Quant Strategies #2: Statistical Arbitrage in Cryptocurrency Trading

Statistical arbitrage is a popular quantitative strategy in cryptocurrency trading that seeks to exploit price inefficiencies between related crypto assets.

By leveraging mathematical models and historical data, traders can identify and capitalize on temporary price discrepancies.

What is Statistical Arbitrage?

Statistical arbitrage involves trading pairs or baskets of cryptocurrencies that historically move together.

When their prices diverge beyond a typical range, the strategy assumes they will revert to their mean, allowing traders to profit from the convergence.

Key Steps to Implement Statistical Arbitrage:

Identify Cointegrated Pairs:

Select cryptocurrencies that have a strong historical correlation. For example, $BTC and $ETH often show similar market movements.

Calculate the Spread:

Determine the price difference between the two assets.

Example Calculation:

$BTC Price: $30,000

$ETH Price: $2,000

Spread = $30,000 - ($2,000 * 15) = $0 (assuming 1 BTC ≈ 15 ETH)

Set Trading Thresholds:

Define upper and lower limits for the spread.

If the spread widens beyond the upper limit, sell the overperforming asset and buy the underperforming one.

Reverse the actions when the spread narrows below the lower limit.

Execute Trades Automatically:

Use algorithms to monitor the spread in real-time and execute trades when thresholds are breached.

Example Scenario:

Suppose historical data shows that $BTC and $ETH typically maintain a 15:1 ratio.

Suddenly, $BTC rises to $31,000 while $ETH drops to $1,900.

The spread widens, triggering the strategy:

Sell $BTC at $31,000Buy $ETH at $1,900

When the prices revert to the mean ratio:

$BTC drops to $30,000$ETH rises to $2,000

Buy back $BTC and sell $ETH, locking in the profit from the convergence.

Backtesting Results:

Over the past six months:

Trades Executed: 20

Winning Trades: 14

Losing Trades: 6

Net Profit: +12%