Strategy vault / Market making
Deep Market Making Handicap Control Trading Robot Market Making Tools
The strategy for the exchange. The general strategy is to obtain the current price and at the same time place buy and sell orders to increase depth. You can copy other market trends and refer to other trends.
The strategy for the exchange. The general strategy is to obtain the current price and at the same time place buy and sell orders to increase depth. You can copy other market trends and refer to other trends.
Source Python
# -*- coding: UTF-8 -*-
import requests
import time
import random
import hashlib
import sys
import threading
from api import *
symbol = sys.argv[1]
gap= float(sys.argv[2]) #Density/price difference
baseamount = float(sys.argv[3])
basebuy = float(sys.argv[4])
amount_add = float(sys.argv[5]) #Pending order increment (numeric)(number)
amount_add2 = float(sys.argv[6]) #Pending order increment (numeric)(number)
longperoidlimit = int(sys.argv[7])
bigbase = float(sys.argv[8]) #Large order base quantity
orderlimit = int(sys.argv[9]) #total order volume
api_key = sys.argv[10]
secret_key = sys.argv[11]
shortperoidlimit = 3 #High-frequency order volume
pre_short_id = [] #High flat id marker
pre_long_id = [] #Low frequency id marker
pre_big_id = 0
requests.packages.urllib3.disable_warnings()
def GetTicker():
def GetPV():
def GetDepth():
def GetSign(sign_str):
def GetOrders():
def create_order(side,price,amount):
def CancelOrder(order_id):
def Buy(price,amount):
def Sell(price,amount):
def GetRecords(symbol,period):
def GetPrecision():
def getrr():
def ordersend_shortperoid():
def ordersend_longperoid():
def send_big_order():
def cancel():
if __name__ == '__main__':
precision = GetPrecision()
print(precision)
i = 0
for x in precision:
if precision[i]['symbol'] == symbol:
pricedot = precision[i]['price_precision']
amountdot = precision[i]['amount_precision']
i += 1
pricegap = max(gap,pow(10,-pricedot))
threading_list = [ordersend_shortperoid,ordersend_longperoid,send_big_order,cancel]
threadingList = []
threadingDict = {}
for x in threading_list:
th = threading.Thread(target=x)
threadingList.append(th)
threadingDict[th.__dict__['_name']] = th.__dict__['_target']
th.start()
while True:
try:
time.sleep(200)
for i in threadingList:
if i.is_alive() is False:
threadingList.remove(i)
result = threadingDict.pop(i.name)
th = threading.Thread(target=result)
threadingList.append(th)
threadingDict[th.__dict__['_name']] = th.__dict__['_target']
th.start()
except Exception as e:
print('check error',e)Published by 3piggy on the FMZ strategy square · last modified 2019-12-10. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.
Build this with AgenKit
A spec is not a trading system. Paste this into your coding agent and AgenKit runs a specialist team — architecture, test-first build, risk gates, review — with paper trading on by default.
/agenkit build a market making bot with inventory skew limits and order deduplication from strategies/Deep-Market-Making-Handicap-Control-Trading-Robot-Market-Making-Tools.md- npx agenkit install engineering-kit in your project
- Paste the command into your agent and approve each phase
- Get tests, backtest, risk controls and a paper-trading switch
Understand it first
Before you run this, read the lesson behind the idea and how to backtest it honestly. Specs like this one are the survivors of an unknown number of trials; deflate their results accordingly.
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