liaozhaorun/ProStock
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

feat(data): 添加个股资金流向接口并重构速率限制配置

Browse Source 
- 新增 moneyflow 资金流向数据同步模块
- 实现接口级速率限制配置（sync_config.py）
- 更新流动性相关因子定义
- 添加非对称量化损失函数
This commit is contained in:
liaozhaorun
2026-04-03 23:57:47 +08:00
parent c143815443
commit 9e7d4241c6
18 changed files with 1473 additions and 334 deletions
Download Patch File Download Diff File Expand all files Collapse all files

314
src/experiment/common.py
View File

@@ -123,133 +123,133 @@ SELECTED_FACTORS = [
"GTJA_alpha048",
"GTJA_alpha049",
"GTJA_alpha050",
# "GTJA_alpha051",
# "GTJA_alpha052",
# "GTJA_alpha053",
# "GTJA_alpha054",
# "GTJA_alpha056",
# "GTJA_alpha057",
# "GTJA_alpha058",
# "GTJA_alpha059",
# "GTJA_alpha060",
# "GTJA_alpha061",
# "GTJA_alpha062",
# "GTJA_alpha063",
# "GTJA_alpha064",
# "GTJA_alpha065",
# "GTJA_alpha066",
# "GTJA_alpha067",
# "GTJA_alpha068",
# "GTJA_alpha070",
# "GTJA_alpha071",
# "GTJA_alpha072",
# "GTJA_alpha073",
# "GTJA_alpha074",
# "GTJA_alpha076",
# "GTJA_alpha077",
# "GTJA_alpha078",
# "GTJA_alpha079",
# "GTJA_alpha080",
# "GTJA_alpha081",
# "GTJA_alpha082",
# "GTJA_alpha083",
# "GTJA_alpha084",
# "GTJA_alpha085",
# "GTJA_alpha086",
# "GTJA_alpha087",
# "GTJA_alpha088",
# "GTJA_alpha089",
# "GTJA_alpha090",
# "GTJA_alpha091",
# "GTJA_alpha092",
# "GTJA_alpha093",
# "GTJA_alpha094",
# "GTJA_alpha095",
# "GTJA_alpha096",
# "GTJA_alpha097",
# "GTJA_alpha098",
# "GTJA_alpha099",
# "GTJA_alpha100",
# "GTJA_alpha101",
# "GTJA_alpha102",
# "GTJA_alpha103",
# "GTJA_alpha104",
# "GTJA_alpha105",
# "GTJA_alpha106",
# "GTJA_alpha107",
# "GTJA_alpha108",
# "GTJA_alpha109",
# "GTJA_alpha110",
# "GTJA_alpha111",
# "GTJA_alpha112",
# # "GTJA_alpha113",
# "GTJA_alpha114",
# "GTJA_alpha115",
# "GTJA_alpha117",
# "GTJA_alpha118",
# "GTJA_alpha119",
# "GTJA_alpha120",
# # "GTJA_alpha121",
# "GTJA_alpha122",
# "GTJA_alpha123",
# "GTJA_alpha124",
# "GTJA_alpha125",
# "GTJA_alpha126",
# "GTJA_alpha127",
# "GTJA_alpha128",
# "GTJA_alpha129",
# "GTJA_alpha130",
# "GTJA_alpha131",
# "GTJA_alpha132",
# "GTJA_alpha133",
# "GTJA_alpha134",
# "GTJA_alpha135",
# "GTJA_alpha136",
# # "GTJA_alpha138",
# "GTJA_alpha139",
# # "GTJA_alpha140",
# "GTJA_alpha141",
# "GTJA_alpha142",
# "GTJA_alpha145",
# # "GTJA_alpha146",
# "GTJA_alpha148",
# "GTJA_alpha150",
# "GTJA_alpha151",
# "GTJA_alpha152",
# "GTJA_alpha153",
# "GTJA_alpha154",
# "GTJA_alpha155",
# "GTJA_alpha156",
# "GTJA_alpha157",
# "GTJA_alpha158",
# "GTJA_alpha159",
# "GTJA_alpha160",
# "GTJA_alpha161",
# # "GTJA_alpha162",
# "GTJA_alpha163",
# "GTJA_alpha164",
# # "GTJA_alpha165",
# "GTJA_alpha166",
# "GTJA_alpha167",
# "GTJA_alpha168",
# "GTJA_alpha169",
# "GTJA_alpha170",
# "GTJA_alpha171",
# "GTJA_alpha173",
# "GTJA_alpha174",
# "GTJA_alpha175",
# "GTJA_alpha176",
# "GTJA_alpha177",
# "GTJA_alpha178",
# "GTJA_alpha179",
# "GTJA_alpha180",
# # "GTJA_alpha183",
# "GTJA_alpha184",
# "GTJA_alpha185",
# "GTJA_alpha187",
# "GTJA_alpha188",
# "GTJA_alpha189",
# "GTJA_alpha191",
"GTJA_alpha051",
"GTJA_alpha052",
"GTJA_alpha053",
"GTJA_alpha054",
"GTJA_alpha056",
"GTJA_alpha057",
"GTJA_alpha058",
"GTJA_alpha059",
"GTJA_alpha060",
"GTJA_alpha061",
"GTJA_alpha062",
"GTJA_alpha063",
"GTJA_alpha064",
"GTJA_alpha065",
"GTJA_alpha066",
"GTJA_alpha067",
"GTJA_alpha068",
"GTJA_alpha070",
"GTJA_alpha071",
"GTJA_alpha072",
"GTJA_alpha073",
"GTJA_alpha074",
"GTJA_alpha076",
"GTJA_alpha077",
"GTJA_alpha078",
"GTJA_alpha079",
"GTJA_alpha080",
"GTJA_alpha081",
"GTJA_alpha082",
"GTJA_alpha083",
"GTJA_alpha084",
"GTJA_alpha085",
"GTJA_alpha086",
"GTJA_alpha087",
"GTJA_alpha088",
"GTJA_alpha089",
"GTJA_alpha090",
"GTJA_alpha091",
"GTJA_alpha092",
"GTJA_alpha093",
"GTJA_alpha094",
"GTJA_alpha095",
"GTJA_alpha096",
"GTJA_alpha097",
"GTJA_alpha098",
"GTJA_alpha099",
"GTJA_alpha100",
"GTJA_alpha101",
"GTJA_alpha102",
"GTJA_alpha103",
"GTJA_alpha104",
"GTJA_alpha105",
"GTJA_alpha106",
"GTJA_alpha107",
"GTJA_alpha108",
"GTJA_alpha109",
"GTJA_alpha110",
"GTJA_alpha111",
"GTJA_alpha112",
# "GTJA_alpha113",
"GTJA_alpha114",
"GTJA_alpha115",
"GTJA_alpha117",
"GTJA_alpha118",
"GTJA_alpha119",
"GTJA_alpha120",
# "GTJA_alpha121",
"GTJA_alpha122",
"GTJA_alpha123",
"GTJA_alpha124",
"GTJA_alpha125",
"GTJA_alpha126",
"GTJA_alpha127",
"GTJA_alpha128",
"GTJA_alpha129",
"GTJA_alpha130",
"GTJA_alpha131",
"GTJA_alpha132",
"GTJA_alpha133",
"GTJA_alpha134",
"GTJA_alpha135",
"GTJA_alpha136",
# "GTJA_alpha138",
"GTJA_alpha139",
# "GTJA_alpha140",
"GTJA_alpha141",
"GTJA_alpha142",
"GTJA_alpha145",
# "GTJA_alpha146",
"GTJA_alpha148",
"GTJA_alpha150",
"GTJA_alpha151",
"GTJA_alpha152",
"GTJA_alpha153",
"GTJA_alpha154",
"GTJA_alpha155",
"GTJA_alpha156",
"GTJA_alpha157",
"GTJA_alpha158",
"GTJA_alpha159",
"GTJA_alpha160",
"GTJA_alpha161",
# "GTJA_alpha162",
"GTJA_alpha163",
"GTJA_alpha164",
# "GTJA_alpha165",
"GTJA_alpha166",
"GTJA_alpha167",
"GTJA_alpha168",
"GTJA_alpha169",
"GTJA_alpha170",
"GTJA_alpha171",
"GTJA_alpha173",
"GTJA_alpha174",
"GTJA_alpha175",
"GTJA_alpha176",
"GTJA_alpha177",
"GTJA_alpha178",
"GTJA_alpha179",
"GTJA_alpha180",
# "GTJA_alpha183",
"GTJA_alpha184",
"GTJA_alpha185",
"GTJA_alpha187",
"GTJA_alpha188",
"GTJA_alpha189",
"GTJA_alpha191",
"chip_dispersion_90",
"chip_dispersion_70",
"cost_skewness",
@@ -270,12 +270,27 @@ SELECTED_FACTORS = [
"bottom_cost_stability",
"pivot_reversion",
"chip_transition",
# "amivest_liq_20",
# "atr_price_impact",
# "hui_heubel_ratio",
# "corwin_schultz_spread_20",
# "roll_spread_20",
# "gibbs_effective_spread",
# "overnight_illiq_20",
# "illiq_volatility_20",
# "amount_cv_20",
# "amount_skewness_20",
# "low_vol_days_20",
# "liquidity_shock_momentum",
# "downside_illiq_20",
# "upside_illiq_20",
# "illiq_asymmetry_20",
# "pastor_stambaugh_proxy"
]
# 因子定义字典（完整因子库，用于存放尚未注册到metadata的因子）
FACTOR_DEFINITIONS = {"cs_rank_circ_mv": "cs_rank(circ_mv)"}
# =============================================================================
# Label 配置（统一绑定 label_name 和 label_dsl）
# =============================================================================
@@ -308,11 +323,11 @@ def get_label_factor(label_name: str) -> dict:
# 辅助函数
# =============================================================================
def register_factors(
engine: FactorEngine,
selected_factors: List[str],
factor_definitions: dict,
label_factor: dict,
excluded_factors: Optional[List[str]] = None,
engine: FactorEngine,
selected_factors: List[str],
factor_definitions: dict,
label_factor: dict,
excluded_factors: Optional[List[str]] = None,
) -> List[str]:
"""注册因子。
@@ -393,11 +408,11 @@ def register_factors(
def prepare_data(
engine: FactorEngine,
feature_cols: List[str],
start_date: str,
end_date: str,
label_name: str,
engine: FactorEngine,
feature_cols: List[str],
start_date: str,
end_date: str,
label_name: str,
) -> pl.DataFrame:
"""准备数据。
@@ -455,11 +470,11 @@ def stock_pool_filter(df: pl.DataFrame) -> pl.Series:
"""
# 代码筛选（排除创业板、科创板、北交所）
code_filter = (
~df["ts_code"].str.starts_with("30") # 排除创业板
& ~df["ts_code"].str.starts_with("68") # 排除科创板
& ~df["ts_code"].str.starts_with("8") # 排除北交所
& ~df["ts_code"].str.starts_with("9") # 排除北交所
& ~df["ts_code"].str.starts_with("4") # 排除北交所
~df["ts_code"].str.starts_with("30") # 排除创业板
& ~df["ts_code"].str.starts_with("68") # 排除科创板
& ~df["ts_code"].str.starts_with("8") # 排除北交所
& ~df["ts_code"].str.starts_with("9") # 排除北交所
& ~df["ts_code"].str.starts_with("4") # 排除北交所
)
# 在已筛选的股票中，选取流通市值最小的500只
@@ -474,7 +489,6 @@ def stock_pool_filter(df: pl.DataFrame) -> pl.Series:
# 定义筛选所需的基础列
STOCK_FILTER_REQUIRED_COLUMNS = ["circ_mv"]
# =============================================================================
# 输出配置
# =============================================================================
@@ -518,7 +532,7 @@ def get_output_path(model_type: str, test_start: str, test_end: str) -> str:
def get_model_save_path(
model_type: str,
model_type: str,
) -> Optional[str]:
"""生成模型保存路径。
@@ -544,11 +558,11 @@ def get_model_save_path(
def save_model_with_factors(
model,
model_path: str,
selected_factors: list[str],
factor_definitions: dict,
fitted_processors: list | None = None,
model,
model_path: str,
selected_factors: list[str],
factor_definitions: dict,
fitted_processors: list | None = None,
) -> str:
"""保存模型及关联的因子信息和处理器。

46
src/experiment/learn_to_rank.py
View File

@@ -54,31 +54,27 @@ N_QUANTILES = 20
# 排除的因子列表
EXCLUDED_FACTORS = [
"GTJA_alpha010",
"GTJA_alpha005",
"GTJA_alpha002",
"GTJA_alpha027",
"GTJA_alpha051",
"GTJA_alpha044",
"GTJA_alpha041",
"GTJA_alpha131",
"GTJA_alpha103",
"GTJA_alpha087",
"GTJA_alpha093",
"GTJA_alpha092",
"GTJA_alpha073",
"GTJA_alpha127",
"GTJA_alpha117",
"GTJA_alpha124",
"GTJA_alpha162",
"GTJA_alpha177",
"GTJA_alpha188",
"smart_money_accumulation",
"GTJA_alpha014",
"GTJA_alpha056",
"GTJA_alpha085",
"GTJA_alpha154",
"GTJA_alpha141",
'active_market_cap',
'close_vwap_deviation',
'sharpe_ratio_20',
'upper_shadow_ratio',
'volume_ratio_5_20',
'GTJA_alpha090',
'GTJA_alpha084',
'GTJA_alpha066',
'GTJA_alpha150',
'GTJA_alpha148',
'GTJA_alpha106',
'GTJA_alpha109',
'GTJA_alpha108',
'GTJA_alpha176',
'GTJA_alpha169',
'GTJA_alpha156',
'chip_dispersion_70',
'winner_rate_cs_rank',
'atr_price_impact',
'low_vol_days_20',
'liquidity_shock_momentum',
]
# LambdaRank 模型参数配置

79
src/experiment/regression.py
View File

@@ -52,55 +52,36 @@ TRAINING_TYPE = "regression"
# 排除的因子列表
EXCLUDED_FACTORS = [
"GTJA_alpha036",
"GTJA_alpha032",
"GTJA_alpha010",
"GTJA_alpha005",
"CP",
"BP",
"debt_to_equity",
"current_ratio",
"GTJA_alpha002",
"GTJA_alpha027",
"GTJA_alpha064",
"GTJA_alpha062",
"GTJA_alpha043",
"GTJA_alpha044",
"GTJA_alpha120",
"GTJA_alpha117",
"GTJA_alpha103",
"GTJA_alpha104",
"GTJA_alpha105",
"GTJA_alpha073",
"GTJA_alpha077",
"GTJA_alpha085",
"GTJA_alpha090",
"GTJA_alpha087",
"GTJA_alpha083",
"GTJA_alpha092",
"GTJA_alpha133",
"GTJA_alpha131",
"GTJA_alpha126",
"GTJA_alpha124",
"GTJA_alpha162",
"GTJA_alpha164",
"GTJA_alpha157",
"GTJA_alpha177",
"price_to_avg_cost",
"cost_skewness",
"GTJA_alpha191",
"GTJA_alpha180",
"history_position",
"bottom_profit",
"mean_median_dev",
"smart_money_accumulation",
"GTJA_alpha013",
"GTJA_alpha099",
"GTJA_alpha107",
"GTJA_alpha119",
"GTJA_alpha141",
"GTJA_alpha130",
"GTJA_alpha173",
'GTJA_alpha016',
'volatility_20',
'current_ratio',
'GTJA_alpha001',
'GTJA_alpha141',
'GTJA_alpha129',
'GTJA_alpha164',
'amivest_liq_20',
'GTJA_alpha012',
'debt_to_equity',
'turnover_deviation',
'GTJA_alpha073',
'GTJA_alpha043',
'GTJA_alpha032',
'GTJA_alpha028',
'GTJA_alpha090',
'GTJA_alpha108',
'GTJA_alpha105',
'GTJA_alpha091',
'GTJA_alpha119',
'GTJA_alpha104',
'GTJA_alpha163',
'GTJA_alpha157',
'cost_skewness',
'GTJA_alpha176',
'chip_transition',
'amount_skewness_20',
'GTJA_alpha148',
'mean_median_dev',
'downside_illiq_20',
]
# 模型参数配置