Industry Trend

C+ 72 completed
Other
unknown / python · small
53
Files
140,116
LOC
0
Frameworks
4
Languages

Pipeline State

completed
Run ID
#393557
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Skipped
Decision
skip_scaffold_dup
Novelty
37.00
Framework unique
Isolation
Last stage change
2026-04-16 18:15:42
Deduplication group #47299
Member of a group with 1 similar repo(s) — canonical #94036 view group →
Top concepts (2)
Project DescriptionWeb Backend
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AI Prompt

Create a Python-based financial analysis tool that analyzes A-share stocks across 31 Shenwan Level 1 industries. The core functionality must be to determine the trend stage (Bear Market, Bear-to-Bull Transition, Bull Market, Bull-to-Bear Transition) for each industry based on Stan Weinstein's 34-week Moving Average (MA34) analysis. Crucially, the system must use a Finite State Machine (FSM) to manage stage transitions and incorporate an Adaptive True Range (ATR14) calculation to dynamically adjust thresholds ($\alpha$, $\beta$, etc.) instead of using fixed constants, making the analysis robust across different volatility levels. The output should help identify optimal buy/sell points based on these stages.
python finance technical-analysis stock-market time-series stan-weinstein atr fsm
Generated by gemma4:latest

Catalog Information

基于温斯坦(Stan Weinstein)34 周均线阶段分析法,对 A 股 31 个申万一级行业(2021 版)进行趋势阶段判断,帮助识别各行业当前处于熊市、牛市还是转换期。

Description

基于温斯坦(Stan Weinstein)34 周均线阶段分析法,对 A 股 31 个申万一级行业(2021 版)进行趋势阶段判断,帮助识别各行业当前处于熊市、牛市还是转换期。

Novelty

3/10

Tags

python finance technical-analysis stock-market time-series stan-weinstein atr fsm

Technologies

streamlit

Claude Models

claude-opus-4-6

Quality Score

C+
71.7/100
Structure
66
Code Quality
71
Documentation
68
Testing
60
Practices
77
Security
100
Dependencies
60

Strengths

  • Good test coverage (44% test-to-source ratio)
  • Consistent naming conventions (snake_case)
  • Good security practices \u2014 no major issues detected

Weaknesses

  • No LICENSE file \u2014 legal ambiguity for contributors
  • No CI/CD configuration \u2014 manual testing and deployment
  • 153 duplicate lines detected \u2014 consider DRY refactoring
  • 2 'god files' with >500 LOC need decomposition

Recommendations

  • Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
  • Add a linter configuration to enforce code style consistency
  • Add a LICENSE file (MIT recommended for open source)

Security & Health

5.1h
Tech Debt (A)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (0)
Repobility · severity-and-effort ranking · https://repobility.com
Unknown
License
2.7%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

python
76.0%
markdown
23.6%
text
0.2%
yaml
0.1%

Frameworks

None detected

Concepts (2)

Findings curated by Repobility · https://repobility.com
CategoryNameDescriptionConfidence
Source: Repobility analyzer · https://repobility.com
auto_descriptionProject Description基于温斯坦(Stan Weinstein)34 周均线阶段分析法,对 A 股 31 个申万一级行业(2021 版)进行趋势阶段判断,帮助识别各行业当前处于熊市、牛市还是转换期。80%
auto_categoryWeb Backendweb-backend70%

Quality Timeline

1 quality score recorded.

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