Alpha Model (strategy)

Alpha Submission Error Causes

Q: What causes an alpha submission error?

A: After an alpha submit, there is a validation process. This process reviews the submitted algorithm for potential issues such as look-ahead bias. If the algorithm does not satisfy these conditions, the submission will not be completed.

Alpha Model Submission Requirements

Q: What is required for the submission of an alpha model?

A: To submit an alpha model, it is necessary to produce the mandatory output of the Alpha class, which is the get function. This function should create positions according to the specified start and end parameters.

Alpha Output Limitations

Q: What are the limitations on the output of an alpha model?

A: There are several key limitations to be aware of when generating output for an alpha model:

1. Date Range Consistency: All dates between the start and end must be present and typically should reflect business days. Missing dates within the range can lead to errors in the validation process.

2. Data Consistency Across Periods: If your model uses multiple pairs of start and end dates, the data for overlapping periods must remain consistent. This is crucial to prevent look-ahead bias, which can invalidate your model's predictive power.

3. Position Sizing: The sum of all positions for a given date should not exceed 100 million. If you intend to allocate a portion of your portfolio to cash, you can assign up to 70 million to positions, and the system will automatically allocate the remainder to cash. This helps maintain a balanced position sizing and prevents overexposure in the model.

Manipulate date calculation

Q: How can I avoid making mistakes with date calculations?

A: Always use reliable date manipulation functions or classes, such as those provided by datetime or DateConverter, for any date-related calculations. This ensures correct handling of different formats and calendar rules.

Monthly Rebalancing in Alpha Model

Q: How is monthly rebalancing implemented in the alpha model?

A: Monthly rebalancing in an alpha model involves adjusting the portfolio at the end of each month based on the signals generated by the model. The logic is to apply resample.last() to the data to capture the signal on the last day of the month and then use that signal to rebalance the portfolio on the first day of the following month. Here is a sample code for monthly rebalancing:

def monthly_rebalancing(position, pre_start, end):
    date_range = pd.date_range(start=str(pre_start), end=str(end), freq="D")

    # Resample the signal at the end of each month and forward fill missing values
    monthly_position = position.fillna(0).resample("M").last()
    rebalanced_position = monthly_position.reindex(date_range).ffill().reindex(position.index)

    return rebalanced_position

position = monthly_rebalancing(position, pre_start, end)

Precautions:

1. Ensure that pre_start is set correctly to avoid cutting off data at the beginning.

2. Perform this operation before position shifting to prevent look-ahead bias.

3. It is assumed that position is based on trading days.

4. Careful implementation is required to avoid errors, as misunderstanding the rebalancing logic can lead to a high probability of mistakes.

Managing Code Structure

Q: How do I manage functions in separate files during model development?

A: To organize functions in separate files, create .py files for the functions and place them within the model's folder structure. Use relative paths to refer to these files. For example, in a structure like this:

codesample
   ├── am.py
   ├── helper.py
   └── config
       └── config.py

You can import functions as follows:

• To import from helper.py, use from helper import functionName.

• To import from config.py within the config folder, use from config.config import functionName.