Results for “iv-percentile”

Econometrics skill for instrumental variables and treatment effect estimation. Activates when the user asks about: "instrumental variables", "IV estimation", "2SLS", "two-stage least squares", "endogeneity", "weak instruments", "first stage", "Sargan test", "overidentification", "propensity score matching", "PSM", "average treatment effect", "ATT", "LATE", "local average treatment effect", "endogenous regressor", "instrument validity", "工具变量", "两阶段最小二乘", "内生性", "弱工具变量", "倾向得分匹配", "平均处理效应", "处理效应", "局部平均处理效应"

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

Fixes common PyTorch bugs including percentile calculations, LayerNorm for Conv1d, and buffer edge cases in reinforcement learning and neural network code.

Provides a single 0-100 cycle score blending VIX, SPY IV Rank, Put/Call ratio, and valuation percentile to determine offense vs. defense posture, based on Howard Marks' market cycle framework.

Prüft die Sanierungsfähigkeit eines Unternehmens im Insolvenzverfahren anhand von Fortbestehensprognose, Krisenursachen und integrierter Planung; liefert eine Fristen- und Risikoampel mit Sofortschritten.

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4-stage funnel that screens all 500+ Hyperliquid perps down to the top trading opportunities. Scores setups 0-400 across smart money, market structure, technicals, and funding. BTC macro filter, hourly trend gate (counter-trend = hard skip), cross-scan momentum tracking. Near-zero LLM tokens — all computation in Python. Use when scanning for new trading opportunities on Hyperliquid, evaluating setups, or checking market conditions.

Statistic component for displaying statistical values with labels, prefixes, and suffixes. Invoke when user needs to implement statistics displays, metrics dashboards, or data summaries in Naive UI.

Profile and optimize CPU, memory, GC, and rendering performance for mobile QuizVerse.

Generates a single-file HTML funnel infographic from 3-6 ordered stages with absolute values, showing conversion rates and drop-off at each step. Output is a static 9:16 vertical image with inline CSS, no JavaScript, no external dependencies.

Cleans credit risk data and screens variables for pre-loan modeling through an 11-step pipeline covering missing rate calculation, IV/PSI filtering, null importance denoising, and correlation removal.

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.

Partial Indexes

Screen US stocks for Stockbee-style short-term momentum burst setups using 4% breakout, dollar breakout, range expansion, volume expansion, prior range contraction, close-location, failure filters, and risk-distance scoring.

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.

Plan and implement new features, screens, managers, and systems in QuizVerse.

DeFi Primitives

Layer 4 of the keyword research pipeline. Spawns a "skeptical SEO" adversarial sub-agent to argue against every survivor of Layers 1-3. Catches mechanical-classifier blind spots — wrong SERP intent, hidden link-graph gauntlets, AIO trajectory shifts, vanity-rank metrics. Same pattern as quality-check's adversarial draft read, applied to keyword selection.

Analyzes market breadth using Monty's Uptrend Ratio Dashboard data to diagnose the current market environment. Generates a 0-100 composite score from 5 components (breadth, sector participation, rotation, momentum, historical context).

Scores any US stock ticker through Warren Buffett's four-lens framework (business simplicity, moat, management, valuation) and returns a weighted HOLDABLE/WATCHABLE/AVOID verdict.

Researches VC funds and partners, providing thesis matching, portfolio analysis, and approach strategy for outreach.

Quantize large language models to 4-bit with minimal accuracy loss using GPTQ, enabling deployment of 70B+ models on consumer GPUs with 4× memory reduction and 3-4× faster inference.

VIPER v2.1 — Range-bound liquidity sniper. Enters at support/resistance when Bollinger Bands are tight, ATR is low, RSI is at extremes within the range, and volume is declining. Works when nothing is trending — the chop predator. USD-weighted OI filtering (min $5M). DSL High Water Mode (mandatory). 5-minute scan interval. Second-best performer in the Senpi zoo at +6.2% ROI.

Screen US stocks for high-quality dividend opportunities combining value characteristics, attractive yields, and consistent growth using a two-stage screening approach with FINVIZ Elite and FMP APIs.

ScepticAgent UI and styles reference. Use whenever the user wants to change the side panel UI, modify highlight colours, update the markdown renderer, work with CSS architecture, add or change category colours, or understand how the sidepanel renders analysis output.

Skeptical pushback on visual placement — both density and quality. Reads the annotated outline plus the visuals manifest and asks (a) whether the article hits the density target from editorial-principles-visuals.md, (b) whether each [VISUAL:...] earns its place, (c) whether sections without one would benefit. One revision pass on FAIL (BLOG_AGENT_VISUALS_REVISION_BUDGET, default 1).

Score and rank options contracts for any ticker using a multi-factor model covering liquidity, IV attractiveness, Greeks balance, and risk/reward. Returns scored option chains with the best contracts highlighted.

Compute TAM/SAM/SOM with top-down and bottoms-up methods, plan survey sample sizes with per-segment minimums, and score candidate segments against Kotler's five criteria.

Chunks a book into canonical retrieval units with heading-aware structure splitting, recursive token targets, and contextual prefixes for downstream RAG ingestion.

Decompose an unknown quantity into 3-5 estimable factors and produce a defensible order-of-magnitude answer without needing precise data. Load when the user needs to size something without data — market size, resource requirements, effort estimates, user numbers, costs — or when a decision is blocked by "we don't know the numbers". Also triggers on "ballpark this", "rough estimate", "how big is this market", "how long would this take", "how many users", or when deep-thinking diagnoses a sizing/estimation frame. The goal is not precision — it is a defensible answer that enables a decision to be made. Based on Enrico Fermi's estimation method.

SA-1B: Segment Anything 1 Billion Masks Dataset

Delivers a daily DeFi regime verdict (RISK-ON/NEUTRAL/RISK-OFF) from five named inputs, top TVL movers with causal reasoning, and a sustainable-vs-incentive yield split to distinguish real product-market fit from emissions-pumped APY.

Creates a 9-page 3:4 vertical carousel for Xiaohongshu or Instagram with warm pastel backgrounds, dashed sticker cards, and page dots.

SVIT: Scaling up Visual Instruction Tuning

Nemotron-4 340B Technical Report