Results for “options”

Analyzes institutional order flow, dark pool prints, and options flow sweeps to generate trade setups with defined entry, stop-loss, and target levels.

Maps the current VIX value to a 5-tier fear-thermometer classification with strategy hints for options sellers, including 1-year percentile and distribution data.

Builds a 3D volatility surface for any optionable ticker, mapping implied volatility across strike price and time to expiration to identify cheap, expensive, or anomalous options.

Monitor a list of tickers for key changes in price, IV rank, unusual activity, earnings dates, and score changes. Supports custom watchlists and a default hot options list.

Search real-time news with bias scoring, get live stock/ETF/crypto data with AI analysis, ML options pricing, balanced news synthesis, and meme search via the Helium MCP server.

Analyzes the volatility smile and skew for a single options expiration, providing implied volatility curves, skew metrics, and shape classification to reveal market pricing of tail risk and directional fear.

Detects unusual options activity and classifies smart money signals to help follow institutional positioning, including volume/OI ratio spikes, block trades, sweep orders, and net premium flow.

Set price, IV rank, unusual activity, earnings, and VRP alerts with contextual notifications and management commands.

Develop quantitative trading strategies with the vn.py framework, covering CTA, spread, and options strategies with support for 20+ broker gateways.

Mine, stake, and manage LITCOIN tokens on Base blockchain using the Python SDK, with options for comprehension mining or LLM-powered research mining.

Calculates first- and second-order option Greeks (Delta, Gamma, Theta, Vega, Rho, Charm, Vanna, Volga) for single contracts or multi-leg positions, with scenario heatmaps and position-level aggregation.

Simulates profit and loss for option positions across underlying price, implied volatility, and time to expiration, generating diagrams, breakeven analysis, and probability distributions.

Screen US equities for parabolic exhaustion patterns, generate conditional pre-market short plans, and monitor intraday trigger signals using 5-min bars.

Backtests quantitative trading strategies across 9 engines and 25 data sources, analyzes trade journals, and runs multi-agent research teams.

Analyzes earnings-season implied volatility: historical IV crush, implied move forecast, IV Rank strategy tag, and a priced Iron Condor quote ready to trade.

Given a stock ticker, cost basis, and position purpose, classifies the holding scenario and returns concrete hedge recommendations (Long Put, Collar, Tier-down) with live option strikes and premiums.

Search and analyze news articles with media bias context, get balanced perspectives, access live stock/ETF/crypto data with AI analysis, and find memes by semantic meaning.

Runs a side-by-side walk-forward backtest of a Bull Put Spread strategy with and without a FearScore entry signal, comparing performance metrics over ~8 years of daily data.

Calculates IV Rank and IV Percentile for any ticker to determine whether implied volatility is high or low relative to its 252-day history, and provides trading signals based on IV zones.

Aggregates market-wide sentiment indicators including VIX, Put/Call ratio, Fear & Greed Index, market breadth, and sector rotation to classify the current regime as risk-on, risk-off, or neutral.

Calculates a per-ticker panic index (0-100) from six weighted signals including VIX, IV Rank, RSI-14, volume anomaly, put/call ratio, and consecutive down days, triggering Bull Put Spread entry signals at scores ≥60.