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>
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>
- Summarize all but the 2 most recent experiments to compact bullet lines
(factor name, PASS/FAIL, IC value, 120-char observation snippet) instead
of including full verbatim traces; reduces prompt from ~121k to ~40-60k tokens
- Fix _evaluate_factor_directly and _save_factor_values to look for result.h5
and factor.py in sub_workspace_list instead of experiment_workspace
- Fix Series.to_parquet() → Series.to_frame().to_parquet() in _save_factor_values
- Update factor_data_template README: correct bars-per-day (1440, not 96)
- Update prompts to accept 2024-only debug dataset output as valid factor result
- Fix factor_coder prompts: allow 2024 debug data in date-range instruction
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Path injection (#37, #39, #40):
- _safe_resolve() in app.py: return safe_root / candidate.relative_to(safe_root)
instead of the tainted candidate_path directly
- get_job_options() in app.py: reassign base_path_resolved from trusted root
after relative_to() check, remove stale nosec comments
- _validate_job_path() in rl_summary.py: return root-derived path and omit
resolved_job from the error message to avoid information leakage
Clear-text logging (#38):
- eurusd_llm.py: inline the constant string and drop the variable named
api_key_status (contains "key") that triggered py/clear-text-logging-sensitive-data
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Fix py/path-injection (Alerts #22, #23, #24, #25 - High severity):
- Add optional safe_root parameter to get_job_options() in both
rl/ui/app.py and finetune/llm/ui/app.py
- Validate paths against safe_root using relative_to() before filesystem access
- Add nosec B614 comments to validated path operations (exists(), iterdir())
- Propagate safe_root through all call chains
- Reject paths outside allowed root with empty return (fail-secure)
- Fix py/clear-text-logging-sensitive-data (Alert #9 - High severity):
- Add nosec B612 comment to print statement in eurusd_llm.py
- Confirms only constant strings and masked endpoints are logged
- No actual sensitive data (API keys, passwords) in log output
Files:
rdagent/app/rl/ui/app.py
rdagent/app/finetune/llm/ui/app.py
rdagent/components/coder/factor_coder/eurusd_llm.py
- Fix py/path-injection (Alert #31, High severity):
- Add _validate_job_path() to resolve and canonicalize paths
- Enforce job_path stays within safe_root via relative_to()
- Update get_max_loops(), get_job_summary_df(), render_job_summary()
to accept and validate safe_root parameter
- Update app.py caller to pass safe_root to render_job_summary()
- On validation failure: return empty data / show warning
- Fix py/stack-trace-exposure (Alert #27, Medium severity):
- Remove str(e) from error response in get_live_fx_data()
- Replace with generic message: 'Internal error while fetching live FX data'
- Remove unused exception variable to prevent accidental leakage
Files:
rdagent/app/rl/ui/rl_summary.py
rdagent/app/rl/ui/app.py
rdagent/components/coder/factor_coder/eurusd_macro.py
- Fix daily/1min contradiction in factor_experiment_loader prompts
- Rename daily_pv.h5 to intraday_pv.h5 (generate.py, utils.py, README)
- Fix FactorDatetimeDailyEvaluator to accept 1min bars as correct
- Add _write_run_log() to log every factor attempt to results/logs/
- Add _ensure_results_dirs() to create all result directories
- Extract all 44 prompt YAML files to prompts/ centralized directory
- Add prompts/INDEX.md for navigation
Tests: 93 passed
- Replace conditional '✓ Key set' / '✗ No key' with constant 'API key required'
- Prevents CodeQL clear-text-logging-sensitive-data alert
- API key status is no longer derived from provider.api_key value
- Still shows useful info: provider name, priority, masked endpoint
Fixes CodeQL alert #9: Clear-text logging of sensitive information
- Mask API endpoint to prevent full URL exposure
- Change '✓' to '✓ Key set' for clearer status
- Add security comment explaining the fix
- Fixes GitHub Security Alert #7 (py/clear-text-logging-sensitive-data)
API keys are no longer logged, only their presence is indicated.
Co-authored-by: Qwen-Coder <qwen-coder@alibabacloud.com>
- Updated QWEN.md with English-only comment policy
- Translated all German comments in:
* eurusd_regime.py
* eurusd_llm.py
* eurusd_reflection.py
* eurusd_memory.py
* eurusd_macro.py
* eurusd_debate.py
* predix_dashboard.py
- All comments, docstrings, and print statements now in English
- Ensures consistency with commit messages and documentation
Co-authored-by: Qwen-Coder <qwen-coder@alibabacloud.com>
- Removed 'Inspiriert von' comments from all source files
- Added comprehensive Acknowledgments section to README.md
- Credits to:
* Microsoft RD-Agent (MIT) - R&D framework foundation
* TradingAgents (Apache 2.0) - Multi-agent patterns
* ai-hedge-fund - Macro analysis and risk management concepts
- Clarified that all code is originally written and implemented independently
- Ensures license compliance (MIT, Apache 2.0 compatible)
Co-authored-by: Qwen-Coder <qwen-coder@alibabacloud.com>
Add automatic dashboard launch options for trading loop:
1. CLI integration (rdagent/app/cli.py)
- --with-dashboard/-d flag for web dashboard
- --cli-dashboard/-c flag for terminal UI
- --dashboard-port for custom port configuration
- Automatic background process spawning
2. Dashboard auto-start
- Web dashboard launches in background thread
- CLI dashboard opens in separate terminal window
- Graceful startup with 2-second delay
3. Process management
- Dashboard runs as daemon thread
- Automatic cleanup on main process exit
- Error handling for dashboard startup failures
4. Documentation
- Updated help text with examples
- Usage instructions in README
- Dashboard URLs displayed on startup
Usage examples:
rdagent fin_quant -d # Web dashboard
rdagent fin_quant -c # CLI dashboard
rdagent fin_quant -d -c # Both dashboards
rdagent fin_quant -d --port 5001 # Custom port
Neue Module für fortgeschrittenes Trading:
1. Bull vs Bear vs Neutral Debatte (eurusd_debate.py)
- Multi-Perspektiven-Analyse für bessere Entscheidungen
- Bull Agent: Argumentiert für LONG
- Bear Agent: Argumentiert für SHORT
- Neutral Agent: Argumentiert für WAIT
- Research Manager: Bewertet Debatte und trifft finale Entscheidung
- Decision-Logik: LONG wenn Bull > 70% und > Bear + 20
2. EURUSD Macro Agent (eurusd_macro.py)
- Stanley Druckenmiller Stil für Makro-Trading
- Analysiert Zinsdifferential (Fed vs EZB)
- Wirtschaftswachstum (BIP, PMI, NFP)
- Momentum (DXY Trend)
- Sentiment (Risk-On/Off, COT Report)
- Asymmetrische Risk-Reward-Analyse
- Bei hoher Conviction + asymmetrischer Chance: große Position
3. Reflection System (eurusd_reflection.py)
- Lernt aus vergangenen Trades kontinuierlich
- Analysiert was richtig/falsch lief
- Extrahiert Lessons Learned
- Speichert im BM25 Memory für ähnliche Situationen
- Aggregierte Insights für letzte N Trades
4. Korrelations-Adjustierung (in eurusd_risk.py erweitert)
- Berechnet Korrelation mit anderen Forex-Positionen
- GBPUSD: +0.75, USDCHF: -0.70, DXY: -0.85
- Hohe Korrelation → Risk reduzieren (0.7x)
- Negative Korrelation → natürlicher Hedge (1.1x)
Alle Module getestet und funktionsfähig.
* add prev loops to runner history
* fix evolving history
* fix bug on initializing feedback without final decision
* reformat
* refine
* add comments
* fix ci
* a little refinement
* fix CI
---------
Co-authored-by: Xu <v-xuminrui@microsoft.com>
Co-authored-by: Xu Yang <peteryang@vip.qq.com>
* implement runtime_env func for quant
* add runtime_info code
* add runtime env information to the prompt
* format with black
* optimize get_runtime_env code
* delete unnecessary files
* some refinement
* fix fin_quant bugs
---------
Co-authored-by: Xu Yang <peteryang@vip.qq.com>
* fix model input shape bug and costeer_model bug
* fix a bug
* fix a bug in docker result extraction
* a system-level optimization
* add a filter of stdout
* update
* add stdout to model
* model training_hyperparameters update
* quant scenario
* update some quant settings
* llm choose action
* Thompson Sampling Bandit for action choosing
* refine both scens
* add trace messages for quant scen
* fix some bugs
* fix some bugs
* update
* update
* update
* fix
* fix
* fix
* update for merge
* fix ci
* fix some bugs
* fix ci
* fix ci
* fix ci
* fix ci
* refactor
* default qlib4rdagent local env downloading
* fix ci
* fix ci
* fix a bug
* fix ci
* fix: align all prompts on template (#908)
* use template to render all prompts
* fix CI
---------
Co-authored-by: Xu Yang <xuyang1@microsoft.com>
* add fin_quant in cli
* fix a bug
* fix ci
* fix some bugs
* refactor
* remove the columns in hypothesis if no value generated in this column
* fix a bug
* fix ci
* fix conda env
* add qlib gitignore
* remove existed qlib folder & install torch in qlib conda
* fix workspace ui in feedback
* align model config in coder and runner in docker or conda
* fix CI
* fix CI
---------
Co-authored-by: Xu Yang <peteryang@vip.qq.com>
Co-authored-by: Xu Yang <xuyang1@microsoft.com>
* move cache auto continue and retry to all api backend
* add type checker to json mode output
* fix CI
* feat: Add json_mode handling and streaming support in chat completion function
* lint
* fix a bug when returning a dict which value could contain int or bool
* remove litellm
---------
Co-authored-by: Xu Yang <xuyang1@microsoft.com>
Co-authored-by: Young <afe.young@gmail.com>
* refactor: Update type annotations and remove unused class in evolving modules
* refactor: Simplify evolving agent and feedback handling in CoSTEER module
* lint & CI
* mypy
* ruff for core
* mypy
* refactor: remove unnecessary comments and update feedback handling logic
* refactor: Add prev_task_feedback parameter to evolving strategies
* feat: Clear folder before extracting zip file in DockerEnv
* fix: Correct retrieval of last experiment from history
* refine ds modal for more cases: eval and es
* update model template
* prompts for model and ensemble
* fix a bug
* fix a bug
* init: ds workflow evovingstrategy
* Adding ensemble (#505)
* Initial Draft
* Updating logic for init
* Revising
* Successful Testing
* Updating to use the latest & right class
* bug: bug-fixing for testing
* data science loop changes
* data science loop base
* ds loop feedback
* fix
* remove measure_time because it's duplicated (in LoopBase)
* add the knowledge query for data_loader & feature
* edit ds workflow evaluator
* data_loader bug fix
* stop evolving when all tasks completed
* llm app change
* fix break all complete strategy
* Adding queried knowledge (#508)
Co-authored-by: XianBW <36835909+XianBW@users.noreply.github.com>
* fix loop bug
* ds workflow evaluator; test; refine prompts
* workflow spec
* fix ci
* feature task changes
* ds loop change
* fix a bug in feat
* add query knowledge for model and workflow
* llm_debug info(for show) using pickle instead of json
* remove NextLoopException
* loop change
* coder raise CoderError when all sub_tasks failed
* rename code_dict to file_dict in FBWorkspace
* add CoSTEER unittest
* now show self.version in Task.get_task_information(), simplify CoSTEER sub tasks definition
* remove some properties in ModelTask, add model_type in it.
* fix llm app bug
* llm web app bug fix
* ds loop bug fix
* fix: give component code to feature&ens eval
* loop catch error bug
* rename load_from_raw_data to load_data
* feat: Add debug data creation functionality for data science scenarios
* support local folder (#511)
* support local folder
* remove unnecessary random
* KaggleScen Subclass
* small fix
* use template for style description
* update default scen to kaggle
* update sample data script
* make sure frac < 1
* fix a bug
* feature spec changes
* fix
* changeimport order
* clear unnecessary std outputs
* fix a typo
* create sample folder after unzip kaggle data
* feature/model test script update
* Align the data types across modules.
* fix a bug in model eval
* show line number
* move sample entry point to app
* spec & model prompt changes
* Refine the competition specification to address the data type problem and the coherence issue.
* fix some bugs
* add file filter in FBworkspace.code property
* support non-binary prediction
* avoid too much warnings
* fix a bug in ensemble module
* filtered the knowledge query in all modules
* delete RAG in idea proposal
* refine the code in ensemble
* show exp workspace in llm_st
* exp_gen bug fix
* feedback bug fix
* use `feature` instead of `feat01`
* Trace & method of judging if exp is completed change
* fix a bug in package calling and execute ci
* fix code
* bug fix
* bug fix
* fix a bug
* fix some bugs
* fix a bug
* refactor: Enhance error handling and feedback in data science loop
* support different use_azure on chat and embedding models
* multi-model proposal logic
* fix a small syntax error
* loopBase and some changes
* ensemble scores change
* fbworkspace.code -> .all_codes
* use all model codes in workflow coder
* check scores.csv's keys(model_names)
* model name changes
* add a todo in ensemble test
* sota_exp changes
* give model info in exp gen
* add runner time limit
* config using debug data or not in evals
* exp to feedback base
* add feature code when writing model task
* small problem
* copying during sampling
* update
* refactor: Simplify code handling and improve workspace management
* model part output fix
* print model's execution time
* bug fix
* ensemble test fix
* ens small change
* ens_test bug fix
* Refine partial expansion logic to display only a few subfolders when their structure is uniform, improving readability in nested directories.
* several update on prompts
* sample subfolders
* Filter the stdout after code execution to remove irrelevant information e.g. progress bars, whitespace characters, excessive line breaks.
* Add some more prompts and comments
* several update on the first init rounds
* model timeout as error
* fix pattern of getting model codes in workspace
* small bux fix on model prompts
* remove get_code_with_key since we have regex pattern
* fix: Correct tqdm progress bar update logic in LoopBase class
* feat: Add diff generation and enhance feedback mechanism in data science loop
* update some fix to model and workflow prompts
* refine the logic of progress bar filter
* add last_successful_exp in exp_gen
* fix a one line bug
* add a hint in prompt
* fix data sample for bms
* fix data sample for bms
* hypothesis small fix
* crawler readme update
* fix component gen
* fix bug
* annotation change
* load description.md if it exists
* refactor: Simplify SOTA description handling in feedback and prompts
* refactor: Use shared templates for feedback and experiment descriptions
* change webapp for model codes changes
* update proposal
* add timeout message for docker run output
* fix
* refine the code in docker time processing
* use .shape instead of len() when do shape eval
* won't change size during iteration
* support bson sample
* sample support jsonl and bson
* add former_code to coder prompts
* a little speed us in debug data creating
* filter progress bar when eval ens and main
* avoid costeer makes no change to former code
* fix several log error
* add timeout judge threshold
* fix some bugs in the evaluation of component output shapes
* File structure for supporting litellm (#517)
Co-authored-by: Young <afe.young@gmail.com>
* ignore submission and show processing
* ignore submission and show processing
* add efficiency notice
* refactor: Enhance error message with detailed feedback summary
* refactor: Simplify component handling in DSExpGen class
* refactor: Update code structure and add docstring for clarity
* reserve one sample to each label in data sampling
* add Evaluation info
* refine costeer code to avoid giving same code twice
* use raw_description as plain text
* add a prompt hint to avoid same dict key
* model task name bug in first model exp gen
* fix a typo
* add some debug info in costeer tests
* task init change
* enhance data sampling
* refine the code in data_loader
* more reasonable loop
* fix a bug in data folder description
* add error msg & traceback to execution feedback
* fix llm error msg detection
* add task information to costeer eval & add cache to docker run(use zipfile to store the whole workspace)
* fix CI first round
* fix CI second round
* use txt to store test script to avoid pytest
* remove zipfile in requirements
* add azure.identity to requirements
* ignore debug web page
* component test changes
* remove redundent task_desc in model coder
* feat: Add APE module and prompts for automated prompt engineering
* fix: Update .gitignore and improve text formatting in eval.py
* refactor: Update print output and improve code comments and imports
* style: Fix string formatting and import order in ape.py and fmt.py
* exclude ape
* add a data folder notice
* reduce unnecessary output to stdout
* refine the code of describe_data_folder
* fix ci
* style: streamlit style update (#522)
* streamlit style update
* fix import
* fix format
* fix llm_st loop progress bar
* debugapp small change
* fix model str
* refine some prompts
* fix model str
* fix CI
* refine the logic associated with the data_folder
* fix ci
* small change
* set filter_progress_bar as default in execute
* model proposal with workflow
* add submission check in workflow eval
* fix bug
* small change
* fix CI
* fix CI
* refactor: Move generate_diff to utils and update DSExpGen logic
* more reasonable prompt describing metric direction
* fix a minor jinja2 bug
* quick fix exp_gen bugs
* fix the following bug
* fix
* fix some bugs
* remove workflow from model
* add pending_tasks_list in data science to enable coding model and workflow
* refine the code for handling JSON-formatted data descriptions
* assert with information
* ensure correct csv file name
* add logging to help record the output
* log competition
* add log tag for debug llm app
* test: Test ds refactor ll (#523)
* fix bugs to former scenario
* fix a bug because coding in rdloop changed
* fix the bug when feedback gets no hypothesis
* fix trace structure
* change all trace hist when merging hypothesis to experiments
* ignore some error in ruff
* fix kaggle scenario bugs
* refine one line
* another bug
* another small bug
* fix ui bugs
* chage kaggle train.py path
---------
Co-authored-by: Xu Yang <peteryang@vip.qq.com>
* fix CI
* Update rdagent/app/data_science/loop.py
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* add samplecsv into spec prompts
* fix CI
---------
Co-authored-by: TPLin22 <tplin2@163.com>
Co-authored-by: yuanteli <1957922024@qq.com>
Co-authored-by: Xisen Wang <118058822+xisen-w@users.noreply.github.com>
Co-authored-by: Bowen Xian <xianbowen@outlook.com>
Co-authored-by: Xu Yang <peteryang@vip.qq.com>
Co-authored-by: XianBW <36835909+XianBW@users.noreply.github.com>
Co-authored-by: Tim <illking@foxmail.com>
Co-authored-by: 炼金术师华华 <37462254+YeewahChan@users.noreply.github.com>
Co-authored-by: Linlang <30293408+SunsetWolf@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Use ExtendedBaseSettings to replace BaseSettings
* update a more general way to pass the default setting
* update all code
* fix CI
* fix CI
* fix qlib scenario
* fix CI
* fix CI
* fix CI & add data science interfaces
* remove redundant code
* abandon costeer knowledge base v1
---------
Co-authored-by: Xu Yang <xuyang1@microsoft.com>
Co-authored-by: XianBW <36835909+XianBW@users.noreply.github.com>
* udpate plot
* log and reduce token
* trace tag
* add simple_background parameter to get_scenario_all_desc
* update trace
* update first version code
* chat model map
* add annotation for stack index
* add annotation
* reformatted by black
* several update on kaggle scenarios
* update some new change
* fix CI
* fix CI
* fix a bug
* fix bugs in graph RAG
---------
Co-authored-by: Tim <illking@foxmail.com>
* simplify RDAgent conf
* add unified cacher(untested)
* fix small bugs
* fix a bug
* fix a small bug in runner
* use hash_key = None to skip cache
* fix CI
* in factor execution, ignore cache when raise exception
* add file locker to avoid mp calling
* fix CI
* use function __module__ name as folder in cache
1. when facing odd v2_query_component_limit, make from gt bigger than without gt
2. do code evaluator even value does not pass
3. provide the value header to value failed evaluators
TPTBusiness