Statistical Arbitrage In CryptoCurrency markets with Python Code to experiment

Statistical arbitrage models such as pairs trading has a rich history that dates back to the 1980s, involving major Wall Street banks like Morgan Stanley. Academic research extensively explores this strategy, suggesting various techniques that appear promising based on historical data. However, translating these findings into live markets isn’t always straightforward. Statistical relationships can break down or change unpredictably.

Many pair trading strategies can be automated using computers. However, not all strategies are equally suitable for trading due as some may work well and some don’t. Some work well for a while then stop and another model might work better. In fast-paced markets, simple models often outperform complex ones that might suffer from delays in decision-making.

Key stages in pair trading models:

1. Identifying suitable pairs: This involves finding assets based on similar business models or similar industries/sectors etc. The code example below uses BNB and ETH as two coins that are similar. In this example they are both L1 Crypto assets. Other pairings need to be logically thought out first. E.g. — STORJ and FIL both Blockchain storage related coins.
2. Timeseries modelling: This involves…