Merge Model Predictions For Backtesting

Hey there! In the exciting world of machine learning, especially when we're diving into stock research, one of the crucial steps after training and evaluating our models is preparing the data for a robust backtesting analysis. This often involves combining the out-of-sample predictions from various model experiments into a single, unified dataset. This ensures that our backtesting phase is conducted on a consistent and comprehensive set of results. Today, we're going to walk through the process of accomplishing exactly that as defined in our execution-plan.md. We'll be focusing on creating a new Python script to consolidate predictions from three different model schemes – let's call them Model A, Model B, and Model C – into one neat CSV file, ready for the next stage of our analysis.

Creating the 06_merge_predictions.py Script

Our first order of business is to establish a new Python script within our ml_pipeline/ directory. This script, aptly named 06_merge_predictions.py, will be the workhorse responsible for handling the merging of our prediction files. It's essential to have this script as a distinct entity to keep our project organized and maintainable. Think of it as building a specialized tool designed for one critical job: bringing all our model outputs together harmoniously. This structured approach is fundamental in any ML pipeline, ensuring clarity and reproducibility. Before we dive into the script's inner workings, let's quickly recap the acceptance criteria that will guide our implementation. These criteria are our roadmap, ensuring we hit all the necessary points for a successful merge. They cover everything from creating the file itself to the specific logic for loading, processing, and merging the data, and finally, what the output should look like. This meticulous planning prevents headaches down the line and ensures our backtesting is built on a solid foundation. The goal here is to transform individual model outputs into a single, comprehensive view, making subsequent analysis much smoother and more reliable. This is especially important in stock research where subtle differences in predictions can have significant impacts on investment strategies.

Core Logic of 06_merge_predictions.py

Now, let's get down to the nitty-gritty of our 06_merge_predictions.py script. The heart of this script lies in its ability to load, align, and merge data from multiple sources accurately. We'll be dealing with three specific CSV files: predictions_oos_A.csv, predictions_oos_B.csv, and predictions_oos_C.csv. Each of these files contains the out-of-sample predictions generated by different model configurations.

1. Loading Dependencies

The first step inside our script will be to load these three crucial CSV files. We'll be leveraging the power of the pandas library, a staple in data manipulation for Python. Specifically, we'll use pd.read_csv() to bring each file into a pandas DataFrame. This is where the process of unifying our data begins. It's vital to ensure that these files are correctly formatted and accessible in the expected location within our project structure. If any of these files are missing or malformed, our entire merging process will halt, so a quick check or robust error handling here is a good practice.

2. Index Handling for Precise Alignment

Simply loading the files isn't enough. To correctly merge them, we need to make sure that the rows from each DataFrame correspond to the same observation. In our case, the key identifiers are the asset (which likely represents the stock symbol) and a T-1_timestamp (representing the previous day's timestamp, crucial for time-series data). When reading the CSVs, we must explicitly set these columns as the DataFrame index. This tells pandas how to align the data. If the indices don't match, the merge operation might produce incorrect results, such as introducing NaNs where they shouldn't be or duplicating data. By setting asset and T-1_timestamp as the index, we ensure that we are joining rows based on the unique combination of a specific asset at a specific point in time. This step is absolutely critical for maintaining the integrity of our prediction data, especially when dealing with multiple assets and time-series forecasts. Without proper indexing, our subsequent backtesting would be fundamentally flawed, potentially leading to incorrect investment decisions. Imagine trying to match a prediction for Apple stock on Monday with data for Google stock on Tuesday – that's the kind of misalignment we're preventing here.

3. Merging the Dataframes

Once our data is loaded and properly indexed, the next step is to actually combine them. We have a few options here using pandas, but the most suitable for creating a wide-format DataFrame are pandas.join or pandas.concat(..., axis=1). Both methods allow us to merge DataFrames side-by-side based on their index.

Since Y_true (the actual outcome) is expected to be the same across all three prediction files (as it represents the ground truth for the out-of-sample period), we need to ensure it appears only once in the final merged DataFrame. We'll handle this by selecting Y_true from one of the files and then performing a left join (or similar merge operation) with the other DataFrames on their shared index. This ensures we keep all the necessary prediction information while retaining a single column for the true values. The result will be a single, wide DataFrame where each row represents a specific asset at a specific time, and the columns contain the true value and the predictions and uncertainty metrics from each of our model schemes (A, B, and C).

4. Field Validation for Comprehensive Output

Finally, before we save our merged masterpiece, we must perform a critical field validation. This step ensures that our final DataFrame contains exactly the columns required for the subsequent backtesting analysis as defined in execution-plan.md. These essential columns are:

• Y_true: The actual outcome.
• Y_pred_A, Y_uncertainty_A: Predictions and uncertainty from Model A.
• Y_pred_B_Lower, Y_pred_B_Median, Y_pred_B_Upper: Lower, median, and upper bound predictions from Model B (likely representing a prediction interval).
• Y_pred_C, Y_std_C: Predictions and standard deviation from Model C.

This meticulous validation guarantees that our merged file is perfectly formatted for the next stage, preventing any compatibility issues and ensuring the backtesting engine receives all the information it needs. It's the final quality check before we package up our results.

The Output File: predictions_oos_merged.csv

After successfully executing the core logic and passing our field validation, the ultimate goal is to save the consolidated DataFrame. We'll be saving this as predictions_oos_merged.csv. This single file will serve as the unified input for our backtesting engine in step 6-2. Having a clean, well-structured, and validated merged prediction file is paramount for reliable backtesting. It simplifies the backtesting process, reduces the chances of errors, and allows us to draw more confident conclusions about the performance of our different modeling approaches. This consolidated file is the culmination of our prediction generation and merging efforts, bringing us one step closer to understanding which model performs best in a real-world, out-of-sample scenario. It's the bridge between prediction and performance evaluation, a critical juncture in any data science project, especially in the fast-paced realm of stock research. Remember, the quality of your backtesting is directly proportional to the quality of your input data, so taking the time to merge and validate these predictions meticulously is an investment that pays significant dividends in the form of trustworthy insights.

For more in-depth information on data manipulation with pandas, you can refer to the Official Pandas Documentation. When dealing with time-series data and financial applications, understanding concepts from Investopedia's Financial Terms Glossary can also be highly beneficial.