- 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>
- Rule 7 extended: session-based aggregations (London/NY/Asian) must also
be shifted by 1 trading day before use — same as daily aggregations
- Rule 8 added: prefer pure intraday rolling factors (RSI, Bollinger, VWAP
deviation, rolling std) that have no look-ahead risk and vary every minute
- predix_full_eval.py: apply _shift_daily_constant_factor_if_needed before IC
- predix_gen_strategies_real_bt.py: improved swing prompt with daily-level
signal logic guidance for daily-constant factors
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Daily factors (e.g. daily_log_return) carried same-day close data at 00:00,
giving the model end-of-day information at bar open — a classic look-ahead bias
that produced spurious IC=0.25 and Sharpe=24 with 98% win rate.
Changes:
- factor_runner.py: add _shift_daily_constant_factor_if_needed() that detects
factors where >90% of days have a single unique intraday value, then shifts
them by 1 trading day before IC computation
- prompts.yaml: add rule #7 instructing LLM to always shift(1) daily aggregates
before forward-filling to minute bars
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Add explicit warning against .date on datetime index (causes data loss
to single year, only 314 entries instead of 2020-2026)
- Add explicit warning against df.merge() which destroys MultiIndex
(causes RangeIndex output instead of required MultiIndex)
- Enforce column name must be exactly factor_name, not a shortened alias
- Require transform() over apply() for per-group calculations to
preserve row count
- Add MultiIndex assertion before saving to result.h5
- Document expected output: ~1500+ daily entries for full 2020-2026 range
* refactor: unify qlib experiment configs, runners, and templates
* fix: use PropSetting instances instead of class attributes in qlib runners
* docs: add configurable train/valid/test time segments for fintech scenarios
* 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>
* add qlib_factor_strategy
* refine the code of action choosing
* fix a bug
* feat: template for kaggle (#308)
* init for s3e26
* ci issue
* fix a small bug in model runner which might cause error when model is the first try (#309)
* update
---------
Co-authored-by: Haoran Pan <167847254+TPLin22@users.noreply.github.com>
Co-authored-by: Xu Yang <peteryang@vip.qq.com>
* store code into FBImplementation
* fix path related bugs
* fix a bug
* fix factor related small bugs
* re-submit all model related code
* new code to model coder
* finish the model evolving code
---------
Co-authored-by: xuyang1 <xuyang1@microsoft.com>
* use CoSTEER as component name
* rename factorimplementation to avoid confusion
* rename modelimplementation
* align benchmark and evolving evaluators
* add scenario to evaluator init function
* rename all factorimplementationknowledge in CoSTEER
* remove all scenario related information in component
* remove useless code
---------
Co-authored-by: xuyang1 <xuyang1@microsoft.com>
* update all code
* save code
* update first version of factor proposal
* change a comment
* remove a useless comment
---------
Co-authored-by: xuyang1 <xuyang1@microsoft.com>
TPTBusiness