Repository Analysis

hello245m/free-stockdb

面向 A 股日K、分钟K与ETF分钟数据的本地量化引擎,集成增量同步、本地缓存、复权、批量查询、回测与指标计算。

4.2 Likely human-written View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of hello245m/free-stockdb, a HTML project with 1,684 GitHub stars. SynthScan v2.0 examined 7,461 lines of code across 33 source files, recording 31 pattern matches distributed across 6 syntactic categories. The overall adjusted score of 4.2 places this repository in the Likely human-written band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

4.2
Adjusted Score
4.2
Raw Score
100%
Time Factor
2026-07-29
Last Push
1.7K
Stars
HTML
Language
7.5K
Lines of Code
33
Files
31
Pattern Hits
2026-08-02
Scan Date
0.00
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

Longitudinal tracking requires multiple scan runs. Once this repository is re-scanned after new commits land, this chart will visualise how the synthetic code signal evolves over time — enabling you to detect whether AI authorship is growing, stabilising, or being actively corrected by human engineers.

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

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 0HIGH 0MEDIUM 0LOW 31

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 31 distinct pattern matches across 6 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. 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 diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Hyper-Verbose Identifiers8 hits · 8 pts
SeverityFileLineSnippetContext
LOW数据网页版.html404 function normalizeCodeSuggestQuery(raw) {CODE
LOW数据网页版.html425 function normalizeDateSuggestQuery(raw) {CODE
LOW数据网页版.html1123 function tradingIndexToClockMinutes(tradingMinute) {CODE
LOWpybao/zhibiao.py396def _format_param_mapping_error(title: str, names: list[str], n: Any) -> str:CODE
LOWpybao/stock_sdk.py595 def build_time_query_for_retrieval(self, start: Optional[str], end: Optional[str], desc: bool, frequency: str) -> stCODE
LOW调用方式/excel/查看数据.html404 function normalizeCodeSuggestQuery(raw) {CODE
LOW调用方式/excel/查看数据.html425 function normalizeDateSuggestQuery(raw) {CODE
LOW调用方式/excel/查看数据.html1123 function tradingIndexToClockMinutes(tradingMinute) {CODE
Excessive Try-Catch Wrapping7 hits · 7 pts
SeverityFileLineSnippetContext
LOWpybao/zhibiao.py240 except Exception:CODE
LOWpybao/安装.py51 except Exception as e:CODE
LOWpybao/stock_sdk.py45 except Exception:CODE
LOWpybao/stock_sdk.py418 except Exception:CODE
LOWpybao/native_mcp.py301 except Exception as exc:CODE
LOWpybao/native_mcp.py322 except Exception as exc:CODE
LOW调用方式/ai_mcp/stock_mcp_server.py56 except Exception as e:CODE
Over-Commented Block6 hits · 6 pts
SeverityFileLineSnippetContext
LOWsync_url.txt1# 数据更新同步源配置文件COMMENT
LOWcpp/src/updater.cpp1#include "stockdb/updater.hpp"COMMENT
LOWcpp/src/server.cpp1#include "stockdb/server.hpp"COMMENT
LOWcpp/src/server.cpp21#include <arpa/inet.h>COMMENT
LOW调用方式/http/rd_test.py1#####COMMENT
LOW调用方式/http/rd_test.py101 #->[20260622, 20260623, 20260624, 20260625, 20260626]COMMENT
Unused Imports6 hits · 6 pts
SeverityFileLineSnippetContext
LOWpybao/stock_sdk.py1CODE
LOWpybao/stock_sdk.py3CODE
LOWpybao/native_mcp.py1CODE
LOW调用方式/python/rd_test.py15CODE
LOW调用方式/python/sdk_test.py1CODE
LOW调用方式/http/rd_test.py17CODE
Deep Nesting2 hits · 2 pts
SeverityFileLineSnippetContext
LOWpybao/stock_sdk.py82CODE
LOWpybao/stock_sdk.py371CODE
AI Structural Patterns2 hits · 2 pts
SeverityFileLineSnippetContext
LOWpybao/stock_sdk.py425CODE
LOWpybao/stock_sdk.py514CODE