I would make this much more comprehensive so Claude has clear engineering requirements instead of vague goals. I'd also encourage Claude to **research first, recommend an approach, then implement only after approval**, especially for brokerage integrations and AI trading logic. --- # Trading Bot Expansion Requirements ## Project Objective The existing trading bot is functional. Your responsibility is to expand the platform into a production-ready AI-powered options trading system. **Before writing any code:** 1. Analyze the entire codebase. 2. Explain the existing architecture. 3. Identify every component that will be affected. 4. Produce an implementation roadmap. 5. Recommend the best technologies. 6. Wait for approval before modifying the code. No existing functionality should be removed or changed unless explicitly approved. --- # Feature 1: AI Earnings Prediction Engine Develop an AI-assisted earnings prediction system that estimates whether a company is likely to outperform or underperform market expectations. The system should output one of three recommendations: * Buy Call * Buy Put * No Trade Each recommendation must include a confidence score and an explanation of the contributing factors. Research and compare multiple predictive approaches before implementation. Potential inputs include: * Historical earnings surprises * Analyst estimate revisions * Earnings guidance changes * Revenue and EPS growth trends * Institutional buying/selling activity * Insider transactions * Options implied volatility * Implied move calculations * Unusual options flow * Relative sector strength * Technical indicators * Market sentiment * News sentiment (if available) * Macroeconomic conditions Do not rely on a single factor. Design a weighted scoring model or machine learning pipeline and justify the chosen approach. --- # Feature 2: Earnings Trade Timing Strategy The bot should identify earnings opportunities approximately 2–3 weeks before the earnings date. Research and implement logic to determine whether to: * Enter the trade * Exit before earnings * Hold through earnings * Avoid the trade entirely Provide quantitative reasoning for each decision. The strategy should account for implied volatility expansion and potential IV crush around earnings. --- # Feature 3: Options Selection Algorithm Create an options selection engine that aims to maximize expected return while minimizing premium cost. Evaluate and justify: * Expiration dates (e.g., 30–60 DTE) * Strike selection * Delta ranges * Gamma exposure * Theta decay * Vega exposure * Implied volatility * Open interest * Daily volume * Bid/Ask spread * Probability of Profit (POP) * Probability of Touch (POT) * Risk/Reward ratio Research common options strategies and recommend the most appropriate for this use case. --- # Feature 4: AI Trade Scoring System Every trade candidate should receive a numerical score (e.g., 0–100). The score should incorporate: * Technical analysis * Fundamental analysis * Earnings probability * Options liquidity * Risk metrics * Overall market conditions Allow score thresholds to be configurable. --- # Feature 5: Automated Trade Alerts When a trade meets all configured criteria, generate an alert containing: * Stock symbol * Suggested direction (Call/Put) * Confidence score * Trade score * Suggested strike * Suggested expiration * Estimated risk * Suggested entry window * Suggested exit window * Timestamp * Brief reasoning summary --- # Feature 6: SMS Notification System Research affordable SMS providers and compare options such as: * Twilio * Amazon SNS * Plivo * Telnyx * Vonage Evaluate: * Pricing * Reliability * API quality * Scalability * Ease of implementation Recommend the most cost-effective provider. Every outgoing message must automatically include a configurable disclaimer, such as: > Informational purposes only. Perform your own due diligence before making investment decisions. This is not financial advice. --- # Feature 7: Broker Integration Research brokerage platforms that support secure API integration for automated or semi-automated trading. Evaluate brokers such as: * Schwab API (Thinkorswim) * Interactive Brokers (IBKR) * Alpaca * Tradier * Tastytrade * Webull (if official API support exists) * Robinhood (only if official API support exists) For each broker, compare: * Official API availability * Authentication methods (OAuth/API keys) * Options trading support * Paper trading support * Order management capabilities * Historical market data access * Real-time market data * Rate limits * API reliability * Documentation quality * Pricing and subscription requirements Recommend the best primary integration and explain why. The architecture should allow additional brokers to be added later without major refactoring. --- # Feature 8: Thinkorswim / Schwab Integration If Thinkorswim (via the Schwab API) is selected: Implement support for: * Secure OAuth authentication * Account information retrieval * Buying power * Positions * Orders * Options chain retrieval * Greeks * Market data * Paper trading (if available) * Trade execution (only if explicitly enabled) Support both: * Read-only mode * Trade execution mode Trade execution should be disabled by default and require explicit user confirmation. --- # Feature 9: TradingView Integration Research available TradingView integration methods. Determine whether the project should use: * Official APIs (if available) * Webhooks * TradingView Alerts * Pine Script alerts * Third-party connectors (only if reputable and officially supported) If feasible, support: * Receiving TradingView alerts * Technical indicator signals * Strategy alerts * Watchlist synchronization (if supported) Allow TradingView signals to be used as one input into the overall trade-scoring system rather than the sole basis for a trade. Document any limitations or licensing requirements. --- # Feature 10: AI Decision Engine Create an AI decision engine that combines all available data into a final recommendation. The engine should: * Explain every recommendation. * Output confidence levels. * Highlight conflicting signals. * Estimate expected risk. * Estimate expected reward. * Provide supporting evidence. Recommendations should be explainable rather than "black box." --- # Feature 11: Backtesting Framework Implement a backtesting system capable of evaluating strategies using historical data. Report metrics including: * Win rate * Average return * Profit factor * Maximum drawdown * Sharpe ratio * Sortino ratio * Expectancy * Average holding period * Performance by sector * Performance by market regime Support walk-forward testing or out-of-sample validation to reduce overfitting. --- # Feature 12: User Interface Develop a modern, responsive web interface with: * User registration * Login * Password reset * Email verification * Two-factor authentication (2FA) * Session management * Secure password hashing * CSRF protection * Rate limiting * Role-based permissions (Admin/User) Include dashboards for: * Active trade opportunities * Portfolio overview * Trade history * Performance analytics * Backtest results * Alerts * System logs * Configuration management --- # Feature 13: Logging & Monitoring Implement comprehensive logging for: * AI decisions * Strategy inputs * Signals * Trade recommendations * Order requests * Broker responses * API failures * Authentication events * Performance metrics Support structured logging for easier debugging and analysis. --- # General Engineering Requirements * Modular architecture with clear separation of concerns. * Configuration-driven feature flags for all new functionality. * Secure handling of API credentials using environment variables or a secrets manager. * Unit, integration, and end-to-end tests for new features. * Comprehensive documentation (README, architecture diagrams, API docs, configuration guides). * Backward compatibility with the existing trading bot. * No breaking changes without explicit approval. * Before implementing any major feature, present a design proposal and wait for approval. This specification should give Claude a clear roadmap for researching, designing, and implementing the enhancements while keeping the project maintainable and extensible. One additional suggestion is to include a plugin-based architecture for data providers and broker integrations so that adding support for new services in the future requires implementing a new adapter rather than modifying the core trading engine.