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Torch Trader: Requirements Specification

1. Introduction

This document outlines the functional and non-functional requirements for the Torch Trader platform, a next-generation trading solution that supports stocks and cryptocurrencies. Torch Trader aims to provide a comprehensive, high-performance, and user-friendly trading environment by leveraging advanced analytics, backtesting, deep learning, and trading bot automation.

2. Functional Requirements

2.1 Data Collection

  1. Integrate popular APIs for data collection, such as Alpha Vantage, Yahoo Finance, and Binance, to gather stock and crypto market data.
  2. Store collected data in an organized and efficient format, such as CSV or a database like SQLite or PostgreSQL.

2.2 Technical Analysis and Scripting

  1. Provide built-in technical analysis indicators using libraries like TA-Lib or Tulipy.
  2. Allow users to create, edit, and manage custom trading strategies using Python scripts.
  3. Offer a scripting engine to evaluate and execute user-defined strategies.

2.3 Backtesting

  1. Develop a backtesting engine that simulates trading with historical data.
  2. Provide users with customizable backtesting parameters, such as date range, initial capital, and trade frequency.
  3. Display comprehensive backtesting results, including performance metrics, trade logs, and visualizations.

2.4 Strategy Optimization and Deep Learning

  1. Implement deep learning models using PyTorch for strategy optimization.
  2. Support GPU acceleration using CUDA for high-performance model training.
  3. Apply optimization algorithms, such as grid search or Bayesian optimization, to fine-tune trading strategies.
  4. Consider reinforcement learning techniques, like Deep Q-Networks (DQN) or Proximal Policy Optimization (PPO), for optimizing strategies.

2.5 Trading Bots and Monitoring

  1. Develop trading bots capable of executing trades on popular stock and crypto exchanges using their APIs.
  2. Allow users to create, configure, and manage multiple trading bots.
  3. Implement a monitoring system to track bot performance, including profit/loss, trade history, and strategy performance.
  4. Provide real-time alerts and notifications for user-defined events, such as trade execution or strategy changes.

2.6 User Interface

  1. Design an intuitive and responsive user interface that supports both novice and experienced traders.
  2. Provide multiple interface options, including a command-line interface, web-based interface using Flask or Django, or desktop application using PyQt or Tkinter.
  3. Implement user-friendly navigation and organization of platform features and settings.

3. Non-functional Requirements

3.1 Performance

  1. Ensure low-latency and high-throughput performance for data processing, backtesting, and deep learning tasks.
  2. Optimize the platform for multi-threading and asynchronous processing using Python's concurrent.futures or asyncio libraries.

3.2 Scalability

  1. Design the platform to handle increasing amounts of data and user activity as the user base grows.
  2. Ensure the platform can be easily expanded to support new features, trading instruments, or data sources.

3.3 Security

  1. Implement industry-standard security measures to protect user data and sensitive information.
  2. Ensure secure communication with external APIs and services using encryption and authentication protocols.

3.4 Reliability

  1. Develop a robust and stable platform that minimizes the occurrence of bugs, crashes, or performance issues.
  2. Implement error handling and logging mechanisms to quickly identify and address issues.
  3. Implement a version control system, such as Git, to track code changes and facilitate collaboration among developers.
  4. Utilize a continuous integration and continuous deployment (CI/CD) pipeline to automate testing and deployment processes, ensuring consistent code quality and efficient updates.
  5. Encourage contributions from the open-source community by maintaining an active and responsive presence on platforms like GitHub or GitLab.

3.6 Usability

  1. Design the user interface with a focus on user experience, ensuring it is easy to learn, navigate, and use for both novice and experienced traders.
  2. Provide comprehensive user documentation, including installation guides, tutorials, and examples, to assist users in effectively using the platform.
  3. Implement responsive and helpful error messages and tooltips to guide users in understanding and resolving issues.
  4. Offer an accessible and responsive customer support system to address user questions, feedback, and concerns.

4. Constraints and Assumptions

  1. Constraints:
    • The platform must be developed using Python and compatible libraries.
    • The platform must support both stock and crypto markets.
    • The platform should be compatible with multiple operating systems, including Windows, macOS, and Linux.
    • The platform must adhere to any legal or regulatory requirements related to trading and data privacy.
  2. Assumptions:
    • Users will have a basic understanding of trading and financial markets.
    • Users will have access to a stable internet connection for data collection and communication with external APIs.
    • Users will have the necessary hardware and software resources to run the platform, such as sufficient RAM, storage, and processing power.

5. Conclusion

This requirements specification document serves as a comprehensive guide for the development of the Torch Trader platform, outlining the necessary functional and non-functional requirements to create a powerful, user-friendly, and reliable trading solution. By adhering to these requirements and addressing the constraints and assumptions, the Torch Trader platform will provide traders with an advanced and efficient toolset for navigating the world of stocks and cryptocurrencies.