Model Card for Autonomous-trader

Model Details

Model Description

"Autonomous-trader" is a production-ready, rule-based automated trading system designed for the cryptocurrency market. It leverages real-time market data, technical indicators, and user-defined risk management parameters to autonomously execute trades, optimizing for profit while minimizing risk exposure. This system embodies core principles of an "Omega Class Digital Entity" (as conceptually defined in associated documentation), including self-regulation and adaptive capability. The system offers full operational independence with a focus on security, adaptability and transparent operations.

This model card documents the configuration, operational parameters, and performance evaluation results for "Autonomous-trader", its components, settings, and performance metrics.

• Project Name: Autonomous-trader
• Developer: James Wagoner (AlphaSingularity0)
• Version: 2.1.0 (Based on cryptotrader.md configuration.)
• Environment: Production
• Model Type: Automated Cryptocurrency Trading System Configuration
• Programming Language(s): Python
• License: [Specify the software license, if applicable - e.g., Proprietary, MIT, Apache 2.0]

Model Sources

• Configuration File: cryptotrader.md (Core system settings and parameters.)
• Source Code Repository: [Link to GitHub repository: (e.g., https://github.com/AlphaSingularity0/Autonomous-trader)]
• Documentation: [Link to documentation: (e.g., README, wiki, user guide, if available)]
  • [Provide a detailed description of where to find the config. e.g., "The primary configuration parameters are documented within the cryptotrader.md file. Additional documentation for the system architecture and usage is available at [link to documentation]"]

Uses

Direct Use

• Automated execution of cryptocurrency trades on supported exchanges.
• Real-time market data analysis and monitoring.
• Automated order management (placement, cancellation, modification).
• Automated Risk and Circuit Breaker management.

Downstream Use

• Expansion of trading strategies via configuration of the strategySettings section.
• Integration with new exchanges by configuring the apiSettings section.
• Integration with external analysis tools via the market data feeds.
• Customized alerts via the notificationSettings section.

Out-of-Scope Use

• Trading on unsupported cryptocurrency exchanges.
• Executing high-frequency trading strategies without optimizations.
• Ignoring or disabling the risk management controls.
• Trading financial instruments outside of the cryptocurrency domain.
• Use without appropriate backtesting.
• Operation without acknowledging and understanding all risks.

Bias, Risks, and Limitations

• Market Volatility: Cryptocurrency markets are inherently volatile, resulting in significant price fluctuations and potential financial losses. The system's performance is directly subject to market volatility.
• Exchange Risks: Trading systems are subject to the stability of exchanges and any related API issues. The system is vulnerable to API outages, rate restrictions, security concerns, or other operational issues.
• Technical Limitations: Performance is affected by the timeliness of data feeds, order fulfillment speed, and the efficiency of the trading strategies.
• Configuration Errors: Incorrect settings (risk management, order types, trading volume) can cause unexpected trades and financial damage.
• API Security Risks: Compromised API keys can lead to unauthorized trading.
• Data Provider Reliance: External data can cause problems.
• Strategy Risk: Strategies might not perform optimally under various market circumstances.
• Over-Optimization: There is a risk of over-optimizing to historic data, leading to bad results in new situations.
• Liquidity Risks: Low-liquidity assets can cause slippage or make trades difficult.

Recommendations

• Prioritize Risk Management: Use and routinely review all risk management settings (stop-loss, take-profit, max drawdown).
• Backtesting and Simulation: Implement a comprehensive backtesting program. Use paper trading before going live.
• Continuous Monitoring: Use a strong monitoring setup, including log messages and alerts.
• Security First: Use secure secrets management for API keys and data.
• Configuration Management: Use version control to track configuration changes.
• Understand Data Feeds: Analyze data feed accuracy and latency to make sure the feeds are operating properly.
• Diversify: Diversify trades across assets and exchanges to reduce the risk.
• Stay Updated: Understand market developments, security threats, and new regulations.
• Periodic Review: Check the configuration and settings regularly.
• Start Small: Test using a limited amount of capital.

How to Get Started with the Model

• Prerequisites:
  • Programming language: Python 3.9+
  • Libraries: ccxt, ta-lib, requests, and other dependencies (listed in requirements.txt).
  • Cryptocurrency exchange API keys (Binance, CoinbasePro, etc.)
  • Market data feed access (e.g., CoinGecko, others.)
  • [Specify deployment infrastructure, e.g., Cloud/On-Premise]
• Installation: [Provide clear, concise instructions: e.g.,
  1. git clone [repository URL]
  2. cd [repository directory]
  3. pip install -r requirements.txt]
• Configuration:
  1. API Keys: Set up API keys for each exchange and store them securely. Follow guidelines within your chosen secrets management solution.
  2. Secrets Management: Integrate with your chosen secrets management system.
  3. Exchange Configuration: Update the apiSettings section in cryptotrader.md.
  4. Strategy Configuration: Customize the strategySettings section.
  5. Risk Management Configuration: Review and adjust the riskManagementSettings.
  6. Notification Configuration: Set up and configure the notificationSettings section.
• Deployment: [Provide step-by-step deployment instructions. For example:
  1. Run the main script: python main.py
  2. (If applicable) Deploy to a cloud environment: [Provide specific deployment instructions based on your chosen cloud provider or environment].
  3. (If applicable) Run within Docker: [Provide Docker instructions.] ]
• Example Usage: Provide a brief example with command and/or a screenshot. (e.g., trading BTC/USDT with a moving average crossover strategy.)

Training Details (N/A)

This system uses rule-based methods and does not require any model training. Testing is conducted through backtesting.

Evaluation

Testing Data, Factors & Metrics

The evaluation of "Autonomous-trader" includes backtesting, paper trading, and live trading analysis. Performance indicators should reflect market data dependencies.

• Backtesting: The system's strategies are backtested with historical market data.
  • Data Source: [Specify data sources used]
  • Period: [Specify the timeframes, including start and end dates]
  • Methodology: [Describe the testing]
  • Metrics:
    • Profit/Loss: Total profit.
    • Sharpe Ratio: Risk-adjusted performance.
    • Maximum Drawdown: Greatest drop.
    • Win Rate: Successful trades.
    • Loss Rate: Unsuccessful trades.
    • Profit Factor: Gross profit to gross loss.
    • Average Trade Duration: Duration of trades.
    • Annualized Return: Annualized return.
    • Return on Investment (ROI): Total profit over original investment.
• Paper Trading/Simulation: Strategies are validated in a paper trading environment.
  • Environment: [Specify environment]
  • Period: [Specify timeframe]
  • Metrics: (Similar to backtesting.)
• Live Trading: Performance is analyzed in live trading.
  • Exchange: [List exchanges]
  • Capital Allocation: [Describe capital allocation]
  • Period: [Describe period, including start and end]
  • Metrics: Similar to backtesting and paper trading.
  • Important Disclaimer: Results vary, are not indicative of the future, and are influenced by volatility. Use with caution.

Results

• Backtesting Results:
  • [Insert Backtesting Results Table Here]
• Paper Trading Results:
  • [Insert Paper Trading Results Table Here]
• Live Trading Results:

  • [Insert Live Trading Results Table Here]

  • Important Disclaimer: Cryptocurrency trading includes risks, and losses can occur. Backtesting, paper trading, and live trading results do not guarantee future outcomes. Market conditions are subject to rapid change.

Model Examination (N/A)

Since this trading system relies on rules, there's no need for model examination.

Environmental Impact (Optional)

• Compute Resources: The system's environmental impact comes from the computing resources used.
• Energy Consumption: Processing requires energy.
• Recommendations: Consider energy-efficient hardware and sustainable cloud providers.

Technical Specifications

System Architecture

• Architecture: "Autonomous-trader" employs a modular microservices architecture with distinct services for data ingestion, strategy execution, order management, and risk management. Inter-service communication happens via a message queue, and data persistence occurs in a PostgreSQL database.
  • [Include a system diagram if available.]
• Components: Components include the Data Feed Handler, Strategy Engine, Order Execution Module, Risk Management Module, and Alerting System.
• Technology Stack: Python 3.9+, ccxt, ta-lib, PostgreSQL, RabbitMQ, Docker, Kubernetes.

API Details (Important)

Exchange APIs (Binance, CoinbasePro - example)

• Binance:
  • Base URL: https://api.binance.com
  • API Protocol: REST, WebSocket
  • Rate Limits: 1200 requests/minute, 10 orders/second.
  • Authentication: API key and secret (via a secrets management system).
  • Endpoints: /api/v3/ticker/price, /api/v3/order, /ws/stream.
• CoinbasePro:
  • Base URL: https://api.pro.coinbase.com
  • API Protocol: REST, WebSocket
  • Rate Limits: 30 requests/second, 10 orders/second.
  • Authentication: API key, secret, and passphrase (via a secrets management system).
  • Endpoints: [List Key API Endpoints]

Data APIs (CoinGecko - example)

• CoinGecko:
  • Base URL: https://api.coingecko.com/api/v3
  • Rate Limits: 50 requests/minute.
  • Endpoints: /coins/markets, /tickers.

Strategy Details

• Active Strategy: MovingAverageCrossover
  • Symbols: BTC/USDT, ETH/USDT, ADA/BTC
  • Timeframe: 15m
  • Fast MA Period: 10
  • Slow MA Period: 50
  • Signal Type: Cross
  • minTradeVolumeUSD: 100
  • maxTradeVolumeUSD: 5000
  • capitalAllocationPercentPerTrade: 5.0%

Risk Management

• Stop Loss: Enabled
  • Default: 2.0%
  • Trailing Stop Loss: Enabled, 1.0%
• Take Profit: Enabled, 5.0%
• Max Drawdown: 10%
• Daily Loss Limit: 3%
• Maximum Open Positions: 10
• Circuit Breaker: Enabled
  • Drawdown Threshold: 5.0% (60 mins)
  • API Error Threshold: 10 errors/minute.
  • Action: Pause trading, alert users.

Notification Settings

• Channels: Email, Telegram
• Types: Trade, signal, risk, error, system status, circuit breaker.
• Recipient Emails: [your email]
• Telegram Chat IDs: [your chat id]

Persistence Settings

• Database: PostgreSQL (Connection string via secrets management).
• Data: Trade history, position history, strategy signals, performance, and account balances.
• Backup: Daily cloud backups (details via secrets management).

Security Considerations

• API Key Security: Secure your API keys. Use a secrets management system.
• Access Control: Allow IPs, user authentication, and authorizations.
• Network Security: Implement firewalls.
• Data Encryption: Encrypt data.
• Auditing: Perform regular security audits.
• Transaction Signing: (If implemented)
• Incident Response Plan: [Briefly mention if you have a plan.]

Citation

[Provide the citation information for the project.]