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xxx.py
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xxx.py
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import os
import requests
import pandas as pd
import datetime
import numpy as np
import sys
from utils import *
from bokeh.io import output_file, show
from bokeh.layouts import column, row, gridplot, layout
from bokeh.plotting import figure
from bokeh.models import LinearAxis, Range1d, VBar, DatetimeTickFormatter
from scipy.stats import linregress
# [inst_id, auction]
inst_ids_info = [['sc2402', 1], ['sc2403', 1],
['au2402', 1], ['au2404', 1],
['ag2402', 1], ['ag2404', 1],
['zn2402', 1], ['zn2403', 1],
['al2402', 1], ['al2403', 1],
['cu2402', 1], ['cu2403', 1],
['CF405', 0],
]
sina_usd_symbol_dict = {
'sc': 'OIL',
'au': 'GC',
'ag': 'SI',
'cu': 'CAD',
'zn': 'ZSD',
'al': 'AHD',
}
headers = {
"Accept": "application/json, text/plain, */*",
"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": "gu.sina.cn",
"Proxy-Connection": "keep-alive",
'Sec-Fetch-Site': 'same-site',
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:106.0) Gecko/20100101 Firefox/106.0",
}
def get_usd_price():
se = requests.session()
URL = 'https://gu.sina.cn/ft/api/jsonp.php/var_DATA=/GlobalService.getMink?symbol={}&type=5'
for variety in sina_usd_symbol_dict:
print(variety)
usd_symbol = sina_usd_symbol_dict[variety]
path = os.path.join(data_dir, usd_symbol+'_5min'+'.csv')
url = URL.format(usd_symbol)
r = se.get(url, verify=False, headers=headers)
s = r.text
s=s.replace('},', '')
s=s.replace('"', '')
s=s.replace('}]);', '')
z = s.split('{')[1:]
datas = []
for i in range(len(z)):
data = z[i].split(',')[:7]
data[0] = data[0][2:]
for k in range(1, len(data)):
data[k] = data[k].split(':')[1]
datas.append(data)
df = pd.DataFrame(columns=['time','open','high','low','close','v','p'], data=datas)
df.to_csv(path, encoding='utf-8', index=False)
def get_usd_cny():
se = requests.session()
url = 'https://vip.stock.finance.sina.com.cn/forex/api/jsonp.php/DATA=/NewForexService.getMinKline?symbol=fx_susdcny&scale=5&datalen=1440'
print('USDCNY')
path = os.path.join(data_dir, 'USDCNY_5min'+'.csv')
r = se.get(url, verify=False, headers=headers)
s = r.text
s=s.replace('},', '')
s=s.replace('"', '')
s=s.replace('}]);', '')
z = s.split('{')[1:]
datas = []
for i in range(len(z)):
data = z[i].split(',')[:7]
data[0] = data[0][2:]
for k in range(1, len(data)):
data[k] = data[k].split(':')[1]
datas.append(data)
df = pd.DataFrame(columns=['time','open','high','low','close'], data=datas)
df.to_csv(path, encoding='utf-8', index=False)
def update_au_ag_td_intraday_data():
se = requests.session()
url = 'https://api-ddc-wscn.awtmt.com/market/kline?prod_code=AUTD.SGE&tick_count=360&period_type=60&adjust_price_type=forward&fields=tick_at,open_px,close_px,high_px,low_px,turnover_volume'
r = se.get(url)
data_json = r.json()
df = pd.DataFrame(data_json['data']['candle']['AUTD.SGE']['lines'])
df.columns = ['open', 'close', 'high', 'low', 'volume', 'time']
df = df[['time', 'open', 'high', 'low', 'close', 'volume']]
df['time'] = df['time'].apply(lambda x:datetime.datetime.fromtimestamp(x).strftime('%Y-%m-%d %H:%M:%S'))
print(df)
def xxx_price(display):
now = datetime.datetime.now()
df = pd.DataFrame()
for inst_id_info in inst_ids_info:
time.sleep(0.25)
inst_id = inst_id_info[0]
auction = inst_id_info[1]
if inst_id[1].isdigit():
variety = inst_id[0]
else:
variety = inst_id[0:2]
for exchange in exchange_dict:
if variety in exchange_dict[exchange]:
break
path = os.path.join(future_price_dir, exchange, variety+'.csv')
if not os.path.exists(path):
print("error", path)
exit()
fut_df = pd.read_csv(path, header=[0,1])
fut_t = pd.DatetimeIndex(pd.to_datetime(fut_df['time']['Unnamed: 0_level_1'], format='%Y-%m-%d'))
path = os.path.join(option_price_dir, exchange, inst_id+'.csv')
if not os.path.exists(path):
print("error", path)
exit()
opt_df = pd.read_csv(path, header=[0,1,2])
opt_t = pd.DatetimeIndex(pd.to_datetime(opt_df['time']['time']['time'], format='%Y-%m-%d'))
# usd price
if (auction == 1):
path = os.path.join(data_dir, sina_usd_symbol_dict[variety]+'_5min'+'.csv')
usd_df = pd.read_csv(path)
usd_t = pd.DatetimeIndex(pd.DatetimeIndex(pd.to_datetime(usd_df['time'], format='%Y-%m-%d %H:%M:%S')))
# usdcny
path = os.path.join(data_dir, 'USDCNY_5min'+'.csv')
usdcny_df = pd.read_csv(path)
usdcny_t = pd.DatetimeIndex(pd.to_datetime(usdcny_df['time'], format='%Y-%m-%d %H:%M:%S'))
if (now.strftime('%Y-%m-%d') != opt_t[-1].strftime('%Y-%m-%d')):
print('wrong time 2,', variety)
exit()
if (now.strftime('%Y-%m-%d') != fut_t[-1].strftime('%Y-%m-%d')):
print('wrong time 3,', variety)
exit()
price_chg = 0
if (auction == 1):
if (now.strftime('%Y-%m-%d') != usd_t[-1].strftime('%Y-%m-%d')):
print('wrong time 1,', variety)
exit()
if (now.strftime('%Y-%m-%d') != usdcny_t[-1].strftime('%Y-%m-%d')):
print('wrong time 4,', variety)
exit()
if now.hour >= 15:
usd_start_time = usd_t[-1].strftime('%Y-%m-%d') + ' 15:00:00'
else:
print('wrong time 5,', variety)
exit()
usd_start_time_dt = pd.to_datetime(usd_start_time, format='%Y-%m-%d %H:%M:%S')
w = np.where(usd_t == usd_start_time_dt)[0]
if (len(w) == 0):
usd_start_time = usd_t[-1].strftime('%Y-%m-%d') + ' 14:55:00'
usd_start_time_dt = pd.to_datetime(usd_start_time, format='%Y-%m-%d %H:%M:%S')
w = np.where(usd_t == usd_start_time_dt)[0]
if (len(w) == 0):
print('wrong time 6,', variety)
exit()
w = w[0]
start_price = (usd_df.loc[w, 'open'] + usd_df.loc[w, 'high'] + \
usd_df.loc[w, 'low'] + usd_df.loc[w, 'close']) / 4
end_price = (usd_df.loc[len(usd_df)-1, 'open'] + usd_df.loc[len(usd_df)-1, 'high'] + \
usd_df.loc[len(usd_df)-1, 'low'] + usd_df.loc[len(usd_df)-1, 'close']) / 4
w = np.where(usdcny_t == usd_start_time_dt)[0]
if (len(w) == 0):
print('wrong time 7,', variety)
exit()
w = w[0]
start_fx = (usdcny_df.loc[w, 'open'] + usdcny_df.loc[w, 'high'] + \
usdcny_df.loc[w, 'low'] + usdcny_df.loc[w, 'close']) / 4
end_fx = (usdcny_df.loc[len(usdcny_df)-1, 'open'] + usdcny_df.loc[len(usdcny_df)-1, 'high'] + \
usdcny_df.loc[len(usdcny_df)-1, 'low'] + 3*usdcny_df.loc[len(usdcny_df)-1, 'close']) / 6
price_chg = (end_price*end_fx - start_price*start_fx) / (start_price*start_fx)
cny_price_start = None
temp_fut_df = fut_df.loc[len(fut_df)-1, :]
cs = ['c1','c2','c3','c4','c5','c6','c7','c8','c9']
for c in cs:
if (temp_fut_df[c]['inst_id'] == inst_id):
cny_price_start = temp_fut_df[c]['close']
break
if cny_price_start is None:
print('cny_price_start is None')
exit()
if (auction == 1):
cny_price_end = cny_price_start * (1 + price_chg)
else:
cny_price_end = cny_price_start
print(inst_id, cny_price_end, str(round(price_chg*100,2))+'%')
col = opt_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 = []
call_price = []
call_delta = []
put_price = []
put_delta = []
for strike_str in strikes_str:
strike.append(float(strike_str))
call_price.append(opt_df.loc[len(opt_df)-1, pd.IndexSlice['C', strike_str, 'close']])
call_delta.append(opt_df.loc[len(opt_df)-1, pd.IndexSlice['C', strike_str, 'delta_c']])
put_price.append(opt_df.loc[len(opt_df)-1, pd.IndexSlice['P', strike_str, 'close']])
put_delta.append(opt_df.loc[len(opt_df)-1, pd.IndexSlice['P', strike_str, 'delta_c']])
strike = np.array(strike, dtype=float)
call_price = np.array(call_price, dtype=float)
call_delta = np.array(call_delta, dtype=float)
put_price = np.array(put_price, dtype=float)
put_delta = np.array(put_delta, dtype=float)
col = ['inst_id', 'price', 'delta']
data = [[inst_id, cny_price_end, 0]]
for i in range(len(strike)):
if (strike[i] <= cny_price_end*1.15) and (strike[i] >= cny_price_end*0.85):
# call
if exchange == 'shfe' or exchange == 'czce':
opt_inst_id = inst_id + 'C' + strikes_str[i]
else:
opt_inst_id = inst_id + '-C-' + strikes_str[i]
price = call_price[i] + call_delta[i]*(cny_price_end - cny_price_start)
data.append([opt_inst_id, price, call_delta[i]])
# put
if exchange == 'shfe' or exchange == 'czce':
opt_inst_id = inst_id + 'P' + strikes_str[i]
else:
opt_inst_id = inst_id + '-P-' + strikes_str[i]
price = put_price[i] + put_delta[i]*(cny_price_end - cny_price_start)
data.append([opt_inst_id, price, put_delta[i]])
temp_df = pd.DataFrame(columns=col, data=data)
df = pd.concat([df, temp_df], axis=0)
if (display):
# plot
opt_inst_id = np.array(temp_df.loc[1:, 'inst_id'], dtype=str)
price = np.array(temp_df.loc[1:, 'price'], dtype=float)
delta = np.array(temp_df.loc[1:, 'delta'], dtype=float)
w = np.where(np.logical_not(np.isnan(price)))[0]
opt_inst_id = opt_inst_id[w]
price = price[w]
delta = delta[w]
L = len(temp_df.loc[0, 'inst_id'])
c_strike = []
c_price = []
c_delta = []
p_strike = []
p_price = []
p_delta = []
for i in range(len(opt_inst_id)):
if 'C' in opt_inst_id[i][L:]:
if ('-' in opt_inst_id[i][L:]):
c_strike.append(float(opt_inst_id[i][L+3:]))
else:
c_strike.append(float(opt_inst_id[i][L+1:]))
c_price.append(price[i])
c_delta.append(delta[i])
if 'P' in opt_inst_id[i][L:]:
if ('-' in opt_inst_id[i][L:]):
p_strike.append(float(opt_inst_id[i][L+3:]))
else:
p_strike.append(float(opt_inst_id[i][L+1:]))
p_price.append(price[i])
p_delta.append(delta[i])
c_strike = np.array(c_strike, dtype=float)
c_price = np.array(c_price, dtype=float)
c_delta = np.array(c_delta, dtype=float)
sort = np.argsort(c_strike)
c_strike = c_strike[sort]
c_price = c_price[sort]
c_delta = c_delta[sort]
p_strike = np.array(p_strike, dtype=float)
p_price = np.array(p_price, dtype=float)
p_delta = np.array(p_delta, dtype=float)
sort = np.argsort(p_strike)
p_strike = p_strike[sort]
p_price = p_price[sort]
p_delta = p_delta[sort]
fig1 = figure(frame_width=1400, frame_height=300, tools=TOOLS, title=temp_df.loc[0, 'inst_id'])
fig1.line(c_strike, c_price, line_width=2, line_color='red', legend_label='CALL PRICE')
fig1.circle(x=c_strike, y=c_price, color='red', legend_label='CALL PRICE')
fig1.line(p_strike, p_price, line_width=2, line_color='darkgreen', legend_label='PUT PRICE')
fig1.circle(x=p_strike, y=p_price, color='darkgreen', legend_label='PUT PRICE')
fig2 = figure(frame_width=1400, frame_height=300, tools=TOOLS, x_range=fig1.x_range)
fig2.line(c_strike, c_delta, line_width=2, line_color='red', legend_label='CALL DELTA')
fig2.circle(x=c_strike, y=c_delta, color='red', legend_label='CALL DELTA')
fig2.line(p_strike, p_delta, line_width=2, line_color='darkgreen', legend_label='PUT DELTA')
fig2.circle(x=p_strike, y=p_delta, color='darkgreen', legend_label='PUT DELTA')
show(column(fig1, fig2))
path = os.path.join('D:\CTP', 'opt_price'+'.csv')
df.to_csv(path, encoding='utf-8', index=False)
if __name__=="__main__":
get_usd_price()
get_usd_cny()
# TODO: 早盘盘前竞价
xxx_price(len(sys.argv) == 1)
# update_au_ag_td_intraday_data()
pass