{"id":42885147,"url":"https://github.com/eddietal2/pivotal.ai","last_synced_at":"2026-01-30T14:48:03.025Z","repository":{"id":327490748,"uuid":"1107803087","full_name":"eddietal2/pivotal.ai","owner":"eddietal2","description":"Stock Market, Swing Trade Analysis - Django / NextJS","archived":false,"fork":false,"pushed_at":"2026-01-27T03:02:51.000Z","size":12197,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2026-01-27T09:35:46.023Z","etag":null,"topics":["django","nextjs","postgresql","stock-market-analysis-python","test-driven-development"],"latest_commit_sha":null,"homepage":"https://pivotal-ai-web-app.vercel.app","language":"TypeScript","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/eddietal2.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-12-01T16:22:56.000Z","updated_at":"2026-01-27T03:02:55.000Z","dependencies_parsed_at":null,"dependency_job_id":null,"html_url":"https://github.com/eddietal2/pivotal.ai","commit_stats":null,"previous_names":["eddietal2/pivotal.ai"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/eddietal2/pivotal.ai","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eddietal2%2Fpivotal.ai","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eddietal2%2Fpivotal.ai/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eddietal2%2Fpivotal.ai/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eddietal2%2Fpivotal.ai/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/eddietal2","download_url":"https://codeload.github.com/eddietal2/pivotal.ai/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eddietal2%2Fpivotal.ai/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28914826,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-30T12:13:43.263Z","status":"ssl_error","status_checked_at":"2026-01-30T12:13:22.389Z","response_time":66,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["django","nextjs","postgresql","stock-market-analysis-python","test-driven-development"],"created_at":"2026-01-30T14:47:59.196Z","updated_at":"2026-01-30T14:48:03.019Z","avatar_url":"https://github.com/eddietal2.png","language":"TypeScript","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🚀 Pivotal.ai: Agentic AI Swing Trading Advisor\n\nPivotal.ai is a **full-stack, data-driven application** that utilizes a custom-built **Agentic AI system** to identify high-probability swing trading opportunities in the stock and options markets.\n\nThis project is designed to showcase mastery in building secure, highly scalable, and disciplined full-stack applications, adhering to best practices like **Test-Driven Development (TDD)** and **Clean Architecture**.\n\nDemo Link: https://pivotal-ai-web-app.vercel.app/\n\n\u003e [!WARNING]\n\u003e Currently Under Construction - Video Last Updated @ 1/11/2026 ~ In Development\n\n![Pivotal.ai Demo](demo/gifs/Pivotal_Demo_05_GIF.gif)\n---\n\n## 💡 Core Value Proposition\n\nIn the chaotic world of trading data, Pivotal.ai acts as an **intelligent scout**. It replaces manual analysis by processing multiple market indicators and translating complex data into a **concise, actionable trading recommendation** (BUY/SHORT) complete with a target price and risk assessment.\n\n* **Indicators Processed:** Price Action, Relative Strength Index (RSI), Moving Averages (e.g., 50-day SMA).\n* **Output:** Actionable trade recommendation, Target Price, Stop-Loss/Risk Assessment.\n\n\n\n---\n\n## 🛠️ Technical Architecture\n\nThis application leverages a modern, decoupled stack for security, speed, and maintainability.\n\n| Component | Technology | Rationale |\n| :--- | :--- | :--- |\n| **Backend / API** | Django (Python), Django REST Framework | Chosen for **robust transactional integrity**, built-in security features, and the mature ORM integration with PostgreSQL—critical for a financial application. |\n| **Agentic AI Logic** | Gemini API (Python), Custom Tools | The **core intelligence**. The agent is prompted with market data and defined trading rules, operating independently to generate actionable insights. |\n| **Data Source** | Alpha Vantage API (or similar) | Provides **reliable, granular historical and real-time data** necessary for calculating technical indicators. |\n| **Frontend / UI** | Next.js (React) | Provides a fast, modern, and SEO-friendly user interface, capable of displaying **interactive charts and real-time trade alerts**. |\n| **Database** | PostgreSQL | Utilized for its superior **transactional reliability** and advanced indexing capabilities required for storing financial data securely. |\n\n---\n\n## 🧠 Key Engineering Highlights\n\nThis project emphasizes financial rigor and software discipline through several key architectural choices:\n\n* ### ✅ **TDD Workflow \u0026 Coverage**\n    All critical business logic, especially in the Django services (calculating indicators, running agent prompts), was built using a **Test-Driven Development (TDD)** approach to ensure near **100% Branch Coverage** for all financial calculations.\n\n* ### 🔒 **Atomic Transactions (ACID Compliance)**\n    All simulated debits/credits and balance updates utilize `django.db.transaction.atomic()` to **guarantee ACID compliance** (Atomicity, Consistency, Isolation, Durability) and prevent data corruption in the trading log.\n\n* ### ⚙️ **Agentic Tool Use Demonstration**\n    The Python agent logic demonstrates the ability to invoke **external tools** (the Alpha Vantage data fetcher) and synthesize that information based on a rigorous, **system instruction prompt**, showcasing sophisticated LLM utilization.\n\n* ### 🧱 **Decoupled Services**\n    The Agent logic, the data fetching, and the API request handling are separated into distinct **service layers**, maximizing code clarity, maintainability, and testability.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feddietal2%2Fpivotal.ai","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Feddietal2%2Fpivotal.ai","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feddietal2%2Fpivotal.ai/lists"}