High Frequency Intertemporal Arbitrage Strategy

Trading target: Bitcoin(BTC) Spread data: BTC Perpetual - BTC Quarterly (cointegration test omitted) Trading cycle: 1 minute Position matching:1:1 Trade type: same commodity long/short spread Conditions for opening a…

PythonArbitrage Pairs & stat-arb

Author: 巴啦啦小魔仙
Complexity: Starter · 75 lines
Parameters: 0

Jump to code View in vault Original on FMZ

Strategy Description

Trading target: Bitcoin(BTC)

Spread data: BTC Perpetual - BTC Quarterly (cointegration test omitted)

Trading cycle: 1 minute

Position matching:1:1

Trade type: same commodity long/short spread

Conditions for opening a long spread position: If the current account has no positions and the spread < (long-term spread level - threshold), go long the spread. That is: buy BTC perpetually and sell BTC quarterly.

Short spread opening conditions: If the current account has no positions and the spread > (long-term spread level + threshold), short the spread. That is: sell BTC perpetually and buy BTC quarterly.

Long spread closing conditions: If the current account holds BTC perpetual long orders, and holds BTC quarterly short orders, and the spread > the long-term spread level, the long spread will be closed. That is: sell flat BTC permanently, buy flat BTC quarterly.

Short spread closing conditions: If the current account holds a BTC perpetual short order and holds a BTC quarterly long order, and the spread is < the long-term spread level, the spread will be closed. That is: buy flat BTC permanently, sell flat BTC quarterly.

For example,Assume BTC Perpetual and BTC The spread of the current season long remains at 35 About. If on a certain day the price difference reaches 50 ,We expect the spread to return to 35 And below. Then it can be sold BTC Perpetual, buy at the same time BTC During the current season, go short this price difference. Vice versa, note BTC Perpetual and BTC The spread of the current season will always return to0Nearby (delivery at maturity), so when the price difference is positive, the priority is to go short the spread, and when the price difference is negative, the priority is to go long the spread.

Contact Information

:point_right: If you are interested in this strategy, please+V:Irene11229 (Click on my homepage to continuously update more strategies, and also access market analysis data from several leading exchanges)

Source Python

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import json
import time

from kumex.client import Trade, Market


class Hf(object):

    def __init__(self):
        # read configuration from json file
        with open('config.json', 'r') as file:
            config = json.load(file)

        self.api_key = config['api_key']
        self.api_secret = config['api_secret']
        self.api_passphrase = config['api_passphrase']
        self.sandbox = config['is_sandbox']
        self.symbol_a = config['symbol_a']
        self.symbol_b = config['symbol_b']
        self.spread_mean = float(config['spread_mean'])
        self.leverage = float(config['leverage'])
        self.size = int(config['size'])
        self.num_param = float(config['num_param'])
        self.trade = Trade(self.api_key, self.api_secret, self.api_passphrase, is_sandbox=self.sandbox)
        self.market = Market(self.api_key, self.api_secret, self.api_passphrase, is_sandbox=self.sandbox)

    def get_symbol_price(self, symbol):
        ticker = self.market.get_ticker(symbol)
        return float(ticker['price'])


if __name__ == '__main__':
    hf = Hf()
    while 1:
        # ticker of symbols
        price_af = hf.get_symbol_price(hf.symbol_a)
        price_bf = hf.get_symbol_price(hf.symbol_b)
        # position of symbols
        position_a = hf.trade.get_position_details(hf.symbol_a)
        position_a_qty = int(position_a['currentQty'])
        position_b = hf.trade.get_position_details(hf.symbol_b)
        position_b_qty = int(position_b['currentQty'])
        # interval of price
        new_spread = price_af - price_bf
        print('new_spread =', new_spread)

        if position_a_qty == position_b_qty == 0 and new_spread < (hf.spread_mean - hf.num_param):
            buy_order = hf.trade.create_limit_order(hf.symbol_a, 'buy', hf.leverage, hf.size, price_af + 1)
            print('buy %s,order id =%s' % (hf.symbol_a, buy_order['orderId']))
            sell_order = hf.trade.create_limit_order(hf.symbol_b, 'sell', hf.leverage, hf.size, price_bf - 1)
            print('sell %s,order id =%s' % (hf.symbol_b, sell_order['orderId']))
        elif position_a_qty == position_b_qty == 0 and new_spread > (hf.spread_mean + hf.num_param):
            buy_order = hf.trade.create_limit_order(hf.symbol_a, 'sell', hf.leverage, hf.size, price_af - 1)
            print('sell %s,order id =%s' % (hf.symbol_a, buy_order['orderId']))
            sell_order = hf.trade.create_limit_order(hf.symbol_b, 'buy', hf.leverage, hf.size, price_bf + 1)
            print('buy %s,order id =%s' % (hf.symbol_b, sell_order['orderId']))
        elif position_a_qty > 0 and position_b_qty < 0 and new_spread > hf.spread_mean:
            buy_order = hf.trade.create_limit_order(hf.symbol_a, 'sell', position_a['realLeverage'],
                                                    position_a_qty, price_af + 1)
            print('sell %s,order id =%s' % (hf.symbol_a, buy_order['orderId']))
            sell_order = hf.trade.create_limit_order(hf.symbol_b, 'buy', position_a['realLeverage'],
                                                     position_a_qty, price_bf - 1)
            print('buy %s,order id =%s' % (hf.symbol_b, sell_order['orderId']))
        elif position_a_qty < 0 and position_b_qty > 0 and new_spread < hf.spread_mean:
            buy_order = hf.trade.create_limit_order(hf.symbol_a, 'buy', position_a['realLeverage'],
                                                    position_a_qty, price_af - 1)
            print('buy %s,order id =%s' % (hf.symbol_a, buy_order['orderId']))
            sell_order = hf.trade.create_limit_order(hf.symbol_b, 'sell', position_a['realLeverage'],
                                                     position_a_qty, price_bf + 1)
            print('sell %s,order id =%s' % (hf.symbol_b, sell_order['orderId']))

        time.sleep(60)

Published by 巴啦啦小魔仙 on the FMZ strategy square · last modified 2021-03-04. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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