├── .github ├── FUNDING.yml └── ISSUE_TEMPLATE │ ├── bug_report.md │ ├── custom.md │ └── feature_request.md ├── LICENSE ├── Logic MM.jpg ├── MMFlow.jpeg ├── Market Making.py └── README.md /.github/FUNDING.yml: -------------------------------------------------------------------------------- 1 | https://www.buymeacoffee.com/vishnugovind 2 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/bug_report.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Bug report 3 | about: Create a report to help us improve 4 | title: '' 5 | labels: '' 6 | assignees: '' 7 | 8 | --- 9 | 10 | **Describe the bug** 11 | A clear and concise description of what the bug is. 12 | 13 | **To Reproduce** 14 | Steps to reproduce the behavior: 15 | 1. Go to '...' 16 | 2. Click on '....' 17 | 3. Scroll down to '....' 18 | 4. 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We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /Logic MM.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/vishnugovind10/MarketMaking/cd6f9729488fc347b0ce66f7af8183975bb8ebd3/Logic MM.jpg -------------------------------------------------------------------------------- /MMFlow.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/vishnugovind10/MarketMaking/cd6f9729488fc347b0ce66f7af8183975bb8ebd3/MMFlow.jpeg -------------------------------------------------------------------------------- /Market Making.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import json 3 | import datetime 4 | import requests 5 | 6 | # Define your API endpoints and keys for Binance and Kraken 7 | binance_api_url = 'https://api.binance.com/api/v3/' 8 | kraken_api_url = 'https://api.kraken.com/0/public/' 9 | 10 | # Fetch initial account balance from the exchange (Replace with actual API call) 11 | initial_balance_response = requests.get(binance_api_url + 'account', headers={'X-MBX-APIKEY': 'your-api-key'}) 12 | initial_balance_data = initial_balance_response.json() 13 | initial_balance = float(initial_balance_data['balances'][0]['free']) 14 | 15 | # Fetch trade data using API (Replace with actual API calls) 16 | trades_response = requests.get(binance_api_url + 'trades', params={'symbol': 'IOTABTC', 'limit': 1000}) 17 | trades_data = trades_response.json() 18 | 19 | # Create a DataFrame to store trade data 20 | trades = pd.DataFrame(trades_data) 21 | 22 | # Fetch order book data using API (Replace with actual API calls) 23 | order_book_response = requests.get(binance_api_url + 'depth', params={'symbol': 'IOTABTC'}) 24 | order_book_data = order_book_response.json() 25 | 26 | # Create a DataFrame to store order book data 27 | orders = pd.DataFrame(order_book_data) 28 | orders['asks'] = orders['asks'].apply(lambda x: x[0]) 29 | orders['bids'] = orders['bids'].apply(lambda x: x[0]) 30 | 31 | # Define the exchange fee rate for trading 32 | exchange_fee_rate = 0.0025 33 | 34 | # Initialize variables 35 | flag = 0 36 | max_loss_percentage = 5 # Example: 5% as maximum loss percentage 37 | current_balance = initial_balance 38 | 39 | # Lists to store trading positions and P&L 40 | col0 = [] # Bid positions 41 | col1 = [] # Ask positions 42 | col2 = [] # Trade prices 43 | col3 = [] # P&L column 44 | 45 | # Iterate through trade data 46 | for index, row_t in trades.iterrows(): 47 | if index % 1000 == 0: 48 | # Create a DataFrame to store positions and P&L data 49 | positions = pd.DataFrame(list(zip(col0, col1, col2, col3)), columns=['Bid', 'Ask', 'Price', 'P&L']) 50 | positions.to_csv('positions.csv', index=False) 51 | 52 | # Extract timestamp and last price from trade data 53 | timestamp2 = row_t['time'] 54 | lastprice_t = float(row_t['price']) 55 | 56 | # Iterate through order book data 57 | for index, row_o in orders.iterrows(): 58 | timestamp1 = row_o['lastUpdateId'] 59 | if timestamp1 > timestamp2: 60 | break 61 | highestbid = row_o["bids"] 62 | lowestask = row_o["asks"] 63 | 64 | # Calculate fair value and spread 65 | fv = (highestbid + lowestask) / 2 66 | spread = lowestask - highestbid 67 | spreadpercent = (spread / lowestask) * 100 68 | 69 | # Skip trade if spread is below transaction costs 70 | if spreadpercent <= exchange_fee_rate: 71 | continue 72 | 73 | # Calculate bid and ask prices 74 | bid = max(lastprice_t - spread / 2, fv - spread / 2) 75 | ask = min(lastprice_t + spread / 2, fv + spread / 2) 76 | 77 | # Perform buy or sell based on bid/ask 78 | if (flag == 0 or flag == 2) and bid == lastprice_t: 79 | col0.append(1) 80 | col1.append(0) 81 | col2.append(lastprice_t) 82 | flag = -1 83 | elif (flag == 0 or flag == 1) and ask == lastprice_t: 84 | col0.append(0) 85 | col1.append(1) 86 | col2.append(lastprice_t) 87 | flag = -1 88 | 89 | # Calculate P&L dynamically, update current balance, and check stop condition 90 | if flag == -1: 91 | pnl = (sum(col0) - sum(col1)) * (lastprice_t * exchange_fee_rate) 92 | col3.append(pnl) 93 | 94 | current_balance = initial_balance + sum(col3) # Update current balance 95 | 96 | # Calculate current P&L as a percentage 97 | current_pnl_percentage = ((current_balance - initial_balance) / initial_balance) * 100 98 | 99 | # Check stop condition 100 | if current_pnl_percentage <= -max_loss_percentage: 101 | print("Stop trading due to excessive loss.") 102 | positions = pd.DataFrame(list(zip(col0, col1, col2, col3)), columns=['Bid', 'Ask', 'Price', 'P&L']) 103 | positions.to_csv('positions.csv', index=False) 104 | exit() # Exit the algorithm 105 | 106 | if sum(col0) - sum(col1) > 10: 107 | flag = 1 108 | elif sum(col1) - sum(col0) > 10: 109 | flag = 2 110 | else: 111 | flag = 0 112 | 113 | # Create a final DataFrame with positions and P&L data 114 | positions = pd.DataFrame(list(zip(col0, col1, col2, col3)), columns=['Bid', 'Ask', 'Price', 'P&L']) 115 | positions.to_csv('positions.csv', index=False) 116 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Enhanced Crypto Market-Making Algorithm with Stop Condition 2 | 3 | ### Overview 4 | This repository contains an advanced crypto market-making trading algorithm that focuses on the "IOTA/BTC" trading pair on the Binance exchange. The algorithm is designed to leverage market-making strategies to profit from bid-ask spreads while effectively managing risks arising from price fluctuations and inventory changes. 5 | 6 | 7 | ![Flow Chart](https://github.com/vishnugovind10/MarketMaking/blob/main/Logic%20MM.jpg) 8 | 9 | 10 | ### Algorithm Highlights 11 | - Dynamic Initial Balance Calculation: The algorithm queries exchange APIs to determine the initial account balance, forming a reliable baseline for P&L calculations. 12 | - Advanced Order Book Analysis: Accurate market-making decisions are informed by in-depth order book analysis, optimizing bid and ask prices for optimal trade execution. 13 | - Real-time P&L Monitoring: Trades are executed dynamically, with the algorithm consistently updating the Profit and Loss (P&L) value after each trade. 14 | - Stop Condition Implementation: A stop condition mechanism is embedded to mitigate risk. If the current P&L falls below a predefined percentage of the initial balance, the algorithm halts trading operations. 15 | 16 | ### Instructions for Use 17 | 1. Replace API endpoints and keys in the code with actual credentials for Binance and Kraken exchanges. 18 | 2. Incorporate the code into your trading infrastructure, ensuring thorough testing in a controlled environment before deploying it for live trading. 19 | 3. Utilize the provided CSV output to monitor trading positions, P&L performance, and adherence to the stop condition. 20 | 21 | ### Important Notes 22 | - Use this code responsibly and after thorough testing. 23 | - The integrated stop condition feature provides a safeguard against significant losses, enhancing the algorithm's viability for successful crypto market-making endeavors. 24 | 25 | ### External Link 26 | If you are interested in learning more about Market Making, refer to my medium article: https://vishnugovind10.medium.com/unveiling-the-world-of-decentralized-finance-defi-and-decentralized-exchanges-dexes-2e180a440e52 27 | --------------------------------------------------------------------------------