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High-frequency cross-currency arbitrage

Author: It's all right., Date: 2020-05-12 21:55:43
Tags:

The strategy

Bitcoin (BTC) is the most traded currency in the world.

The price difference data: BTC Sustainable - BTC Quarterly (excluding Co-Integrity Check)

Trading cycle: 1 minute

头寸匹配:1:1

Type of transaction: cross-section of the same variety

If the current account has no holdings and the price difference is < (long-term price difference level - threshold), then the price difference is made.

If the current account does not have any holdings and the spread is > (long-term spread level + threshold), then the spread is made; i.e.: sell BTC permanently, buy BTC quarterly.

If the current account holds a number of BTC perpetual orders and holds a quarterly BTC blank order, and the spread is > the level of the long-term spread, then the spread is a perpetual spread.

If the current account holds BTC perpetual and holds more than one BTC per quarter, and the price difference is

For example:Assuming that the price differential between BTC and BTC in a season is around 35 for the long term. If the price differential reaches 50 in a day, we expect the price differential to return to 35 or below at some point in the future. Then we can sell BTC permanently and buy BTC in the season to make this difference.

How to contact

:point_right: If you are interested in this policy, please +V:Irene11229 (Click on my homepage, I'll keep updating more strategies, and get market analysis data from some of the top exchanges)


#!/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)

More

takerAre you saying the opposite, the permanent price is almost the same as the spot price, the price is much higher, it must be the quarterly price, how can it be a quarterly buy, should be a quarterly buy, sell?