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https://github.com/The-Swarm-Corporation/AutoHedge.git
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cleanup
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Kye Gomez
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import asyncio
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import csv
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from dataclasses import dataclass, field
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from datetime import datetime
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import json
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import logging
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import math
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import os
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import random
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import time
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from typing import Dict, List, Optional, Tuple
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import aiohttp
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import numpy as np
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import pandas as pd
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from loguru import logger
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import websockets
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# Market Making Strategy Configuration
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@dataclass
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class MarketMakingConfig:
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"""Configuration for market making strategy."""
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trading_pair: str = 'BTC/USDT' # Default trading pair
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total_capital: float = 10000.0 # Total capital to allocate
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spread_percentage: float = 0.001 # 0.1% spread
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order_size_percentage: float = 0.01 # 1% of total capital per order
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max_inventory_exposure: float = 0.2 # Max 20% of total capital in one asset
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rebalance_threshold: float = 0.1 # 10% deviation triggers rebalance
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min_profit_threshold: float = 0.002 # 0.2% minimum profit target
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@dataclass
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class MarketData:
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"""Holds real-time market data for a trading pair."""
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timestamp: float = field(default_factory=time.time)
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best_bid: float = 0.0
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best_ask: float = 0.0
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last_price: float = 0.0
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volume: float = 0.0
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class MarketMaker:
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"""Advanced Market Making Algorithm for Crypto Trading."""
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def __init__(self, config: MarketMakingConfig):
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"""
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Initialize the market maker with given configuration.
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Args:
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config (MarketMakingConfig): Configuration for market making strategy
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"""
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self.config = config
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self.market_data = MarketData()
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# Logging setup
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logger.add("market_maker.log", rotation="10 MB")
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# Trading state tracking
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self.current_inventory = {
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'base': 0.0, # e.g., BTC amount
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'quote': config.total_capital # e.g., USDT amount
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}
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# Order tracking
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self.active_orders: Dict[str, Dict] = {}
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# CSV logging setup
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self.csv_filename = f"market_making_{config.trading_pair}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv"
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self.initialize_csv_log()
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def initialize_csv_log(self):
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"""Initialize CSV log with headers."""
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with open(self.csv_filename, 'w', newline='') as csvfile:
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fieldnames = [
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'timestamp', 'event_type', 'price', 'amount',
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'base_inventory', 'quote_inventory', 'total_value'
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]
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writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
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writer.writeheader()
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async def fetch_market_data(self) -> MarketData:
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"""
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Fetch real-time market data from multiple free sources.
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Returns:
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MarketData: Current market data snapshot
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"""
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async with aiohttp.ClientSession() as session:
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try:
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# Coinbase public ticker (free, no API key)
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async with session.get(f"https://api.coinbase.com/v2/prices/{self.config.trading_pair.replace('/', '-')}/spot") as response:
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if response.status == 200:
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data = await response.json()
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last_price = float(data['data']['amount'])
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# Simulating bid/ask spread
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spread = last_price * self.config.spread_percentage
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return MarketData(
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timestamp=time.time(),
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best_bid=last_price - spread/2,
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best_ask=last_price + spread/2,
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last_price=last_price,
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volume=0.0 # Coinbase API doesn't provide volume in free tier
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)
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except Exception as e:
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logger.error(f"Market data fetch error: {e}")
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# Fallback to alternative free source if first fails
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try:
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async with session.get(f"https://api.binance.com/api/v3/ticker/price?symbol={self.config.trading_pair.replace('/', '')}") as response:
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if response.status == 200:
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data = await response.json()
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last_price = float(data['price'])
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spread = last_price * self.config.spread_percentage
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return MarketData(
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timestamp=time.time(),
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best_bid=last_price - spread/2,
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best_ask=last_price + spread/2,
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last_price=last_price,
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volume=0.0
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)
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except Exception as inner_e:
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logger.error(f"Backup market data fetch error: {inner_e}")
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# Completely fallback to a simulated market data
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return MarketData(
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timestamp=time.time(),
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best_bid=50000.0,
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best_ask=50100.0,
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last_price=50050.0,
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volume=100.0
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)
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def calculate_order_size(self) -> float:
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"""
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Calculate appropriate order size based on current strategy and inventory.
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Returns:
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float: Order size in base asset
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"""
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total_value = (
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self.current_inventory['base'] * self.market_data.last_price +
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self.current_inventory['quote']
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)
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order_size = (
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self.config.total_capital *
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self.config.order_size_percentage /
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self.market_data.last_price
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)
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# Risk management: Ensure order size doesn't exceed max inventory exposure
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max_order_size = (
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self.config.total_capital *
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self.config.max_inventory_exposure /
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self.market_data.last_price
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)
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return min(order_size, max_order_size)
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def simulate_order(self, order_type: str, price: float, amount: float) -> Dict:
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"""
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Simulate an order execution without actual trading.
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Args:
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order_type (str): 'buy' or 'sell'
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price (float): Order price
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amount (float): Order amount
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Returns:
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Dict: Simulated order details
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"""
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order_id = f"sim_{int(time.time() * 1000)}"
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if order_type == 'buy':
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# Simulate buying
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if self.current_inventory['quote'] >= price * amount:
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self.current_inventory['base'] += amount
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self.current_inventory['quote'] -= price * amount
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logger.info(f"Simulated BUY: {amount} @ {price}")
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else:
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logger.warning("Insufficient funds for buy order")
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return {}
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elif order_type == 'sell':
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# Simulate selling
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if self.current_inventory['base'] >= amount:
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self.current_inventory['base'] -= amount
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self.current_inventory['quote'] += price * amount
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logger.info(f"Simulated SELL: {amount} @ {price}")
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else:
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logger.warning("Insufficient base asset for sell order")
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return {}
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# Log to CSV
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with open(self.csv_filename, 'a', newline='') as csvfile:
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writer = csv.DictWriter(csvfile, fieldnames=[
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'timestamp', 'event_type', 'price', 'amount',
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'base_inventory', 'quote_inventory', 'total_value'
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])
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writer.writerow({
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'timestamp': time.time(),
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'event_type': order_type.upper(),
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'price': price,
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'amount': amount,
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'base_inventory': self.current_inventory['base'],
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'quote_inventory': self.current_inventory['quote'],
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'total_value': (self.current_inventory['base'] * price + self.current_inventory['quote'])
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})
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return {
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'order_id': order_id,
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'type': order_type,
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'price': price,
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'amount': amount,
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'status': 'filled'
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}
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async def market_making_strategy(self):
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"""
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Core market making strategy implementation.
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Continuously fetches market data and places orders.
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"""
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while True:
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try:
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# Fetch latest market data
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self.market_data = await self.fetch_market_data()
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# Calculate order parameters
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order_size = self.calculate_order_size()
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# Place buy order slightly below market
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buy_price = self.market_data.best_bid * (1 - self.config.spread_percentage/2)
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self.simulate_order('buy', buy_price, order_size)
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# Place sell order slightly above market
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sell_price = self.market_data.best_ask * (1 + self.config.spread_percentage/2)
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self.simulate_order('sell', sell_price, order_size)
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# Wait before next iteration
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await asyncio.sleep(5) # Adjust based on desired trading frequency
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except Exception as e:
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logger.error(f"Market making strategy error: {e}")
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await asyncio.sleep(10) # Pause on error
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async def run(self):
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"""
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Main execution method to start market making process.
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"""
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logger.info(f"Starting Market Making for {self.config.trading_pair}")
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await self.market_making_strategy()
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def main():
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"""
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Main entry point for the market making algorithm.
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Allows configuration of trading pairs and capital.
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"""
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# Example configurations
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configs = [
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MarketMakingConfig(trading_pair='BTC/USDT', total_capital=10000.0),
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MarketMakingConfig(trading_pair='ETH/USDT', total_capital=5000.0)
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]
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async def run_market_makers():
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market_makers = [MarketMaker(config) for config in configs]
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await asyncio.gather(*[mm.run() for mm in market_makers])
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asyncio.run(run_market_makers())
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if __name__ == "__main__":
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main()
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# Backtest Evaluation Script
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def backtest_market_maker(historical_data_path: str, config: MarketMakingConfig):
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"""
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Backtest the market making strategy on historical data.
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Args:
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historical_data_path (str): Path to CSV with historical price data
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config (MarketMakingConfig): Market making configuration
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Returns:
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Dict: Performance metrics
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"""
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# Load historical data
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df = pd.read_csv(historical_data_path)
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# Initialize backtest state
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initial_capital = config.total_capital
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current_inventory = {
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'base': 0.0,
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'quote': initial_capital
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}
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# Tracking variables
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trades = []
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# Simulate strategy
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for _, row in df.iterrows():
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current_price = row['close']
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# Market making logic (simplified)
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spread = current_price * config.spread_percentage
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buy_price = current_price - spread/2
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sell_price = current_price + spread/2
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order_size = (initial_capital * config.order_size_percentage) / current_price
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# Simulate buy
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if current_inventory['quote'] >= buy_price * order_size:
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current_inventory['base'] += order_size
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current_inventory['quote'] -= buy_price * order_size
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trades.append({
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'type': 'BUY',
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'price': buy_price,
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'amount': order_size
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})
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# Simulate sell
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if current_inventory['base'] >= order_size:
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current_inventory['base'] -= order_size
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current_inventory['quote'] += sell_price * order_size
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trades.append({
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'type': 'SELL',
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'price': sell_price,
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'amount': order_size
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})
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# Calculate performance
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final_value = (
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current_inventory['base'] * df.iloc[-1]['close'] +
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current_inventory['quote']
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)
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return {
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'initial_capital': initial_capital,
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'final_value': final_value,
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'total_return_percentage': ((final_value - initial_capital) / initial_capital) * 100,
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'total_trades': len(trades)
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}
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# Example usage for backtest
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if __name__ == "__main__":
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# Assuming you have a historical price data CSV
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backtest_results = backtest_market_maker(
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'historical_prices.csv',
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MarketMakingConfig(trading_pair='BTC/USDT')
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)
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print(json.dumps(backtest_results, indent=2))
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