Options Analytics Dashboard and Analyzer Enhancements
Budget: $250 – $750 USD
Project Objective
Develop and enhance an existing options analytics dashboard used for analyzing U.S. index options. The platform streams real-time market data, performs quantitative analysis, and displays results on a web-based dashboard with alerting capabilities.
|Component|Stack|
|---|---|
|Backend|Python (Flask)|
|Frontend|HTML/CSS + JavaScript|
|Database|PostgreSQL|
|APIs|REST-based external data APIs|
|Hosting|Linux VPS (Ubuntu), virtualenv|
|Dashboard|Browser-accessible Flask
Core Requirements
Modular Analyzer Integration**
- Refactor existing dashboard to support **multiple analyzers**
- Dynamic loading based on configuration or session selection
- Cache results where applicable for performance
2 **Data Stream Management**
- Interface with a real-time data streamer (already operational)
- Ensure robust ingestion into PostgreSQL
- Enable custom filters and snapshot logic as needed
3. Analyzer Framework (Placeholder)**
- Implement multiple placeholder analyzers that will be detailed later
- Each analyzer will:
- Query real-time and historical data
- Store results in dedicated tables
- Output JSON to the dashboard frontend
- Results must be timestamped and queryable by date/session
4.**Alert System**
- Build a lightweight alerting engine
- Alerts triggered from analyzer results (logic to be defined internally)
- Display alerts on the dashboard with time and trigger metadata
- Optional: Webhook support for Slack/email later
5. **API Integration**
- Extend/modify an existing REST API script for external data ingestion (Tiingo or similar)
- Ensure fail-safe error handling, timestamp alignment, and storage logic
- Support daily and intraday fetch modes
---
Dashboard Requirements
- User-selectable analyzer dropdown or tabbed view
- Results display for each analyzer:
- Tables
- Charts (if applicable)
- Real-time summaries
- Alert display panel with recent signals
- Data refresh toggle (auto/manual)
---
Data Structure & Persistence
- Each analyzer must have a **dedicated PostgreSQL table** (schema will be co-developed)
- Include:
- Unique ID
- Timestamp
- Analyzer output metrics (flexible columns)
- Alert triggers (boolean or signal column)
- Create 2–3 summary views or materialized views for quick loading on the frontend
---
Deliverables
- Updated Flask backend with integrated analyzers
- Responsive frontend with dynamic analyzer loading
- Alert system integrated with database and frontend
- SQL schema scripts and migrations
- Deployment instructions + environment config files
- Inline and external documentation (README + API descriptions)
Develop and enhance an existing options analytics dashboard used for analyzing U.S. index options. The platform streams real-time market data, performs quantitative analysis, and displays results on a web-based dashboard with alerting capabilities.
|Component|Stack|
|---|---|
|Backend|Python (Flask)|
|Frontend|HTML/CSS + JavaScript|
|Database|PostgreSQL|
|APIs|REST-based external data APIs|
|Hosting|Linux VPS (Ubuntu), virtualenv|
|Dashboard|Browser-accessible Flask
Core Requirements
Modular Analyzer Integration**
- Refactor existing dashboard to support **multiple analyzers**
- Dynamic loading based on configuration or session selection
- Cache results where applicable for performance
2 **Data Stream Management**
- Interface with a real-time data streamer (already operational)
- Ensure robust ingestion into PostgreSQL
- Enable custom filters and snapshot logic as needed
3. Analyzer Framework (Placeholder)**
- Implement multiple placeholder analyzers that will be detailed later
- Each analyzer will:
- Query real-time and historical data
- Store results in dedicated tables
- Output JSON to the dashboard frontend
- Results must be timestamped and queryable by date/session
4.**Alert System**
- Build a lightweight alerting engine
- Alerts triggered from analyzer results (logic to be defined internally)
- Display alerts on the dashboard with time and trigger metadata
- Optional: Webhook support for Slack/email later
5. **API Integration**
- Extend/modify an existing REST API script for external data ingestion (Tiingo or similar)
- Ensure fail-safe error handling, timestamp alignment, and storage logic
- Support daily and intraday fetch modes
---
Dashboard Requirements
- User-selectable analyzer dropdown or tabbed view
- Results display for each analyzer:
- Tables
- Charts (if applicable)
- Real-time summaries
- Alert display panel with recent signals
- Data refresh toggle (auto/manual)
---
Data Structure & Persistence
- Each analyzer must have a **dedicated PostgreSQL table** (schema will be co-developed)
- Include:
- Unique ID
- Timestamp
- Analyzer output metrics (flexible columns)
- Alert triggers (boolean or signal column)
- Create 2–3 summary views or materialized views for quick loading on the frontend
---
Deliverables
- Updated Flask backend with integrated analyzers
- Responsive frontend with dynamic analyzer loading
- Alert system integrated with database and frontend
- SQL schema scripts and migrations
- Deployment instructions + environment config files
- Inline and external documentation (README + API descriptions)