In this post, we will delve into the application of machine learning algorithms, specifically Decision Trees and Random Forests, for developing cryptocurrency trading strategies. Topics covered include:
- Strategy ideation and implementation
- Technical indicators and feature engineering
- Data mining and preprocessing
- Backtesting and performance metrics
- Limitations and future directions
We will explore how these machine-learning techniques, combined with Python libraries and tools like Scikit-Learn and VectorBt, can be used to build robust, data-driven trading systems for highly volatile cryptocurrency markets.
Who is this blog for?
This blog is for you if you are motivated by:
- Ideation: Exploring innovative ways to utilise machine learning in quantitative trading and technical analysis.
- Implementation: Learning step-by-step approaches to creating, testing, and refining trading strategies using algorithms like Decision Trees and Random Forests.
- Performance Optimisation: Understanding metrics such as Sharpe Ratio, Profit Factor, and Win Rate to evaluate trading strategy efficiency.
Reading Level: Intermediate to Advanced
Prerequisites
Before diving into this blog, you should ensure the following:
- You are aware of practical examples of how machine learning is applied in trading strategies, such as in the EPAT projects:
- Predicting Stock Trends with Technical Analysis and Random Forests: Read here: https://blog.quantinsti.com/predicting-stock-trends-technical-analysis-random-forests/
- Building a Random Forest Regression Model for Forex: Read here: https://blog.quantinsti.com/building-random-forest-regression-model-forex-project-christos/
- Algo Trading Project Presentation Highlights: Watch and explore: https://blog.quantinsti.com/algo-trading-epat-projects-13-april-2021/
2. You have a basic understanding of algorithmic trading and technical analysis.
3. You are familiar with how strategies are built using machine learning models such as Decision Trees and Random Forests and know how to apply these concepts in trading.
4. You have read about cryptocurrency trading strategies, particularly algorithmic trading with cryptocurrency.
5. You are aware of practical examples and case studies where machine learning is applied in trading, such as Machine Learning with Decision Trees in Trading.
6. Additionally, you have explored the use of technical indicators in trading strategies, covered in detail in Using Technical Indicators for Algorithmic Trading.
By covering these fundamentals, you’ll be better equipped to understand and implement the concepts discussed in this blog.
Strategy Idea
The idea is to use "machine learning in trading" and its techniques like Decision Trees or other algorithms, if better one is found during research for Buying, Holding, and Selling cryptocurrencies.
The decision tree model is trained on historical data using a set of technical indicators and statistical relationships between these indicators and prices as inputs. The model then learns to make trading decisions (buy or sell signals) based on these inputs or a subset of these inputs.
The initial Idea is to use Decision Trees and compare it with other models mentioned in the coursework, with a final possibility of combining them to yield better results. Ultimately the goal is to have a high win rate and Sharpe ratio as compared to what I have achieved in the paper with stocks that I have mentioned below for cryptocurrencies, as it is easier to go long and short on crypto, and there is higher volatility in this market.
I have already worked on a Decision Tree based long only strategy for trading stocks in the NIFTY50 index after reading about a similar strategy from the textbook given in the course.
While it had a good Sharpe ratio, it’s win rate in the testing data was around ~48.15% and it was a long only strategy. I want to build a bidirectional strategy [long and short] to improve win rate while maintaining or increasing the Sharpe ratio, here is the link to the paper that I wrote about the strategy for stocks: https://arxiv.org/pdf/2405.13959.
Intraday trading of Bitcoin using technical indicators and Random Forests
Project Abstract
This article aims to explore the effectiveness of Random Forests in developing intraday trading strategies using established technical indicators for the Bitcoin-US Dollar (BTC-USD) pair.
Unlike traditional methods that depend on a static rule set derived from combinations of technical indicators formulated by human traders, the proposed approach utilizes Random Forests to generate trading rules, potentially enhancing trading performance and efficiency.
By rigorously backtesting the strategy, a trader can ascertain the viability of employing the rules generated by the Random Forests algorithm for any market. Random Forest-based strategies have been observed to outperform the simple buy-and-hold strategy in various instances.
The findings underscore the proficiency of Random Forests as a powerful tool for augmenting intraday trading performance. A rules-based strategy becomes more important in highly volatile Cryptocurrency markets.
Dataset
The Dataset will be intraday data 1 minute OHLCV data of BTCUSD [Bitcoin USD] or
BTCUSDT [Bitcoin Tether] for at least the last two years.
Project Motivation
Intraday trading involves executing buy and sell orders within the same day to capitalise on minor price fluctuations in the market, accumulating small profits over the trading period. Technical analysis is a well-established method in intraday trading that employs historical market data to generate indicators, recognise patterns, and make trading decisions based on the identified patterns.
However, conventional technical analysis methods rely on a fixed set of rules based on combinations of technical indicators, which can be time-consuming to develop and may not perform consistently across all assets. Moreover, these methods may not account for individual asset characteristics, leading to suboptimal trading decisions.
Previously, I have worked on a decision tree-based strategy for the equities market [1]. This strategy utilized a set of technical indicators across various stocks and was a long-only strategy. Inspired by this experience, I decided to develop a strategy for the cryptocurrency market, specifically focusing on the Bitcoin-US Dollar (BTC-USD) pair.
Due to the highly volatile nature of cryptocurrencies and the larger datasets involved, a decision tree-based strategy did not perform well in backtesting. To address this challenge, I upgraded the model to Random Forests, an ensemble learning method that combines multiple decision trees to improve predictive accuracy and robustness.
The cryptocurrency market presents an appealing opportunity for several reasons. Firstly, it allows for both long and short positions, providing more flexibility in trading strategies. Secondly, the market operates 24/7, offering a higher frequency of trading opportunities compared to traditional equity markets. These factors motivated me to explore algorithmic trading strategies in the cryptocurrency market using Random Forests.
Data Mining
To develop the algorithmic trading strategy for the BTC-USD market, historical data is essential. In this project, the data was obtained from Alpaca, a platform that provides free access to cryptocurrency data through its API. The API offers 1-minute level OHLC (Open, High, Low, Close) data. A dataset spanning two years was collected, comprising approximately 900,000 rows of 1-minute OHLC data for the BTC-USD pair. This extensive data set allows for a comprehensive analysis of the market, enabling the development of a robust trading strategy.
Data Analysis
With the collected OHLC data, various technical indicators were computed to capture the underlying market trends and patterns. These indicators serve as features for the Random Forests model, enabling it to generate. The input features and indicators used for the model are listed below:
- Returns [percent change]
- 15 period percent change
- Relative Strength Index [RSI]
- Average Directional Index [ADX]
- Simple Moving Average [SMA]
- Ratio between SMA and Close Price
- Correlation between SMA and Close Price
- Volatility — Standard deviation of returns
- Standard deviation of 15 period returns
The output which the model predicts on is the future percent change which is just the next return value [greater than 0 -> 1, 0 = 0, lower than 0 -> -1].
Key Findings
When it comes to random forests, there are many hyperparameters, the most important are:
- n_estimators — The number of estimators/decision trees in the model.
- max_tree_depth — The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples.
- criterion — can be either “gini”, “entropy”, “log_loss”
The gini criterion was used for the model and the maximum tree depth was set to None, so the model can expand the trees as necessary. As for the number of estimators, I have tested various values and have settled on 11. Odd number of estimators have worked better than even number of estimators in my analysis.
I have included charts showing various key performance indicators in relation to the number of estimators below. In the code repository, a report can be found which lists various metrics of the strategy in comparison to the buying-and-holding the asset itself [Filename: Random-Forest-BTCUSD.html]. A summary of important metrics of the strategy:
- Sharpe Ratio: 4.47
- Total Return: 367.05%
- Max Drawdown: -22.93%
- Win Rate: 53.53%
- Profit Factor: 1.06
Challenges/Limitations
Although the API also provides volume data, it was observed that the volume was zero for most of the rows. This inconsistency in volume data could be attributed to data quality issues (I was using the free API after all). As a result, volume and volume-based indicators were excluded from the strategy development process to ensure the reliability and robustness of the trading signals. Addition of volume based indicators might have been useful as it proved useful for my previous equity based strategy.
Implementation Methodology (if live/practical project)
For this project, the Random Forest Classifier model was created using the Scikit Learn library. The vectorized backtesting for the strategy was performed using the VectorBt library. The code is explained and can be found in the linked repo [Filename: backtest_script.py]. Some of the generated trees of the model are given below:
Conclusion
The results demonstrated that the Random Forest-based strategy outperformed the simple buy-and-hold strategy, showcasing the potential of Random Forests as a valuable tool for enhancing intraday trading performance in the cryptocurrency market.
Future work includes further hyperparameter tuning of the Random Forests model, incorporating additional features, and exploring other ensemble learning methods to improve the strategy's performance. Additionally, extending the strategy to other cryptocurrency pairs and assessing its performance in different market conditions could provide valuable insights for traders seeking to refine their trading strategies.
In conclusion, the proposed algorithmic trading strategy using Random Forests offers a promising approach for traders looking to capitalize on the unique opportunities presented by the cryptocurrency market.
Annexure/Codes
[1] GitHub Repository: https://github.com/sharathnirmala16/btc-ml-epat-project
Bibliography
[1] Daniya, T., et al. “Classification and regression trees with Gini Index.” Advances in Mathematics: Scientific Journal, vol. 9, no. 10, 23 Sept. 2020, pp. 8237–8247, https://doi.org/10.37418/amsj.9.10.53
[2] Shah, Ishan, and Rekhit Pachanekar. “Chap-ter 12 - Decision Trees.” Machine Learning in Trading, QuantInsti Quantitative Learning Pvt. Ltd., Mumbai, Maharastra, 2023, pp. 143–155.
[3] Filho, Mario. “Do Decision Trees Need Feature Scaling or Normalization?” Forecastegy, 24 Mar. 2023, forecastegy.com/posts/do-decision-trees-need-feature-scaling-ornormalization/#:~:text=In%20general%2C%20no.,as%20we%27ll% 20see%20later
[4] Shafi, Adam. “Random Forest Classification with Scikit-Learn.” DataCamp, DataCamp, 24 Feb. 2023, www.datacamp.com/tutorial/random-forests-classifier-python.
[5] “Randomforestclassifier.” Scikit, scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html. Accessed 23 July 2024.
[6] My preprint paper which is yet to be published: https://arxiv.org/pdf/2405.13959
Project Summary
In this project, I explored the effectiveness of Random Forests in developing intraday trading strategies for the Bitcoin-US Dollar (BTC-USD) pair using technical indicators. Unlike traditional methods, I utilized Random Forests to generate trading rules, aiming to enhance performance and efficiency. I developed the strategy using two years of 1-minute OHLC data from Alpaca, with various technical indicators as features. The strategy I developed achieved a Sharpe Ratio of 4.47 and a total return of 367.05%, outperforming a simple buy-and-hold approach. I faced challenges with inconsistent volume data, hence I excluded volume from the analysis.
NOTE: This project demonstrates the theoretical approach to applying Random Forests in trading. It shouldn't be applied by itself in the markets as it trades quite frequently and is impractical in its current state. It should only be used as a conceptual base for building more advanced strategies, which I am currently working on.
If you wish to learn more about Machine Learning in trading, you must explore the learning track titled "Learning Track: Machine Learning & Deep Learning in Trading Beginners". This bundle of courses is highly recommended for those interested in machine learning and its applications in trading. From data cleaning aspects to predicting the correct market trend and optimising AI models, these courses are perfect for beginners.
File in the download
- Machine Learning to generate intraday Buy and Sell Signals for Cryptocurrency- Python notebook
About the Author
My name is Sharath Chandra Nirmala, and I'm from Hyderabad, India. I completed my Bachelor of Engineering in Computer Science and Engineering from the National Institute of Engineering, Mysuru in 2024. Currently, I'm working at Fidelity Investments, India as an Executive Graduate Trainee—Full Stack Engineer in the Asset Management Technology business unit. I'm passionate about coding, machine learning, and finance, which naturally led me to algorithmic trading. Feel free to connect with me on LinkedIn: https://www.linkedin.com/in/snirmala20/ or check out my projects on GitHub: https://github.com/sharathnirmala16/.
Disclaimer:The information in this project is true and complete to the best of our Student’s knowledge. All recommendations are made without guarantee on the part of the student or QuantInsti®. The student and QuantInsti® disclaim any liability in connection with the use of this information. All content provided in this project is for informational purposes only and we do not guarantee that by using the guidance you will derive a certain profit.
