Average price

Market making bots typically utilize an average price mechanism to determine the optimal pricing for buy and sell orders. The average price helps in mitigating the effects of price volatility and allows the bot to make informed decisions based on historical price data.

▎How Average Price Works in Market Making

1. Price Calculation:

   • The average price is usually calculated based on a predefined time frame (e.g., last X trades, last Y minutes).

   • It can be a simple moving average (SMA) or an exponential moving average (EMA), depending on the strategy.

2. Order Placement:

   • The bot places buy and sell orders around this average price, typically at a certain spread.

   • This helps in capturing profits from the bid-ask spread.

3. Updating the Average Price:

   • The average price is updated continuously as new trades occur or at regular intervals.

   • This ensures that the bot adapts to changing market conditions.

4. Risk Management:

   • By using the average price, the bot can set stop-loss and take-profit levels more effectively.

▎Example Code for Average Price Feature

Here’s a simple implementation in Python for calculating the average price and placing orders:

import numpy as np from collections import deque

class MarketMakingBot: def init(self, window_size=10): self.price_history = deque(maxlen=window_size) # Store the last 'window_size' prices self.current_price = None self.spread = 0.01 # Example spread of 1%

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 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()

#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 encapsulates the market-making logic.

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

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

• place_orders(): Calculates buy and sell prices based on the average price and prints them out (in a real scenario, this would place actual orders).

• run(): Simulates receiving new prices and processes them.