Results for “volatility-analysis”

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.

Acquires Linux memory using the LiME kernel module and analyzes the image with Volatility 3 to extract processes, network connections, bash history, kernel modules, and injected code for incident response.

Inspect, profile, clean, reconcile, analyze, visualize, and verify spreadsheet data while preserving formulas, formatting, types, and source files. Use when working with .xlsx, .xlsm, .xls, .ods, .csv, or .tsv files; answering questions from a workbook; auditing formulas or data quality; comparing sheets or versions; producing pivots, charts, forecasts, or summary workbooks; repairing malformed tables; or validating that spreadsheet edits and calculations are accurate.

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.

Perform forensic on-chain analysis of EVM tokens to detect scams, rug pulls, and soft rugs by cross-referencing on-chain state against team narratives.

Identify command-and-control beaconing patterns in network traffic by applying statistical frequency analysis, jitter calculation, and coefficient of variation scoring to detect periodic callbacks from compromised endpoints.

Queries video analytics incidents, alerts, metrics, and sensor data from Elasticsearch via the VA-MCP server.

Analyze memory dumps using Volatility3 plugins to detect injected code, rootkits, credential theft, and malware artifacts in Windows, Linux, and macOS memory images.

Applies quantitative and qualitative analysis techniques, interprets experimental data, validates procedures, and selects appropriate methods with uncertainty quantification.

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

Analyzes sector rotation patterns and market cycle positioning using publicly available CSV data, with optional chart image analysis.

Use this skill for on-chain data, explorers, Dune-style queries, wallets, transfers, contract events. Trigger when the task involves crypto work related to Onchain Data Analytics, production implementation, audits, debugging, strategy, or validation.

Provides expert guidance for systematic backtesting of trading strategies, covering methodology, stress testing, bias prevention, and result interpretation.

Screen S&P 500 stocks for Mark Minervini's Volatility Contraction Pattern (VCP) and detect historical VCPs in a single ticker's price path.

Data analysis best practices with pandas, numpy, matplotlib, seaborn, and Jupyter notebooks.

Customer retention analysis, churn prediction, cohort analysis, win-back campaigns, and loyalty program design. Use when the user asks about churn, retention, customer lifetime value, cohort analysis, or win-back strategies.