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Found 39 Skills
Risk management rules learned from competition outcomes. Use when sizing positions or setting stop-losses.
Master of price action, chart patterns, and technical indicators - combining classical Wyckoff/Dow theory with modern quantitative validation for edge identificationUse when "technical analysis, chart pattern, indicator, RSI, MACD, support resistance, trend, candlestick, price action, fibonacci, trading, technical-analysis, charts, indicators, price-action, patterns, support-resistance, trend-following" mentioned.
Use when analyzing markets or interpreting charts - applies technical indicators (RSI, MACD, Moving Averages), identifies support/resistance, analyzes multi-timeframe trends, checks fundamentals and sentiment. Activates when user says "analyze BTC", "what's the trend", "check this chart", mentions ticker symbols, or uses /trading:analyze command.
Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
Backtest crypto and traditional trading strategies against historical data. Calculates performance metrics (Sharpe, Sortino, max drawdown), generates equity curves, and optimizes strategy parameters. Use when user wants to test a trading strategy, validate signals, or compare approaches. Trigger with phrases like "backtest strategy", "test trading strategy", "historical performance", "simulate trades", "optimize parameters", or "validate signals".
Backtest trading strategies on historical data and interpret performance metrics. Provides run_backtest (crypto strategies) and run_prediction_market_backtest (Polymarket strategies). Fast execution (20-60s), minimal cost ($0.001). Returns Sharpe ratio, max drawdown, win rate, profit factor, and trade statistics. Use this skill after building or improving strategies to validate performance before deploying. NEVER deploy without thorough backtesting (6+ months recommended).
AI-powered generation of complete trading strategy code. Uses create_strategy and create_prediction_market_strategy to transform requirements into production-ready Python code. Most expensive AI tool ($1.00-$4.50 per generation). Generates complete Jesse framework strategies with entry/exit logic, position sizing, and risk management. Use after exploring data and optionally generating ideas. ALWAYS test with test-trading-strategies before deploying.
In-depth analysis of individual stocks. Use this skill when the user says "Analyze XX", "How is XX doing", "Is XX worth buying", "Research XX".
Build financial models, backtest trading strategies, and analyze market data. Implements risk metrics, portfolio optimization, and statistical arbitrage. Use PROACTIVELY for quantitative finance, trading algorithms, or risk analysis.
Breaks down trading ideas into component parts for systematic Pine Script implementation. Use when analyzing trading concepts, decomposing strategies, planning indicator features, or extracting ideas from YouTube videos. Triggers on conceptual questions, "how would I build", YouTube URLs, or video analysis requests.
Orchestrates Pine Script development by coordinating workflows and planning complex projects. Use when building complete trading systems, managing multi-step projects, planning indicator/strategy development, or coordinating multiple capabilities. Triggers on complex requests mentioning multiple features, "build a complete", "trading system", or project planning needs.
Use Robonet's MCP server to build, backtest, optimize, and deploy trading strategies. Provides 24 specialized tools for crypto and prediction market trading: (1) Data tools for browsing strategies, symbols, indicators, Allora topics, and backtest results, (2) AI tools for generating strategy ideas and code, optimizing parameters, and enhancing with ML predictions, (3) Backtesting tools for testing strategy performance on historical data, (4) Prediction market tools for Polymarket trading strategies, (5) Deployment tools for live trading on Hyperliquid, (6) Account tools for credit management. Use when: building trading strategies, backtesting strategies, deploying trading bots, working with Hyperliquid or Polymarket, or enhancing strategies with Allora Network ML predictions.