Results for “da”

Extract, validate, and categorize invoice data against purchase orders and GL codes. Use when the user requests invoice processing or provides relevant inputs for this workflow.

Manage Xero accounting data through the Maton gateway: contacts, invoices, payments, accounts, bank transactions, and financial reports with managed OAuth.

Compresses long K-line (candlestick) data into a fixed-length sequence using OHLC aggregation rules and applies min-max normalization.

Interact with the Plaid finance platform via a CLI to link institutions, fetch account balances, and query transactions by date range.

Convert daily market observations into structured research tickets and export validated candidate specs for a trading strategy pipeline.

Executes on-chain token trades and queries data on Solana, Ethereum, BSC, and Base via the XXYY Open API.

Provides startup metrics frameworks and stage-specific benchmarks for SaaS, Marketplace, Consumer, and B2B models, including investor-ready dashboard generation.

Build financial projections, P&L statements, DCF models, and valuation analyses from assumptions and historical data. Use when the user requests financial modeling or provides relevant inputs for this workflow.

Interact with the Taco crypto trading platform via API to fetch market data, manage positions, and compute technical indicators across multiple exchanges.

Provides real-time access to SEC EDGAR filings, BLS employment statistics, and USDA crop prices through three MCP tools.

Interact with the Plaid finance platform via a CLI: link accounts from various institutions, query balances, and retrieve transactions by date range.

Interact with the Plaid finance platform to link bank accounts, fetch balances, and query transactions by date range using the plaid-cli tool.

Tracks crypto prices, wallet balances, portfolio values, and CEX history via a command-line interface, with support for Polymarket prediction markets.

Detects whether current market conditions match David Tepper's historic panic-buy signal by combining VIX, FearScore, and a quality filter.

Access 31 pay-per-use tools for quantum security, agent safety, research, data, and earn on Base, paid via x402 protocol.

Evaluate feature investments using revenue impact, cost structure, ROI, and strategic value to make data-driven build/don't-build decisions.

Aggregate and rank signals from multiple edge-finding skills into a prioritized conviction dashboard with weighted scoring, deduplication, and contradiction detection.

Performs financial ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction for strategic decision-making using financial statements and spreadsheet data.

Evaluate the financial impact of pricing changes using ARPU, conversion, churn risk, NRR, and payback analysis to make data-driven go/no-go decisions.

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

Convert daily market observations and news reactions into structured edge hints, with optional LLM augmentation, outputting a canonical hints.yaml for downstream concept synthesis.

Enriches inbound leads with firmographic and technographic data. Takes minimal lead info and enriches via WebSearch, Apollo, or ZoomInfo to produce a complete lead record with company size, revenue, tech stack, social profiles, and decision-maker details.

Analyzes stocks using the AlphaGBM Five Pillars framework, returning a composite score, risk assessment, target price, and AI-generated report from real market data.

Analyze weekly price charts for stocks, indices, crypto, or forex to identify trends, support/resistance, and probabilistic scenarios based purely on chart data.

Develops and backtests quantitative trading strategies for the Chinese securities market using the QMT terminal's built-in Python framework, covering data retrieval, order placement, and position management.

Runs a nightly automated retrospective on autonomous trading strategy performance, computing win rates, fee drag, holding period buckets, direction bias, and producing data-driven improvement suggestions.

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.

Master the AI tools that transform financial operations. From bookkeeping to forecasting, manage money smarter and make data-driven financial decisions. Use when "finance, accounting, budgeting, forecasting, FP&A, bookkeeping, expense management, finance, accounting, budgeting, forecasting, fpa, expenses" mentioned.

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.

Systematically profile Node.js, Python, and Go applications to identify CPU, memory, and I/O bottlenecks, generate flamegraphs, analyze bundle sizes, optimize database queries, and run load tests with k6 and Artillery.

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.

Quantifies whether a stock is suitable for long-term holding or requires tiered profit-taking using a novel 'rollercoaster rate' metric, running 15 exit strategies over ~10 years of daily history per ticker.

Retrieves upcoming earnings announcements for US stocks using the Financial Modeling Prep API, filtered by market cap over $2B, and organizes them into a markdown table grouped by date and timing.

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.

Track investment theses across their lifecycle — from screening idea to closed position with postmortem. Register theses from screener outputs, manage state transitions, attach position sizing, review due dates, and generate postmortem reports with P&L and MAE/MFE analysis.

Configure, simulate, debug, and analyze radarDataGenerator within radarScenario. Use for: interactively building radar detection scenarios from datasheets or performance requirements; diagnosing missed detections and configuration errors; interpreting sensor spherical, body, and scenario-frame outputs; deriving ReferenceRange from hardware specs via link budget; scan mode configuration (mechanical, electronic/AESA, hybrid); and validating simulation results against analytical predictions.