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Found 46 Skills
Run a historical backtest using npx neural-trader with Rust/NAPI engine (8-19x faster) and walk-forward validation
Apply the Efficient Market Hypothesis (Fama, 1970) to evaluate information incorporation in asset prices across weak, semi-strong, and strong forms. Use this skill when the user needs to assess market efficiency, determine if a trading strategy can generate abnormal returns, evaluate event studies, or when they ask 'can technical analysis work', 'does the market already know this', or 'is this anomaly exploitable'.
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).
Use when developing or documenting trading strategies - guides edge hypothesis formation, validates statistical significance, documents strategy rules systematically (entry, exit, risk management). Activates when user says "research this strategy", "document my approach", "test this idea", mentions "trading strategy", "edge", or uses /trading:research command.
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.
NautilusTrader algorithmic trading platform reference. NautilusTrader 量化交易框架参考。 Use this skill when: - Working with NautilusTrader API (使用 NautilusTrader API) - Implementing trading strategies (实现交易策略) - Running backtests (运行回测) - Configuring data feeds and adapters (配置数据源和适配器) - Debugging NautilusTrader code (调试 NautilusTrader 代码) - Understanding trading concepts like positions, orders, and fills (理解持仓、订单、成交等概念) Keywords: NautilusTrader, strategy, backtest, trading, adapter, Binance, quantitative, 量化, 策略, 回测
Backtest crypto trading strategies from natural language ideas. Use when: user describes trading ideas, wants to validate strategies, mentions "backtest", "trading strategy", "buy low sell high", "RSI", "MACD", "oversold", "overbought", "crypto strategy", "validate strategy", "backtest", "DCA", or similar.
Analyze, rank, and prioritize trading strategies using multi-factor scoring. Use when creating prioritization plans, ranking strategies, analyzing strategy portfolios, comparing strategy performance, or making strategy selection decisions.
Guides users through their first trade on Senpi/Hyperliquid. Walks through discovery (top traders), creating a mirror strategy with a chosen trader, monitoring, and closing the strategy. Use when user says "let's trade", "first trade", "teach me to trade", "how do I trade", or when state is AWAITING_FIRST_TRADE. Can also run when state is not READY (e.g. after entrypoint Step 3); then prompts for wallet funding before starting when needed. Requires Senpi MCP to be connected.
Use when the task requires operating exchanges with the ritmex-bot CLI, including capability checks, market/account/position queries, order operations, strategy run, dry-run simulation, and JSON output parsing.
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.
POLAR v2.4 — ETH Alpha Hunter (sniper recalibration). Single-asset ETH lifecycle scanner with conviction-scaled leverage, move-exhaustion scoring, and same-direction re-entry cooldown. v2.4 recalibration after -31.7% ROE on 381 trades: MIN_SCORE raised 8→10 (Cheetah v5.1 APEX pattern), leverage tiers shifted to 7x at 10-11 / 10x at 12+, cooldown raised 120→240 min, new MIN_SM_ACCEL_PCT=0.3 hard gate on 15m velocity. DSL exit managed by plugin runtime via runtime.yaml.