
data_t = temp_df1.loc[i, 'time']
w = np.where(fut_t == data_t)[0]
if len(w) > 0:
S0 = fut_price[w[0]]
T = (float(expiration_timestamp) - float(temp_df1.loc[i, 'ticks'])) / year_milliseconds
# print(S0, K, T, r, temp_df1.loc[i, 'close'])
imp_vol, delta = calculate_crypto_greeks(S0, K, T, rate, temp_df1.loc[i, 'close']*S0, opt_type)
else:
imp_vol = np.nan
delta = np.nan
temp_df1.loc[i, 'imp_vol'] = imp_vol
temp_df1.loc[i, 'delta'] = delta
col1 = ['time', opt_type, opt_type, opt_type, opt_type, opt_type]
col2 = ['time', strike, strike, strike, strike, strike]
col3 = ['time', 'close', 'volume', 'oi', 'imp_vol', 'delta']
if (len(temp_df1) > 0):
temp_df1 = temp_df1[['time', 'close', 'volume', 'oi', 'imp_vol', 'delta']]
temp_df1.columns = [col1, col2, col3]
if temp_df.empty:
temp_df = temp_df1.copy()
else:
temp_df = pd.concat([temp_df, temp_df1], axis=0)
temp_df.drop_duplicates(subset=[('time','time','time')], keep='last', inplace=True)
start_time_ts = end_time_ts
if len(temp_df) > 0:
# 最后一行数据是截至到当前的数据, 会被下一次拿的数据替换掉, 所以把 oi 放在倒数第二个数据里
if (len(temp_df) > 1):
temp_df.loc[len(temp_df)-1-1, pd.IndexSlice[opt_type, strike, 'oi']] = summary_dict[inst_id][1]
else:
temp_df.loc[len(temp_df)-1, pd.IndexSlice[opt_type, strike, 'oi']] = summary_dict[inst_id][1]
if (df.empty):
df = temp_df.copy()
else:
df = pd.merge(df, temp_df, on=[('time','time','time')], how='outer')
if len(df) > 0:
temp_info_df = pd.DataFrame()
temp_info_df['time'] = df['time']['time']['time']
temp_info_df[name] = name
if info_df.empty:
info_df = temp_info_df.copy()
else:
info_df = pd.merge(info_df, temp_info_df, on='time', how='outer')
if not(os.path.exists(opt_path)):
df.to_csv(opt_path, encoding='utf-8', index=False)
else:
old_df = pd.read_csv(opt_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(opt_path, encoding='utf-8', index=False)
if len(info_df) > 0:
print(info_df)
cols = info_df.columns.tolist()
cols.remove('time')
for i in range(len(info_df)):
s = ''
for col in cols:
if type(info_df.loc[i, col]) == str:
s += info_df.loc[i, col]
s += ','
info_df.loc[i, 'inst_ids'] = s
info_df = info_df[['time', 'inst_ids']]
path = os.path.join(option_price_dir, 'deribit', 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_deribit_future_data():
headers = {
"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8",
"Accept-Language": "zh-CN,zh;q=0.8,zh-TW;q=0.7,zh-HK;q=0.5,en-US;q=0.3,en;q=0.2",
"Accept-Encoding": "gzip, deflate, br",
"Cache-Control": "no-cache",
"Content-Type": "application/x-www-form-urlencoded; charset=UTF-8",
"Host": "www.deribit.com",
"Connection": "keep-alive",
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:106.0) Gecko/20100101 Firefox/106.0",
}
SUMMARY_URL = 'https://www.deribit.com/api/v2/public/get_book_summary_by_currency?currency={}&kind=future'
INST_URL = 'https://www.deribit.com/api/v2/public/get_instruments?currency={}&kind=future'
DATA_URL = 'https://www.deribit.com/api/v2/public/get_tradingview_chart_data?instrument_name={}&start_timestamp={}&end_timestamp={}&resolution={}'
# session = requests.session()
proxy = {'https':'127.0.0.1:8889'}
se = requests.session()
for variety in ['BTC', 'ETH']:
inst_url = INST_URL.format(variety)
while (1):
try:
r = se.get(inst_url, verify=False, headers=headers, proxies=proxy)
break
except Exception as e:
print(e)
time.sleep(5)
cookies = r.cookies
data_json = json.loads(r.content)
# print(data_json)
inst_df = pd.DataFrame(data_json["result"])
inst_df = inst_df[['instrument_name']]
print(inst_df)
# BOOK SUMMARY
url = SUMMARY_URL.format(variety)
r = se.get(url, verify=False, headers=headers, cookies=cookies, proxies=proxy)
data_json = json.loads(r.content)
# print(data_json)
summary_df = pd.DataFrame(data_json["result"])
summary_dict = {}
for i in range(len(summary_df)):
summary_dict[summary_df.loc[i, 'instrument_name']] = summary_df.loc[i, 'open_interest']
fut_path = os.path.join(future_price_dir, 'deribit', variety+'.csv')
last_line_time = get_last_line_time(fut_path, variety+' future', '2023-07-22 00:00:00', 19, '%Y-%m-%d %H:%M:%S')
tmp = time.strptime(last_line_time, '%Y-%m-%d %H:%M:%S')
last_line_time_ts = int(time.mktime(tmp))*1000
tmp = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
tmp = time.strptime(tmp, '%Y-%m-%d %H:%M:%S')
current_time_ts = int(time.mktime(tmp))*1000
df = pd.DataFrame()
n = 1
for i in range(len(inst_df)):
inst_id = inst_df.loc[i, 'instrument_name']
name = get_deribit_file_name(inst_id)
if not('PERPETUAL' in name):
cn = 'c' + str(n)
n = n + 1
else:
cn = 'PERPETUAL'
print(cn)
start_time_ts = last_line_time_ts + 100 # +0.1s
temp_df = pd.DataFrame()
while ((start_time_ts+1000*60*period) < current_time_ts):
if (current_time_ts - start_time_ts) > (1000*60*60*24*20): # 20d
end_time_ts = start_time_ts + 1000*60*60*24*20
else:
end_time_ts = current_time_ts
url = DATA_URL.format(inst_id, str(start_time_ts), str(end_time_ts), str(period))
try:
r = se.get(url, verify=False, headers=headers, cookies=cookies, proxies=proxy)
except:
print('GET ERROR')
time.sleep(5)
data_json = json.loads(r.content)
# print(data_json)
temp_df1 = pd.DataFrame(data_json["result"])
temp_df1['time'] = temp_df1['ticks'].apply(lambda x:time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(float(x)/1000)))
temp_df1['inst_id'] = name
temp_df1['oi'] = np.nan
temp_df1 = temp_df1[['time', 'inst_id', 'close', 'volume', 'oi']]
col1 = ['time', cn, cn, cn, cn]
col2 = ['time', 'inst_id', 'close', 'volume', 'oi']
temp_df1.columns = [col1, col2]
if temp_df.empty:
temp_df = temp_df1.copy()
else:
temp_df = pd.concat([temp_df, temp_df1], axis=0)
temp_df.drop_duplicates(subset=[('time','time')], keep='last', inplace=True)
start_time_ts = end_time_ts
if (temp_df.empty):
return
if (len(temp_df) > 1):
temp_df.loc[len(temp_df)-1-1, pd.IndexSlice[cn,'oi']] = summary_dict[inst_id]
elif (len(temp_df) == 1):
temp_df.loc[len(temp_df)-1, pd.IndexSlice[cn,'oi']] = summary_dict[inst_id]
if (df.empty):
df = temp_df.copy()
else:
df = pd.merge(df, temp_df, on=[('time','time')], how='outer')
# print(df)
if not(os.path.exists(fut_path)):
df.to_csv(fut_path, encoding='utf-8', index=False)
else:
old_df = pd.read_csv(fut_path, header=[0,1])
old_df = pd.concat([old_df, df], axis=0)
old_df.drop_duplicates(subset=[('time','time')], keep='last', inplace=True) # last
old_df.loc[:, pd.IndexSlice['time','time']] = old_df.loc[:, pd.IndexSlice['time','time']].apply(lambda x:pd.to_datetime(x, format='%Y-%m-%d %H:%M:%S'))
old_df.sort_values(by = ('time','time'), inplace=True)
old_df.loc[:, pd.IndexSlice['time','time']] = old_df.loc[:, pd.IndexSlice['time','time']].apply(lambda x:datetime.datetime.strftime(x,'%Y-%m-%d %H:%M:%S'))
old_df.to_csv(fut_path, encoding='utf-8', index=False)
def get_last_friday_expiry_time(year, month):
last_day = calendar.monthrange(year, month)[-1]
dt = datetime.datetime(year=year, month=month, day=last_day)
while (1):
weekday = dt.weekday()
if weekday == 4: # friday
break
dt = dt - pd.Timedelta(days=1)
return datetime.datetime(year=dt.year, month=dt.month, day=dt.day, hour=16)
def get_crypto_full_strike_price(df):
col = df.columns.tolist()
put_strike = [(col[i][1]) for i in range(len(col)) if col[i][0] == 'P']
call_strike = [(col[i][1]) for i in range(len(col)) if col[i][0] == 'C']
res = []
for i in put_strike:
if i not in res:
res.append(i)
put_strike = np.array(res, dtype=float)
res = []
for i in call_strike:
if i not in res:
res.append(i)
call_strike = np.array(res, dtype=float)
return put_strike, call_strike
def crypto_put_call_delta_volatility(df, delta, price, put_strike, call_strike):
# CALL CLOSE
tmp = df.loc[pd.IndexSlice['C', :, 'delta']]
idx1, idx2, delta1, delta2 = column_index_delta(tmp, delta)
o_price = df.loc[pd.IndexSlice['C', :, 'close']]
iv = df.loc[pd.IndexSlice['C', :, 'imp_vol']]
if delta1 == delta:
_25d_call_price = o_price[idx1]
_25d_call_iv = iv[idx1]
else:
w1 = (delta2 - delta)/(delta2 - delta1)
w2 = (delta - delta1)/(delta2 - delta1)
_25d_call_price = w1*o_price[idx1] + w2*o_price[idx2]
_25d_call_iv = w1*iv[idx1] + w2*iv[idx2]
if (price > 0.25):
# print(df)
# print(strike)
# print(price)
idx1, idx2, price1, price2 = column_index_price(call_strike, price)
if price1 == price:
_atm_call_iv = iv[idx1]
else:
w1 = (price2 - price)/(price2 - price1)
w2 = (price - price1)/(price2 - price1)
_atm_call_iv = w1*iv[idx1] + w2*iv[idx2]
else:
_atm_call_iv = np.nan
############################################################################
delta = -delta
# PUT CLOSE
tmp = df.loc[pd.IndexSlice['P', :, 'delta']]
idx1, idx2, delta1, delta2 = column_index_delta(tmp, delta)
o_price = df.loc[pd.IndexSlice['P', :, 'close']]
iv = df.loc[pd.IndexSlice['P', :, 'imp_vol']]
if delta1 == delta:
_25d_put_price = o_price[idx1]
_25d_put_iv = iv[idx1]
else:
w1 = (delta2 - delta)/(delta2 - delta1)
w2 = (delta - delta1)/(delta2 - delta1)
_25d_put_price = w1*o_price[idx1] + w2*o_price[idx2]
_25d_put_iv = w1*iv[idx1] + w2*iv[idx2]
if (price > 0.25):
idx1, idx2, price1, price2 = column_index_price(put_strike, price)
if price1 == price:
_atm_put_iv = iv[idx1]
else:
w1 = (price2 - price)/(price2 - price1)
w2 = (price - price1)/(price2 - price1)
_atm_put_iv = w1*iv[idx1] + w2*iv[idx2]
else:
_atm_put_iv = np.nan
return [_25d_put_price, _25d_put_iv,\
_25d_call_price, _25d_call_iv,\
_atm_put_iv,\
_atm_call_iv
]
def update_deribit_option_info_detail():
for variety in ['BTC', 'ETH']:
print(variety + ' info details')
info_path = os.path.join(option_price_dir, 'deribit', 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, 'deribit', 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
fut_path = os.path.join(future_price_dir, 'deribit', variety+'.csv')
fut_df = pd.read_csv(fut_path, header=[0,1])
fut_t = pd.DatetimeIndex(pd.to_datetime(fut_df['time']['time'], format='%Y-%m-%d %H:%M:%S'))
fut_cols = ['c1','c2','c3','c4','c5','c6','c7']
opt_dict = {}
cols = ['time', 'total_put_volume', 'total_call_volume', 'total_put_oi', 'total_call_oi',
# 本周末到期的期权合约
'total_put_volume_1w', 'total_call_volume_1w', 'total_put_oi_1w', 'total_call_oi_1w',
'atm_put_iv_1w', 'atm_call_iv_1w', '25d_put_iv_1w', '25d_call_iv_1w',
'10d_put_iv_1w', '10d_call_iv_1w', '5d_put_iv_1w', '5d_call_iv_1w',
'put_volume_max1_1w', 'put_volume_max1_strike_1w', 'put_volume_max2_1w', 'put_volume_max2_strike_1w',
'put_volume_max3_1w', 'put_volume_max3_strike_1w', 'put_volume_max4_1w', 'put_volume_max4_strike_1w',
'put_volume_max5_1w', 'put_volume_max5_strike_1w',
'call_volume_max1_1w', 'call_volume_max1_strike_1w', 'call_volume_max2_1w', 'call_volume_max2_strike_1w',
'call_volume_max3_1w', 'call_volume_max3_strike_1w', 'call_volume_max4_1w', 'call_volume_max4_strike_1w',
'call_volume_max5_1w', 'call_volume_max5_strike_1w',
'put_oi_max1_1w', 'put_oi_max1_strike_1w', 'put_oi_max2_1w', 'put_oi_max2_strike_1w',
'put_oi_max3_1w', 'put_oi_max3_strike_1w', 'put_oi_max4_1w', 'put_oi_max4_strike_1w',
'put_oi_max5_1w', 'put_oi_max5_strike_1w',
'call_oi_max1_1w', 'call_oi_max1_strike_1w', 'call_oi_max2_1w', 'call_oi_max2_strike_1w',
'call_oi_max3_1w', 'call_oi_max3_strike_1w', 'call_oi_max4_1w', 'call_oi_max4_strike_1w',
'call_oi_max5_1w', 'call_oi_max5_strike_1w',
# 下周末到期的期权合约
'total_put_volume_2w', 'total_call_volume_2w', 'total_put_oi_2w', 'total_call_oi_2w',
'atm_put_iv_2w', 'atm_call_iv_2w', '25d_put_iv_2w', '25d_call_iv_2w',
'10d_put_iv_2w', '10d_call_iv_2w', '5d_put_iv_2w', '5d_call_iv_2w',
'put_volume_max1_2w', 'put_volume_max1_strike_2w', 'put_volume_max2_2w', 'put_volume_max2_strike_2w',
'put_volume_max3_2w', 'put_volume_max3_strike_2w', 'put_volume_max4_2w', 'put_volume_max4_strike_2w',
'put_volume_max5_2w', 'put_volume_max5_strike_2w',
'call_volume_max1_2w', 'call_volume_max1_strike_2w', 'call_volume_max2_2w', 'call_volume_max2_strike_2w',
'call_volume_max3_2w', 'call_volume_max3_strike_2w', 'call_volume_max4_2w', 'call_volume_max4_strike_2w',
'call_volume_max5_2w', 'call_volume_max5_strike_2w',
'put_oi_max1_2w', 'put_oi_max1_strike_2w', 'put_oi_max2_2w', 'put_oi_max2_strike_2w',
'put_oi_max3_2w', 'put_oi_max3_strike_2w', 'put_oi_max4_2w', 'put_oi_max4_strike_2w',
'put_oi_max5_2w', 'put_oi_max5_strike_2w',
'call_oi_max1_2w', 'call_oi_max1_strike_2w', 'call_oi_max2_2w', 'call_oi_max2_strike_2w',
'call_oi_max3_2w', 'call_oi_max3_strike_2w', 'call_oi_max4_2w', 'call_oi_max4_strike_2w',
'call_oi_max5_2w', 'call_oi_max5_strike_2w',
# 本月末到期的期权合约
'total_put_volume_1m', 'total_call_volume_1m', 'total_put_oi_1m', 'total_call_oi_1m',
'atm_put_iv_1m', 'atm_call_iv_1m', '25d_put_iv_1m', '25d_call_iv_1m',
'10d_put_iv_1m', '10d_call_iv_1m', '5d_put_iv_1m', '5d_call_iv_1m',
'put_volume_max1_1m', 'put_volume_max1_strike_1m', 'put_volume_max2_1m', 'put_volume_max2_strike_1m',
'put_volume_max3_1m', 'put_volume_max3_strike_1m', 'put_volume_max4_1m', 'put_volume_max4_strike_1m',
'put_volume_max5_1m', 'put_volume_max5_strike_1m',
'call_volume_max1_1m', 'call_volume_max1_strike_1m', 'call_volume_max2_1m', 'call_volume_max2_strike_1m',
'call_volume_max3_1m', 'call_volume_max3_strike_1m', 'call_volume_max4_1m', 'call_volume_max4_strike_1m',
'call_volume_max5_1m', 'call_volume_max5_strike_1m',
'put_oi_max1_1m', 'put_oi_max1_strike_1m', 'put_oi_max2_1m', 'put_oi_max2_strike_1m',
'put_oi_max3_1m', 'put_oi_max3_strike_1m', 'put_oi_max4_1m', 'put_oi_max4_strike_1m',
'put_oi_max5_1m', 'put_oi_max5_strike_1m',
'call_oi_max1_1m', 'call_oi_max1_strike_1m', 'call_oi_max2_1m', 'call_oi_max2_strike_1m',
'call_oi_max3_1m', 'call_oi_max3_strike_1m', 'call_oi_max4_1m', 'call_oi_max4_strike_1m',
'call_oi_max5_1m', 'call_oi_max5_strike_1m',
# 本季度末到期的期权合约
'total_put_volume_1q', 'total_call_volume_1q', 'total_put_oi_1q', 'total_call_oi_1q',
'atm_put_iv_1q', 'atm_call_iv_1q', '25d_put_iv_1q', '25d_call_iv_1q',
'10d_put_iv_1q', '10d_call_iv_1q', '5d_put_iv_1q', '5d_call_iv_1q',
'put_volume_max1_1q', 'put_volume_max1_strike_1q', 'put_volume_max2_1q', 'put_volume_max2_strike_1q',
'put_volume_max3_1q', 'put_volume_max3_strike_1q', 'put_volume_max4_1q', 'put_volume_max4_strike_1q',
'put_volume_max5_1q', 'put_volume_max5_strike_1q',
'call_volume_max1_1q', 'call_volume_max1_strike_1q', 'call_volume_max2_1q', 'call_volume_max2_strike_1q',
'call_volume_max3_1q', 'call_volume_max3_strike_1q', 'call_volume_max4_1q', 'call_volume_max4_strike_1q',
'call_volume_max5_1q', 'call_volume_max5_strike_1q',
'put_oi_max1_1q', 'put_oi_max1_strike_1q', 'put_oi_max2_1q', 'put_oi_max2_strike_1q',
'put_oi_max3_1q', 'put_oi_max3_strike_1q', 'put_oi_max4_1q', 'put_oi_max4_strike_1q',
'put_oi_max5_1q', 'put_oi_max5_strike_1q',
'call_oi_max1_1q', 'call_oi_max1_strike_1q', 'call_oi_max2_1q', 'call_oi_max2_strike_1q',
'call_oi_max3_1q', 'call_oi_max3_strike_1q', 'call_oi_max4_1q', 'call_oi_max4_strike_1q',
'call_oi_max5_1q', 'call_oi_max5_strike_1q',
# 年末到期的期权合约
'total_put_volume_1y', 'total_call_volume_1y', 'total_put_oi_1y', 'total_call_oi_1y',
'atm_put_iv_1y', 'atm_call_iv_1y', '25d_put_iv_1y', '25d_call_iv_1y',
'10d_put_iv_1y', '10d_call_iv_1y', '5d_put_iv_1y', '5d_call_iv_1y',
'put_volume_max1_1y', 'put_volume_max1_strike_1y', 'put_volume_max2_1y', 'put_volume_max2_strike_1y',
'put_volume_max3_1y', 'put_volume_max3_strike_1y', 'put_volume_max4_1y', 'put_volume_max4_strike_1y',
'put_volume_max5_1y', 'put_volume_max5_strike_1y',
'call_volume_max1_1y', 'call_volume_max1_strike_1y', 'call_volume_max2_1y', 'call_volume_max2_strike_1y',
'call_volume_max3_1y', 'call_volume_max3_strike_1y', 'call_volume_max4_1y', 'call_volume_max4_strike_1y',
'call_volume_max5_1y', 'call_volume_max5_strike_1y',
'put_oi_max1_1y', 'put_oi_max1_strike_1y', 'put_oi_max2_1y', 'put_oi_max2_strike_1y',
'put_oi_max3_1y', 'put_oi_max3_strike_1y', 'put_oi_max4_1y', 'put_oi_max4_strike_1y',
'put_oi_max5_1y', 'put_oi_max5_strike_1y',
'call_oi_max1_1y', 'call_oi_max1_strike_1y', 'call_oi_max2_1y', 'call_oi_max2_strike_1y',
'call_oi_max3_1y', 'call_oi_max3_strike_1y', 'call_oi_max4_1y', 'call_oi_max4_strike_1y',
'call_oi_max5_1y', 'call_oi_max5_strike_1y',
]
df = pd.DataFrame(columns=cols)
for i in range(start_idx, len(info_t)):
t = info_t[i]
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
keys = ['1w', '2w', '1m', '1q', '1y']
for key in keys:
df.loc[i, key] = ''
inst_ids = info_df.loc[i, 'inst_ids'].split(',')
inst_ids.remove('')
# 到期日dict
expiry_dict = {}
for inst_id in inst_ids:
if not(inst_id in opt_dict):
opt_path = os.path.join(option_price_dir, 'deribit', 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'))
put_strike, call_strike = get_crypto_full_strike_price(opt_df)
opt_dict[inst_id] = [opt_t, opt_df, put_strike, call_strike]
# 星期五下午 16:00:00 到期
ymd_dt = pd.to_datetime(t.strftime('%Y-%m-%d'), format='%Y-%m-%d')
weekday = t.weekday()
hour = t.hour
# 1w
if (weekday < 4) or (weekday == 4 and hour < 16):
# 本周 星期五下午 16:00:00 之前
expiry_dt = ymd_dt + pd.Timedelta(days=(4-weekday)) # to friday
expiry = expiry_dt.strftime('%Y%m%d')
else:
# 之后
expiry_dt = ymd_dt + pd.Timedelta(days=(11-weekday)) # to next friday
expiry = expiry_dt.strftime('%Y%m%d')
expiry_dict['1w'] = expiry[2:]
# 2w
expiry_dt = expiry_dt + pd.Timedelta(days=7)
expiry = expiry_dt.strftime('%Y%m%d')
expiry_dict['2w'] = expiry[2:]
# 1m
expiry_dt = get_last_friday_expiry_time(t.year, t.month)
if (t < expiry_dt):
expiry = expiry_dt.strftime('%Y%m%d')
else:
if t.month == 12:
expiry_dt = get_last_friday_expiry_time(t.year+1, 1)
expiry = expiry_dt.strftime('%Y%m%d')
else:
expiry_dt = get_last_friday_expiry_time(t.year, t.month+1)
expiry = expiry_dt.strftime('%Y%m%d')
expiry_dict['1m'] = expiry[2:]
# 1q
month = t.month
# 本季度末的那个月
month = (month//3 + 1)*3
expiry_dt = get_last_friday_expiry_time(t.year, month)
if (t < expiry_dt):
expiry = expiry_dt.strftime('%Y%m%d')
else:
if t.month == 12:
expiry_dt = get_last_friday_expiry_time(t.year+1, 3)
expiry = expiry_dt.strftime('%Y%m%d')
else:
expiry_dt = get_last_friday_expiry_time(t.year, month+3)
expiry = expiry_dt.strftime('%Y%m%d')
expiry_dict['1q'] = expiry[2:]
# 1y
expiry_dt = get_last_friday_expiry_time(t.year, 12)
if (t < expiry_dt):
expiry = expiry_dt.strftime('%Y%m%d')
else:
expiry_dt = get_last_friday_expiry_time(t.year+1, 12)
expiry = expiry_dt.strftime('%Y%m%d')
expiry_dict['1y'] = expiry[2:]
print(t, expiry_dict)
for key in keys:
# inst_id example: BTC230825
inst_id = variety + expiry_dict[key]
if inst_id in opt_dict:
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,:]
put_strike = opt_dict[inst_id][2]
call_strike = opt_dict[inst_id][3]
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_'+key] = put_volume
df.loc[i, 'total_call_volume_'+key] = 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_'+key] = put_oi
df.loc[i, 'total_call_oi_'+key] = 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]
df.loc[i, 'put_volume_max1_'+key] = P_volume[-1]
df.loc[i, 'put_volume_max1_strike_'+key] = idx[-1]
df.loc[i, 'put_volume_max2_'+key] = P_volume[-2]
df.loc[i, 'put_volume_max2_strike_'+key] = idx[-2]
df.loc[i, 'put_volume_max3_'+key] = P_volume[-3]
df.loc[i, 'put_volume_max3_strike_'+key] = idx[-3]
df.loc[i, 'put_volume_max4_'+key] = P_volume[-4]
df.loc[i, 'put_volume_max4_strike_'+key] = idx[-4]
df.loc[i, 'put_volume_max5_'+key] = P_volume[-5]
df.loc[i, 'put_volume_max5_strike_'+key] = 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]
df.loc[i, 'call_volume_max1_'+key] = C_volume[-1]
df.loc[i, 'call_volume_max1_strike_'+key] = idx[-1]
df.loc[i, 'call_volume_max2_'+key] = C_volume[-2]
df.loc[i, 'call_volume_max2_strike_'+key] = idx[-2]
df.loc[i, 'call_volume_max3_'+key] = C_volume[-3]
df.loc[i, 'call_volume_max3_strike_'+key] = idx[-3]
df.loc[i, 'call_volume_max4_'+key] = C_volume[-4]
df.loc[i, 'call_volume_max4_strike_'+key] = idx[-4]
df.loc[i, 'call_volume_max5_'+key] = C_volume[-5]
df.loc[i, 'call_volume_max5_strike_'+key] = 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]
df.loc[i, 'put_oi_max1_'+key] = P_oi[-1]
df.loc[i, 'put_oi_max1_strike_'+key] = idx[-1]
df.loc[i, 'put_oi_max2_'+key] = P_oi[-2]
df.loc[i, 'put_oi_max2_strike_'+key] = idx[-2]
df.loc[i, 'put_oi_max3_'+key] = P_oi[-3]
df.loc[i, 'put_oi_max3_strike_'+key] = idx[-3]
df.loc[i, 'put_oi_max4_'+key] = P_oi[-4]
df.loc[i, 'put_oi_max4_strike_'+key] = idx[-4]
df.loc[i, 'put_oi_max5_'+key] = P_oi[-5]
df.loc[i, 'put_oi_max5_strike_'+key] = 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]
df.loc[i, 'call_oi_max1_'+key] = C_oi[-1]
df.loc[i, 'call_oi_max1_strike_'+key] = idx[-1]
df.loc[i, 'call_oi_max2_'+key] = C_oi[-2]
df.loc[i, 'call_oi_max2_strike_'+key] = idx[-2]
df.loc[i, 'call_oi_max3_'+key] = C_oi[-3]
df.loc[i, 'call_oi_max3_strike_'+key] = idx[-3]
df.loc[i, 'call_oi_max4_'+key] = C_oi[-4]
df.loc[i, 'call_oi_max4_strike_'+key] = idx[-4]
df.loc[i, 'call_oi_max5_'+key] = C_oi[-5]
df.loc[i, 'call_oi_max5_strike_'+key] = idx[-5]
try:
w = np.where(fut_t == t)[0][0]
except:
continue
fut_temp_df = fut_df.loc[w,:]
for c in fut_cols:
if (fut_temp_df[c]['inst_id'] == inst_id):
fut_price = fut_temp_df[c]['close']
break
df.loc[i, key] = inst_id
ret = crypto_put_call_delta_volatility(temp_df, 0.25, fut_price, put_strike, call_strike)
df.loc[i, '25d_put_iv_'+key] = ret[1]
df.loc[i, '25d_call_iv_'+key] = ret[3]
df.loc[i, 'atm_put_iv_'+key] = ret[4]
df.loc[i, 'atm_call_iv_'+key] = ret[5]
ret = crypto_put_call_delta_volatility(temp_df, 0.1, fut_price, put_strike, call_strike)
df.loc[i, '10d_put_iv_'+key] = ret[1]
df.loc[i, '10d_call_iv_'+key] = ret[3]
ret = crypto_put_call_delta_volatility(temp_df, 0.05, fut_price, put_strike, call_strike)
df.loc[i, '5d_put_iv_'+key] = ret[1]
df.loc[i, '5d_call_iv_'+key] = ret[3]
path = os.path.join(option_price_dir, 'deribit', variety+'_info_detail'+'.csv')
if os.path.exists(path):
df.to_csv(path, mode='a', encoding='utf-8', index=False, header=None)
else:
df.to_csv(path, encoding='utf-8', index=False)
def plot_deribit_option_datas():
for variety in ['BTC', 'ETH']:
path = os.path.join(option_price_dir, 'deribit', 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 len(col) == 2:
data_dict[col] = 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
fut_path = os.path.join(future_price_dir, 'deribit', variety+'.csv')
fut_df = pd.read_csv(fut_path, header=[0,1])
fut_t = pd.DatetimeIndex(pd.to_datetime(fut_df['time']['time'], format='%Y-%m-%d %H:%M:%S'))
fut_price = np.array(fut_df['PERPETUAL']['close'], dtype=float)
contract_1w = data_dict['1w'][-1]
# fut_price_1w = np.array(fut_df['c1']['close'], dtype=float)
# put_oi_max1_strike_1w = np.array(df['put_oi_max1_strike_1w'], dtype=float)
# put_oi_max2_strike_1w = np.array(df['put_oi_max2_strike_1w'], dtype=float)
# put_oi_max3_strike_1w = np.array(df['put_oi_max3_strike_1w'], dtype=float)
# call_oi_max1_strike_1w = np.array(df['call_oi_max1_strike_1w'], dtype=float)
# call_oi_max2_strike_1w = np.array(df['call_oi_max2_strike_1w'], dtype=float)
# call_oi_max3_strike_1w = np.array(df['call_oi_max3_strike_1w'], dtype=float)
# put_oi_max_strike_1w = np.vstack((put_oi_max1_strike_1w, put_oi_max2_strike_1w, put_oi_max3_strike_1w))
# put_oi_max_strike_1w = np.sort(put_oi_max_strike_1w, axis=0)
# call_oi_max_strike_1w = np.vstack((call_oi_max1_strike_1w, call_oi_max2_strike_1w, call_oi_max3_strike_1w))
# call_oi_max_strike_1w = np.sort(call_oi_max_strike_1w, axis=0)
# put_oi_max = np.array(df['put_oi_max1_1w'], dtype=float)
# w = np.where(put_oi_max > 0)[0]
# datas = [
# [[[t,data_dict['5d_put_iv_1w'],'1week '+contract_1w+' 5d_put_iv','color=darkgreen'],
# [t,data_dict['5d_call_iv_1w'],'1week '+contract_1w+' 5d_call_iv','color=red'],
# ],
# [],''],
# [[[t,data_dict['10d_put_iv_1w'],'1week '+contract_1w+' 10d_put_iv','color=darkgreen'],
# [t,data_dict['10d_call_iv_1w'],'1week '+contract_1w+' 10d_call_iv','color=red'],
# ],
# [],''],
# [[
# [t,data_dict['5d_put_iv_1w']-data_dict['5d_call_iv_1w'],'1week '+contract_1w+' 5d_skew',''],
# [t,data_dict['10d_put_iv_1w']-data_dict['10d_call_iv_1w'],'1week '+contract_1w+' 10d_skew',''],
# [t,data_dict['25d_put_iv_1w']-data_dict['25d_call_iv_1w'],'1week '+contract_1w+' 25d_skew',''],
# [t,data_dict['atm_put_iv_1w']-data_dict['atm_call_iv_1w'],'1week '+contract_1w+' atm_skew',''],
# ],
# [],''],
# [[[fut_t,fut_price_1w,variety,'color=black, width=4'],
# [t[w],call_oi_max_strike_1w[0,:][w],'call 持仓量一','color=red'],
# [t[w],call_oi_max_strike_1w[1,:][w],'call 持仓量二','color=orange'],
# [t[w],put_oi_max_strike_1w[2,:][w],'put 持仓量一','color=darkgreen'],
# [t[w],put_oi_max_strike_1w[1,:][w],'put 持仓量二','color=purple'],],[],''],
# [[[t,data_dict['25d_put_iv_1w'],'1week '+contract_1w+' 25d_put_iv','color=darkgreen'],
# [t,data_dict['25d_call_iv_1w'],'1week '+contract_1w+' 25d_call_iv','color=red'],
# ],
# [],''],
# [[[t,data_dict['atm_put_iv_1w'],'1week '+contract_1w+' atm_put_iv','color=darkgreen'],
# [t,data_dict['atm_call_iv_1w'],'1week '+contract_1w+' atm_call_iv','color=red'],
# ],
# [],''],
# ]
# plot_many_figure(datas, max_height=900)
contract_2w = data_dict['2w'][-1]
# fut_price_2w = np.array(fut_df['c2']['close'], dtype=float)
# put_oi_max1_strike_2w = np.array(df['put_oi_max1_strike_2w'], dtype=float)
# put_oi_max2_strike_2w = np.array(df['put_oi_max2_strike_2w'], dtype=float)
# put_oi_max3_strike_2w = np.array(df['put_oi_max3_strike_2w'], dtype=float)
# call_oi_max1_strike_2w = np.array(df['call_oi_max1_strike_2w'], dtype=float)
# call_oi_max2_strike_2w = np.array(df['call_oi_max2_strike_2w'], dtype=float)
# call_oi_max3_strike_2w = np.array(df['call_oi_max3_strike_2w'], dtype=float)
# put_oi_max_strike_2w = np.vstack((put_oi_max1_strike_2w, put_oi_max2_strike_2w, put_oi_max3_strike_2w))
# put_oi_max_strike_2w = np.sort(put_oi_max_strike_2w, axis=0)
# call_oi_max_strike_2w = np.vstack((call_oi_max1_strike_2w, call_oi_max2_strike_2w, call_oi_max3_strike_2w))
# call_oi_max_strike_2w = np.sort(call_oi_max_strike_2w, axis=0)
# put_oi_max = np.array(df['put_oi_max1_2w'], dtype=float)
# w = np.where(put_oi_max > 0)[0]
# datas = [
# [[[t,data_dict['5d_put_iv_2w'],'2week '+contract_2w+' 5d_put_iv','color=darkgreen'],
# [t,data_dict['5d_call_iv_2w'],'2week '+contract_2w+' 5d_call_iv','color=red'],
# ],
# [],''],
# [[[t,data_dict['10d_put_iv_2w'],'2week '+contract_2w+' 10d_put_iv','color=darkgreen'],
# [t,data_dict['10d_call_iv_2w'],'2week '+contract_2w+' 10d_call_iv','color=red'],
# ],
# [],''],
# [[
# [t,data_dict['5d_put_iv_2w']-data_dict['5d_call_iv_2w'],'2week '+contract_2w+' 5d_skew',''],
# [t,data_dict['10d_put_iv_2w']-data_dict['10d_call_iv_2w'],'2week '+contract_2w+' 10d_skew',''],
# [t,data_dict['25d_put_iv_2w']-data_dict['25d_call_iv_2w'],'2week '+contract_2w+' 25d_skew',''],
# [t,data_dict['atm_put_iv_2w']-data_dict['atm_call_iv_2w'],'2week '+contract_2w+' atm_skew',''],
# ],
# [],''],
# [[[fut_t,fut_price_2w,variety,'color=black, width=4'],
# [t[w],call_oi_max_strike_2w[0,:][w],'call 持仓量一','color=red'],
# [t[w],call_oi_max_strike_2w[1,:][w],'call 持仓量二','color=orange'],