Add annualized volatility metric - #326
Open
mameikagou wants to merge 1 commit into
Open
Conversation
Up to standards ✅🟢 Issues
|
| Metric | Results |
|---|---|
| Complexity | 0 |
| Duplication | 0 |
NEW Get contextual insights on your PRs based on Codacy's metrics, along with PR and Jira context, without leaving GitHub. Enable AI reviewer
TIP This summary will be updated as you push new changes.
mameikagou
force-pushed
the
codex/add-volatility-metric
branch
from
August 24, 2026 07:20
1fcdb85 to
26e8f9d
Compare
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Closes #324.
Adds a standalone
Volatilitymetric alongside Sharpe and Sortino. It computes the standard deviation of net PnL changes and annualizes it using the inferred samples per day and configurable trading days per year.Also exports the metric, adds it to the API reference, and covers the annualization calculation with a focused test.
Test:
pytest py-hftbacktest/tests/test_stats.py -q