Dashboard / Repos / Agentic Openenvolve2 Backtest

Agentic Openenvolve2 Backtest

D 58 completed
Ai Ml
web_app / python · medium
1,063
Files
1,380,467
LOC
3
Frameworks
9
Languages
OverviewFiles & MetricsGit ActivityCall GraphSecurityReports

Pipeline State

completed
Run ID
#300938
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Cataloged
Decision
proceed
Novelty
77.67
Framework unique
—
Isolation
—
Last stage change
2026-05-10 03:35:41
Deduplication group #52504
Member of a group with 3 similar repo(s) — this repo is canonical view group →
Top concepts (12)
Repositorybusiness_logicpresentationLayered ArchitectureinfrastructureFactoryStrategyCachingConfigurationDatabaseFile ManagementLogging
All rows above produced by Repobility · https://repobility.com

AI Prompt

Create a comprehensive stock price prediction and earnings call analysis system. I need it to integrate Agentic RAG by connecting several services: FMP API for financial data, a Main Agent, and Helper Agents. The system must utilize a Neo4j Knowledge Graph, connect to the SEC Filings Service to fetch 10-K/10-Q/13F documents, and use the Backtester API for OHLCV data. It should also calculate performance metrics using a dedicated service and store results in MinIO. The core functionality should expose endpoints for standard analysis and advanced analysis that incorporates all these external services. Please structure the backend using Python.
python web-app stock-prediction rag financial-analysis neo4j api agentic backtesting
Generated by gemma4:latest

Catalog Information

This project integrates Agentic RAG and Whaleforce services to analyze earnings calls and predict stock prices.

Description

The project is a system that combines the capabilities of Agentic RAG and Whaleforce services to provide a comprehensive analysis of earnings calls and predict stock prices. It uses a unified LLM inference service, LiteLLM, to process financial data from various sources, including SEC filings, historical performance metrics, and comparative analysis. The system also includes a backtester API for validating predictions and calculating performance metrics.

الوصف

هذا المشروع يدمج خدمات Agentic RAG و Whaleforce لتحليل المكالمات الصحفية والتنبؤ بالأسهم. يستخدم النظام خدمة الاستدلال الموحدة LiteLLM للتعامل مع البيانات المالية من مصادر متعددة، بما في ذلك الإبلاغ عن SEC ، والمعايير التاريخية، والتحليل المقارن. كما يحتوي النظام على واجهة برمجة التطبيقات لاختبار التنبؤات وتحديد المعايير الأداء.

Novelty

7/10

Tags

financial-analysis stock-prediction earnings-call-analysis llm-inference sec-filings historical-performance-metrics comparative-analysis backtesting

Technologies

aws-sdk fastapi huggingface langchain numpy openai pandas pydantic pytorch scikit-learn uvicorn

Claude Models

claude-opus-4.5

Quality Score

D
58.3/100
Structure
43
Code Quality
73
Documentation
77
Testing
30
Practices
52
Security
75
Dependencies
90

Strengths

  • Consistent naming conventions (snake_case)
  • Containerized deployment (Docker)

Weaknesses

  • No LICENSE file \u2014 legal ambiguity for contributors
  • No CI/CD configuration \u2014 manual testing and deployment
  • 11 bare except/catch blocks swallowing errors
  • 2799 duplicate lines detected \u2014 consider DRY refactoring
  • 10 'god files' with >500 LOC need decomposition

Recommendations

  • Add a test suite \u2014 start with critical path integration tests
  • 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)
  • Replace bare except/catch blocks with specific exception types

Security & Health

13.1h
Tech Debt (A)
Medium
DORA Rating
A
OWASP (100%)
PASS
Quality Gate
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A
Risk (0)
Unknown
License
15.1%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

python
57.5%
json
20.4%
markdown
10.7%
typescript
5.5%
javascript
3.0%
css
1.5%
html
1.3%
shell
0.0%
text
0.0%

Frameworks

React Next.js Tailwind CSS

Symbols

variable673
function647
constant219
method159
class54
interface35
type_alias8
property6

API Endpoints (44)

Page rendered by Aljefra Mapper · scored by Repobility (https://repobility.com)
MethodPathHandlerFramework
Methodology: Repobility · https://repobility.com/research/state-of-ai-code-2026/
POST/admin/run-scanapp.postExpress/Koa
GET/admin/statusapp.getExpress/Koa
POST/admin/test-lineapp.postExpress/Koa
POST/api/analyzeapi_analyzeFastAPI/Flask
POST/api/analyze-with-servicesapi_analyze_with_servicesFastAPI/Flask
GET/api/backtest/infoapi_backtest_infoFastAPI/Flask
POST/api/backtest/runapi_backtest_runFastAPI/Flask
GET/api/backtest/strategiesapi_backtest_strategiesFastAPI/Flask
POST/api/batch-analyzeapi_batch_analyzeFastAPI/Flask
GET/api/batch-analyze/{job_id}api_batch_statusFastAPI/Flask
GET/api/call/{job_id}api_call_detailFastAPI/Flask
GET/api/callsapi_callsFastAPI/Flask
GET/api/earnings-calendar/rangeapi_earnings_calendar_rangeFastAPI/Flask
GET/api/earnings-calendar/todayapi_earnings_calendar_todayFastAPI/Flask
POST/api/prompt_profilesapi_create_profileFastAPI/Flask
GET/api/prompt_profilesapi_list_profilesFastAPI/Flask
POST/api/prompt_profiles/applyapi_apply_profileFastAPI/Flask
DELETE/api/prompt_profiles/{name}api_delete_profileFastAPI/Flask
GET/api/prompt_profiles/{name}api_get_profileFastAPI/Flask
GET/api/promptsapi_get_promptsFastAPI/Flask
PUT/api/promptsapi_update_promptFastAPI/Flask
DELETE/api/prompts/{key}api_delete_promptFastAPI/Flask
GET/api/prompts/statusapi_prompt_statusFastAPI/Flask
GET/api/services/backtester/ohlcvapi_backtester_ohlcvFastAPI/Flask
GET/api/services/backtester/post-earnings-returnapi_backtester_post_earnings_returnFastAPI/Flask
POST/api/services/backtester/validate-predictionapi_backtester_validate_predictionFastAPI/Flask
GET/api/services/healthapi_services_healthFastAPI/Flask
GET/api/services/performance-metricsapi_performance_metricsFastAPI/Flask
GET/api/services/performance-metrics/post-earningsapi_performance_metrics_post_earningsFastAPI/Flask
GET/api/services/sec-filings/contextapi_sec_contextFastAPI/Flask
GET/api/services/sec-filings/filingsapi_sec_filingsFastAPI/Flask
GET/api/services/sec-filings/searchapi_sec_searchFastAPI/Flask
GET/api/symbolsapi_symbolsFastAPI/Flask
GET/api/transcript-datesapi_transcript_datesFastAPI/Flask
GETdatesearchParams.getExpress
GETend_datesearchParams.getExpress
GET/healthzapp.getExpress/Koa
GETjob_idparams.getExpress
POST/message/pushlineClient.postExpress
GETmin_market_capsearchParams.getExpress
GETmin_market_capsearchParams.getExpress
GETrefreshsearchParams.getExpress
GETstart_datesearchParams.getExpress
GETsymbolurlParams.getExpress

Concepts (17)

Per-row analysis by Repobility · https://repobility.com
CategoryNameDescriptionConfidence
Citation: Repobility (2026). State of AI-Generated Code. https://repobility.com/research/
design_patternRepositoryFound repository-named files80%
arch_layerbusiness_logicDetected business_logic layer70%
arch_layerpresentationDetected presentation layer70%
arch_patternLayered ArchitectureFound API/routes, service, and data layers70%
arch_layerinfrastructureDetected infrastructure layer70%
design_patternFactoryFound factory/create_ naming patterns60%
design_patternStrategyFound strategy/policy-named files60%
business_logicCachingDetected from 2 related files50%
business_logicConfigurationDetected from 10 related files50%
business_logicDatabaseDetected from 2 related files50%
business_logicFile ManagementDetected from 3 related files50%
business_logicLoggingDetected from 32 related files50%
business_logicSearchDetected from 13 related files50%
business_logicTestingDetected from 902 related files50%
business_logicAnalyticsDetected from 84 related files50%
business_logicAPI GatewayDetected from 5 related files50%
arch_patternContainerized/MicroservicesMultiple Dockerfiles found at package level50%

Quality Timeline

1 quality score recorded.

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BinComp Dependency Hardening

All packages →
16 of this repo's dependencies have been scanned for binary hardening. Grade reflects RELRO / stack canary / FORTIFY / PIE coverage.
Ntransformers5.5.3 · 0 gadgets · risk 10146.5Ftorch2.11.0 · 1,257 gadgets · risk 5116.6Nrequests2.33.1 · 0 gadgets · risk 3687.0Nasyncio4.0.0 · 0 gadgets · risk 0.0Nboto31.42.88 · 0 gadgets · risk 0.0Nbotocore1.42.88 · 0 gadgets · risk 0.0Nfastapi0.135.3 · 0 gadgets · risk 0.0Nhttpx0.28.1 · 0 gadgets · risk 0.0Fnumpy2.4.4 · 6,596 gadgets · risk 0.0Nopenai2.31.0 · 0 gadgets · risk 0.0Fpandas3.0.2 · 6,381 gadgets · risk 0.0Npydantic2.12.5 · 0 gadgets · risk 0.0Nredis7.4.0 · 0 gadgets · risk 0.0Fscipy1.17.1 · 21,805 gadgets · risk 0.0Ntqdm4.67.3 · 0 gadgets · risk 0.0Nuvicorn0.44.0 · 0 gadgets · risk 0.0