Loading...
Loading...
Found 29 Skills
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
This skill should be used when analyzing market breadth charts, specifically the S&P 500 Breadth Index (200-Day MA based) and the US Stock Market Uptrend Stock Ratio charts. Use this skill when the user provides breadth chart images for analysis, requests market breadth assessment, positioning strategy recommendations, or wants to understand medium-term strategic and short-term tactical market outlook based on breadth indicators. All analysis and output are conducted in English.
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.
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.
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.
Give your agent a budget, a target, and a deadline — it does the rest. Orchestrates DSL + Opportunity Scanner + Emerging Movers into a full autonomous trading loop on Hyperliquid. Race condition prevention, conviction collapse cuts, cross-margin buffer math, speed filter. 3 risk profiles: conservative, moderate, aggressive. Use when setting up autonomous trading, creating a trading strategy, or running a scan-evaluate-trade-protect loop.
Auto-mirror top Discovery traders on Hyperliquid. Scans top 50 traders, scores on PnL rank (35%), win rate (25%), consistency (20%), hold time (10%), drawdown (10%). Creates 2-5 mirror strategies with overlap checks. Daily rebalance with 2-day watch period before swaps. Use when setting up trader mirroring, copy trading, or portfolio auto-rebalancing based on Discovery leaderboard performance.
Opinionated trailing stop loss preset for Hyperliquid perps with tighter defaults than DSL v4. 4 tiers with per-tier breach counts that tighten as profit grows (3→2→2→1), auto-calculated price floors from entry and leverage, stagnation take-profit that closes if ROE ≥8% but high-water stalls for 1 hour. Same ROE-based engine as DSL v4 — different defaults, fewer knobs. Use when you want aggressive profit protection with minimal configuration.
Build, test, and deploy DeFi trading strategies using the Almanak SDK. ALWAYS use this skill when the user mentions almanak, DeFi strategy, trading strategy, yield farming, liquidity provision, token swap, borrowing, lending, perpetuals, staking, vault deposit, bridging tokens, backtesting, paper trading, or on-chain execution. Use for writing strategy.py files, composing intents (Swap, LP, Borrow, Supply, Perp, Bridge, Stake, Vault, Prediction), working with config.json strategy parameters, running almanak strat or almanak gateway CLI commands, or debugging strategy execution on Anvil forks. Do NOT use for general smart contract development, Solidity code, or non-strategy SDK internals.
Analyze option volatility by combining vol surface data, option pricing with Greeks, and historical price data to assess implied vs realized volatility. Use when pricing options, analyzing volatility surfaces, computing Greeks, assessing vol premiums, or evaluating vol trading strategies.
SCORPION v2.0 — Momentum Event Consensus. Complete rewrite. Uses leaderboard_get_momentum_events (real-time threshold crossings) to detect when 2+ quality SM traders cross momentum thresholds on the same asset/direction within 60 minutes. Confirmed by market concentration + volume. Enters with the momentum. Replaces the v1.1 whale-mirroring scanner (406 trades, -24.2% ROI, stale position data).
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.