Intramarket Indicator Differences | Algorithmic Crypto Trading Strategy in Python

Summary

This video explores intra-market differencing, a straightforward technique for crypto analysis, using Bitcoin and Ethereum as examples. It discusses the correlation between the two cryptocurrencies and introduces the concept of intermarket differencing by normalizing indicators like RSI and the difference between closing price and moving average, adjusted by the average true range. The video demonstrates how to code this normalized indicator and applies a simple trading strategy based on the intra-market difference indicator. The strategy's performance is evaluated across various parameter settings, showing robustness. The video concludes by suggesting that this analysis can be extended to other indicators and markets.

Takeaways

• 📈 The video discusses intermarket differencing, a technique in technical analysis used to compare two different markets or symbols.
• 🔗 The correlation between Bitcoin and Ethereum's hourly log returns is 0.84, indicating they often move together.
• 📊 Intermarket differencing can be done by measuring an indicator like RSI on both Bitcoin and Ethereum and then subtracting the readings to create a new indicator.
• 📉 The video uses the normalized closing price minus a moving average indicator as an example, which is scaled using the average true range (ATR) to account for different price scales and volatilities.
• 💻 The code provided in the video implements the normalized indicator using open, high, low, and close data, along with the moving average and ATR look back periods.
• 📌 The intermarket indicator is created by taking the difference between the indicators measured on both symbols, which should have the same scale for effective comparison.
• 📈 A simple trading strategy is proposed: going long on Ethereum when the intermarket difference indicator crosses above a threshold and holding until it returns to zero.
• 📊 The video presents a histogram of the intermarket difference to help determine an appropriate threshold for the trading strategy.
• 📉 The strategy's performance is evaluated across a range of parameter values, showing robustness with most parameter sets resulting in a profit factor above one.
• 🌐 Intermarket differencing can be applied to various indicators and across different markets, not just cryptocurrencies, providing a versatile tool for comparative analysis.

Q & A

What is intermarket differencing?

Intermarket differencing is a simple multi-symbol analysis technique that involves comparing two or more markets to identify when one market is doing something different compared to the others. It can be done by measuring the same indicator on different symbols and then subtracting the readings to create a new indicator.

Why are Bitcoin and Ethereum used as examples in the script?

Bitcoin and Ethereum are used as examples because they are two of the most prominent cryptocurrencies, and their prices tend to move together. This makes them suitable for demonstrating intermarket differencing, where the correlation between their hourly log returns is 0.84 from 2018 to 2023.

What is the correlation between the hourly log returns of Bitcoin and Ethereum from 2018 to 2023?

The correlation between the hourly log returns of Bitcoin and Ethereum from 2018 to 2023 is 0.84, indicating a strong positive correlation between the two cryptocurrencies.

Why is the RSI indicator used in the example of intermarket differencing?

The RSI indicator is used in the example because it is a widely recognized and used indicator in technical analysis. However, the script suggests that any indicator that is stationary and normalized could be used for intermarket differencing.

What is meant by 'stationary' and 'normalized' in the context of indicators for intermarket differencing?

In the context of intermarket differencing, 'stationary' means that the indicator should not have a trend over time, and 'normalized' means that the indicator should have the same scale for both symbols being compared to ensure a fair comparison.

How is the scaling issue addressed when using the close minus moving average indicator?

The scaling issue is addressed by dividing the difference between the close and a moving average by the average true range (ATR). This normalizes the scale of the indicator across different symbols with varying price levels and volatilities.

What is the purpose of using logarithmic prices in intermarket differencing?

Logarithmic prices are used to address scaling issues in intermarket differencing by allowing for a more comparable analysis of price movements across symbols with different price scales.

What is the average true range (ATR) and why is it used in the normalization process?

The average true range (ATR) is a technical analysis indicator that measures market volatility. It is used in the normalization process to account for different levels of volatility between symbols, ensuring that the intermarket differencing indicator is comparable across markets.

How is the 'threshold revert signal' function used in the strategy described in the script?

The 'threshold revert signal' function is used to generate trading signals based on the intermarket difference indicator. It enters a long position when the indicator crosses above a certain threshold and a short position when it crosses below the negative threshold, holding these positions until the indicator returns to zero.

What is the significance of the profit factor in the context of the strategy discussed in the script?

The profit factor is a measure of the strategy's performance, indicating the ratio of the total profits to the total losses. A profit factor above one suggests that the strategy is profitable, as it shows that the total profits outweigh the total losses.

How can the intermarket differencing technique be applied to different markets or indicators?

Intermarket differencing can be applied to various markets by comparing indicators across different symbols or even different asset classes. It can also be used with different indicators such as ADX, RSI, MACD, etc., to identify relative outperformance or underperformance between markets.