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.
