# Avellaneda-Stoikov Market Making Strategy: A Practical Guide for Crypto Traders

# Avellaneda-Stoikov Market Making Strategy: A Practical Guide

Market making is a popular trading strategy used by traders to provide liquidity to financial markets. The goal of market making is to earn a profit by buying and selling a particular asset at bid and ask prices respectively. In this blog post, we will discuss the Avellaneda-Stoikov market making strategy and how it can be applied to the crypto market using code snippets.

# Avellaneda-Stoikov Market Making Strategy

The Avellaneda-Stoikov model is a popular model for predicting the dynamics of limit order books (LOBs). The model takes into account the impact of market liquidity and microstructure on LOB dynamics. The Avellaneda-Stoikov model has been widely used in traditional financial markets and has shown to be effective in predicting market dynamics during periods of high volatility.

The Avellaneda-Stoikov market making strategy is a market making strategy that utilizes the insights from the Avellaneda-Stoikov model. The strategy involves placing limit orders at a certain distance away from the current market price. These limit orders are then updated based on the market conditions and the insights provided by the Avellaneda-Stoikov model.

The Avellaneda-Stoikov market making strategy involves the following steps:

- Calculate the fair value of the asset using the Avellaneda-Stoikov model.
- Place limit orders at a certain distance away from the fair value.
- Update the limit orders based on the market conditions and the insights provided by the Avellaneda-Stoikov model.

# Implementing the Avellaneda-Stoikov Market Making Strategy in Python

To implement the Avellaneda-Stoikov market making strategy in Python, we will use the `ccxt`

library to interact with a cryptocurrency exchange. We will also use the `pandas`

library to handle data and the `matplotlib`

library to visualize the results.

The following code snippets demonstrate how to implement the Avellaneda-Stoikov market making strategy in Python:

`import ccxt`

import pandas as pd

import matplotlib.pyplot as plt

# Initialize exchange

exchange = ccxt.binance({

'apiKey': 'YOUR_API_KEY',

'secret': 'YOUR_SECRET_KEY',

})# Set trading parameters

symbol = 'BTC/USDT'

spread = 0.0005

amount = 0.01

num_levels = 10# Get market data

ohlcv = exchange.fetch_ohlcv(symbol, '1m')

df = pd.DataFrame(ohlcv, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])

df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')

df.set_index('timestamp', inplace=True)# Calculate fair value using Avellaneda-Stoikov model

df['mid_price'] = (df['high'] + df['low']) / 2

df['log_return'] = np.log(df['mid_price']).diff()

df['cumulative_volume'] = df['volume'].cumsum()

df['delta'] = 0.01 * df['cumulative_volume'] / df['mid_price']

df['fair_value'] = df['mid_price'] + spread * df['delta']

df.dropna(inplace=True)# Place limit orders

for i in range(num_levels):

bid_price = df['fair_value'].iloc[-1] * (1 - (i + 1) * spread)

ask_price = df['fair_value'].iloc[-1] * (1 + (i + 1) * spread)

exchange.create_limit_buy_order(symbol, amount, bid_price)

exchange.create_limit_sell_order(symbol, amount, ask_price)# Update limit orders

while True:

# Get market data

ohlcv = exchange.fetch_ohlcv(symbol, '1m')

df = pd.DataFrame(ohlcv, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])

df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')

df.set_index('timestamp', inplace=True) # Calculate fair value using Avellaneda-Stoikov model

df['mid_price'] = (df['high'] + df['low']) / 2

df['log_return'] = np.log(df['mid_price']).diff()

df['cumulative_volume'] = df['volume'].cumsum()

df['delta'] = 0.01 * df['cumulative_volume'] / df['mid_price']

df['fair_value'] = df['mid_price'] + spread * df['delta']

df.dropna(inplace=True) # Update limit orders

orders = exchange.fetch_open_orders(symbol)

for order in orders:

if order['side'] == 'buy':

bid_price = df['fair_value'].iloc[-1] * (1 - (order['info']['price'] / df['fair_value'].iloc[-1] - 1) / spread)

exchange.edit_limit_buy_order(order['id'], amount, bid_price)

elif order['side'] == 'sell':

ask_price = df['fair_value'].iloc[-1] * (1 + (order['info']['price'] / df['fair_value'].iloc[-1] - 1) / spread)

exchange.edit_limit_sell_order(order['id'], amount, ask_price) # Plot market data and fair value

plt.plot(df['mid_price'])

plt.plot(df['fair_value'])

plt.legend(['Mid Price', 'Fair Value'])

plt.show()

This code snippet demonstrates how to implement the Avellaneda-Stoikov market making strategy in Python using the Binance exchange. The code calculates the fair value of the asset using the Avellaneda-Stoikov model and places and updates limit orders based on the fair value.

# Conclusion

The Avellaneda-Stoikov market making strategy is a promising strategy for traders in the crypto market. This strategy utilizes the insights provided by the Avellaneda-Stoikov model to place and update limit orders based on the fair value of the asset. By implementing the strategy in Python using the `ccxt`

library, traders can easily apply this strategy to the cryptocurrency exchange of their choice.