def __init__(self, *args, **kwargs):
self.api = ccxt.deribit()
super().__init__(*args, **kwargs)
instrument_name = {
'instrument_name': self.market,
}
currency = {'currency': self.market[:3]}
self.position = self.api.private_get_get_position(instrument_name)
self.account = self.api.private_get_get_account_summary(currency)
def ex_api(self):
if self.ex == "ftx":
exchange = ccxt.ftx({
'apiKey': self.apiKey,
'secret': self.secret,
'enableRateLimit': True
})
elif self.ex == "deribit":
exchange = ccxt.deribit({
'apiKey': self.apiKey,
'secret': self.secret,
'enableRateLimit': True,
"urls": {
"api": "https://test.deribit.com"
}
})
return exchange
import ccxt
import random
import numpy as np
import pandas as pd
import datetime as dt
from math import exp, sqrt
from py_vollib.black_scholes_merton.implied_volatility import implied_volatility
from resources import pricing
dbt = ccxt.deribit()
def get_iv(row, side):
try:
iv = implied_volatility(
row['{}_price'.format(side)] * row['index_price'],
row['index_price'], row['strike'], row['until_expiry'],
row['interest_rate'], row['q'], row['flag'])
iv = round(100 * iv, 2)
except Exception as e:
print(e)
iv = np.nan
return iv
def get_iv_custom(row, side):
try:
if not np.isnan(row['mid_price']):
iv = pricing.get_implied_vol(
row['{}_price'.format(side)] * row['index_price'],
import ccxt
import credentials
import time
# import pandas as pd
import numpy as np
import sys
exchange = ccxt.deribit({
'apiKey': credentials.client_id,
'secret': credentials.client_secret
})
# _perpetual = 'BTC-PERPETUAL'
# _sep = 'BTC-25SEP20'
# _dec = 'BTC-25DEC20'
# _mar = 'BTC-26MAR21'
# delay = 1
#
#
# def order_book(_symbol):
# count_loop = 1
# while True:
# orderBook = exchange.fetch_order_book(_symbol, 5)
# orderBook_bid = orderBook['bids']
# orderBook_ask = orderBook['asks']
# list_bid = pd.DataFrame(orderBook_bid)
# list_ask = pd.DataFrame(orderBook_ask)
# list_bid.rename(columns={0: 'Price', 1: 'Size'}, inplace=True)
# list_ask.rename(columns={0: 'Price', 1: 'Size'}, inplace=True)
# # print("Ask")
# # print(list_ask)
# time.sleep(delay)
# id_index = pandas.concat(raw_index_pnl)
# id_trend = pandas.concat(raw_bt_pnl)
# trade_count = pandas.Series(raw_bt_count)
# daily_w1 = pandas.DataFrame(raw_index_w1)
# rolling_w1 = daily_w1.rolling(20).mean()
# daily_spot = pandas.DataFrame([i.sum().rename(i.index[0].date()) for i in raw_spot_pnl])
# daily_index = pandas.DataFrame([i.sum().rename(i.index[0].date()) for i in raw_index_pnl])
# daily_trend = pandas.DataFrame([i.sum().rename(i.index[0]) for i in raw_bt_pnl])
#
# agg_trend = daily_trend.mul(daily_w1.shift(1).fillna(1 / daily_w1.shape[1])).sum(axis=1)
# print(pandas.Series({'ret': agg_trend.mean() * 260,
# 'vol': agg_trend.std() * (260 ** 0.5),
# 'sharpe': agg_trend.mean() / agg_trend.std() * (260 ** 0.5),
# 'neg_days': len(agg_trend[agg_trend < 0]),
# 'total_days': len(agg_trend),
# 'mdd': (1 - agg_trend.cumsum().div(agg_trend.cumsum().cummax())).max(),
# 'daily_trade_count': trade_count.mean()}))
# agg_trend.cumsum().plot()
with open('pickle', 'rb') as f:
import pickle
agg_trend = pickle.load(f)
retu = (agg_trend.cumsum() / 50 + 1).pct_change()
fig = pf.create_returns_tear_sheet(retu, return_fig=True)
fig.savefig('/Users/sweetdreams/code/fintend/alpha/alpha.png')
plt.show()
import ccxt
d = ccxt.deribit().fetch_ohlcv