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Found 29 Skills
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".
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.
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.
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.
FOX v0.1 — Fully autonomous multi-strategy trading for Hyperliquid perps via Senpi MCP. Forked from Wolf v7 + v7.1 data-driven optimizations (14-trade analysis: 2W/12L). Tighter absolute floor (0.02/lev, ~20% max ROE loss), aggressive Phase 1 timing (30min hard timeout, 15min weak peak, 10min dead weight), green-in-10 floor tightening, time-of-day scoring (+1 for 04-14 UTC, -2 for 18-02 UTC), rank jump minimum (≥15 OR vel>15). Scoring system (6+ pts), NEUTRAL regime support, tiered margin (6 entries max), BTC 1h bias alignment, market regime refresh 4h. 8-cron architecture. Independent from Wolf. Requires Senpi MCP, python3, mcporter CLI, OpenClaw cron system.
ORCA v1.1 — Hardened dual-mode emerging movers scanner. Every lesson from 5+ days of live trading across 22 agents baked into the code. v1.1 adds the DSL state template directly in scanner output — eliminating the dsl-profile.json override bugs that broke Fox, Grizzly, Jackal, and every Wolf-based agent. XYZ equities banned at scan level. Leverage 7-10x enforced. Stagnation TP mandatory. 10% daily loss limit. 2-hour per-asset cooldown. Conviction-scaled Phase 1 timing per-signal. The agent cannot override any of these — they are in the scanner, not instructions.
Framework for developing, testing, and deploying trading strategies for prediction markets. Use when creating new strategies, implementing signals, or building backtesting logic.
Risk management rules learned from competition outcomes. Use when sizing positions or setting stop-losses.
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".
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 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.