SignalSage is a web application that allows users to automatically process and structure trading signals from Telegram channels using AI. It leverages the power of OpenAI's language models to transform raw, often unstructured text from Telegram into clean, usable JSON data. This saves traders time and effort, allowing them to focus on analysis and decision-making, rather than manual data extraction.

 

Active Traders: Individuals who actively trade stocks, cryptocurrencies, forex, or other financial instruments and rely on Telegram channels for trading signals.

Quantitative Analysts (Quants): Traders and analysts who use algorithmic or automated trading strategies and require structured data for their models.

Financial Data Aggregators: Services that collect and consolidate financial data from various sources, including Telegram.

Trading Tool Developers: Developers who are building tools for traders and need a way to easily integrate structured trading signals from Telegram.

 

Key Features:

 

Secure Connection: Users can securely connect their Telegram account to SignalSage (using Telegram's official API, not by providing their password directly to the site). This grants SignalSage access to read messages from specified channels.

Channel Selection: Users can select which Telegram channels they want to monitor for trading signals. The interface will guide them through the process of identifying channels. Ideally, this would involve a channel search/browse feature, not just manual entry of usernames.

Real-time Monitoring: SignalSage continuously monitors the selected Telegram channels for new messages.

 

AI-Powered Signal Processing:

 

Automated Extraction: When a new message is detected in a monitored channel, SignalSage automatically extracts the message text.

OpenAI Integration: The extracted text is sent to an OpenAI language model (e.g., GPT-3.5 Turbo or GPT-4) to be processed and converted into structured JSON format. The prompts used with the OpenAI API are crucial for getting the correct output. The web application would likely allow users to customize these prompts (more on this later).

Customizable Output: Users can define the structure of the output JSON. This is essential. Different users will need different data fields (e.g., "symbol", "entry_price", "stop_loss", "take_profit", "signal_type" (long/short), "timeframe", "confidence_level").

Error Handling: The system should gracefully handle cases where the AI fails to process a signal or produces invalid JSON. This might involve alerting the user, retrying with a different prompt, or logging the error for later analysis.

 

Data Management and Display:

 

Signal History: Processed signals are stored in a database (e.g., PostgreSQL, MySQL, or a NoSQL database like MongoDB).

Dashboard: A user-friendly dashboard displays the processed signals in a clear, organized format (e.g., a table with sortable columns).

Filtering and Search: Users can filter signals by channel, date, time, asset type, and any other relevant criteria defined in the JSON structure.

Data Export: Users can export the processed data in various formats (e.g., CSV, JSON, Excel) for use in other applications or trading platforms.

 

User Management:

 

Registration/Login: Secure user accounts with email verification.

Subscription Plans: Different tiers of service could be offered (e.g., free tier with limited channels/signals, paid tiers with more features and higher limits).

API Keys: Users can generate API keys to access the processed data programmatically, enabling integration with other trading tools and scripts.

 

Alerting and Notifications (Optional, but Highly Valuable):

 

Real-time Alerts: Users can set up real-time alerts (e.g., email, push notifications, webhooks) when new signals matching specific criteria are processed. This allows them to react quickly to trading opportunities.

 

Custom Prompt Management (Advanced Feature):

 

Prompt Library: A library of pre-built prompts for different types of trading signals (e.g., cryptocurrency signals, stock signals, forex signals).

Prompt Editor: An interface that allows users to create and edit their own prompts for the OpenAI API. This provides maximum flexibility and allows users to fine-tune the signal processing to their specific needs. The editor should provide syntax highlighting and validation. A "test" feature to run the prompt against a sample Telegram message would be essential.

Prompt Versioning: Track changes to prompts and allow users to revert to previous versions.

Backtesting (Advanced Feature):

Allow users to backtest their chosen signals/strategies with historical data, potentially providing performance metrics.

Technology Stack (Suggestions):

Frontend:

React, Vue.js, or Angular (for a dynamic, responsive user interface)

HTML, CSS, JavaScript

UI libraries like Material UI, Bootstrap, or Tailwind CSS for styling

Backend:

Python (Flask or Django) - You already have a Python codebase, so this makes sense.

python-telegram-bot or Telethon (for interacting with the Telegram API)

openai (for interacting with the OpenAI API)

Database:

PostgreSQL or MySQL (for relational data, user accounts, signal history)

MongoDB (for flexibility in storing the processed JSON data, especially if the structure might vary)

Deployment:

AWS, Google Cloud Platform, Azure, Heroku, DigitalOcean (cloud hosting)

Docker (for containerization)

Nginx or Apache (web server)

Other:

Redis (for caching and potentially as a message queue for handling real-time updates)

Celery (for asynchronous task processing, like sending requests to OpenAI and processing large volumes of data)

Monetization Strategy:

Subscription Model: Offer different subscription tiers with varying features and limits (e.g., number of channels, number of signals processed per month, access to advanced features like custom prompts and alerting).

API Access: Charge for API access to the processed data.

Development Roadmap (Phased Approach):

MVP (Minimum Viable Product):

Basic Telegram integration (connect to a single, hardcoded channel).

Simple OpenAI processing (using a fixed prompt).

Display processed signals in a basic table.

User authentication (registration/login).

Beta Version:

Allow users to select multiple Telegram channels.

Implement basic filtering and search.

Add data export functionality.

Introduce basic subscription plans.

Version 1.0:

Implement customizable output JSON structure.

Add error handling and logging.

Introduce alerting/notifications.

Future Enhancements:

Custom prompt management.

API access with API keys.

Backtesting capabilities.

Integration with other trading platforms.

Support for other messaging platforms (e.g., Discord).

This detailed project description provides a solid foundation for building a valuable web application. It outlines the core functionality, target users, technology choices, and a potential roadmap for development. Remember to prioritize features based on user needs and iterate based on feedback.