diff --git a/rdagent/app/factor_implementation/eval_implement.py b/rdagent/app/factor_implementation/eval_implement.py index 06b653c5..0ae577b8 100644 --- a/rdagent/app/factor_implementation/eval_implement.py +++ b/rdagent/app/factor_implementation/eval_implement.py @@ -1,18 +1,18 @@ -from rdagent.core.conf import BenchmarkSettings +from rdagent.benchmark.conf import BenchmarkSettings from rdagent.core.utils import import_class from rdagent.benchmark.eval_method import FactorImplementEval -from rdagent.benchmark.data_process import load_eval_data +from rdagent.factor_implementation.task_loader.json_loader import FactorTestCaseLoaderFromJsonFile # 1.read the settings bs = BenchmarkSettings() # 2.read and prepare the eval_data -test_cases = load_eval_data(bs.bench_version) +test_cases = FactorTestCaseLoaderFromJsonFile().load(bs.bench_data_path) # 3.declare the method to be tested and pass the arguments. -# TODO: Whether it is necessary to define two Eval method classes for two data type? + method_cls = import_class(bs.bench_method_cls) -generate_method = method_cls(bs.bench_method_extra_kwargs) +generate_method = method_cls() # 4.declare the eval method and pass the arguments. eval_method = FactorImplementEval( @@ -23,8 +23,4 @@ eval_method = FactorImplementEval( ) # 5.run the eval -eval_method.eval() - -# 6.save the result -eval_method.save(output_path = bs.bench_result_path) - +res = eval_method.eval() diff --git a/rdagent/benchmark/conf.py b/rdagent/benchmark/conf.py index 10241b90..bcbbc030 100644 --- a/rdagent/benchmark/conf.py +++ b/rdagent/benchmark/conf.py @@ -2,24 +2,22 @@ from dotenv import load_dotenv load_dotenv(verbose=True, override=True) from dataclasses import field from pathlib import Path -from typing import Literal, Optional, Union +from typing import Optional from pydantic_settings import BaseSettings DIRNAME = Path(__file__).absolute().resolve().parent -BENCHMARK_VERSION = Literal["paper", "amcV01", "amcV02train", "amcV02test"] - class BenchmarkSettings(BaseSettings): ground_truth_dir: Path = DIRNAME / "ground_truth" - bench_version: Union[BENCHMARK_VERSION, str] = "paper" + bench_data_path: Path = DIRNAME / "example.json" - bench_test_round: int = 20 + bench_test_round: int = 10 bench_test_case_n: Optional[int] = None # how many test cases to run; If not given, all test cases will be run - bench_method_cls: str = "scripts.factor_implementation.baselines.naive.one_shot.OneshotFactorGen" + bench_method_cls: str = "rdagent.factor_implementation.CoSTEER.CoSTEERFG" bench_method_extra_kwargs: dict = field( default_factory=dict, ) # extra kwargs for the method to be tested except the task list diff --git a/rdagent/benchmark/eval_method.py b/rdagent/benchmark/eval_method.py index 2735e70b..262d248b 100644 --- a/rdagent/benchmark/eval_method.py +++ b/rdagent/benchmark/eval_method.py @@ -3,7 +3,7 @@ from typing import List, Tuple, Union from tqdm import tqdm from collections import defaultdict -from rdagent.core.conf import RD_AGENT_SETTINGS +from rdagent.factor_implementation.share_modules.factor_implementation_config import FACTOR_IMPLEMENT_SETTINGS from rdagent.core.exception import ImplementRunException from rdagent.core.task import ( TaskImplementation, @@ -102,29 +102,25 @@ class BaseEval: class FactorImplementEval(BaseEval): def __init__( self, - test_case: TestCase, + test_cases: TestCase, method: TaskGenerator, test_round: int = 10, *args, **kwargs, ): - # evaluator collection for online evaluation - online_evaluator_l = ( - [ - FactorImplementationCorrelationEvaluator, - FactorImplementationIndexEvaluator, - FactorImplementationIndexFormatEvaluator, - FactorImplementationMissingValuesEvaluator, - FactorImplementationRowCountEvaluator, - FactorImplementationSingleColumnEvaluator, - FactorImplementationValuesEvaluator, - ], - ) - super().__init__(online_evaluator_l, test_case, method, *args, **kwargs) + online_evaluator_l = [ + FactorImplementationSingleColumnEvaluator(), + FactorImplementationIndexFormatEvaluator(), + FactorImplementationRowCountEvaluator(), + FactorImplementationIndexEvaluator(), + FactorImplementationMissingValuesEvaluator(), + FactorImplementationValuesEvaluator(), + FactorImplementationCorrelationEvaluator(hard_check=False), + ] + super().__init__(online_evaluator_l, test_cases, method, *args, **kwargs) self.test_round = test_round def eval(self): - gen_factor_l_all_rounds = [] test_cases_all_rounds = [] res = defaultdict(list) @@ -139,25 +135,22 @@ class FactorImplementEval(BaseEval): print("Manually interrupted the evaluation. Saving existing results") break - if len(gen_factor_l) != len(self.test_cases): + if len(gen_factor_l.corresponding_implementations) != len(self.test_cases.ground_truth): raise ValueError( "The number of cases to eval should be equal to the number of test cases.", ) - gen_factor_l_all_rounds.extend(gen_factor_l) - test_cases_all_rounds.extend(self.test_cases) - - eval_res_l = [] + gen_factor_l_all_rounds.extend(gen_factor_l.corresponding_implementations) + test_cases_all_rounds.extend(self.test_cases.ground_truth) eval_res_list = multiprocessing_wrapper( [ - (self.eval_case, (gt_case.ground_truth, gen_factor)) + (self.eval_case, (gt_case, gen_factor)) for gt_case, gen_factor in zip(test_cases_all_rounds, gen_factor_l_all_rounds) ], - n=RD_AGENT_SETTINGS.evo_multi_proc_n, + n=FACTOR_IMPLEMENT_SETTINGS.evo_multi_proc_n, ) for gt_case, eval_res, gen_factor in tqdm(zip(test_cases_all_rounds, eval_res_list, gen_factor_l_all_rounds)): - res[gt_case.task.factor_name].append((gen_factor, eval_res)) - eval_res_l.append(eval_res) + res[gt_case.target_task.factor_name].append((gen_factor, eval_res)) return res diff --git a/rdagent/benchmark/example.json b/rdagent/benchmark/example.json new file mode 100644 index 00000000..b69ffd8e --- /dev/null +++ b/rdagent/benchmark/example.json @@ -0,0 +1,30 @@ +{ + "Turnover_Rate_Factor": { + "description": "A traditional factor based on 20-day average turnover rate, adjusted for market capitalization, which is further improved by applying the information distribution theory.", + "formulation": "\\text{Adjusted Turnover Rate} = \\frac{\\text{mean}(20\\text{-day turnover rate})}{\\text{Market Capitalization}}", + "variables": { + "20-day turnover rate": "Average turnover rate over the past 20 days.", + "Market Capitalization": "Total market value of a company's outstanding shares." + }, + "gt_code": "import pandas as pd\n\ndata_f = pd.read_hdf('daily_f.h5')\n\ndata = data_f.reset_index()\nwindow_size = 20\n\nnominator=data.groupby('instrument')[['30\u65e5\u6362\u624b\u7387']].rolling(window=window_size).mean().reset_index(0, drop=True)\n# transfer to series\nnew=nominator['30\u65e5\u6362\u624b\u7387']\ndata['Turnover_Rate_Factor']=new/data['\u6d41\u901aA\u80a1']\n\n# # set the datetime and instrument as index and drop the original index\nresult=pd.DataFrame(data['Turnover_Rate_Factor']).set_index(data_f.index)\n\n# transfer the result to series\nresult=result['Turnover_Rate_Factor']\nresult.to_hdf(\"result.h5\", key=\"data\")\n" + }, + "PctTurn20": { + "description": "A factor representing the percentage change in turnover rate over the past 20 trading days, market-value neutralized.", + "formulation": "\\text{PctTurn20} = \\frac{1}{N} \\sum_{i=1}^{N} \\left( \\frac{\\text{Turnover}_{i, t} - \\text{Turnover}_{i, t-20}}{\\text{Turnover}_{i, t-20}} \\right)", + "variables": { + "N": "Number of stocks in the market.", + "Turnover_{i, t}": "Turnover of stock i at day t.", + "Turnover_{i, t-20}": "Turnover of stock i at day t-20." + }, + "gt_code": "import pandas as pd\nfrom statsmodels import api as sm\n\n\ndef fill_mean(s: pd.Series) -> pd.Series:\n return s.fillna(s.mean()).fillna(0.0)\n\n\ndef market_value_neutralize(s: pd.Series, mv: pd.Series) -> pd.Series:\n s = s.groupby(\"datetime\", group_keys=False).apply(fill_mean)\n mv = mv.groupby(\"datetime\", group_keys=False).apply(fill_mean)\n\n df_f = mv.to_frame(\"\u5e02\u503c\")\n df_f[\"const\"] = 1\n X = df_f[[\"\u5e02\u503c\", \"const\"]]\n\n # Perform the Ordinary Least Squares (OLS) regression\n model = sm.OLS(s, X)\n results = model.fit()\n\n # Calculate the residuals\n df_f[\"residual\"] = results.resid\n df_f[\"norm_resi\"] = df_f.groupby(level=\"datetime\", group_keys=False)[\"residual\"].apply(\n lambda x: (x - x.mean()) / x.std(),\n )\n return df_f[\"norm_resi\"]\n\n\n# get_turnover\ndf_pv = pd.read_hdf(\"daily_pv.h5\", key=\"data\")\ndf_f = pd.read_hdf(\"daily_f.h5\", key=\"data\")\nturnover = df_pv[\"$money\"] / df_f[\"\u6d41\u901a\u5e02\u503c\"]\n\nf = turnover.groupby(\"instrument\").pct_change(periods=20)\n\nf_neutralized = market_value_neutralize(f, df_f[\"\u6d41\u901a\u5e02\u503c\"])\n\nf_neutralized.to_hdf(\"result.h5\", key=\"data\")\n" + }, + "PB_ROE": { + "description": "Constructed using the ranking difference between PB and ROE, with PB and ROE replacing original PB and ROE to obtain reconstructed factor values.", + "formulation": "\\text{rank}(PB\\_t) - rank(ROE_t)", + "variables": { + "\\text{rank}(PB_t)": "Ranking PB on cross-section at time t.", + "\\text{rank}(ROE_t)": "Ranking single-quarter ROE on cross-section at time t." + }, + "gt_code": "#!/usr/bin/env python\n\nimport pandas as pd\n\ndata_f = pd.read_hdf('daily_f.h5')\n\ndata = data_f.reset_index()\n\n# Calculate the rank of PB and ROE\ndata['PB_rank'] = data.groupby('datetime')['B/P'].rank()\ndata['ROE_rank'] = data.groupby('datetime')['ROE'].rank()\n\n# Calculate the difference between the ranks\ndata['PB_ROE'] = data['PB_rank'] - data['ROE_rank']\n\n# set the datetime and instrument as index and drop the original index\nresult=pd.DataFrame(data['PB_ROE']).set_index(data_f.index)\n\n# transfer the result to series\nresult=result['PB_ROE']\nresult.to_hdf(\"result.h5\", key=\"data\")\n" + } +} \ No newline at end of file diff --git a/rdagent/core/task.py b/rdagent/core/task.py index 8e544948..bb2d758d 100644 --- a/rdagent/core/task.py +++ b/rdagent/core/task.py @@ -118,8 +118,8 @@ class TestCase: def __init__( self, - target_task: BaseTask, - ground_truth: TaskImplementation, + target_task: list[BaseTask] = [], + ground_truth: list[TaskImplementation] = [], ): self.ground_truth = ground_truth self.target_task = target_task diff --git a/rdagent/factor_implementation/evolving/factor.py b/rdagent/factor_implementation/evolving/factor.py index 6fd0cf7d..f2cee55d 100644 --- a/rdagent/factor_implementation/evolving/factor.py +++ b/rdagent/factor_implementation/evolving/factor.py @@ -173,7 +173,7 @@ class FileBasedFactorImplementation(TaskImplementation): FACTOR_IMPLEMENT_SETTINGS.file_based_execution_data_folder, ) self.workspace_path.mkdir(exist_ok=True, parents=True) - + source_data_path.mkdir(exist_ok=True, parents=True) code_path = self.workspace_path / f"{self.target_task.factor_name}.py" code_path.write_text(self.code) diff --git a/rdagent/factor_implementation/share_modules/factor_implementation_config.py b/rdagent/factor_implementation/share_modules/factor_implementation_config.py index 515e1193..16607143 100644 --- a/rdagent/factor_implementation/share_modules/factor_implementation_config.py +++ b/rdagent/factor_implementation/share_modules/factor_implementation_config.py @@ -9,13 +9,13 @@ SELECT_METHOD = Literal["random", "scheduler"] class FactorImplementSettings(BaseSettings): file_based_execution_data_folder: str = str( - (Path().cwd() / "factor_implementation_source_data").absolute(), + (Path().cwd() / "git_ignore_folder" / "factor_implementation_source_data").absolute(), ) file_based_execution_workspace: str = str( - (Path().cwd() / "factor_implementation_workspace").absolute(), + (Path().cwd() / "git_ignore_folder" / "factor_implementation_workspace").absolute(), ) implementation_execution_cache_location: str = str( - (Path().cwd() / "factor_implementation_execution_cache").absolute(), + (Path().cwd() / "git_ignore_folder" / "factor_implementation_execution_cache").absolute(), ) enable_execution_cache: bool = True # whether to enable the execution cache diff --git a/rdagent/factor_implementation/task_loader/json_loader.py b/rdagent/factor_implementation/task_loader/json_loader.py index 722b2ecc..d626d98a 100644 --- a/rdagent/factor_implementation/task_loader/json_loader.py +++ b/rdagent/factor_implementation/task_loader/json_loader.py @@ -1,7 +1,8 @@ import json from pathlib import Path from rdagent.core.task import TaskLoader -from rdagent.factor_implementation.evolving.factor import FactorImplementTask +from rdagent.factor_implementation.evolving.factor import FactorImplementTask, FileBasedFactorImplementation +from rdagent.core.task import TestCase class FactorImplementationTaskLoaderFromDict(TaskLoader): @@ -21,7 +22,8 @@ class FactorImplementationTaskLoaderFromDict(TaskLoader): class FactorImplementationTaskLoaderFromJsonFile(TaskLoader): def load(self, json_file_path: Path) -> list: - factor_dict = json.load(json_file_path) + with open(json_file_path, 'r') as file: + factor_dict = json.load(file) return FactorImplementationTaskLoaderFromDict().load(factor_dict) @@ -29,3 +31,22 @@ class FactorImplementationTaskLoaderFromJsonString(TaskLoader): def load(self, json_string: str) -> list: factor_dict = json.loads(json_string) return FactorImplementationTaskLoaderFromDict().load(factor_dict) + +class FactorTestCaseLoaderFromJsonFile(TaskLoader): + def load(self, json_file_path: Path) -> list: + with open(json_file_path, 'r') as file: + factor_dict = json.load(file) + TestData = TestCase() + for factor_name, factor_data in factor_dict.items(): + task = FactorImplementTask( + factor_name=factor_name, + factor_description=factor_data["description"], + factor_formulation=factor_data["formulation"], + variables=factor_data["variables"], + ) + gt = FileBasedFactorImplementation(task, code=factor_data["gt_code"]) + gt.execute() + TestData.target_task.append(task) + TestData.ground_truth.append(gt) + + return TestData \ No newline at end of file