Volatility strategy

The Volatility strategy in market making focuses on adjusting trading behavior based on the volatility of the asset. High volatility can present opportunities for profit but also increases risk, while low volatility may lead to tighter spreads and less trading activity.

▎How Volatility Strategy Works in Market Making

1. Volatility Measurement:

   • Market makers can measure volatility using various metrics, such as standard deviation of price movements over a certain period or using indicators like the Average True Range (ATR) or Bollinger Bands.

   • A higher volatility indicates larger price swings, which may warrant wider spreads to capture the increased risk.

2. Spread Adjustment:

   • In periods of high volatility, the bot might widen its bid-ask spread to account for the increased uncertainty and potential price movements.

   • Conversely, during low volatility, the bot could tighten the spread to attract more trades.

3. Order Placement:

   • The bot continuously adjusts its buy and sell orders based on the calculated volatility, ensuring that it remains competitive while managing risk.

▎Example Code for Volatility Strategy Feature

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

import numpy as np from collections import deque

class MarketMakingBot: def init(self, window_size=10, base_spread=0.01): self.price_history = deque(maxlen=window_size) self.current_price = None self.base_spread = base_spread # Base spread when volatility is low self.position = 0 # Net position (positive for long, negative for short)

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_volatility(self):
    if len(self.price_history) < 2:
        return 0
    return np.std(list(self.price_history))

def adjust_spread(self):
    volatility = self.calculate_volatility()
    # Adjust spread based on volatility
    if volatility > 1:  # Example threshold for high volatility
        spread_multiplier = 2  # Widen spread for high volatility
    else:
        spread_multiplier = 1  # Normal spread for low volatility
    
    return self.base_spread * spread_multiplier

def place_orders(self):
    avg_price = self.calculate_average_price()
    if avg_price is not None:
        spread = self.adjust_spread()
        buy_price = avg_price * (1 - spread)
        sell_price = avg_price * (1 + 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()

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 market-making logic with a focus on volatility 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_volatility(): Calculates the standard deviation of the price history to measure volatility.

• adjust_spread(): Adjusts the bid-ask spread based on the calculated volatility. In this example, if volatility exceeds a threshold (e.g., 1), it doubles the base spread.

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

• run(): Simulates receiving new prices and processes them while managing order placements based on volatility.