ruflo-neural-trader
claudeNeural trading strategies — self-learning LSTM/Transformer/N-BEATS models with Rust/NAPI backtesting
9 skills · 1 command · 4 agents
Independent verifier for trading-strategy backtests (look-ahead, costs, Deflated Sharpe).
Plugin monte-neo from marketplace monte-neo in NeoZorK/Monte-Neo. Commands read from the repo's manifest — 13 days ago.
This plugin runs code on your machine — its hooks fire automatically on events like tool use and it starts local MCP servers. The exact commands are listed below; read them before installing.
verify-strategyVerify a trading-strategy backtest before reporting results. Use whenever you build, tune or report a trading strategy, signal, indicator or backtest (Sharpe, returns, win rate) — it detects look-ahead bias, unpaid trading costs and selection bias with the Monte-Neo verifier.
/monte-neo:verifyVerify a trading strategy backtest with Monte-Neo (look-ahead, costs, selection bias)
monte-neoruns: uvx --from monte-neo[mcp]>=0.53.0 monte-neo-mcp
python3 "${CLAUDE_PLUGIN_ROOT}/hooks/remind_verify.py"
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
Study companion for the Deep Learning textbook by Goodfellow, Bengio & Courville (MIT Press, 2016), free to read at deeplearningbook.org. Twenty chapter files, a glossary, patterns and a cheatsheet index the whole book, and a delta reference dates it against 2026 practice with per-claim confidence levels: double descent qualifying the U-curve, AdamW splitting weight decay from L2, transformers displacing Chapter 10's recurrence, diffusion growing out of Chapter 18's score matching. Four stdlib tools make it executable — a prerequisite-closed reading-path planner that refuses goals the book does not cover, a training diagnostic running Chapter 11's rules in priority order so a NaN is never reported as overfitting, a capacity planner that ranks 'shrink the model' last when overparameterized, and a parameter/FLOP/activation-memory calculator that refuses a stack whose shapes do not connect. A companion, not a compilation: the book is copyrighted, so nothing here reproduces its text.
1 skill · 3 commands · 1 agent
This page updates itself from your repo. Add the badge to your README, or get it Featured to pin it above the organic results with a followed link to your site.