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Found 25 Skills
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.
Auxiliary development skill for Tongda Xin quantitative platform (TdxQuant). Use this skill when users mention "Tongda Xin", "TDX", "TdxQuant", "quantitative trading" or ask about the usage of the `tqcenter` module. It can help with environment setup, API calling, market data acquisition and trading strategy implementation.
Run a historical backtest using npx neural-trader with Rust/NAPI engine (8-19x faster) and walk-forward validation
Analyze historical downtrend durations and generate interactive HTML histograms showing typical correction lengths by sector and market cap.
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'.
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
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.
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.
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).
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.