Delta strategy

The Delta strategy in market making involves adjusting the bot's position based on the delta of the asset, which is a measure of how much the price of an option or asset is expected to change when the underlying asset's price changes. In the context of market making, this strategy can be applied to manage inventory and hedge risk effectively.

▎How Delta Strategy Works in Market Making

1. Delta Definition:

   • Delta represents the sensitivity of an asset's price to changes in the underlying asset's price. In options trading, delta values range from 0 to 1 for calls and -1 to 0 for puts.

   • For market makers, maintaining a delta-neutral position means balancing buy and sell orders to minimize risk from price movements.

2. Position Management:

   • The bot continuously monitors its net delta position (the sum of all positions) and adjusts its orders accordingly.

   • If the bot has a positive delta (more long positions), it may place more sell orders to bring the delta back towards zero.

   • Conversely, if it has a negative delta (more short positions), it may place more buy orders.

3. Dynamic Adjustments:

   • The bot recalculates its delta regularly based on new trades and adjusts its market-making strategy to maintain a neutral delta position.

▎Example Code for Delta Strategy Feature

Here’s a simple implementation in Python that incorporates a delta strategy into a market-making bot:

import numpy as np from collections import deque

class MarketMakingBot: def init(self, window_size=10, target_delta=0): self.price_history = deque(maxlen=window_size) self.current_price = None self.spread = 0.01 # Example spread of 1% self.position = 0 # Net position (positive for long, negative for short) self.target_delta = target_delta

def update_price(self, new_price):
    self.current_price = new_price
    self.price_history.append(new_price)

def calculate_average_price(self):
    if not self.price_history:
        return None
    return np.mean(self.price_history)

def calculate_delta(self):
    # In this example, we'll assume delta is simply the net position
    return self.position

def adjust_position(self):
    current_delta = self.calculate_delta()
    if current_delta > self.target_delta:
        # Too many long positions, place more sell orders
        print("Adjusting Position: Placing more sell orders.")
        self.position -= 1  # Simulate selling
    elif current_delta < self.target_delta:
        # Too many short positions, place more buy orders
        print("Adjusting Position: Placing more buy orders.")
        self.position += 1  # Simulate buying

def place_orders(self):
    avg_price = self.calculate_average_price()
    if avg_price is not None:
        buy_price = avg_price * (1 - self.spread)
        sell_price = avg_price * (1 + self.spread)
        print(f"Placing Buy Order at: {buy_price}")
        print(f"Placing Sell Order at: {sell_price}")

def run(self, new_prices):
    for price in new_prices:
        self.update_price(price)
        self.place_orders()
        self.adjust_position()

# Example usage

if name == "main": bot = MarketMakingBot(window_size=10) sample_prices = [100, 101, 102, 99, 98, 100, 103, 104, 105, 102] bot.run(sample_prices)

▎Explanation of the Code

• MarketMakingBot Class: This class implements the market-making logic with a focus on delta management.

• update_price(): Updates the current price and appends it to the price history.

• calculate_average_price(): Computes the average price from the stored prices.

• calculate_delta(): Returns the current delta position based on the bot's inventory.

• adjust_position(): Adjusts the bot's position by placing more buy or sell orders based on the current delta compared to the target delta.

• place_orders(): Calculates and prints buy and sell prices based on the average price.

• run(): Simulates receiving new prices and processes them while managing position adjustments.