Sentiment Analysis Dashboard Development
Budget: $750 – $1,500 USD
Project Scope: Quantitative Analyzer & Dashboard System for Options Market
Objective
Develop a modular, Flask-based dashboard application that runs, manages, and visualizes a set of custom real-time financial analyzers. These analyzers will process live market data from SPX options (primarily 0DTE), store relevant metrics, and expose them through a structured dashboard interface and backend API.
The system must support dynamic component registration, individual analyzer isolation, scalable visualization, and future-ready expansion for order routing and machine learning integration. No trading logic or proprietary strategies will be disclosed or implemented in this phase.
---
Core Deliverables
1. Backend Infrastructure
- Build a **component registry system** that supports dynamic loading of analyzer modules without modifying core routes.
- Implement an **analyzer engine** that runs each analyzer on a scheduled interval (e.g., every 15–30 seconds).
- All analyzers must:
- Accept live data input (via database or stream)
- Output structured results to the UI/API
- Optionally log to PostgreSQL for historical analysis
---
2. Analyzers to Build
Seven analyzers will be implemented as isolated Python modules, each with a specific focus:
1. Mispricing Analyzer**
- Compares theoretical option prices vs. real-time market midpoint
- Flags overpricing/underpricing deviations
2. Heat Map Analyzer**
- Computes rate of delta change across adjacent strikes
- Visualizes “gamma zones” of sensitivity
3. Spread Tracker**
- Tracks credit pricing trends for fixed-width vertical spreads
- Logs running averages, highs, and lows for selected delta bands
4. Skew Mapping**
- Fits and logs the implied volatility curve per expiry and option type
- Captures slope, curvature, and IV anomalies
5. Skew + Open Interest Analyzer**
- Ranks put and call strikes with above-average implied volatility and trader interest
- Uses open interest and volume to prioritize flow targets
6. **Iron Condor Analyzer**
- Already completed but need to fit to new dashboard.
7. **Short Vertical Spread Analyzer**
-Finished, but fit to new dashboard.
---
3. Flask Dashboard System
- Develop a modular, tabbed dashboard using Flask templates
- Each analyzer will have:
- Dedicated backend route
- JavaScript rendering file
- HTML panel under `/templates/analyzers/`
- Must include:
- Tables and charts for each analyzer
- Optional filters (expiry, delta range, etc.)
- Real-time refresh behavior
- Use reusable JS components (e.g., Chart.js or Plotly)
---
4. Data Logging and Access
- Use PostgreSQL for time-series data storage
- Each analyzer will have a corresponding result or log table
- Ensure all logs are timestamped and labeled by expiry date and option type
- Provide SQL scripts to create required tables and views
---
5. Extensibility Requirements
- Component registration must allow future analyzers to be added without hardcoding
- The dashboard must support:
- Analyzer discovery at runtime
- Injection of new UI tabs or panels dynamically
- System must be designed to allow:
- Trade execution module in a future release
- AI module to consume stored analyzer results
---
Excluded from Scope
- No trade execution or order routing in this phase
- No direct connection to broker APIs
- No UI mockups or style branding unless requested
---
Technology Stack
- Python 3.10+
- Flask (modular architecture)
- PostgreSQL (existing)
- Chart.js or Plotly.js (whichever the developer prefers)
- Optional: SQLAlchemy or psycopg2
---
Estimated Completion Timeline
| Task Group | Estimated Duration |
|------------|---------------------|
| Backend setup + registry + base dashboard | 2–3 business days |
| Analyzer implementation (7 modules total) | 8–10 business days |
| Frontend integration and charting | 4–5 business days |
| QA, testing, and adjustments | 2 business days |
Total estimated time: 14–20 business days (3–4 weeks)
Objective
Develop a modular, Flask-based dashboard application that runs, manages, and visualizes a set of custom real-time financial analyzers. These analyzers will process live market data from SPX options (primarily 0DTE), store relevant metrics, and expose them through a structured dashboard interface and backend API.
The system must support dynamic component registration, individual analyzer isolation, scalable visualization, and future-ready expansion for order routing and machine learning integration. No trading logic or proprietary strategies will be disclosed or implemented in this phase.
---
Core Deliverables
1. Backend Infrastructure
- Build a **component registry system** that supports dynamic loading of analyzer modules without modifying core routes.
- Implement an **analyzer engine** that runs each analyzer on a scheduled interval (e.g., every 15–30 seconds).
- All analyzers must:
- Accept live data input (via database or stream)
- Output structured results to the UI/API
- Optionally log to PostgreSQL for historical analysis
---
2. Analyzers to Build
Seven analyzers will be implemented as isolated Python modules, each with a specific focus:
1. Mispricing Analyzer**
- Compares theoretical option prices vs. real-time market midpoint
- Flags overpricing/underpricing deviations
2. Heat Map Analyzer**
- Computes rate of delta change across adjacent strikes
- Visualizes “gamma zones” of sensitivity
3. Spread Tracker**
- Tracks credit pricing trends for fixed-width vertical spreads
- Logs running averages, highs, and lows for selected delta bands
4. Skew Mapping**
- Fits and logs the implied volatility curve per expiry and option type
- Captures slope, curvature, and IV anomalies
5. Skew + Open Interest Analyzer**
- Ranks put and call strikes with above-average implied volatility and trader interest
- Uses open interest and volume to prioritize flow targets
6. **Iron Condor Analyzer**
- Already completed but need to fit to new dashboard.
7. **Short Vertical Spread Analyzer**
-Finished, but fit to new dashboard.
---
3. Flask Dashboard System
- Develop a modular, tabbed dashboard using Flask templates
- Each analyzer will have:
- Dedicated backend route
- JavaScript rendering file
- HTML panel under `/templates/analyzers/`
- Must include:
- Tables and charts for each analyzer
- Optional filters (expiry, delta range, etc.)
- Real-time refresh behavior
- Use reusable JS components (e.g., Chart.js or Plotly)
---
4. Data Logging and Access
- Use PostgreSQL for time-series data storage
- Each analyzer will have a corresponding result or log table
- Ensure all logs are timestamped and labeled by expiry date and option type
- Provide SQL scripts to create required tables and views
---
5. Extensibility Requirements
- Component registration must allow future analyzers to be added without hardcoding
- The dashboard must support:
- Analyzer discovery at runtime
- Injection of new UI tabs or panels dynamically
- System must be designed to allow:
- Trade execution module in a future release
- AI module to consume stored analyzer results
---
Excluded from Scope
- No trade execution or order routing in this phase
- No direct connection to broker APIs
- No UI mockups or style branding unless requested
---
Technology Stack
- Python 3.10+
- Flask (modular architecture)
- PostgreSQL (existing)
- Chart.js or Plotly.js (whichever the developer prefers)
- Optional: SQLAlchemy or psycopg2
---
Estimated Completion Timeline
| Task Group | Estimated Duration |
|------------|---------------------|
| Backend setup + registry + base dashboard | 2–3 business days |
| Analyzer implementation (7 modules total) | 8–10 business days |
| Frontend integration and charting | 4–5 business days |
| QA, testing, and adjustments | 2 business days |
Total estimated time: 14–20 business days (3–4 weeks)