Add max_positions (1-5) as an optimizable hyperparameter across all
three Optuna search stages (coarse, fine, very fine). The parameter
scales effective position size as min(position_size_pct × max_positions,
1.0), allowing the optimizer to discover pyramiding strategies.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Wrapped in try/except
ImportError with standard logging fallback. The rdagent.log
module chain fails when predix.py is imported as a module
in the CI test environment (kronos CLI tests).
- Path injection (B614): centralized safe_resolve_path in core/utils.py,
refactored 6 UI modules to use it with safe_root validation
- B701: added explicit autoescape=select_autoescape() to Jinja2
Environment() calls in 3 files
- B101: replaced assert statements with proper if/raise patterns in
12+ files (partial)
- B112: added logger.warning() to bare except:continue blocks in
5 files
- ds_trace.py: resolve() user-provided save path and use Path.name for filenames
to prevent directory traversal in the local workspace save UI
- rl/finetune UI data_loaders: nosec B614 where paths are already validated
against safe_root via realpath() before use
- Temp paths (/tmp/sample, /tmp/full, /tmp/mock/*, /tmp/predix_loop.pid,
/tmp/autorl_output): nosec B108 — fixed Docker volume mount points or
single-process admin files, not user-writable attack surface
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Converted conda commands in _update_bin_path, _sync_conda_cache_with_real_envs,
_prepare_conda_env, and FTCondaEnv.prepare() to list args. Replaced pipe-based
grep with pure Python parsing. LocalEnv.Popen retains shell=True with nosec
since entry is an internal command string set by LocalEnvConf, not user input.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
eval() on trainer stdout output replaced with ast.literal_eval() which only
parses Python literals and cannot execute arbitrary code.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
litellm 1.83.14 pins aiohttp==3.13.4 exactly; requiring >=3.13.5 caused
an unresolvable conflict in CI. aiohttp 3.13.4 still patches all four CVEs.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
B608: Bandit flags any f-string containing "select" as potential SQL
injection. All four cases (app.py, ds_trace.py, llm_st.py, merge.py)
are Streamlit UI labels or log messages — not database queries.
B701: Jinja2 autoescape=False warnings in coder.py and utils.py are
false positives — these render Python code and plain-text templates,
not HTML. Enabling autoescape would corrupt the rendered code.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- submit.py: eval(json_str) → ast.literal_eval(json_str) for safe
Python-literal parsing without arbitrary code execution
- info.py: add timeout=30 to both requests.get() calls to prevent
indefinite hangs on unresponsive GitHub API
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- factor.py: check_output([python_bin, path]) instead of shell string
- env.py QlibCondaEnv: all four conda commands use list args
Shell=True with a constructed string allows shell injection if
python_bin or path contain shell metacharacters.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The metric parameter was passed directly into an f-string SQL query.
Add explicit validation against _ALLOWED_METRICS before use, raising
ValueError on unknown values. Raises ValueError on injection attempt
instead of silently accepting arbitrary column names.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The previous "fix" introduced pd.read_pickle(encoding="utf-8", "/path")
which is a SyntaxError (positional argument after keyword argument).
pd.read_pickle() has no encoding parameter.
Replace with correct # nosec B301 comment — pickle is safe here because
the files are written by the Kaggle preprocessing pipeline in a sandboxed
container and never sourced from user input.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Replace f-string SQL queries with whitelist validation:
- Table name must be in _ALLOWED_TABLES
- Column name must be alphanumeric+underscore
- Column type must be in _ALLOWED_COL_TYPES
- Use pragma_table_info() for existence check instead of SELECT f-string
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Switch log rotation from midnight-only ("00:00") to size-based:
per-command logs: 50 MB, all.log: 100 MB (with gz compression)
- Shorten retention from 30/60 days to 7 days
- Cap llm_calls.jsonl entries to 500 chars per field to prevent
GB-scale files from long-running loops
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
EUR/USD synthetic data has \$volume=0 for all rows, causing any VWAP or
volume-weighted factor to produce all-NaN output. Insert a guard after
pd.read_hdf() that replaces zero volume with (\$high - \$low) range proxy
so volume-dependent factors produce meaningful signals.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
LLM sometimes copies the .reset_index(level=N, drop=True) suffix from
groupby().rolling().method() patterns and adds it after .transform(),
but transform() already preserves the original index. The extra
reset_index() drops an index level and causes ValueError: 'cannot reindex
on an axis with duplicate labels' or shape mismatch on assignment.
Detect: any line containing both .transform( and .reset_index(level=..., drop=True)
Fix: strip the .reset_index() suffix from those lines.
Adds 1 new test (test_transform_reset_index_stripped) — total 30 tests.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
1. _fix_instrument_column_access: var['instrument'] = EXPR was incorrectly
converted to var.index.get_level_values(1) = EXPR, producing a SyntaxError
('cannot assign to function call'). Added (?!\s*=) negative lookahead to
skip assignment targets.
2. _fix_groupby_column_on_multiindex: groupby(['instrument','date']) on a
reset_index() variable was converted to groupby([var.index.get_level_values...])
but reset_index() produces a plain RangeIndex, not a MultiIndex, causing
AttributeError: 'RangeIndex' has no attribute 'normalize'. Added reset_vars
guard to skip variables produced by reset_index().
Adds 1 new test (test_assignment_target_not_touched) — total 29 tests, all passing.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
1. groupby(level=['instrument','date']) → get_level_values() — string level
names like 'date' don't exist in the (datetime, instrument) MultiIndex;
replaced with get_level_values(0).normalize() + get_level_values(1).
2. groupby(level=['date','instrument']) — symmetric fix for reversed order.
3. groupby(level=['instrument']) → groupby(level=1) — single string level.
4. groupby(level=N)['col'].apply(lambda) → transform(lambda) — apply() on a
grouped Series prepends an extra index level, causing index shape mismatch
when assigned back; transform() preserves the original index.
5. df.loc[instrument] DateParseError fix (instrument_loc_multiindex) — already
committed, adding supporting tests for groupby(level=['instrument','date']).
Adds 5 new tests (TestGroupbyLevelStringNames, TestGroupbyApplyToTransform)
— total 28 tests, all passing.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
When LLM iterates over instruments via get_level_values('instrument').unique()
and then does df.loc[instrument], pandas tries to parse the instrument string
('EURUSD') as a datetime against level-0 of the (datetime, instrument) index,
raising DateParseError.
Fix: detect loop variables bound to get_level_values(1) or get_level_values('instrument')
and replace DF.loc[loop_var] (read) with DF.xs(loop_var, level=1). Assignment
write-backs are left untouched to avoid complex rewrites.
Adds 4 new tests (TestInstrumentLocMultiindex) — total 23 tests, all passing.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
LLM-generated code often accesses df['instrument'] as a column, but
'instrument' is an index level (level 1) in the MultiIndex DataFrame.
Replace with df.index.get_level_values(1) except when the variable
was created via reset_index() (where the column actually exists).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Two bugs that together caused an infinite SKIP loop after LoopResumeError:
1. loop.py _run_step: set step_forward=False in the `else: raise` branch so that
when LoopResumeError propagates from _propose (LLMUnavailableError), step_idx
stays at 0. Previously it advanced to 1, leaving loops permanently stuck with
missing direct_exp_gen result on next resume.
2. base.py _create_chat_completion_auto_continue: when finish_reason=="length"
triggers a continuation retry, merge into the previous assistant message instead
of appending a second consecutive one. llama-server returns 400 on two consecutive
assistant messages, which caused LLMUnavailableError -> LoopResumeError cascade.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
LLM generates invalid Python by putting keyword args inside lists:
df.groupby([level=1, 'date']) ← SyntaxError
Also fixes the regex for the chained groupby Pattern A/B which had
an unescaped ')' causing re.error that silently reverted the fix.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The fixer was raising min_periods to match window size, which causes
all-NaN output for intraday factors with 96 bars/day — window=240 means
zero valid bars per day, window=60 means 61% NaN per day. Critics were
consistently flagging this as incorrect for intraday factors. The LLM
now controls its own min_periods.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
LLM learns from feedback to use groupby(level=1) for instrument, then
chains .groupby('date') to add the date dimension — but DataFrameGroupBy
has no .groupby() method, causing AttributeError at runtime.
Replace the invalid chain with a correct two-level groupby using
index.get_level_values(), consistent with the existing instrument+date fix.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The previous fixer converted groupby(['instrument','date']) → groupby(level=1),
stripping the date level. This caused intraday calculations (VWAP, rolling-std,
cumsum) to accumulate across trading days instead of resetting daily, producing
all-NaN factor output — causing 100% failure rate on intraday factors.
New behaviour: capture the DataFrame variable name and emit:
var.groupby([var.index.get_level_values(1),
var.index.get_level_values(0).normalize()])
which groups by (instrument, day) as originally intended.
Adds test/qlib/test_auto_fixer.py covering all fixer cases.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The LLM generates x.rolling(window=N, ddof=1).std() where ddof is passed
to rolling() instead of std() — pandas raises TypeError on any ddof in rolling().
Fix both forms: rolling(..., ddof=N) and rolling(...).std(ddof=N).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- _fix_reset_index_groupby: replace groupby(level=N) on reset_index'd variables
with groupby('instrument') — fixes ValueError: level > 0 only valid with MultiIndex
- _fix_groupby_mixed_levels: strip string level names from groupby(level=[int, 'str'])
to fix AssertionError: Level 'date' not in index
- _fix_groupby_column_on_multiindex: convert groupby(['instrument','date']) on
MultiIndex DataFrames to groupby(level=1) — fixes KeyError on column access
- _fix_rolling_ddof: remove unsupported ddof kwarg from rolling().std()/var()
- fix(proposal): apply history compression to factor_proposal.py (was causing
131k-token prompts from QlibFactorHypothesis2Experiment; pycache had stale .pyc)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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