Results for “quantitative-trading”

Build financial models, backtest trading strategies, and analyze market data. Implements risk metrics, portfolio optimization, and statistical arbitrage. Use PROACTIVELY for quantitative finance, trading algorithms, or risk analysis.

Analyze URLs or text to discover ranked Polymarket, Hyperliquid, and Deribit markets with Quotient edge scores, paid via on-chain x402 settlement.

Optimizes Qdrant query performance for large limits across multiple shards by using Poisson-distributed subsampling to reduce inter-shard data transfer.

Reads and preprocesses 5-minute stock candlestick CSV data, then clusters the time series using tslearn's TimeSeriesKMeans, including data cleaning, percentage change calculation, model training, saving, and representative sample extraction.

This skill should be used when the user asks to "buy crypto", "sell tokens", "swap ETH", "trade on Base", "exchange tokens", "cross-chain swap", "bridge tokens", "convert ETH to WETH", or any token trading operation. Provides guidance on supported chains, amount formats, and swap operations.

`task-agent`/`review-agent`: use when broker delivery, ordering, acknowledgement, DLQ, backpressure, or replay changes; skip synchronous retry without message semantics.

Analyze and simulate options trading strategies using Black-Scholes pricing, Greeks calculation, and risk management guidance.

Multi-timeframe backtesting combining 15Min + 1Hour model signals. Trigger when: (1) multi-TF backtest, (2) combining timeframe signals in backtest, (3) validating multi-TF strategy, (4) --multi-tf CLI flag.