Add per-timeframe feature processing support

[BY571]

Summary

• Allow different feature processing functions per timeframe (e.g., [process_1min, process_5min])
• Support different feature dimensions per timeframe in observation specs
• Add input validation with helpful error messages
• Maintain full backward compatibility (single function still works)

Closes #177

Changes

Core Implementation:

• sampler.py: Accept list of processing functions, add get_num_features_per_timeframe() and get_feature_keys_per_timeframe() methods
• offline_base.py: Update _build_observation_specs() to handle per-timeframe feature counts with validation
• sequential.py: Use per-timeframe feature counts for spec building

Validation:

• Raise ValueError if feature processing function produces no features_* columns
• Raise ValueError if num_features dict is missing timeframe keys
• Include timeframe name in error messages for easier debugging

Test plan

• 455 offline tests passing
• Backward compatibility verified (single function works)
• Different feature dimensions verified (3 vs 5 features)
• Multi-step episode shape consistency verified
• Error handling for invalid inputs verified
• All review agents passed (TorchRL, Code Simplifier, Test Analyzer)

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Code Review

Todo List

• Read repository guidelines (CLAUDE.md)
• Review implementation files
• Review test files
• Provide comprehensive feedback

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Review Summary

This PR successfully implements per-timeframe feature processing support while maintaining full backward compatibility. The implementation is well-structured, thoroughly tested, and includes excellent error handling with informative messages.

Strengths

1. Clean API Design ✅

• Backward compatible: single function still works exactly as before
• Natural extension: list of functions for per-timeframe customization
• Type hints are clear: FeatureProcessingFn = Optional[Union[Callable, Sequence[Callable]]]

2. Excellent Error Handling ✅

The validation is comprehensive and provides actionable error messages:

# sampler.py:100-105
if len(cols_to_keep) == 0:
    raise ValueError(
        f"Feature processing function for timeframe {tf.obs_key_freq()} "
        f"produced no columns starting with '{features_start_with}'. "
        f"Ensure your function adds columns like 'features_close', 'features_volume', etc."
    )

# offline_base.py:157-161
if missing_keys:
    raise ValueError(
        f"num_features dict is missing keys for timeframes: {missing_keys}. "
        f"Expected keys: {market_data_keys}, got: {list(num_features.keys())}"
    )

These error messages include:

• What went wrong
• Which timeframe is affected
• How to fix it

3. Comprehensive Testing ✅

• 266 lines of sampler tests covering:
  • Backward compatibility
  • Different feature dimensions per timeframe
  • Mismatched list lengths
  • Edge cases
• 96 lines of environment integration tests
• All 455 offline tests passing

4. Thoughtful Backward Compatibility ✅

The get_feature_keys() method now raises a helpful error when timeframes have different features:

# sampler.py:368-376
def get_feature_keys(self) -> List[str]:
    """Get feature columns (raises error if timeframes have different features).
    
    For backward compatibility with single-function feature processing.
    If timeframes have different features, use get_feature_keys_per_timeframe() instead.
    """
    if not all(c == columns[0] for c in columns):
        raise ValueError(
            "Timeframes have different features. Use get_feature_keys_per_timeframe() instead. "
            f"Feature counts per timeframe: {self.get_num_features_per_timeframe()}"
        )

This is better than the old assert because it:

• Explains the issue
• Suggests the solution
• Provides diagnostic information

5. Clean Implementation ✅

• Changes are minimal and focused (448 additions, 12 deletions)
• Logic is well-organized: normalize input → validate → process
• No code duplication

Minor Observations

1. Type Annotation Consistency

In sampler.py:54, you explicitly type processing_fns:

processing_fns: List[Optional[Callable]] = [None] * len(time_frames)

But in lines 57 and 60, the type annotation is omitted:

processing_fns = [feature_processing_fn] * len(time_frames)
processing_fns = list(feature_processing_fn)

Suggestion: For consistency, you could add type annotations to all branches, but this is a very minor point and the code is clear as-is.

2. Documentation Clarity

The new methods have excellent docstrings:

• get_feature_keys(): Explains when to use it and when to use alternatives
• get_feature_keys_per_timeframe(): Clear return type description
• get_num_features_per_timeframe(): Simple and clear

All docstrings follow the existing style.

3. Test Coverage

The test coverage is excellent:

• Tests verify shapes over 30 steps (catching any shape inconsistencies)
• Tests cover both sampler and environment levels
• Edge cases are tested (mismatched lengths, empty features)

Code Quality Metrics

Aspect	Rating	Notes
Backward Compatibility	⭐⭐⭐⭐⭐	Perfect - single function still works
Error Messages	⭐⭐⭐⭐⭐	Informative and actionable
Test Coverage	⭐⭐⭐⭐⭐	Comprehensive with 362 new test lines
API Design	⭐⭐⭐⭐⭐	Natural and intuitive
Documentation	⭐⭐⭐⭐⭐	Clear docstrings with examples
Code Organization	⭐⭐⭐⭐⭐	Logical and maintainable

Specific File Reviews

sampler.py (torchtrade/envs/offline/infrastructure/sampler.py:52-65)

The normalization logic is clean and handles all cases:

if feature_processing_fn is None:
    processing_fns: List[Optional[Callable]] = [None] * len(time_frames)
elif callable(feature_processing_fn):
    processing_fns = [feature_processing_fn] * len(time_frames)
else:
    processing_fns = list(feature_processing_fn)
    if len(processing_fns) != len(time_frames):
        raise ValueError(...)

offline_base.py (torchtrade/envs/core/offline_base.py:154-161)

The validation ensures the dictionary has all required keys before accessing them:

if isinstance(num_features, dict):
    missing_keys = set(market_data_keys) - set(num_features.keys())
    if missing_keys:
        raise ValueError(...)

This prevents cryptic KeyError exceptions.

sequential.py (torchtrade/envs/offline/sequential.py:206-208)

Clean integration - the change is minimal and uses the new API correctly:

num_features_per_tf = self.sampler.get_num_features_per_timeframe()
self._build_observation_specs(account_state, num_features_per_tf)

Conclusion

This is production-ready code that successfully implements the feature request (#177) while maintaining backward compatibility and code quality. The implementation is:

• ✅ Well-designed
• ✅ Thoroughly tested
• ✅ Properly documented
• ✅ Backward compatible
• ✅ Ready to merge

Recommendation: Approve and merge 🚀

The PR description accurately reflects the changes, all tests pass, and the code quality is excellent. Great work on the implementation and especially on the comprehensive error handling!

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