Navigating the dynamic and complex world of finance often hinges on staying ahead of the curve. Devising a trading strategy may seem like an arduous task, but the advent of advanced technology has made it remarkably simpler. In this guide, we’ll walk you through the process of constructing a long-short strategy for Dow30 stocks using detailed balance sheet data, various price indicators, and the power of our no-code platform, ML Studio.
In our contemporary, data-rich financial landscape, utilizing machine learning in trading strategies offers a significant edge. Machine learning’s capacity to process enormous volumes of data, recognize patterns, and make informed decisions surpasses traditional trading models.
Machine learning models, specifically ranking models, are vital in this context due to their exceptional predictive capabilities. These models can classify stocks based on specific predictive features, enabling traders to take long positions on the highest-ranked stocks and short positions on the lowest-ranked ones. This balanced risk-return approach considerably enhances your trading strategy.
Feature engineering is the cornerstone of machine learning’s effectiveness. It involves creating new features or modifying existing ones to boost the model’s predictive performance. In stock trading, features could include a broad spectrum of fundamental and technical indicators.
From the balance sheet data, we can derive features such as current ratio (current assets/current liabilities), quick ratio ((current assets-inventory)/current liabilities), and debt-to-equity ratio (total liabilities/total equity). As for the price indicators, we can incorporate moving averages, relative strength index (RSI), and Bollinger Bands, among others.
The quality and relevance of the features used directly impact the model’s performance. Reliable and well-engineered features allow your model to have an accurate understanding of the market scenario, making its predictions more precise and advantageous for your trading strategy.
ML Studio, our no-code platform, is designed to simplify the process of developing a data-driven trading strategy. It bridges the gap between complex machine learning processes and users without extensive coding knowledge. Let’s see how ML Studio supports you at each step:
Building a long-short strategy for Dow30 stocks using balance sheet data and an array of price indicators is not an intimidating process with ML Studio. It makes feature engineering and machine learning accessible and manageable, whether you’re a seasoned trader looking to fine-tune your strategies or a novice eager to understand algorithmic trading. It’s crucial to remember that this isn’t about proposing a guaranteed profitable strategy, but rather about demonstrating how to build intricate machine learning models in finance, made easy with ML Studio.
It’s now time to explore how we can bring this all together in ML Studio, step-by-step, to craft an effective long-short trading strategy for Dow30 stocks. Let’s dive in!
Step 1: Data Acquisition and Preparation
Start by acquiring the necessary data from the marketplace. ML Studio streamlines this process, ensuring you have the most up-to-date and relevant data to start your trading strategy. You’ll need two types of data – balance sheet data and price data. Balance sheet data provides crucial financial information about the companies, while price data will give you insights into the market’s past and current behavior.
Data marketplace offers easy access to external data, such as financials and weather forecasts, without the need to maintain additional data pipelines.
ML Studio comes with 130+ data connectors and a rich marketplace
Once you have obtained both the balance sheet and price data, the next crucial step is to combine, clean, and manipulate these datasets to prepare them for analysis. Data preparation might seem daunting, but ML Studio makes this process smooth with its no-code Data Prep Canvas. You can easily merge datasets, handle missing values, create new variables, and perform a variety of other data transformations with intuitive drag-and-drop tools.
Here’s a screenshot showing how you can leverage ML Studio’s Data Prep Canvas to combine the balance sheet and price data:
No code nodes to perform data operations
With ML Studio, data acquisition and preparation become less of a hurdle and more of a straightforward process, enabling you to focus on crafting your trading strategy. This simplicity is key to making advanced trading accessible to a broader audience.
Step 2: Adding Balance Sheet Data Features
The next step involves incorporating features derived from balance sheet data. This step requires domain knowledge, the sort of understanding that typically comes from a background in finance. This is precisely why we created ML Studio – to empower people with domain knowledge to implement their ideas and build machine learning models without having to handle the underlying operations.
The features you’ll include are:
Current Ratio (Current Assets / Current Liabilities): This ratio is a liquidity ratio that measures a company’s ability to pay short-term obligations. It provides insight into the company’s financial health.
Debt Equity Ratio (Total Liabilities / Total Equity): This ratio is used to evaluate a company’s financial leverage and is calculated by dividing total liabilities by shareholder equity. A lower ratio could be a good indicator as it suggests less risk.
Cash to Debt Ratio (Cash / Total Debt): This ratio shows how long a company could survive if it used only its cash to pay off its debt. The higher the ratio, the more financially stable the company.
Equity Ratio (Equity / Total Assets): This measures the proportion of the total assets that are financed by stockholders and not creditors. It’s an indication of financial stability; higher values are generally seen as positive.
Long-Term Debt to Capitalization (Long-Term Debt / (Long-Term Debt + Equity)): This ratio indicates how much of the company’s capital structure is comprised of long-term debt. A lower ratio may suggest less risk for investors.
Keep track of your feature calculations visually
Step 3: Adding Price and Volume Related Features
The third step involves adding price and volume-related features. These are key technical indicators that can provide valuable insights into market trends and help in predicting future price movements. ML Studio offers no-code nodes for performing these calculations. If you prefer to do coding, ML Studio also supports Python scripting. In this example, features to be added include:
Moving Averages: This indicator smoothens price data to create a line that traders can use to identify price trends. A rising moving average typically suggests an uptrend, while a falling moving average might indicate a downtrend.
Bollinger Bands: Bollinger Bands consist of a middle band (simple moving average) with two outer bands, standard deviations away from the middle band. This indicator helps traders identify overbought and oversold conditions. When the price crosses the upper band, it may signal an overbought condition, and when it crosses the lower band, it might indicate an oversold condition.
MACD (Moving Average Convergence Divergence): MACD is a trend-following momentum indicator that shows the relationship between two moving averages of a security’s price. A MACD crossover of the signal line can indicate a potential buy or sell opportunity.
Volume Moving Averages: This is similar to price moving averages, but it is applied to trade volume. It can be used to identify trends in trading volume, which is often a precursor to significant price moves.
Switch to coding whenever you feel it
Step 4: Creating a Machine Learning Model
Next, we’ll create a machine learning model, specifically a ranking model. A ranking model is a type of machine learning model used to rank items in a specific order, which is beneficial when you want to prioritize one outcome over another. For our case, ranking is an excellent way to order stocks based on predictive features, helping traders decide which stocks to take long and short positions on.
With ML Studio, you simply need to select the input data and specify the target – in this case, whether the stock price will go up or down in the next month. Once this is done, ML Studio handles all the heavy lifting. It takes care of feature scaling, missing value imputation, hyperparameter optimization, model selection, and much more, thus ensuring the model’s performance is optimized for your specific needs.
Build accurate machine learning models with one click
This automated handling of complex tasks ensures you focus on what’s essential – interpreting the model’s results and making informed trading decisions. Furthermore, ML Studio’s flexibility means it can be easily tailored to different scenarios, should you decide to modify your trading strategy in the future.
Understand how your models make decisions
Step 5: Deploying the Model
Once your model is ready, ML Studio facilitates its deployment with a single click. After deploying, it’s time to connect your model to real-time data sources, ensuring your strategy stays up-to-date with the latest market trends and developments.
One of ML Studio’s significant advantages is the ability to schedule your model’s predictions. This feature allows for automatic retraining and forecasting, providing you with updated rankings for your long-short strategy at the frequency you prefer – daily, hourly, or even every few minutes. This automated scheduling feature keeps your model fresh and responsive to market changes.
Additionally, ML Studio’s deployment features are designed to seamlessly integrate with your existing infrastructure. Whether you’re planning to manually execute trades based on the model’s signals or automate the trading process, ML Studio can interact with your existing systems, ensuring a smooth end-to-end workflow.
ML Studio’s deployment capabilities translate to a higher degree of operational efficiency, enabling you to focus on the crucial task of making informed trading decisions. The result is a trading strategy that’s not only data-driven but also effortlessly up-to-date.
Schedule your predictions with ease
Step 6: Implementing the Strategy
The strategy we’ve built is to take long positions on the top 3 ranked stocks and short positions on the bottom 3. Once the model is deployed and connected to real-time data, you can see the strategy in action through an interactive dashboard. This visual tool will give you a clear view of how the model is performing in real-time.
This outline provides a straightforward roadmap for constructing a long-short strategy for Dow30 stocks using balance sheet data, price indicators, and ML Studio. The beauty of ML Studio lies in its ability to simplify complex processes, making machine learning and feature engineering accessible to anyone with domain knowledge in finance.
Get in touch with us for a personal demo and we’ll show you how ML Studio can help your business to succeed.