This comprehensive course focuses on algorithmic trading, machine learning, and quantitative strategies using Python. It introduces participants to three distinct trading strategies: an unsupervised learning strategy using S&P 500 data and K-means clustering, a Twitter sentiment-based strategy for NASDAQ 100 stocks, and an intraday strategy employing a GARCH model for volatility prediction on simulated data. The course covers data preparation, feature engineering, backtesting strategies, and the role of machine learning in trading, while emphasizing that the content is for educational purposes only and not financial advice. Practical steps for implementing these strategies in Python are demonstrated, including data download, indicator calculation, and portfolio construction and analysis.
Podcast
Listen or Download Podcast – Algorithmic Trading: Machine Learning
Algorithmic Trading Fundamentals and Opportunities
Based on the sources, here is a discussion of algorithmic trading basics:
Algorithmic trading is defined as trading on a predefined set of rules. These rules are combined into a strategy or a system. The strategy or system is developed using a programming language and is run by a computer.
Algorithmic trading can be used for both manual and automated trading. In manual algorithmic trading, you might use a screener developed algorithmically to identify stocks to trade, or an alert system that notifies you when conditions are triggered, but you would manually execute the trade. In automated trading, a complex system performs calculations, determines positions and sizing, and executes trades automatically.
Python is highlighted as the most popular language used in algorithmic trading, quantitative finance, and data science. This is primarily due to the vast amount of libraries available in Python and its ease of use. Python is mainly used for data pipelines, research, backtesting strategies, and automating low complexity systems. However, Python is noted as a slow language, so for high-end, complicated systems requiring very fast trade execution, languages like Java or C++ might be used instead.
The sources also present algorithmic trading as a great career opportunity within a huge industry, with potential jobs at hedge funds, banks, and prop shops. Key skills needed for those interested in this field include Python, backtesting strategies, replicating papers, and machine learning in trading.
Machine Learning Strategies in Algorithmic Trading
Drawing on the provided sources, machine learning plays a significant role within algorithmic trading and quantitative finance. Algorithmic trading itself involves trading based on a predefined set of rules, which are combined into a strategy or system developed using a programming language and run by a computer. Machine learning can be integrated into these strategies.
Here’s a discussion of machine learning strategies as presented in the sources:
Role and Types of Machine Learning in Trading
Machine learning is discussed as a key component in quantitative strategies. The course overview explicitly includes “machine learning in trading” as a topic. Two main types of machine learning are mentioned in the context of their applications in trading:
- Supervised Learning: This can be used for signal generation by making predictions, such as generating buy or sell signals for an asset based on predicting its return or the sign of its return. It can also be applied in risk management to determine position sizing, the weight of a stock in a portfolio, or to predict stop-loss levels.
- Unsupervised Learning: The primary use case highlighted is to extract insights from data. This involves analyzing financial data to discover patterns, relationships, or structures, like clusters, without predefined labels. These insights can then be used to aid decision-making. Specific unsupervised learning techniques mentioned include clustering, dimensionality reduction, anomaly detection, market regime detection, and portfolio optimization.
Specific Strategies Covered in the Course
The course develops three large quantitative projects that incorporate or relate to machine learning concepts:
- Unsupervised Learning Trading Strategy (Project 1): This strategy uses unsupervised learning (specifically K-means clustering) on S&P 500 stocks. The process involves collecting daily price data, calculating various technical indicators (like Garmon-Class Volatility, RSI, Bollinger Bands, ATR, MACD, Dollar Volume) and features (including monthly returns for different time horizons and rolling Fama-French factor betas). This data is aggregated monthly and filtered to the top 150 most liquid stocks. K-means clustering is then applied to group stocks into similar clusters based on these features. A specific cluster (cluster 3, hypothesized to contain stocks with good upward momentum based on RSI) is selected each month, and a portfolio is formed using efficient frontier optimization to maximize the Sharpe ratio for stocks within that cluster. This portfolio is held for one month and rebalanced. A notable limitation mentioned is that the project uses a stock list that likely has survivorship bias.
- Twitter Sentiment Investing Strategy (Project 2): This project uses Twitter sentiment data on NASDAQ 100 stocks. While it is described as not having “machine learning modeling”, the core idea is to demonstrate how alternative data can be used to create a quantitative feature for a strategy. An “engagement ratio” is calculated (Twitter comments divided by Twitter likes). Stocks are ranked monthly based on this ratio, and the top five stocks are selected for an equally weighted portfolio. The performance is then compared to the NASDAQ benchmark (QQQ ETF). The concept here is feature engineering from alternative data sources. Survivorship bias in the stock list is again noted as a limitation that might skew results.
- Intraday Strategy using GARCH Model (Project 3): This strategy focuses on a single asset using simulated daily and 5-minute intraday data. It combines signals from two time frames: a daily signal derived from predicting volatility using a GARCH model in a rolling window, and an intraday signal based on technical indicators (like RSI and Bollinger Bands) and price action patterns on 5-minute data. A position (long or short) is taken intraday only when both the daily GARCH signal and the intraday technical signal align, and the position is held until the end of the day. While GARCH is a statistical model, not a typical supervised/unsupervised ML algorithm, it’s presented within this course framework as a quantitative prediction method.
Challenges in Applying Machine Learning
Applying machine learning in trading faces significant challenges:
- Theoretical Challenges: The reflexivity/feedback loop makes predictions difficult. If a profitable pattern predicted by a model is exploited by many traders, their actions can change the market dynamics, making the initial prediction invalid (the strategy is “arbitraged away”). Predicting returns and prices is considered particularly hard, followed by predicting the sign/direction of returns, while predicting volatility is considered “not that hard” or “quite straightforward”.
- Technical Challenges: These include overfitting (where the model performs well on training data but fails on test data) and generalization issues (the model doesn’t perform the same in real-world trading). Nonstationarity in training data and regime shifts can also ruin model performance. The black box nature of complex models like neural networks can make them difficult to interpret.
Skills for Algorithmic Trading with ML
Key skills needed for a career in algorithmic trading and quantitative finance include knowing Python, how to backtest strategies, how to replicate research papers, and understanding machine learning in trading. Python is the most popular language due to its libraries and ease of use, suitable for research, backtesting, and automating low-complexity systems, though slower than languages like Java or C++ needed for high-end, speed-critical systems.
In summary, machine learning in algorithmic trading involves using models, primarily supervised and unsupervised techniques, for tasks like signal generation, risk management, and identifying patterns. The course examples illustrate building strategies based on clustering (unsupervised learning), engineering features from alternative data, and utilizing quantitative prediction models like GARCH, while also highlighting the considerable theoretical and technical challenges inherent in this field.
Algorithmic Trading Technical Indicators and Features
Technical indicators are discussed in the sources as calculations derived from financial data, such as price and volume, used as features and signals within algorithmic and quantitative trading strategies. They form part of the predefined set of rules that define an algorithmic trading system.
The sources mention and utilize several specific technical indicators and related features:
- Garmon-Class Volatility: An approximation to measure the intraday volatility of an asset, used in the first project.
- RSI (Relative Strength Index): Calculated using the pandas_ta package, it’s used in the first project. In the third project, it’s combined with Bollinger Bands to generate an intraday momentum signal. In the first project, it was intentionally not normalized to aid in visualizing clustering results.
- Bollinger Bands: Includes the lower, middle, and upper bands, calculated using pandas_ta. In the third project, they are used alongside RSI to define intraday trading signals based on price action patterns.
- ATR (Average True Range): Calculated using pandas_ta, it requires multiple data series as input, necessitating a group by apply methodology for calculation per stock. Used as a feature in the first project.
- MACD (Moving Average Convergence Divergence): Calculated using pandas_ta, also requiring a custom function and group by apply methodology. Used as a feature in the first project.
- Dollar Volume: Calculated as adjusted close price multiplied by volume, often divided by 1 million. In the first project, it’s used to filter for the top 150 most liquid stocks each month, rather than as a direct feature for the machine learning model.
- Monthly Returns: Calculated for different time horizons (1, 2, 3, 6, 9, 12 months) using the percent_change method and outliers are handled by clipping. These are added as features to capture momentum patterns.
- Rolling Factor Betas: Derived from Fama-French factors using rolling regression. While not traditional technical indicators, they are quantitative features calculated from market data to estimate asset exposure to risk factors.
In the algorithmic trading strategies presented, technical indicators serve multiple purposes:
- Features for Machine Learning Models: In the first project, indicators like Garmon-Class Volatility, RSI, Bollinger Bands, ATR, and MACD, along with monthly returns and factor betas, form an 18-feature dataset used as input for a K-means clustering algorithm. These features help the model group stocks into clusters based on their characteristics.
- Signal Generation: In the third project, RSI and Bollinger Bands are used directly to generate intraday trading signals based on price action patterns. Specifically, a long signal occurs when RSI is above 70 and the close price is above the upper Bollinger band, and a short signal occurs when RSI is below 30 and the close is below the lower band. This intraday signal is then combined with a daily signal from a GARCH volatility model to determine position entry.
The process of incorporating technical indicators often involves:
- Calculating the indicator for each asset, frequently by grouping the data by ticker symbol. Libraries like pandas_ta simplify this process.
- Aggregating the calculated indicator values to a relevant time frequency, such as taking the last value for the month.
- Normalizing or scaling the indicator values, particularly when they are used as features for machine learning models. This helps ensure features are on a similar scale.
- Combining technical indicators with other data types, such as alternative data (like sentiment in Project 2, though not a technical indicator based strategy) or volatility predictions (like the GARCH model in Project 3), to create more complex strategies.
In summary, technical indicators are fundamental building blocks in the algorithmic trading strategies discussed, serving as crucial data inputs for analysis, feature engineering for machine learning models, and direct triggers for trading signals. Their calculation, processing, and integration are key steps in developing quantitative trading systems.
Algorithmic Portfolio Optimization and Strategy
Based on the sources, portfolio optimization is a significant component of the quantitative trading strategies discussed, particularly within the context of machine learning applications.
Here’s a breakdown of how portfolio optimization is presented:
- Role in Algorithmic Trading Portfolio optimization is explicitly listed as a topic covered in the course, specifically within the first module focusing on unsupervised learning strategies. It’s also identified as a use case for unsupervised learning in trading, alongside clustering, dimensionality reduction, and anomaly detection. The general idea is that after selecting a universe of stocks, optimization is used to determine the weights or magnitude of the position in each stock within the portfolio.
- Method: Efficient Frontier and Maximizing Sharpe Ratio In the first project, the strategy involves using efficient frontier optimization to maximize the Sharpe ratio for the stocks selected from a particular cluster. This falls under the umbrella of “mean variance optimization”. The goal is to find the weights that yield the highest Sharpe ratio based on historical data.
- Process and Inputs To perform this optimization, a function is defined that takes the prices of the selected stocks as input. The optimization process involves several steps:
- Calculating expected returns for the stocks, using methods like mean_historical_return.
- Calculating the covariance matrix of the stock returns, using methods like sample_covariance.
- Initializing the EfficientFrontier object with the calculated expected returns and covariance matrix.
- Applying constraints, such as weight bounds for individual stocks. The sources mention potentially setting a maximum weight (e.g., 10% or 0.1) for diversification and a dynamic lower bound (e.g., half the weight of an equally weighted portfolio).
- Using a method like max_sharpe on the efficient frontier object to compute the optimized weights.
- The optimization requires at least one year of historical daily price data prior to the optimization date for the selected stocks.
- Rebalancing Frequency In the first project, the portfolio is formed using the optimized weights and held for one month, after which it is rebalanced by re-optimizing the weights for the next month’s selected stocks.
- Challenges and Workarounds A practical challenge encountered during the implementation is that the optimization solver can sometimes fail, resulting in an “infeasible” status. When the Max Sharpe optimization fails, the implemented workaround is to default to using equal weights for the portfolio in that specific month.
- Contrast with Other Strategies Notably, the second project, the Twitter sentiment investing strategy, is explicitly described as not having “machine learning modeling”, and it does not implement efficient frontier optimization. Instead, it forms an equally weighted portfolio of the top selected stocks each month. This highlights that while portfolio optimization, particularly using sophisticated methods like Efficient Frontier, is a key strategy, simpler approaches like equal weighting are also used depending on the strategy’s complexity and goals.
Twitter Sentiment Trading Strategy Using Engagement Ratio
Based on the sources, Sentiment analysis is discussed in the context of a specific quantitative trading strategy referred to as the Twitter sentiment investing strategy. This strategy forms the basis of the second project covered in the course.
Here’s what the sources say about sentiment analysis and its use in this strategy:
- Concept: Sentiment investing focuses on analyzing how people feel about certain stocks, industries, or the overall market. The underlying assumption is that public sentiment can impact stock prices. For example, if many people express positive sentiment about a company on Twitter, it might indicate that the company’s stock has the potential to perform well.
- Data Source: The strategy utilizes Twitter sentiment data specifically for NASDAQ 100 stocks. The data includes information like date, symbol, Twitter posts, comments, likes, impressions, and a calculated “Twitter sentiment” value provided by a data provider.
- Feature Engineering: Rather than using the raw sentiment or impressions directly, the strategy focuses on creating a derivative quantitative feature called the “engagement ratio”. This is done to potentially create more value from the data.
- The engagement ratio is calculated as Twitter comments divided by Twitter likes.
- The reason for using the engagement ratio is to gauge the actual engagement people have with posts about a company. This is seen as more informative than raw likes or comments, partly because there can be many bots on Twitter that skew raw metrics. A high ratio (comments as much as or more than likes) suggests genuine engagement, whereas many likes and few comments might indicate bot activity.
- Strategy Implementation:
- The strategy involves calculating the average engagement ratio for each stock every month.
- Stocks are then ranked cross-sectionally each month based on their average monthly engagement ratio.
- For portfolio formation, the strategy selects the top stocks based on this rank. Specifically, the implementation discussed selects the top five stocks for each month.
- A key characteristic of this particular sentiment strategy, in contrast to the first project, is that it does not use machine learning modeling.
- Instead of portfolio optimization methods like Efficient Frontier, the strategy forms an equally weighted portfolio of the selected top stocks each month.
- The portfolio is rebalanced monthly.
- Purpose: The second project serves to demonstrate how alternative or different data, such as sentiment data, can be used to create a quantitative feature and a potential trading strategy.
- Performance: Using the calculated engagement ratio in the strategy showed that it created “a little bit of value above the NASDAQ itself” when compared to the NASDAQ index as a benchmark. Using raw metrics like average likes or comments for ranking resulted in similar or underperformance compared to the benchmark.
By Amjad Izhar
Contact: amjad.izhar@gmail.com
https://amjadizhar.blog
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