Repository Analysis

sngyai/Sequoia-X

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19.3 Moderate AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of sngyai/Sequoia-X, a Python project with 6,464 GitHub stars. SynthScan v2.0 examined 1,706 lines of code across 27 source files, recording 32 pattern matches distributed across 4 syntactic categories. The overall adjusted score of 19.3 places this repository in the Moderate AI signal 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).

19.3

Adjusted Score

19.3

Raw Score

100%

Time Factor

2026-07-10

Last Push

6.5K

Stars

Python

Language

1.7K

Lines of Code

Files

Pattern Hits

2026-09-03

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 1LOW 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 32 distinct pattern matches across 4 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.

Excessive Try-Catch Wrapping12 hits · 13 pts

Severity	File	Line	Snippet	Context
LOW	main.py	95	except Exception:	CODE
LOW	main.py	99	except Exception:	CODE
LOW	sequoia_x/data/engine.py	241	except Exception as exc:	CODE
LOW	sequoia_x/data/engine.py	322	except Exception as e:	CODE
LOW	sequoia_x/strategy/private_placement.py	32	except Exception as exc:	CODE
LOW	sequoia_x/strategy/rps_breakout.py	20	except Exception as exc:	CODE
MEDIUM	sequoia_x/strategy/rps_breakout.py	16	def run(self) -> list[str]:	CODE
LOW	sequoia_x/strategy/ma_volume.py	58	except Exception as exc:	CODE
LOW	sequoia_x/strategy/high_tight_flag.py	67	except Exception as exc:	CODE
LOW	sequoia_x/strategy/uptrend_limit_down.py	62	except Exception as exc:	CODE
LOW	sequoia_x/strategy/limit_up_shakeout.py	60	except Exception as exc:	CODE
LOW	sequoia_x/strategy/turtle_trade.py	101	except Exception as exc:	CODE

Hyper-Verbose Identifiers10 hits · 10 pts

Severity	File	Line	Snippet	Context
LOW	sequoia_x/core/config.py	20	def settings_customise_sources(cls, settings_cls, **kwargs): # type: ignore[override]	CODE
LOW	tests/test_data_engine.py	33	def test_unique_symbol_date_constraint(symbol: str, trade_date: date) -> None:	CODE
LOW	tests/test_strategy.py	24	def test_strategy_run_returns_list_of_str(symbols: list[str]) -> None:	CODE
LOW	tests/test_feishu.py	31	def test_notification_contains_all_symbols(symbols: list[str]) -> None:	CODE
LOW	tests/test_feishu.py	52	def test_notification_uses_config_url(webhook_url: str) -> None:	CODE
LOW	tests/test_feishu.py	68	def test_http_failure_logs_error(status_code: int) -> None:	CODE
LOW	tests/test_config.py	13	def test_env_overrides_default(db_path: str, monkeypatch) -> None:	CODE
LOW	tests/test_config.py	25	def test_missing_required_field_raises() -> None:	CODE
LOW	tests/test_main.py	17	def test_main_exits_nonzero_on_exception(error_msg: str) -> None:	CODE
LOW	tests/test_logger.py	10	def test_get_logger_same_instance(name: str) -> None:	CODE

Unused Imports8 hits · 8 pts

Severity	File	Line	Context
LOW	main.py	13	CODE
LOW	sequoia_x/core/config.py	22	CODE
LOW	sequoia_x/strategy/ma_volume.py	3	CODE
LOW	sequoia_x/strategy/high_tight_flag.py	3	CODE
LOW	sequoia_x/strategy/limit_up_shakeout.py	3	CODE
LOW	tests/test_feishu.py	4	CODE
LOW	tests/test_feishu.py	7	CODE
LOW	tests/test_main.py	3	CODE

Deep Nesting2 hits · 2 pts

Severity	File	Line	Snippet	Context
LOW	sequoia_x/data/engine.py	158		CODE
LOW	sequoia_x/strategy/turtle_trade.py	26		CODE