ruflo-neural-trader
claudeNeural trading strategies — self-learning LSTM/Transformer/N-BEATS models with Rust/NAPI backtesting
9 skills · 1 command · 4 agents
17 cross-model skills with a Python research toolkit and paper-only workflows
Plugin marketpilot-ai from marketplace marketpilot-ai-marketplace in voyager67/MarketPilot-AI. Commands read from the repo's manifest — 5 days ago.
marketpilot-alertsCreate and manually check one-shot local price alerts with explicit symbol, threshold and direction.
marketpilot-analystSummarize reported SEC XBRL metrics and recent filing metadata while preserving periods, units, and missing-data caveats.
marketpilot-brokerPrepare and record local paper-only tickets after a passing risk check and explicit paper confirmation; this skill has no live-order capability.
marketpilot-budgetCalculate whole-share position size from account equity, per-trade risk, stop distance, slippage, fees and a notional cap.
marketpilot-data-qualityAudit OHLCV data integrity, timestamps, missing volume and source completeness before any signal or simulation.
marketpilot-disclosuresIndex public U.S. House Periodic Transaction Reports and explain disclosure lag, transaction ranges and source limitations.
marketpilot-entryCheck whether a specified long setup threshold has triggered on a completed, sufficiently fresh market bar.
marketpilot-exitBuild a transparent long-only ATR stop and reward/risk target plan from user-specified entry and historical bars.
marketpilot-journalSummarize user-supplied recorded trades and identify process patterns without presenting unaudited journal statistics as verified performance.
marketpilot-newsCollect recent public headlines and SEC filing metadata for a user-supplied watchlist; classify catalysts without turning headlines into predictions.
marketpilot-portfolioCompute supplied cash and position weights to surface concentration; no broker account is connected.
marketpilot-regimeDescribe broad trend and volatility state from historical bars without forecasting direction.
marketpilot-riskValidate the exact proposed long-only order against user-defined loss, exposure, trade-count, reward/risk and data-freshness constraints.
marketpilot-scannerScan a supplied symbol list or public Nasdaq Trader universe for predefined chart setups. Use after the user asks for a scan, watchlist, breakout candidates, or oversold names.
marketpilot-strategy-labRun a chronological training/holdout breakout study and audit for look-ahead, sample size and parameter overfitting.
marketpilot-time-machineRun reproducible historical and bootstrap scenarios for a user-specified entry, stop, and target.
marketpilot-trade-reviewReview an already completed trade against its original plan, realized fill, slippage and process adherence.
Neural trading strategies — self-learning LSTM/Transformer/N-BEATS models with Rust/NAPI backtesting
9 skills · 1 command · 4 agents
The official CCXT MCP server plus the ccxt-mcp usage skill — market data, balances and opt-in trading across 100+ crypto exchanges and prediction markets, with keys kept local.
1 MCP server
Quantitative analysis, algorithmic trading strategies, financial modeling, portfolio risk management, and backtesting
2 skills · 2 agents
Price bonds, analyze yield curves, evaluate FX carry trades, value options, and build macro dashboards using LSEG financial data and analytics.
8 skills · 8 commands · 1 MCP server
Price bonds, analyze yield curves, evaluate FX carry trades, value options, and build macro dashboards using LSEG financial data and analytics.
8 skills · 8 commands · 1 MCP server
Enterprise / cross-functional Research Operations domain — the managed counterpart to the academic research/ domain. v2.9.0 ships 5 skills: orchestrator (context: fork), clinical-research (protocol synopsis, endpoint selection, sample-size/power for means/proportions/survival, phase-gate feasibility), research-finance (R&D budgeting with F&A split, burn/runway, capitalize-vs-expense routing, portfolio ROI), market-research (TAM/SAM/SOM top-down and bottoms-up, survey sampling with FPC, Kotler segmentation), and product-research (goal-matched study design, method-based saturation, insight synthesis flagging single-source anecdotes). Hard rules: outputs are estimates routed to a named clinical or finance owner, market sizes show method + assumptions, product insights require recurrence across independent participants. 24 stdlib Python tools, 12 reference docs. Distinct from ra-qm-team, finance, research/grants, product-team, and marketing-skill.
6 skills · 7 commands · 2 agents
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