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hugo2046/QuantsPlaybook

量化研究-券商金工研报复现

17.9 Elevated pattern signal View on GitHub

Maintenance and Governance Review

This report summarizes scanner evidence for hugo2046/QuantsPlaybook, a Jupyter Notebook project with 6,515 GitHub stars. SynthScan examined 118,980 lines of code across 643 source files, recording 1240 pattern matches distributed across 12 syntactic categories. The legacy adjusted pattern signal is 17.9 (Elevated pattern signal); it is not an authorship estimate.

Use the findings to ask whether maintainers can explain intent, verify dependencies, simplify unnecessary scaffolding, and focus review effort. Risk Payload v1 adds ownership, supply-chain, over-engineering, and review-effort vectors when available. These are prompts for human verification, not probabilities, security findings, or measured maintenance costs.

17.9
Adjusted Signal
17.9
Raw Pattern Score
100%
Time Factor
2026-10-03
Last Push
6.5K
Stars
Jupyter Notebook
Language
119.0K
Lines of Code
643
Files
1.2K
Pattern Hits
2026-10-12
Scan Date
0.34
HC Hit Rate

Risk Review · v3

Use each vector to guide manual verification of the underlying findings.

Cognitive Ownership Risk 27.5/100 · MEDIUM

Can maintainers explain intent, assumptions, and trade-offs? 700 finding(s).

Hallucinated Dependency & Supply Chain Risk 14.7/100 · LOW

Are imports declared and package/API names verified? 284 finding(s).

Agent Over-Engineering Ratio 48.0/100 · MEDIUM

Are wrappers, guards, and repeated structures necessary? 842 finding(s).

PR Review Velocity Impact 63.0/100 · HIGH

Which changes need focused human review? 1240 finding(s).

What These Metrics Mean

Adjusted Signal
Legacy pattern score normalized per 1,000 scanned lines and multiplied by the repository-level time factor. It is not an estimate of AI authorship.
Raw Pattern Score
Sum of severity- and context-weighted pattern findings before applying the time factor.
Time Factor
A coarse multiplier based on repository last-push metadata. It does not identify when individual code was written.
Pattern Hits
Count of configured pattern findings. Review each location and surrounding code before acting.
HC Hit Rate
High- and critical-severity findings per scanned file. Severity is a configured detector weight, not proof of a vulnerability or authorship.
Lines / Files
Scan coverage denominators used by the legacy normalized score.

Pattern-Signal History

Run additional scans to build a history of the legacy pattern signal. Score changes are not a measure of AI adoption or code quality.

No multi-scan history yet — run the scanner again to build trend data.

Severity Breakdown

Groups findings by configured severity weight. Severity is not a confidence estimate, security rating, or proof that a package is missing. Verify imports against the project's manifests and inspect each finding in its local design context.

CRITICAL 0HIGH 218MEDIUM 18LOW 1004

Directory Score Breakdown

This horizontal bar chart shows where configured pattern scores accumulate across directories. Treat the bars as locations to inspect, not as evidence of authorship or code quality. Use the file-level findings to decide whether a directory needs a focused ownership, dependency, complexity, or review check.

Pattern Findings

The scanner identified 1240 distinct pattern matches across 12 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a detector match to inspect in context. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the configured finding weight (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — legacy comment-context matches receive a ×1.5 score weight. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier under the legacy scoring model.

Cross-File Repetition165 hits · 825 pts
SeverityFileLineSnippetContext
HIGHB-因子构建类/企业生命周期/scr/utils.py0creates rolling-window 'blocks' of length `window` from `a`. note that the orientation of rows/columns follows that of pSTRING
HIGHC-择时类/技术分析算法框架与实战/py/technical_analysis_patterns.py0creates rolling-window 'blocks' of length `window` from `a`. note that the orientation of rows/columns follows that of pSTRING
HIGH…ion/build_timing_signal/technical_analysis_patterns.py0creates rolling-window 'blocks' of length `window` from `a`. note that the orientation of rows/columns follows that of pSTRING
HIGHB-因子构建类/高频价量相关性，意想不到的选股因子/Hugos_tools/Tdays.py0构造交易日历 args: trade_dates (pd.datatimeindex, optional): 交易日. defaults to none. returns: pd.dataframe: 交易日历表STRING
HIGHB-因子构建类/个股动量效应的识别及球队硬币因子/src/dataservice/trade_cal.py0构造交易日历 args: trade_dates (pd.datatimeindex, optional): 交易日. defaults to none. returns: pd.dataframe: 交易日历表STRING
HIGHC-择时类/特征分布建模择时/scr/ts_trade_days.py0构造交易日历 args: trade_dates (pd.datatimeindex, optional): 交易日. defaults to none. returns: pd.dataframe: 交易日历表STRING
HIGHB-因子构建类/高频价量相关性，意想不到的选股因子/Hugos_tools/Tdays.py0日期偏移 args: watch_date (str): 观察日 count (int): 偏离日 freq (str):频率,d-日度,w-周度,m-月份,y-年度 returns: dt.datetime: 目标日期STRING
HIGHB-因子构建类/个股动量效应的识别及球队硬币因子/src/dataservice/trade_cal.py0日期偏移 args: watch_date (str): 观察日 count (int): 偏离日 freq (str):频率,d-日度,w-周度,m-月份,y-年度 returns: dt.datetime: 目标日期STRING
HIGHC-择时类/特征分布建模择时/scr/ts_trade_days.py0日期偏移 args: watch_date (str): 观察日 count (int): 偏离日 freq (str):频率,d-日度,w-周度,m-月份,y-年度 returns: dt.datetime: 目标日期STRING
HIGH…/高频价量相关性，意想不到的选股因子/Hugos_tools/Stragegy_performance.py0determines the information ratio of a strategy. parameters ---------- returns : :py:class:`pandas.series` or pd.dataframSTRING
HIGHhugos_toolkit/BackTestReport/performance.py0determines the information ratio of a strategy. parameters ---------- returns : :py:class:`pandas.series` or pd.dataframSTRING
HIGHC-择时类/成交量的奥秘_另类价量共振指标的择时/scr/performance.py0determines the information ratio of a strategy. parameters ---------- returns : :py:class:`pandas.series` or pd.dataframSTRING
HIGHC-择时类/基于点位效率理论的个股趋势预测研究/py/performance.py0determines the information ratio of a strategy. parameters ---------- returns : :py:class:`pandas.series` or pd.dataframSTRING
HIGH…/高频价量相关性，意想不到的选股因子/Hugos_tools/Stragegy_performance.py0returns a new :py:class:`pandas.series` adjusted by adjustment_factor. optimizes for the case of adjustment_factor beingSTRING
HIGHhugos_toolkit/BackTestReport/performance.py0returns a new :py:class:`pandas.series` adjusted by adjustment_factor. optimizes for the case of adjustment_factor beingSTRING
HIGHC-择时类/成交量的奥秘_另类价量共振指标的择时/scr/performance.py0returns a new :py:class:`pandas.series` adjusted by adjustment_factor. optimizes for the case of adjustment_factor beingSTRING
HIGHC-择时类/基于点位效率理论的个股趋势预测研究/py/performance.py0returns a new :py:class:`pandas.series` adjusted by adjustment_factor. optimizes for the case of adjustment_factor beingSTRING
HIGHB-因子构建类/个股动量效应的识别及球队硬币因子/src/factor_analyze.py0预处理因子数据 args: factor_data (pd.dataframe): multiindex level0:datetime level1:instrument multicolumns level0:feature levelSTRING
HIGHB-因子构建类/筹码因子/scr/factor_analyze.py0预处理因子数据 args: factor_data (pd.dataframe): multiindex level0:datetime level1:instrument multicolumns level0:feature levelSTRING
HIGHB-因子构建类/凸显理论STR因子/scr/factor_analyze.py0预处理因子数据 args: factor_data (pd.dataframe): multiindex level0:datetime level1:instrument multicolumns level0:feature levelSTRING
HIGHB-因子构建类/个股动量效应的识别及球队硬币因子/src/plotting.py0author: hugo2046 shen.lan123@gmail.com date: 2023-03-27 15:02:44 lasteditors: hugo2046 shen.lan123@gmail.com lastedittimSTRING
HIGHB-因子构建类/筹码因子/scr/plotting.py0author: hugo2046 shen.lan123@gmail.com date: 2023-03-27 15:02:44 lasteditors: hugo2046 shen.lan123@gmail.com lastedittimSTRING
HIGHB-因子构建类/凸显理论STR因子/scr/plotting.py0author: hugo2046 shen.lan123@gmail.com date: 2023-03-27 15:02:44 lasteditors: hugo2046 shen.lan123@gmail.com lastedittimSTRING
HIGHB-因子构建类/个股动量效应的识别及球队硬币因子/src/plotting.py0预测值分组收益 args: pred_label (pd.dataframe): _description_ n (int, optional): 分组数. defaults to 5. returns: pd.dataframe: _deSTRING
HIGHB-因子构建类/筹码因子/scr/plotting.py0预测值分组收益 args: pred_label (pd.dataframe): _description_ n (int, optional): 分组数. defaults to 5. returns: pd.dataframe: _deSTRING
HIGHB-因子构建类/凸显理论STR因子/scr/plotting.py0预测值分组收益 args: pred_label (pd.dataframe): _description_ n (int, optional): 分组数. defaults to 5. returns: pd.dataframe: _deSTRING
HIGHB-因子构建类/个股动量效应的识别及球队硬币因子/src/plotting.py0ic自回归系数 args: pred_label (pd.dataframe): _description_ lag (int, optional): _description_. defaults to 1. returns: pd.daSTRING
HIGHB-因子构建类/筹码因子/scr/plotting.py0ic自回归系数 args: pred_label (pd.dataframe): _description_ lag (int, optional): _description_. defaults to 1. returns: pd.daSTRING
HIGHB-因子构建类/凸显理论STR因子/scr/plotting.py0ic自回归系数 args: pred_label (pd.dataframe): _description_ lag (int, optional): _description_. defaults to 1. returns: pd.daSTRING
HIGHB-因子构建类/个股动量效应的识别及球队硬币因子/src/plotting.py0计算组合换手率 args: pred_label (pd.dataframe): _description_ n (int, optional): 分组. defaults to 5. lag (int, optional): 滞后期. dSTRING
HIGHB-因子构建类/筹码因子/scr/plotting.py0计算组合换手率 args: pred_label (pd.dataframe): _description_ n (int, optional): 分组. defaults to 5. lag (int, optional): 滞后期. dSTRING
HIGHB-因子构建类/凸显理论STR因子/scr/plotting.py0计算组合换手率 args: pred_label (pd.dataframe): _description_ n (int, optional): 分组. defaults to 5. lag (int, optional): 滞后期. dSTRING
HIGHB-因子构建类/个股动量效应的识别及球队硬币因子/src/plotting.py0画ic,rank_ic args: pred_label (pd.dataframe): multiindex-datetime,code columns-score,label ax (plt.axes, optional): defauSTRING
HIGHB-因子构建类/筹码因子/scr/plotting.py0画ic,rank_ic args: pred_label (pd.dataframe): multiindex-datetime,code columns-score,label ax (plt.axes, optional): defauSTRING
HIGHB-因子构建类/凸显理论STR因子/scr/plotting.py0画ic,rank_ic args: pred_label (pd.dataframe): multiindex-datetime,code columns-score,label ax (plt.axes, optional): defauSTRING
HIGHB-因子构建类/个股动量效应的识别及球队硬币因子/src/dataservice/utils.py0格式化日期 args: dt (union[dt.datetime, dt.date, str]): fm (str, optional): 格式化表达. defaults to '%y-%m-%d'. returns: strSTRING
HIGHD-组合优化/MLT_TSMOM/src/utils.py0格式化日期 args: dt (union[dt.datetime, dt.date, str]): fm (str, optional): 格式化表达. defaults to '%y-%m-%d'. returns: strSTRING
HIGHC-择时类/特征分布建模择时系列之二/scr/utils.py0格式化日期 args: dt (union[dt.datetime, dt.date, str]): fm (str, optional): 格式化表达. defaults to '%y-%m-%d'. returns: strSTRING
HIGHC-择时类/特征分布建模择时/scr/utils.py0格式化日期 args: dt (union[dt.datetime, dt.date, str]): fm (str, optional): 格式化表达. defaults to '%y-%m-%d'. returns: strSTRING
HIGHhugos_toolkit/BackTestReport/tear.py0净值表现情况 # args: # price (pd.series): idnex-date values # result (list): 回测结果 #STRING
HIGHC-择时类/成交量的奥秘_另类价量共振指标的择时/scr/tear.py0净值表现情况 # args: # price (pd.series): idnex-date values # result (list): 回测结果 #STRING
HIGHC-择时类/特征分布建模择时系列之二/scr/bt_func.py0净值表现情况 # args: # price (pd.series): idnex-date values # result (list): 回测结果 #STRING
HIGHC-择时类/特征分布建模择时/scr/bt_func.py0净值表现情况 # args: # price (pd.series): idnex-date values # result (list): 回测结果 #STRING
HIGHhugos_toolkit/BackTestReport/tear.py0交易情况 args: price (pd.dataframe): index-date ohlcv数据 result (_type_): _description_STRING
HIGHC-择时类/C-VIX中国版VIX编制手册/scr/bt_func.py0交易情况 args: price (pd.dataframe): index-date ohlcv数据 result (_type_): _description_STRING
HIGHC-择时类/特征分布建模择时系列之二/scr/bt_func.py0交易情况 args: price (pd.dataframe): index-date ohlcv数据 result (_type_): _description_STRING
HIGHC-择时类/特征分布建模择时/scr/bt_func.py0交易情况 args: price (pd.dataframe): index-date ohlcv数据 result (_type_): _description_STRING
HIGHhugos_toolkit/BackTestReport/timeseries.py0determines peak, valley, and recovery dates given an 'underwater' dataframe. an underwater dataframe is a dataframe thatSTRING
HIGHC-择时类/成交量的奥秘_另类价量共振指标的择时/scr/timeseries.py0determines peak, valley, and recovery dates given an 'underwater' dataframe. an underwater dataframe is a dataframe thatSTRING
HIGHC-择时类/特征分布建模择时系列之二/scr/timeseries.py0determines peak, valley, and recovery dates given an 'underwater' dataframe. an underwater dataframe is a dataframe thatSTRING
HIGHC-择时类/特征分布建模择时/scr/timeseries.py0determines peak, valley, and recovery dates given an 'underwater' dataframe. an underwater dataframe is a dataframe thatSTRING
HIGHhugos_toolkit/BackTestReport/timeseries.py0finds top drawdowns, sorted by drawdown amount. parameters ---------- returns : pd.series daily returns of the strategy,STRING
HIGHC-择时类/成交量的奥秘_另类价量共振指标的择时/scr/timeseries.py0finds top drawdowns, sorted by drawdown amount. parameters ---------- returns : pd.series daily returns of the strategy,STRING
HIGHC-择时类/特征分布建模择时系列之二/scr/timeseries.py0finds top drawdowns, sorted by drawdown amount. parameters ---------- returns : pd.series daily returns of the strategy,STRING
HIGHC-择时类/特征分布建模择时/scr/timeseries.py0finds top drawdowns, sorted by drawdown amount. parameters ---------- returns : pd.series daily returns of the strategy,STRING
HIGHhugos_toolkit/BackTestReport/timeseries.py0# places top drawdowns in a table. # parameters # ---------- # returns : pd.series # daily returns of the strategy, noncSTRING
HIGHC-择时类/成交量的奥秘_另类价量共振指标的择时/scr/timeseries.py0# places top drawdowns in a table. # parameters # ---------- # returns : pd.series # daily returns of the strategy, noncSTRING
HIGHC-择时类/特征分布建模择时系列之二/scr/timeseries.py0# places top drawdowns in a table. # parameters # ---------- # returns : pd.series # daily returns of the strategy, noncSTRING
HIGHC-择时类/特征分布建模择时/scr/timeseries.py0# places top drawdowns in a table. # parameters # ---------- # returns : pd.series # daily returns of the strategy, noncSTRING
HIGHhugos_toolkit/VectorbtStylePlotting/plotting.py0画每年累计收益 args: returns (pd.series): index-date values-returns returns: _type_: _description_STRING
105 more matches not shown…
Hyper-Verbose Identifiers539 hits · 524 pts
SeverityFileLineSnippetContext
LOW…）：形态识别，均线的收敛与发散/FactorArithmetic/convergence_factor.py16def calculate_convergence_factor(CODE
LOW⚡…评论（91）：形态识别，均线的收敛与发散/src/analyzer/streamlit_analyze.py627 def mean_return_spread_by_quantile(self):CODE
LOW⚡…评论（91）：形态识别，均线的收敛与发散/src/analyzer/streamlit_analyze.py632 def mean_return_spread_std_by_quantile(self):CODE
LOW⚡…评论（91）：形态识别，均线的收敛与发散/src/analyzer/streamlit_analyze.py637 def calc_cumulative_return_by_quantile(CODE
LOW…评论（91）：形态识别，均线的收敛与发散/src/analyzer/streamlit_analyze.py94 def __gen_clean_factor_and_forward_returns(self):CODE
LOW…评论（91）：形态识别，均线的收敛与发散/src/analyzer/streamlit_analyze.py171 def calc_mean_return_by_quantile(CODE
LOW…评论（91）：形态识别，均线的收敛与发散/src/analyzer/streamlit_analyze.py222 def compute_mean_returns_spread(CODE
LOW…评论（91）：形态识别，均线的收敛与发散/src/analyzer/streamlit_analyze.py299 def calc_factor_information_coefficient(CODE
LOW…评论（91）：形态识别，均线的收敛与发散/src/analyzer/streamlit_analyze.py332 def calc_mean_information_coefficient(CODE
LOW…评论（91）：形态识别，均线的收敛与发散/src/analyzer/streamlit_analyze.py370 def calc_average_cumulative_return_by_quantile(CODE
LOW…评论（91）：形态识别，均线的收敛与发散/src/analyzer/streamlit_analyze.py416 def calc_quantile_turnover_mean_n_days_lag(self, n=10):CODE
LOW…评论（91）：形态识别，均线的收敛与发散/src/analyzer/streamlit_analyze.py444 def calc_autocorrelation_n_days_lag(self, n=10, rank=False):CODE
LOW…评论（91）：形态识别，均线的收敛与发散/src/analyzer/streamlit_analyze.py554 def mean_return_std_by_quantile(self):CODE
LOW…评论（91）：形态识别，均线的收敛与发散/src/analyzer/streamlit_analyze.py726 def calc_top_down_cumulative_returns(CODE
LOW…评论（91）：形态识别，均线的收敛与发散/src/analyzer/streamlit_analyze.py815 def cumulative_return_by_quantile(self):CODE
LOW…评论（91）：形态识别，均线的收敛与发散/src/analyzer/streamlit_analyze.py833 def top_down_cumulative_returns(self):CODE
LOW…评论（91）：形态识别，均线的收敛与发散/src/analyzer/streamlit_analyze.py954 def plot_quantile_statistics_table(self, make_pretty: bool = True):CODE
LOW…评论（91）：形态识别，均线的收敛与发散/src/analyzer/streamlit_analyze.py1031 def plot_quantile_returns_bar(CODE
LOW…评论（91）：形态识别，均线的收敛与发散/src/analyzer/streamlit_analyze.py1059 def plot_quantile_returns_violin(CODE
LOW…评论（91）：形态识别，均线的收敛与发散/src/analyzer/streamlit_analyze.py1083 def plot_mean_quantile_returns_spread_time_series(CODE
LOW…评论（91）：形态识别，均线的收敛与发散/src/analyzer/streamlit_analyze.py1111 def plot_factor_auto_correlation(self, periods=None, rank=True) -> List[go.Figure]:CODE
LOW…评论（91）：形态识别，均线的收敛与发散/src/analyzer/streamlit_analyze.py1133 def plot_top_bottom_quantile_turnover(self, periods=None) -> List[go.Figure]:CODE
LOW…评论（91）：形态识别，均线的收敛与发散/src/analyzer/streamlit_analyze.py1202 def plot_top_down_cumulative_returns(CODE
LOW…评论（91）：形态识别，均线的收敛与发散/src/analyzer/streamlit_analyze.py1242 def plot_cumulative_returns_by_quantile(CODE
LOW…评论（91）：形态识别，均线的收敛与发散/src/analyzer/streamlit_analyze.py1277 def plot_quantile_average_cumulative_return(CODE
LOW…评论（91）：形态识别，均线的收敛与发散/src/analyzer/streamlit_analyze.py1341 def create_summary_tear_sheet(self, demeaned=False, group_adjust=False) -> None:CODE
LOW…评论（91）：形态识别，均线的收敛与发散/src/analyzer/streamlit_analyze.py1397 def create_returns_tear_sheet(CODE
LOW…评论（91）：形态识别，均线的收敛与发散/src/analyzer/streamlit_analyze.py1470 def create_information_tear_sheet(self, group_adjust=False, by_group=False):CODE
LOW…评论（91）：形态识别，均线的收敛与发散/src/analyzer/streamlit_analyze.py1494 def create_turnover_tear_sheet(self, turnover_periods=None):CODE
LOW…评论（91）：形态识别，均线的收敛与发散/src/analyzer/streamlit_analyze.py1522 def create_event_returns_tear_sheet(CODE
LOW…评论（91）：形态识别，均线的收敛与发散/src/analyzer/streamlit_analyze.py1567 def plot_disable_chinese_label(self):CODE
LOW…开源证券-开源量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/plotting.py100def plot_quantile_statistics_table(factor_data):CODE
LOW…开源证券-开源量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/plotting.py243def plot_quantile_returns_bar(CODE
LOW…开源证券-开源量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/plotting.py323def plot_quantile_returns_violin(return_by_q, ylim_percentiles=None, ax=None):CODE
LOW…开源证券-开源量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/plotting.py374def plot_mean_quantile_returns_spread_time_series(CODE
LOW…开源证券-开源量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/plotting.py459def plot_factor_rank_auto_correlation(CODE
LOW…开源证券-开源量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/plotting.py489def plot_top_bottom_quantile_turnover(quantile_turnover, period=1, ax=None):CODE
LOW…开源证券-开源量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/plotting.py581def plot_top_down_cumulative_returns(factor_returns, period=1, ax=None):CODE
LOW…开源证券-开源量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/plotting.py600def plot_cumulative_returns_by_quantile(CODE
LOW…开源证券-开源量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/plotting.py633def plot_quantile_average_cumulative_return(CODE
LOW…开源证券-开源量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/plotting.py762def plot_missing_events_distribution(CODE
LOW…量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/plotly_plotting.py322def plot_quantile_returns_bar(CODE
LOW…量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/plotly_plotting.py373def plot_quantile_returns_violin(CODE
LOW…量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/plotly_plotting.py452def plot_mean_quantile_returns_spread_time_series(CODE
LOW…量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/plotly_plotting.py556def plot_factor_rank_auto_correlation(factor_autocorrelation, period=1):CODE
LOW…量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/plotly_plotting.py606def plot_top_bottom_quantile_turnover(CODE
LOW…量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/plotly_plotting.py801def plot_top_down_cumulative_returns(CODE
LOW…量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/plotly_plotting.py841def plot_cumulative_returns_by_quantile(CODE
LOW…量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/plotly_plotting.py917def plot_average_cumulative_return(CODE
LOW…量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/plotly_plotting.py994def plot_average_cumulative_return_by_quantile(CODE
LOW…量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/plotly_plotting.py1071def plot_quantile_average_cumulative_return(CODE
LOW…建类/开源证券-开源量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/utils.py16def get_forward_returns_columns(columns):CODE
LOW…建类/开源证券-开源量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/utils.py21def convert_to_forward_returns_columns(period):CODE
LOW…证券-开源量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/exceptions.py20def non_unique_bin_edges_error(func):CODE
LOW…券-开源量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/performance.py60def factor_information_coefficient(CODE
LOW…券-开源量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/performance.py104def mean_information_coefficient(CODE
LOW…券-开源量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/performance.py400def compute_mean_returns_spread(mean_returns, upper_quant, lower_quant, std_err=None):CODE
LOW…券-开源量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/performance.py507def average_cumulative_return_by_quantile(CODE
LOW…券-开源量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/performance.py552 def average_cumulative_return(q_fact, demean_by):CODE
LOW…券-开源量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/performance.py626def get_quantile_turnover_mean(quantile_turnover: pd.DataFrame) -> pd.DataFrame:CODE
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Unused Imports260 hits · 255 pts
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LOW…评论（91）：形态识别，均线的收敛与发散/src/analyzer/streamlit_analyze.py3CODE
LOW…评论（91）：形态识别，均线的收敛与发散/src/analyzer/streamlit_analyze.py3CODE
LOW…类/开源证券-开源量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/compat.py5CODE
LOW…开源证券-开源量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/plotting.py12CODE
LOW…开源证券-开源量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/plotting.py12CODE
LOW…开源证券-开源量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/__init__.py3CODE
LOW…量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/plotly_plotting.py9CODE
LOW…量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/plotly_plotting.py9CODE
LOW…量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/plotly_plotting.py30CODE
LOW…量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/plotly_plotting.py30CODE
LOW…建类/开源证券-开源量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/utils.py13CODE
LOW…/开源证券-开源量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/prepare.py4CODE
LOW…/开源证券-开源量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/analyze.py3CODE
LOW…/开源证券-开源量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/analyze.py3CODE
LOWB-因子构建类/基于量价关系度量股票的买卖压力/py/CalFunc.py3CODE
LOWB-因子构建类/企业生命周期/scr/get_dataset.py12CODE
LOWB-因子构建类/企业生命周期/scr/get_dataset.py12CODE
LOWB-因子构建类/企业生命周期/scr/get_dataset.py12CODE
LOWB-因子构建类/企业生命周期/scr/get_dataset.py14CODE
LOWB-因子构建类/企业生命周期/scr/my_scr.py9CODE
LOWB-因子构建类/企业生命周期/scr/my_scr.py9CODE
LOWB-因子构建类/企业生命周期/scr/my_scr.py11CODE
LOWB-因子构建类/企业生命周期/scr/my_scr.py19CODE
LOWB-因子构建类/企业生命周期/scr/my_scr.py23CODE
LOWB-因子构建类/企业生命周期/scr/my_scr.py25CODE
LOWB-因子构建类/企业生命周期/scr/my_scr.py26CODE
LOWB-因子构建类/企业生命周期/scr/plotting.py9CODE
LOWB-因子构建类/企业生命周期/scr/plotting.py9CODE
LOWB-因子构建类/企业生命周期/scr/plotting.py9CODE
LOWB-因子构建类/企业生命周期/scr/plotting.py9CODE
LOWB-因子构建类/企业生命周期/scr/my_factor.py8CODE
LOWB-因子构建类/企业生命周期/scr/my_factor.py8CODE
LOWB-因子构建类/企业生命周期/scr/my_factor.py8CODE
LOWB-因子构建类/企业生命周期/scr/my_factor.py8CODE
LOWB-因子构建类/企业生命周期/scr/analyze_func.py9CODE
LOWB-因子构建类/企业生命周期/scr/analyze_func.py18CODE
LOWB-因子构建类/企业生命周期/scr/analyze_func.py18CODE
LOWB-因子构建类/企业生命周期/scr/utils.py8CODE
LOWB-因子构建类/企业生命周期/scr/utils.py8CODE
LOWB-因子构建类/企业生命周期/factor_tools/composition_factor.py41CODE
LOWB-因子构建类/企业生命周期/factor_tools/composition_factor.py47CODE
LOWB-因子构建类/企业生命周期/factor_tools/composition_factor.py48CODE
LOWB-因子构建类/企业生命周期/factor_tools/composition_factor.py53CODE
LOWB-因子构建类/企业生命周期/factor_tools/ipca.py10CODE
LOWB-因子构建类/企业生命周期/factor_tools/ipca.py18CODE
LOWB-因子构建类/企业生命周期/factor_tools/ipca.py21CODE
LOWB-因子构建类/高频价量相关性，意想不到的选股因子/Hugos_tools/Tdays.py1CODE
LOWB-因子构建类/高频价量相关性，意想不到的选股因子/Hugos_tools/Tdays.py1CODE
LOWB-因子构建类/高频价量相关性，意想不到的选股因子/Hugos_tools/Tdays.py1CODE
LOWB-因子构建类/高频价量相关性，意想不到的选股因子/Hugos_tools/Tdays.py1CODE
LOWB-因子构建类/高频价量相关性，意想不到的选股因子/Hugos_tools/Tdays.py1CODE
LOWB-因子构建类/高频价量相关性，意想不到的选股因子/Hugos_tools/Tdays.py1CODE
LOWB-因子构建类/高频价量相关性，意想不到的选股因子/Hugos_tools/Tdays.py4CODE
LOWB-因子构建类/高频价量相关性，意想不到的选股因子/Hugos_tools/__init__.py1CODE
LOWB-因子构建类/高频价量相关性，意想不到的选股因子/Hugos_tools/BuildStockPool.py8CODE
LOWB-因子构建类/高频价量相关性，意想不到的选股因子/Hugos_tools/BuildStockPool.py11CODE
LOWB-因子构建类/高频价量相关性，意想不到的选股因子/Hugos_tools/BuildStockPool.py12CODE
LOWB-因子构建类/高频价量相关性，意想不到的选股因子/Hugos_tools/BuildStockPool.py12CODE
LOWB-因子构建类/高频价量相关性，意想不到的选股因子/Hugos_tools/BuildStockPool.py12CODE
LOWB-因子构建类/高频价量相关性，意想不到的选股因子/Hugos_tools/BuildStockPool.py12CODE
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Cross-Language Confusion24 hits · 122 pts
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HIGHB-因子构建类/开源证券-开源量化评论（91）：形态识别，均线的收敛与发散/src/preprocess.py4LastEditors: error: error: git config user.name & please set dead value or install git && error: git config user.email &STRING
HIGHC-择时类/相对旋转图RRG行业轮动/tests/test_factor_algo.py414 assert rs.index.equals(benchmark.index)CODE
HIGHC-择时类/相对旋转图RRG行业轮动/tests/test_factor_algo.py434 assert rs.index.equals(benchmark.index)CODE
HIGH⚡C-择时类/相对旋转图RRG行业轮动/src/plotting.py117 if (x > cx && y > cy) t = '领先';CODE
HIGH⚡C-择时类/相对旋转图RRG行业轮动/src/plotting.py118 else if (x < cx && y > cy) t = '改善';CODE
HIGH⚡C-择时类/相对旋转图RRG行业轮动/src/plotting.py119 else if (x < cx && y < cy) t = '滞后';CODE
HIGH⚡C-择时类/相对旋转图RRG行业轮动/src/plotting.py120 else if (x > cx && y < cy) t = '疲软';CODE
HIGHC-择时类/相对旋转图RRG行业轮动/src/factor_algo.py336 if not rs_ratio.columns.equals(rs_mom.columns):CODE
HIGHC-择时类/基于稀疏自编码器的指数择时/tests/test_tushare_source.py128 assert q.index.equals(pd.DatetimeIndex(idx))CODE
HIGHC-择时类/基于稀疏自编码器的指数择时/tests/test_tushare_source.py164 assert stored.index.equals(q.index)CODE
HIGHC-择时类/基于稀疏自编码器的指数择时/tests/test_tushare_source.py186 assert sessions.equals(expected)CODE
HIGHC-择时类/基于稀疏自编码器的指数择时/tests/test_tushare_source.py383 assert q.index.equals(pd.DatetimeIndex(idx)) # 合并后40日无缺无重CODE
HIGHC-择时类/基于稀疏自编码器的指数择时/tests/test_tushare_source.py404 assert q.index.equals(expected) # 补头后返回区间连续无缺日CODE
HIGHC-择时类/基于稀疏自编码器的指数择时/tests/test_tushare_source.py476 assert q.index.equals(pd.DatetimeIndex(idx))CODE
HIGHC-择时类/基于稀疏自编码器的指数择时/tests/test_tushare_source.py652 assert q.index.equals(pd.DatetimeIndex(idx))CODE
HIGHC-择时类/基于稀疏自编码器的指数择时/tests/test_store.py184 assert store.read_quotes("000905.SH").index.equals(raw.index)CODE
HIGHC-择时类/基于稀疏自编码器的指数择时/src/store.py205 if sessions.hasnans or not expected.equals(raw.index):STRING
HIGHC-择时类/基于稀疏自编码器的指数择时/src/store.py374 if not frame.index.equals(pd.RangeIndex(len(frame))):STRING
HIGHC-择时类/基于稀疏自编码器的指数择时/src/generator.py158 if not actual.equals(pd.DatetimeIndex(expected)):CODE
HIGHC-择时类/基于稀疏自编码器的指数择时/src/report_reference.py29 if not close.index.equals(signal.index):CODE
HIGHC-择时类/基于稀疏自编码器的指数择时/src/report_reference.py89 if not returns.index.equals(positions.index):CODE
HIGHC-择时类/基于稀疏自编码器的指数择时/src/data_provider.py235 if not observed.equals(expected):CODE
HIGHC-择时类/基于稀疏自编码器的指数择时/src/analyze.py41 if not close.index.equals(signal.index) or not signal.isin([-1, 0, 1]).all():CODE
HIGHC-择时类/基于稀疏自编码器的指数择时/src/analyze.py81 if not portfolio.wrapper.index.equals(signal.index):CODE
Docstring Block Structure21 hits · 105 pts
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HIGHB-因子构建类/基于隔夜与日间的网络关系因子/factor_computation.py62 计算领先-滞后得分 对应论文中的得分计算逻辑，基于聚类结果计算领导者和滞后者股票的得分。 Args: adjacency_matrix (np.ndarray):STRING
HIGHB-因子构建类/基于隔夜与日间的网络关系因子/factor_computation.py113 按分值排序选择股票 Args: scores (Dict[str, float]): 股票评分字典 {股票代码: 评分} quantile (float, STRING
HIGHB-因子构建类/基于隔夜与日间的网络关系因子/factor_computation.py144 生成交易信号 基于领先股票的得分和收益率生成交易信号。 对应论文公式：S = (1/|C_a|) * sum(returns) Args: leaSTRING
HIGHB-因子构建类/基于隔夜与日间的网络关系因子/factor_computation.py187 根据信号方向选择多空股票 对应论文4.1节： "go long the top 20% and short the bottom 20% of stocks in C_lag" STRING
HIGHB-因子构建类/基于隔夜与日间的网络关系因子/factor_computation.py238 计算完整的因子值时间序列 Args: adjacency_matrices (np.ndarray): 形状为(n,m,m)，邻接矩阵序列 signed_mSTRING
HIGHC-择时类/C-VIX中国版VIX编制手册/data_service/utils.py14将ser类型转为datetime Args: ser (pd.Series): _description_ Raises: TypeError: _description_ ReSTRING
HIGHC-择时类/成交量的奥秘_另类价量共振指标的择时/scr/create_signal.py22HMA均线 Args: price (pd.Series): 价格数据. index-date values window (int): 计算窗口 Raises: ValuSTRING
HIGHC-择时类/特征分布建模择时系列之二/scr/calc_func.py31HMA均线 Args: price (pd.Series): 价格数据. index-date values window (int): 计算窗口 Raises: ValuSTRING
HIGHC-择时类/技术分析算法框架与实战/py/technical_analysis_patterns.py664使用多进程匹配 Args: price (pd.Series): 价格数据 n (int): 窗口期 reset_window (int, optional): 字典更新窗口. DeSTRING
HIGH…ion/build_timing_signal/technical_analysis_patterns.py587使用多进程匹配 Args: price (pd.Series): 价格数据 n (int): 窗口期 reset_window (int, optional): 字典更新窗口. DeSTRING
HIGH…ttern Recognition/build_timing_signal/timing_signal.py167计算低延迟趋势线 Args: price (pd.Series): 价格数据. index-date values alpha (float): 窗口期的倒数.比如想要窗口期为5,则为1/5 STRING
HIGH…ttern Recognition/build_timing_signal/timing_signal.py199 广发提出了一种新的低延迟均线系统。 OLSTL(Ordinary Least Square Trend Line)是基于普通最小二乘法的思想构建均线指标。 普通最小二乘法的思想是通过对自变量和因变量序列进行拟合,找STRING
HIGH…ttern Recognition/build_timing_signal/timing_signal.py232分形自适应移动平均(FRactal Adaptive Moving Average,FRAMA)利用了投资品价格序列的分形特征 Args: price (pd.Series): 价格数据. index-date vSTRING
HIGH…ttern Recognition/build_timing_signal/timing_signal.py291HMA均线 Args: price (pd.Series): 价格数据. index-date values window (int): 计算窗口 Raises: ValuSTRING
HIGH…ttern Recognition/build_timing_signal/timing_signal.py322 1. 计算每天日收益率的五阶矩,计算公式下式所示,计算数据长度为20。 $$v_k=\frac{\sum^n_{i=1}x^k_i}{N}$$ 2. 在T日收盘后,计算出T日(含)之前的五阶矩。 3. 对STRING
HIGH…ttern Recognition/build_timing_signal/timing_signal.py377 "强者恒强、弱者恒弱"常为市场所证实。个股或市场的强弱表现其本身就是基本面、资金面、投资者情绪等多种因素的综合作用下的体现。 通常市场强势与否,可以用市场相对强弱 RPS 指标来表示。 计算方法: RPSSTRING
HIGH…ttern Recognition/build_timing_signal/timing_signal.py420 相对强弱 RPS下波动率差值 1. 计算相应指数相对强弱RPS 2. 计算相应指数上行波动率、下行波动率,并计算二者差值 3. 计算当天波动率差值的移动均值天数由 RPS 值确定、RPS 值越STRING
HIGH…ttern Recognition/build_timing_signal/timing_signal.py479 from 《量化投资:策略于技术》丁鹏 策略篇-量化择时-熊牛线 Args: price (pd.Series): 价格数据 n (int): 采样点数 STRING
HIGH…ttern Recognition/build_timing_signal/timing_signal.py508geometric_mrownian_motion过程 Args: price (pd.Series): 价格数据 mu (pd.Series): 均值 sigma (pd.SeriSTRING
HIGHC-择时类/特征分布建模择时/scr/calc_func.py17HMA均线 Args: price (pd.Series): 价格数据. index-date values window (int): 计算窗口 Raises: ValuSTRING
HIGHC-择时类/特征分布建模择时/data_service/get_data.py130_summary_ Args: start_date (str, optional): _description_. Defaults to None. end_date (str, optionaSTRING
Decorative Section Separators16 hits · 54 pts
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MEDIUMB-因子构建类/企业生命周期/factor_tools/composition_factor.py476# ----------COMMENT
MEDIUMhugos_toolkit/BackTestReport/timeseries.py168# ----------COMMENT
MEDIUMC-择时类/相对旋转图RRG行业轮动/tests/test_optimizer.py292# ---------------------------------------------------------------------------COMMENT
MEDIUMC-择时类/相对旋转图RRG行业轮动/tests/test_optimizer.py294# ---------------------------------------------------------------------------COMMENT
MEDIUMC-择时类/相对旋转图RRG行业轮动/tests/test_exposure.py125# ── aggregate_industry_weights ─────────────────────────────────────────────COMMENT
MEDIUMC-择时类/相对旋转图RRG行业轮动/tests/test_exposure.py319# ── ETFExposureBuilder（缓存 + 编排）────────────────────────────────────────COMMENT
MEDIUM⚡C-择时类/相对旋转图RRG行业轮动/tests/test_backtest.py602# ---------------------------------------------------------------------------COMMENT
MEDIUM⚡C-择时类/相对旋转图RRG行业轮动/tests/test_backtest.py604# ---------------------------------------------------------------------------COMMENT
MEDIUM⚡C-择时类/相对旋转图RRG行业轮动/tests/test_backtest.py809# ---------------------------------------------------------------------------COMMENT
MEDIUM⚡C-择时类/相对旋转图RRG行业轮动/tests/test_backtest.py811# ---------------------------------------------------------------------------COMMENT
MEDIUMC-择时类/相对旋转图RRG行业轮动/tests/test_backtest.py450# ---------------------------------------------------------------------------COMMENT
MEDIUMC-择时类/相对旋转图RRG行业轮动/tests/test_backtest.py452# ---------------------------------------------------------------------------COMMENT
MEDIUMC-择时类/相对旋转图RRG行业轮动/tests/test_backtest.py700# ── 境外（港股）ETF 剔除 ───────────────────────────────────────────────────COMMENT
MEDIUMC-择时类/相对旋转图RRG行业轮动/src/exposure.py271 # ── 三阶段 ──────────────────────────────────────────────────────────COMMENT
MEDIUMC-择时类/相对旋转图RRG行业轮动/src/exposure.py304 # ── 复位 ────────────────────────────────────────────────────────────COMMENT
MEDIUMC-择时类/基于点位效率理论的个股趋势预测研究/py/performance.py130# ----------COMMENT
Over-Commented Block49 hits · 48 pts
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LOWB-因子构建类/企业生命周期/factor_tools/composition_factor.py481# 滚动窗口COMMENT
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LOWB-因子构建类/股票网络与网络中心度因子研究/src/factor_algo.py161 N: int = corr_matrix.shape[0] # 股票数量COMMENT
LOWB-因子构建类/股票网络与网络中心度因子研究/src/factor_algo.py321 # 然后除以该时间点的市场标准差，得到标准化偏离度COMMENT
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LOWD-组合优化/MLT_TSMOM/src/core.py361# return weights_tensor, returns_tensorCOMMENT
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LOWhugos_toolkit/BackTestReport/performance.py161# 对比策略与基准在回撤区间内的收益COMMENT
LOWhugos_toolkit/BackTestReport/performance.py181COMMENT
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LOW…ttern Recognition/build_timing_signal/Approximation.py641COMMENT
LOW…ttern Recognition/build_timing_signal/Approximation.py781 '''COMMENT
LOWC-择时类/相对旋转图RRG行业轮动/docs/ETF轮动回测说明_20260602.md41COMMENT
LOWC-择时类/相对旋转图RRG行业轮动/src/store.py341 UserWarning,COMMENT
LOWC-择时类/基于点位效率理论的个股趋势预测研究/py/Approximation.py641COMMENT
LOWC-择时类/基于点位效率理论的个股趋势预测研究/py/Approximation.py781 '''COMMENT
LOWC-择时类/基于点位效率理论的个股趋势预测研究/py/performance.py121# benchmark_code: str = "000300.SH",COMMENT
LOWC-择时类/基于点位效率理论的个股趋势预测研究/py/performance.py141# phase_change = compare_phase_change(returns, benchmark_code, top)COMMENT
LOWC-择时类/基于点位效率理论的个股趋势预测研究/py/performance.py161# '''COMMENT
LOWC-择时类/基于点位效率理论的个股趋势预测研究/py/performance.py181COMMENT
AI Structural Patterns47 hits · 46 pts
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LOWB-因子构建类/筹码因子/scr/qlib_workflow.py199CODE
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LOWB-因子构建类/凸显理论STR因子/scr/qlib_workflow.py84CODE
LOWB-因子构建类/凸显理论STR因子/scr/qlib_workflow.py127CODE
LOWB-因子构建类/凸显理论STR因子/scr/qlib_workflow.py176CODE
LOWB-因子构建类/凸显理论STR因子/scr/qlib_workflow.py210CODE
LOWB-因子构建类/凸显理论STR因子/scr/qlib_workflow.py241CODE
LOWB-因子构建类/凸显理论STR因子/scr/qlib_workflow.py279CODE
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LOWhugos_toolkit/VectorbtStylePlotting/plotting.py733CODE
LOWC-择时类/成交量的奥秘_另类价量共振指标的择时/scr/vectorbt_style_plotting.py733CODE
LOWC-择时类/QRS择时信号/backtrader_utils/bt_template.py83CODE
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LOWC-择时类/相对旋转图RRG行业轮动/src/plotting.py734CODE
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LOWC-择时类/相对旋转图RRG行业轮动/src/plotting.py1013CODE
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Modern Structural Boilerplate44 hits · 44 pts
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LOWC-择时类/相对旋转图RRG行业轮动/src/plotting.py132def _setup_cjk_font() -> None:STRING
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Deep Nesting44 hits · 42 pts
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LOWC-择时类/基于点位效率理论的个股趋势预测研究/py/recover_approximation.py69CODE
LOWC-择时类/基于点位效率理论的个股趋势预测研究/py/recover_approximation.py505CODE
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Empty Error Handlers8 hits · 40 pts
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HIGH…ttern Recognition/build_timing_signal/Approximation.py325except KeyError:CODE
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HIGHC-择时类/基于点位效率理论的个股趋势预测研究/py/recover_approximation.py443except KeyError:CODE
HIGHC-择时类/基于点位效率理论的个股趋势预测研究/py/Approximation.py173except KeyError:CODE
HIGHC-择时类/基于点位效率理论的个股趋势预测研究/py/Approximation.py325except KeyError:CODE
Excessive Try-Catch Wrapping23 hits · 23 pts
SeverityFileLineSnippetContext
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LOW…/开源证券-开源量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/prepare.py82 except Exception as e:CODE
MEDIUM…/开源证券-开源量化评论（91）：形态识别，均线的收敛与发散/src/analyzer/prepare.py64def quantile_calc(x, _quantiles, _bins, _zero_aware, _no_raise):CODE
LOWB-因子构建类/个股动量效应的识别及球队硬币因子/src/dump_bin.py575 except Exception:CODE
LOWB-因子构建类/个股动量效应的识别及球队硬币因子/src/factor_analyze.py392 except Exception as e:STRING
MEDIUMB-因子构建类/个股动量效应的识别及球队硬币因子/src/factor_analyze.py306def dec(*args, **kwargs):CODE
LOWB-因子构建类/筹码因子/scr/cyq.py123 except Exception as e:CODE
LOWB-因子构建类/基于隔夜与日间的网络关系因子/dlesc_clustering.py643 except Exception as e:CODE
LOWB-因子构建类/基于隔夜与日间的网络关系因子/dlesc_clustering.py671 except Exception as e:CODE
LOWB-因子构建类/基于隔夜与日间的网络关系因子/lead_lag_network.py863 except Exception as e:CODE
LOWB-因子构建类/基于隔夜与日间的网络关系因子/factor_computation.py291 except Exception as e:CODE
LOWB-因子构建类/股票网络与网络中心度因子研究/docs/使用指南_网络中心度因子.md526except Exception as e:CODE
LOWB-因子构建类/股票网络与网络中心度因子研究/src/factor_algo.py534 except Exception as e:CODE
LOWB-因子构建类/股票网络与网络中心度因子研究/src/factor_algo.py571 except Exception as e:CODE
LOWB-因子构建类/股票网络与网络中心度因子研究/src/generator.py159 except Exception as e:CODE
LOWB-因子构建类/股票网络与网络中心度因子研究/src/generator.py272 except Exception as e:CODE
LOWB-因子构建类/股票网络与网络中心度因子研究/src/analyze.py633 except Exception as e:CODE
LOWD-组合优化/MLT_TSMOM/ts_data_service/tushare_api.py57 except Exception:CODE
LOWC-择时类/ICU均线/src/bt_icu_ind.py33 except Exception as e:CODE
LOWC-择时类/相对旋转图RRG行业轮动/src/store.py131 except Exception:CODE
LOWC-择时类/基于稀疏自编码器的指数择时/src/store.py215 except Exception:STRING
LOWC-择时类/基于稀疏自编码器的指数择时/src/store.py244 except Exception:STRING
LOWC-择时类/基于稀疏自编码器的指数择时/src/store.py359 except Exception:STRING