Full company profile including business description, sector, industry, CEO, employees, IPO date, exchange, country, and website. Output includes a structured summary with key company identifiers and a narrative business overview.

Real-time or latest market quote for any symbol. Returns bid, ask, open, high, low, close, volume, 52-week high/low, market cap, P/E ratio, and day change percentage. Output is a formatted market snapshot.

Historical OHLCV (Open, High, Low, Close, Volume) price data with split and dividend adjustments. Supports custom date ranges and result limits. Output includes adjusted and unadjusted close prices for each trading day.

Side-by-side comparison of peer companies in the same sector/industry. Shows current price, 1-month and 1-year return, market cap, P/E ratio, and RSI for each peer. Useful for sector rotation and relative value analysis.

AI-generated summary of the latest news and sentiment for a symbol. Aggregates recent news articles, extracts key themes, and provides an overall sentiment assessment (bullish/bearish/neutral). Useful for quick fundamental event scanning.

ETF fund facts via Financial Modeling Prep: expense ratio, AUM, asset class, issuer, NAV, top holdings & concentration, and sector/country weightings.

Historical stock split data for a symbol. Shows split dates, split ratios (e.g., 4:1, 3:1, 2:1), and pre/post split prices. Important for understanding historical price context — a stock at $150 that did a 4:1 split was effectively $600. Relevant for back-adjusting prices, options analysis around split events, and historical performance review.

Historical dividend payment data for a symbol. Shows ex-dividend dates, payment dates, dividend amounts, frequency, and yield calculation. Useful for income-focused investors analyzing dividend growth, consistency, and payout trends. Also important for options sellers managing covered call strategies around ex-dividend dates.

Ask AI any market question — uses GPT to answer with context from KlickAnalytics data. Supports free-form queries about stocks, markets, earnings, and more.

AI-powered technical chart pattern analysis. Identifies chart patterns (head & shoulders, cup & handle, triangles, flags, double tops/bottoms), evaluates trend structure, key support/resistance, and provides a narrative summary of the current technical setup. Powered by Claude AI.

Composable chart image generator — build any technical chart by stacking overlays on the price axis and indicator panels below. Overlays: sma(), ema(), bb(), kc(), vwap, env(). Panels: vol, rsi(), macd(), stoch(), adx(), obv, atr(), cci(), willr(), mfi(). Each indicator takes inline params like bb(20,2.5) or macd(12,26,9). Special types: compare (multi-symbol), drawdown, histogram. Custom title via -title. Dark and light themes. Returns image/png or base64 JSON.

Activate a specialized financial agent to analyze a symbol. Each agent has a unique persona, expertise, analytical lens, and output format. The agent auto-runs its priority commands, feeds the data to an LLM, and delivers structured analysis through its specialized lens. Run without arguments to see available agents.

Browse and search all 200+ specialized financial agents. Filter by category or search by keyword. Each agent has a unique expertise, analysis workflow, and communication style.

Free-form chat with a specialized financial agent. Ask any question and the agent responds through its unique expertise lens. Automatically detects symbols in your message and runs relevant analysis commands. Provide -agent to select the persona.

Detected candlestick patterns in recent price history. Identifies reversal and continuation patterns including Doji, Hammer, Shooting Star, Engulfing, Morning/Evening Star, and Harami. Each pattern shows detection date, direction (bullish/bearish), and signal strength.

Classifies the current market regime using five independent statistical lenses: Trend (ADX, +DI/-DI, SMA alignment), Volatility (HV short/long ratio, percentile), Memory (Hurst exponent via R/S analysis), Momentum (return autocorrelation at lag-1 and lag-5), and Structure (price vs SMA50/200, slope). Primary regimes: TRENDING-UP, TRENDING-DOWN, RANGING, BREAKOUT. Volatility regime: HIGH-VOL, NORMAL-VOL, LOW-VOL. Memory: PERSISTENT, RANDOM, MEAN-REVERTING. Suggests optimal strategy per regime.

Liquidity profile — average daily dollar volume, recent vs historical liquidity trend, high-low spread proxy, Amihud illiquidity ratio (return per $M traded), and days-to-trade calculator for $100k / $1M / $10M / $100M positions at 5% and 10% of average daily volume. Includes a liquidity grade from POOR to EXCELLENT.

Fibonacci retracement and extension levels calculated from the most significant recent swing high and low. Shows key levels at 23.6%, 38.2%, 50%, 61.8%, 78.6%, and extension levels at 127.2% and 161.8%. Useful for identifying potential support, resistance, and price targets.

Statistical analysis of historical price gaps. Categorizes gaps by type (gap-up / gap-down), shows fill rate (how often gaps close), average gap size, median time to fill, and frequency. Useful for gap trading strategies and understanding price behavior after gaps.

Price action signal analysis including breakout detection, support/resistance tests, trend line breaks, and inside/outside bar patterns. Shows recent signals with date, pattern type, direction, and historical success rate for each pattern type.

Full technical analysis report combining price data with key indicators. Includes RSI (9/14/20), MACD line/signal/histogram, Bollinger Bands, ATR, ADX/+DI/-DI, all moving averages (SMA/EMA 20/50/100/200), and a composite signal summary. Best for a quick complete technical picture of any symbol.

Comprehensive technical signal pack covering 15+ indicators. Includes MA crossover signals (SMA/EMA 20/50/200), RSI overbought/oversold, MACD crossover, Bollinger Band squeeze/breakout, Stochastic, ADX trend strength, and CCI signals. Output: signal table with direction (buy/sell/neutral) and strength.

Volume profile analysis showing price levels with the highest and lowest trading activity. Identifies the Point of Control (highest volume price), Value Area High/Low (70% of volume range), and high-volume nodes (HVN) / low-volume nodes (LVN). Key for institutional-level support/resistance analysis.

Detects days with statistically unusual trading volume — both spikes and unusually low volume. Shows date, actual volume, average volume, volume ratio, and price return on that day. Useful for spotting institutional accumulation, distribution, or news-driven volume events.

Consolidated chart analysis combining patterns (50+ chart patterns), candle_patterns (doji, hammer, engulfing), levels (support and resistance), and trend (direction, slope, ADX) into a single report. Saves multi-call round-trips for LLM consumers and dashboards. Pure aggregator over existing endpoints.

Relative strength rating (0-99) measuring price performance vs a universe of stocks and a benchmark over multiple periods. A score of 99 means the stock outperformed 99% of the universe. Includes 1-month, 3-month, 6-month, and 1-year RS components with composite score.

Multi-period momentum analysis showing returns across 1-day, 1-week, 1-month, 3-month, 6-month, and 1-year timeframes. Includes momentum rank within peer group, RSI, trend direction, and 52-week high/low context. Useful for momentum screening and trend confirmation.

Comprehensive chart pattern scanner detecting 50+ patterns across 8 categories: reversal (Head & Shoulders, Double/Triple Top/Bottom, V-Top), continuation (Flag, Pennant, Wedge, Cup & Handle), bilateral (Symmetrical Triangle, Broadening), directional (Ascending/Descending Triangle, Channel), volatility (VCP, Inside Bar, NR4/NR7, Compression Breakout), harmonic (ABCD, Gartley, Bat, Butterfly, Crab), price action (BOS, CHOCH, FVG, Liquidity Sweep), and hybrid patterns. Each pattern scored 0-100 for confidence.

Hidden Markov Model regime detection fitting a 2-3 state Gaussian HMM to daily returns via the Baum-Welch (EM) algorithm. Probabilistically identifies bull, bear, and sideways regimes. Returns current regime probabilities, state transition matrix, expected duration per regime, full regime history timeline, and per-regime statistics (mean return, volatility, Sharpe). Pure Python implementation — no external ML dependencies.

How far is price from its mean, and what historically happens next? Computes z-score of close vs SMA20/SMA50/SMA200, Bollinger %B and bandwidth, RSI(14), and percent deviation from each MA. The key insight: finds every historical stretch event (crossing the z-score threshold) in the same direction as the current stretch, then measures forward returns at 5d/10d/20d/63d and reversion probability. Quantifies whether buying the dip or fading the rip has edge on this specific symbol.

Gap fade probability engine — analyzes every historical gap (up/down) by size category (tiny/small/medium/large/huge) and calculates same-day fill rate, multi-day fill rate, average fill time, continuation %, and average close return. Shows today's gap with a fade/no-fade verdict based on historical fill rate for that gap type. Includes day-of-week fill rates, recent gap history, and a fade strategy guide.

Trend exhaustion scanner — detects when a trend is running on fumes before reversal. Combines 5 signals: RSI divergence (price new high, RSI lower high), volume divergence (price extends on fading volume), DeMark TD Sequential countdown (9/13 setups), MA extension z-score (distance from 20/50/200 SMA), and RSI overbought/oversold extremes. Outputs a 0-100 composite exhaustion score with active signal list and per-component detail.

Bollinger Band squeeze detector (TTM Squeeze inspired). When Bollinger Bands contract inside Keltner Channels, volatility is compressed and a directional explosion is imminent. Shows current squeeze status (on/off/fired), squeeze duration, momentum direction bias, BB width percentile, historical squeeze events with 5d/10d/20d post-squeeze returns, win rates, and bullish/bearish outcome ratios. Includes recent bar-by-bar squeeze + momentum table.

Multi-timeframe trend sweep for 1-20 symbols. For each symbol, analyzes trend direction on daily, weekly, and monthly timeframes using SMA alignment (10/20/50/200), ADX trend strength, DI+/DI- directional indicators, and 10-bar rate of change. Master alignment table shows which symbols are aligned across all timeframes (ALIGNED UP/DOWN) vs conflicted. Includes per-symbol detail with price-vs-MA checks, SMA stack order, and ADX strength labels.

Entry/exit zone calculator combining Fibonacci retracements, pivot points (daily/weekly/monthly), VWAP anchored levels (YTD/QTD/MTD with sigma bands), and ATR-based stop/target zones into one actionable table. Shows ATR stops (tight/normal/wide), targets at 1R/2R/3R reward, all pivot levels, Fibonacci retracement and extension levels, nearest support/resistance with distance, and a unified sorted level map with current price marker.

Identifies key support and resistance levels using three methods: volume profile (high-volume nodes), swing pivot analysis (local H/L extremes), and price clustering density (KDE-based). Levels are scored 0-100 based on volume, touches, density, recency, and method overlap. Outputs separate support and resistance lists sorted by proximity to current price.

5-component trend quality score (0-100) covering: R² of log-price regression (30%), ADX strength (25%), Higher High/Higher Low consistency (20%), volume-adjusted slope t-statistic (15%), and percentage of trending days (10%). Output includes multi-timeframe scores (20d, 63d, 126d) plus full window, with detailed component breakdown.

ATR context — current ATR vs historical percentile, expected daily range in dollars, recent range expansion/contraction trend, stop placement at 1/2/3 ATR, and position sizing helper that converts a dollar risk amount into share count given a 1-ATR stop.

Top N best and worst single-day return events in a symbol's history. Shows date, return percentage, and surrounding market context. Useful for understanding tail risk, volatility events, and how the stock behaves during extreme market moves.

Historical (realized) volatility analysis across multiple rolling windows (10d, 20d, 30d, 60d, 90d, 1y). Shows annualized volatility percentages, volatility trend, and comparison to 52-week average. Useful for options pricing, risk assessment, and position sizing.

Monthly and quarterly seasonal return patterns based on full price history. Shows average return, win rate, and best/worst months for each calendar period. Useful for timing entries and exits based on historical tendencies.

Comprehensive quantitative statistics report. Includes CAGR, Sharpe ratio, Sortino ratio, Calmar ratio, max drawdown, win rate, profit factor, average gain/loss, best/worst day, monthly returns breakdown, and risk-adjusted performance metrics.

Trading-focused statistics: win rate, average gain, average loss, profit factor, expectancy, average holding period, largest win/loss, and consecutive win/loss streaks. Helps evaluate the historical tradability and edge of a symbol.

Statistical price distribution analysis showing where a stock has spent most of its time. Includes frequency histogram, percentile ranking of current price, mean, median, standard deviation, and skewness of the price distribution. Useful for understanding price ranges and current price context.

Analyzes historical rally and pullback cycles. Shows average rally duration (days), average rally magnitude (%), average pullback duration, average pullback magnitude, and current cycle status. Useful for estimating how long and how far typical moves last for a given symbol.

Multi-type streak analysis for a symbol. Tracks consecutive up/down price days, days trading above or below key moving averages (SMA20, SMA50, SMA200), consecutive high or low volume days, and RSI zone streaks (overbought, oversold, bullish, bearish). Each streak type shows the current active streak, historical stats (count, average length, max length, average return), and the top longest historical streaks. Use -type to focus on a specific streak category, -threshold to filter noise from small price moves, and -lookback to control the analysis window.

Comprehensive drawdown analysis showing all historical drawdown periods. Includes maximum drawdown, average drawdown, median recovery time, current drawdown from peak, longest drawdown period, and underwater equity curve. Essential for risk assessment and strategy evaluation.

Return distribution deep-dive with daily, weekly, or monthly frequency. Includes histogram with normal overlay comparison, skewness, kurtosis, Jarque-Bera normality test, fat tail analysis (% beyond ±1/2/3 sigma vs normal expectation), empirical VaR and CVaR at 95/99%, best/worst return deciles, and rolling skewness/kurtosis trend. Quantifies whether returns are normal or fat-tailed — critical for position sizing and risk models.

Return skew & asymmetry analysis — left vs right tail asymmetry, skewness/kurtosis, semi-deviation (downside vs upside), down/up volatility ratio, tail means at the worst/best 10%, and rolling 60-day skewness trend with % of windows showing negative skew.

Market entropy / predictability — Hurst exponent (trending vs mean-reverting vs random walk via R/S analysis), Shannon entropy of returns, approximate entropy (regularity measure), runs test for sign randomness, and a composite predictability score 0-100.

Statistical anomaly detector — flags days with returns, range, or volume beyond N standard deviations from a rolling baseline. Returns recent anomalies (last 45 days), top anomalies by max z-score across the lookback, and per-day flags identifying which dimension was extreme.

Realized volatility term structure — annualized vol over 5d / 10d / 20d / 60d / 120d / 252d windows. Short-vs-long vol ratio classifies the shape (CONTANGO if short > long, BACKWARDATION if short < long, FLAT if neutral). Includes vol-of-vol (instability of vol regime) and 20-day vol percentile rank vs trailing year.

Intraday OHLCV price data with computed EMA(9/20/50), RSI(14), and MACD indicators. Returns a session summary (open, high, low, current, change %, volume) plus a recent bar table. Supports multiple timeframes: 1min, 5min, 15min, 30min, 1hour, 4hour. Essential for day traders monitoring live price action and intraday technical signals.

Volume-Weighted Average Price (VWAP) analysis anchored to the session start. Computes VWAP with +/- 1 and 2 standard deviation bands, price deviation from VWAP (% and $), VWAP slope direction (rising/flat/falling), and band position label (above/below/at VWAP). Essential for institutional-style execution and VWAP-based trading strategies.

Computes the Opening Range High (ORH) and Opening Range Low (ORL) from the first N minutes of the most recent session (default 30 min). Reports breakout or breakdown status, extension percentage beyond the range, measured-move targets at 1x and 2x range, ATR comparison to gauge range significance, and session progress. A core setup for opening range breakout (ORB) day trading strategies.

Intraday Relative Volume (RVOL) comparing today's volume to the historical average at the same time of day across recent sessions. Reports per-bar RVOL, cumulative RVOL for the session, projected daily volume, volume trend classification (extremely high/high/normal/low), and RVOL acceleration. Identifies unusual intraday activity signaling institutional accumulation or distribution.

Identifies key intraday support and resistance levels from multiple independent sources: VWAP +/- 1-2 sigma bands, today's session high/low, previous session close/high/low, intraday swing pivots, and volume node clusters. All levels are merged, labeled SUPPORT or RESISTANCE relative to the current price, and ranked by proximity. Essential for intraday entry/exit and stop placement.

Detects the current intraday market regime using five components: ADX + DI+/DI- for trend strength and direction, R-squared of close price for trend linearity, ATR ratio for volatility context, VWAP cross count (high = mean-reverting), and HH/HL consistency for uptrend bars. Primary regimes: TRENDING_UP, TRENDING_DOWN, RANGING, MEAN_REVERTING. Volatility overlay: HIGH_VOL, NORMAL_VOL, LOW_VOL. Guides strategy selection in real time.

Builds a volume-by-price profile from today's intraday session bars. Identifies POC (Point of Control — highest volume price), VAH/VAL (Value Area High/Low — 70% of volume range), HVN (High Volume Nodes — key clusters), and LVN (Low Volume Nodes — breakout acceleration zones). Shows current price position vs profile (above/inside/below value area), balance/imbalance assessment, and an ASCII histogram visualization.

Pre-market gap analysis and level mapping. Shows current pre-market price, previous session stats (close, high, low, VWAP, range), gap size ($ and %), gap classification (FULL_GAP_UP, FULL_GAP_DOWN, PARTIAL, NO_GAP), gap vs daily ATR ratio to gauge significance, and key levels including gap fill target, previous high/low, and extension targets. Includes strategy implications per gap type.

Screens multiple symbols using live intraday data with filterable metrics: change_pct (open-to-current), range_pct (high-low as % of low), gap_pct (gap from previous close), rvol (relative volume), rsi, adx, vwap_dev (% deviation from VWAP), vwap_pos (ABOVE/BELOW/AT), and cumulative volume. Supports custom filter expressions (e.g. 'change_pct > 1 AND rvol > 1.5') and sorting. Ideal for finding the most active stocks intraday.

Income statement, balance sheet, cash flow statement, and key financial metrics from quarterly and annual filings. Shows revenue, gross profit, operating income, net income, EPS, total assets, total debt, free cash flow, margins, ROE, ROIC, and more. Supports quarterly and annual periods with configurable history depth. Essential for fundamental analysis, financial modeling, and company valuation.

Forward analyst estimates for EPS and revenue, price targets, and recommendation consensus (Strong Buy/Buy/Hold/Sell/Strong Sell). Shows consensus EPS estimate, high/low range, number of analysts, and price target range for upcoming quarters. Useful for understanding market expectations and identifying potential earnings surprises when actual results deviate from consensus.

Institutional holder analysis: top holders, percentage of shares held by institutions, and quarterly changes in position size. Shows holder name, shares held, portfolio weight, change from prior quarter, and market value. Useful for tracking smart money positioning — increasing institutional ownership can signal confidence, while decreasing may signal distribution.

Social media sentiment analysis from StockTwits and Twitter/X. Shows post counts, comments, likes, impressions, and sentiment trends over a configurable window (default 30 days). Tracks bullish vs bearish sentiment shifts and volume of social discussion. Useful for gauging retail crowd positioning and identifying sentiment extremes that may signal contrarian opportunities.

Tracks how analyst EPS and revenue estimates have changed over time for upcoming quarters. Rising estimates signal positive earnings momentum, falling estimates signal deterioration. Shows current consensus estimate, estimate 30/60/90 days ago, revision direction and magnitude, number of analysts, and consensus recommendation (strong buy / buy / hold / sell / strong sell). A leading indicator of earnings surprise direction.

Recent insider trading activity from SEC Form 4 filings. Shows insider name, title (CEO, CFO, Director, etc.), transaction type (buy/sell/exercise), shares traded, price, total value, and filing date. Insider buying is a strong bullish signal — executives putting their own money into the stock. Cluster buying (multiple insiders buying) is especially significant.

Quarterly and annual earnings data including EPS (actual vs estimate), revenue (actual vs estimate), surprise percentage, and reporting date. Useful for tracking earnings trends and beat/miss history over time.

Historical earnings reaction analysis: how much the stock moved in the trading session immediately after each earnings report. Shows pre-earnings implied move vs actual move, gap direction, and trend over recent quarters. Useful for options traders and earnings event strategies.

Upcoming and recent earnings announcement dates for a symbol. Shows next earnings date, days until next report, previous earnings dates, and reporting time (before market / after market). Critical for swing traders who need to avoid holding through binary earnings events, and for event traders who position around earnings catalysts.

Summary of recent earnings performance trends. Shows EPS and revenue trajectory over the last 8-12 quarters, growth rates, beat/miss streak, and forward estimates if available. Provides a quick fundamental health check through the earnings lens.

Post-earnings price drift analysis: how the stock performs in the 1, 5, 10, 20, and 60 days after each earnings report. Shows average drift by beat/miss/in-line outcome, and cumulative drift chart. Based on the academic Post-Earnings Announcement Drift (PEAD) effect.

Trading calendar — upcoming and recent events for a symbol: earnings dates with reaction stats from past prints (EPS beat/miss + next-day move), ex-dividend dates with payment date, recent splits, and TTM dividend with current yield. Configurable forward-window in days.

Full earnings-call transcript for a symbol for a specific fiscal quarter and year, fetched from FMP. Useful for sentiment analysis, summary, or quoting management remarks. Requires --year (e.g. 2024) and --quarter (1-4). Returns the transcript as text by default.

Pearson correlation matrix for 2-10 symbols over a selected time period. Shows pairwise correlations from -1 (perfectly inverse) to +1 (perfectly correlated). Useful for portfolio diversification analysis and identifying hedging relationships. Includes a formatted heatmap-style text output.

Side-by-side comparison of 2-10 symbols across key metrics: returns (1d/1m/3m/1y), volatility, Sharpe ratio, beta vs SPY, RSI, ADX, max drawdown, and market cap. Output is a formatted comparison table. Ideal for peer analysis, ETF comparison, and portfolio candidate screening.

Cross-sectional dispersion measuring how spread out returns are within a basket. High dispersion = stock-picking market (alpha opportunity), low dispersion = macro-driven / correlated (trade the index instead). Computes daily cross-sectional standard deviation, rolling dispersion (20d default), current percentile vs historical range, per-symbol deviation from basket mean (outlier detection), dispersion regime (HIGH/NORMAL/LOW/VERY_LOW), and average pairwise correlation.

Cross-correlation at various lags (1-5 days) between all pairs in a basket. Finds predictive relationships: does AMD move before NVDA? Does SPY lead the group or follow? Returns a lead-lag matrix with strongest predictive pairs highlighted, per-pair optimal lag and correlation at that lag, leader/follower ranking (who leads the most pairs), and Granger-style directional score per symbol.

Cross-sectional herding & dispersion analysis — daily dispersion (std of returns across symbols), Cross-Sectional Absolute Deviation (CSAD), directional agreement %, percentage of macro-driven days (>=80% agreement) vs stock-picking days (<=60% agreement), and recent regime detection (MACRO-DRIVEN vs STOCK-PICKING).

Price ratio tracking between 2-3 symbols for relative value monitoring. Shows ratio mean, current z-score, Bollinger Bands on the ratio, and reversion signals. Simpler than the full pairs command (no cointegration or hedge ratios) — focuses on the raw price ratio with band analysis for quick relative value assessment and spread reversion signals.

Quantitative pairs trading analysis for two symbols. Computes hedge ratio via OLS regression, spread statistics, rolling z-score (current divergence from mean), ADF cointegration test with p-value, half-life of mean reversion, correlation, historical spread extremes, and entry/exit signals at configurable z-score thresholds. Supports log-price and raw-price spread types. Essential for statistical arbitrage and market-neutral strategies.

Cross-asset performance curve — multi-symbol normalized cumulative returns from 100, side-by-side total return / vol / max drawdown, relative outperformance vs first symbol over time, and snapshots at start/quartiles/end.

Multi-symbol performance heatmap across timeframes. Shows returns for each symbol across 1-day, 1-week, 1-month, 3-month, 6-month, YTD, and 1-year periods in an ASCII color-coded table. Provides at-a-glance comparison of which symbols are leading or lagging across all timeframes. Ideal for sector comparison, portfolio review, and identifying momentum leaders and laggards in a basket.

Relative Rotation Graph (RRG) analysis placing each symbol in one of 4 quadrants based on JdK RS-Ratio (relative strength vs benchmark, normalized to 100) and JdK RS-Momentum (rate of change of RS-Ratio). Quadrants: LEADING (outperforming & accelerating), WEAKENING (outperforming but decelerating), LAGGING (underperforming & decelerating), IMPROVING (underperforming but accelerating). Shows the clockwise rotation path and identifies which symbols are entering leadership.

Basket performance tracker computing cumulative equal-weight or custom-weighted basket return over time. Like a mini-fund NAV with daily P&L attribution showing which symbols contributed most and least. Includes rolling Sharpe ratio, max drawdown, best/worst contributor per period, and correlation between basket members. Ideal for tracking thematic baskets, sector groups, or custom portfolios.

Multi-factor exposure analysis for 1-10 symbols across five dimensions: Momentum (3m/6m/12m returns, RSI, relative strength), Value (P/E, P/B, EV/EBITDA, dividend yield), Quality (ROE, net margin, operating margin, debt/equity), Size (market cap, rank within group), and Volatility (annualized vol, beta proxy, max drawdown). Scores each factor 1-5 within the group (quintile rank), computes a composite score, and shows a factor radar profile.

Variance-covariance matrix with eigenvalue decomposition for 2-10 symbols. Returns the full covariance matrix, correlation matrix, explained variance by principal components (PCA), diversification ratio, and flags indicating diversification quality. Shows how much of the basket's risk is driven by the first few factors. Essential for portfolio construction, risk budgeting, and understanding concentration risk.

Beta analysis vs a benchmark (default: SPY). Reports beta, alpha, R-squared, correlation, and tracking error over multiple timeframes. Includes rolling beta chart to show how beta has changed over time. Useful for understanding a stock's systematic risk and market sensitivity.

Style box positioning — places each stock on a 3x3 grid of Size (Small/Mid/Large) × Style (Value/Blend/Growth) using market cap, P/E, P/B, and 12-month momentum as factor inputs. Lighter-weight version of the factor command focused specifically on style classification.

Aggregate technical signal scan across a watchlist. For each symbol: RSI zone (overbought/neutral/oversold), MACD signal (bullish/bearish crossover), MA alignment (bullish/bearish stack), ADX trend strength, and a net signal score. One table showing which names have the most bullish or bearish signal confluence. Ideal for quickly scanning a watchlist to find the strongest and weakest names.

Tests the predictive power of a factor across a basket. Computes daily Spearman rank IC (Information Coefficient) between factor at t and forward N-day return at t+N, IR (mean/std of IC), hit rate, and runs a quintile NAV simulation rebalanced daily. The Q-top minus Q-bottom long-short return is the canonical alpha measure. Different from `factor` (which scores known factors) — this validates whether ANY metric has predictive power on YOUR universe. Supported factors include returns (return_1d/1w/1m/3m/6m/1y), oscillators (rsi9/14/20), volatility (stdev_20d/50d, atr14, natr14), trend (adx14), MA distance (close_to_sma20/50/200_percentage), and volume (volume_relative_20d/60d).

Tail dependence analysis via Student-t copula fitting. Pearson correlation misses tail co-movement — this quantifies whether assets crash (or rally) together more than linear correlation implies. Fits t-copula parameters (correlation rho, degrees of freedom nu) via maximum pseudo-likelihood, then computes upper and lower tail dependence coefficients. Compares with Gaussian copula baseline (zero tail dependence). Critical for understanding portfolio risk during market stress.

Portfolio-level analytics for 2-20 symbols with optional position details (shares, cost basis, purchase date). Computes weighted returns, portfolio volatility, Sharpe ratio, correlation matrix, individual risk contribution, and minimum-variance optimization. Holdings format: SYMBOL:shares:avg_cost:purchase_date (all fields after symbol are optional).

Mean-variance portfolio optimization. Solves for optimal weights using historical returns and covariance matrix. Returns weights for: Minimum-Variance (lowest vol), Maximum-Sharpe (best risk-adjusted return), Risk-Parity (equal risk contribution per asset), Equal-Weight (baseline), and Inverse-Volatility (simple risk-parity proxy). Plus the efficient frontier curve and per-portfolio expected return / volatility / Sharpe ratio.

Comprehensive risk dashboard computing: VaR (95% and 99% confidence), CVaR/Expected Shortfall, Ulcer Index, Pain Ratio, Tail Ratio, Skewness, Excess Kurtosis, Downside Deviation, Sortino Ratio, and Calmar Ratio. All computed from raw price history in Python — no reliance on external risk databases.

Deep tail risk analytics for 1-10 symbols. Computes Value-at-Risk (95/99) using historical, normal, and Cornish-Fisher methods. Includes CVaR/Expected Shortfall, skewness, excess kurtosis, tail ratio, multi-horizon risk (1d/5d/20d), drawdown-at-risk, loss frequency distribution, and the 5 worst historical days, weeks, and months. Shows dollar VaR on a $10k position. Multi-symbol mode adds a comparison table. What's the real downside beyond standard deviation?

Performance attribution — splits a stock's return into market beta + alpha components vs a benchmark. Returns OLS beta, annualized alpha, R-squared, tracking error, information ratio, return decomposition (% from beta vs % from alpha), asymmetric beta (up-market vs down-market), and rolling 60-day beta/alpha tracking.

Historical stress test — simulate symbol's behavior across known crisis windows: 2008 GFC, 2018 Q4 selloff, 2020 COVID crash, 2022 rate shock, 2023 banking crisis. Returns total return, max drawdown, annualized vol, worst day, recovery days, and projected dollar impact at current price for each scenario.

Expression-based strategy backtester. Define entry and exit conditions using a custom expression language with OHLCV variables (O, H, L, C, V with bar offsets like C[-1]), operators (+, -, *, /, >, <, and, or), and 50+ functions including SMA, EMA, RSI, ATR, MACD, Bollinger Bands, ADX, Stochastic, CCI, crossup/crossdown, and more. Reports total return, win rate, profit factor, max drawdown, Sharpe, and optional trade-by-trade history table.

Monte Carlo price simulation generating N forward paths based on historical return distribution fitted to a Student-t distribution (captures fat tails). Returns median projected price, 5th and 95th percentile cone, probability of hitting a target price by the horizon, and expected price distribution at horizon. Configurable simulation count, horizon, and target price. Useful for probabilistic price forecasting and position sizing.

Signal decay analysis — after RSI, MACD, MA-cross, Bollinger Band, volume spike, or gap signals fire, what are the forward returns at 1d/3d/5d/10d/20d horizons? Shows hit count, average return, median, win rate, profit factor, t-statistic, and a verdict on whether the signal has statistically significant alpha or is noise. Supports individual signals (rsi, macd, bb, ma, vol, gap) or all at once for comparison. 11 built-in signal types with automatic detection.

Multi-symbol event study inspired by Kensho-style analytics. Define conditions on one or more trigger symbols (e.g. TSLA|C>avg(C,50)) and the engine finds every date where ALL conditions fire simultaneously. Then it measures how a basket of target symbols performs around those events: buy N days before, sell M days after. Returns event count, year/month occurrence matrix, average returns, win rate, profit factor, median return, and best/worst trades per symbol. Historical trades table is available on request with -trades flag.

Ranks 2-20 symbols across 6 quantitative dimensions: Momentum (3m/6m/12m returns), Quality (Sharpe, Sortino), Risk (max drawdown, volatility), Trend (ADX, MA alignment), Mean-Reversion (z-score distance from mean), and Volume (relative volume, OBV trend). Each dimension scored 0-100 within the peer group and weighted into a composite score. Outputs a ranked leaderboard for systematic screening and selection.

Multi-symbol 0-100 composite score leaderboard. Ranks symbols across 6 components: Momentum (25%), Trend Quality (20%), Relative Strength (15%), RSI Positioning (15%), Volume Confirmation (15%), and ATH Distance (10%). Outputs a ranked leaderboard for quick comparative screening.

Advanced multi-table stock screener filtering across price data (returns, volume, range, gap, trend), technical indicators (RSI, SMA/EMA, ADX, Bollinger Bands, CCI, signals), and company fundamentals (sector, industry, market cap, country). Filter syntax: 'field op value' with AND logic. Operators: > < >= <= = != IN NOT IN LIKE. Supports multi-value filters (sector IN Technology,Healthcare), glob patterns (sector LIKE Tech*), and custom sort/limit. Use -fields 1 to see available filter fields.

Custom OHLCV formula scanner. Evaluate any mathematical expression over historical price data and see when the condition was true. Supports functions like avg(), std(), max(), min() over rolling windows. Useful for backtesting entry signals and custom indicator research.

KlickAnalytics Query Language (KQL): a pipe-based, single-symbol query language for market data. Chain a source with relational verbs (where, select, extend, tag, sort, top, group, roll, horizons). Expressions use the unified Formula Scanner / Backtest grammar — O H L C V with offsets like C[-1], the functions sum avg mean min max ref between least greatest abs stdev, plus technical indicators as calls (rsi(C,14), atr(14), macd(C), bbupper(C,20,2), adx(14), crossup/crossdown) and bare-field aliases (rsi14, atr14, bb_upper, macd_hist). Single execution backend at POST /v1/kql.

Rule-based alert scanner — define conditions in plain expression syntax (e.g. 'rsi14<30,price>sma200,return_1m>10') and scan a watchlist. Returns which symbols match each rule, match count, and full per-symbol detail. Supports 25+ metrics including RSI, ADX, ATR, SMAs/EMAs, returns over various windows, and market cap.

Upcoming and recent economic data releases: CPI, Non-Farm Payrolls, PMI, GDP, FOMC decisions, and more with consensus estimates. Shows event name, date/time, country, importance level (high/medium/low), previous value, consensus forecast, and actual result (when released). Filterable by country and importance. Essential for macro traders and anyone who needs to manage event risk.

US Treasury yield curve across all maturities (1M, 3M, 6M, 1Y, 2Y, 3Y, 5Y, 7Y, 10Y, 20Y, 30Y). Shows current rates, historical comparison, 2Y/10Y spread (inversion detection), and curve shape classification (normal, flat, inverted). Tracks yield curve changes over a configurable window. A foundational macro indicator — inversion historically signals recession risk.

TIPS breakeven inflation rates derived from Treasury nominal yields minus TIPS real yields. Shows inflation expectations embedded in the bond market across maturities. Breakeven = Nominal Yield - Real Yield. Rising breakevens signal higher expected inflation; falling breakevens signal disinflation expectations. A key input for real return analysis, TIPS allocation, and macro regime assessment.

Pre-defined market mover reports: top gainers, top losers, most active by volume, highest momentum, and more across configurable timeframes (1d, 1w, 1m, 3m, 6m, YTD, 1y). Filterable by exchange (NASDAQ, NYSE), sector, asset class (EQUITY, ETF, crypto, forex), and currency. Use 'ka movers list' to see all available report types. Ideal for daily market scanning and finding actionable names.

Arithmetic mean (numpy.mean) over a fetched series. Source dictates default field (prices→close, volatility→hv_20, quantstats→sharpe).

Median value (numpy.median) over a fetched series.

Sample standard deviation (ddof=1) over a fetched series.

Sum of values (numpy.sum) over a fetched series.

Maximum value (numpy.max) over a fetched series.

Minimum value (numpy.min) over a fetched series.

Number of observations (len) in the fetched series.

Geometric mean (statistics.geometric_mean) of positive values — use for returns series where compounding matters.

pandas-describe style summary: count, mean, std, min, 25%, 50%, 75%, max.

Z-score transform — returns a series of the same length as the input.

Pearson correlation between two symbols. Uses -symbol2 for the second symbol. Tails are trimmed to common length before correlating.

Percent change over -periods (default 1). Output is a series of same length as input with the warmup bars as null.

Cumulative sum of the series (numpy.cumsum). Useful for converting returns to a cumulative path or summing volume over time.

Rolling mean over -window bars. First (window-1) values are null (warmup).

Rolling sample standard deviation (ddof=1) over -window bars. First (window-1) values are null.

First difference (or -periods difference) of the series. Warmup is null.

Shift the series by -periods. Positive = lag (values shifted to later index, old positions null). Negative = lead. Use for lookahead-free backtesting.

Exponentially-weighted mean with pandas-compatible alpha = 2/(span+1). No warmup nulls — seeds from the first value.

Autocorrelation at -lag (default 1). Detects momentum (positive) vs mean-reversion (negative) regimes.

Keep values where -expr is true. Returns a (possibly shorter) series. Expression grammar: x OP NUMBER with OP in > >= < <= == = !=

True if any value satisfies -expr. Scalar bool. Short-circuits at first match.

True if every value satisfies -expr. Scalar bool.

Keep leading values while -expr is true; stop at first false. Useful for measuring uninterrupted streaks.

Sort the series ascending (default) or descending via -reverse true / -sort_dir desc.

Count frequency of each unique value. Returns a table sorted by count (desc). Useful on discrete fields like dividends, rounded returns, EPS.

Keep values in [-lower, -upper] (inclusive). Either bound optional.

Spearman rank correlation between two symbols (uses -symbol2). Measures monotonic co-movement, robust to outliers and nonlinear relationships.

Sample covariance (ddof=1) between two symbols (uses -symbol2). Feeds into hedge ratios and portfolio variance calculations.

NxN Pearson correlation matrix of N symbols (comma-separated in -s). Max 10 symbols. Returns a matrix renderable as text or JSON.

Concatenate two series: data then -symbol2 data. Useful for stacking sessions or merging pre/post event windows.

Element-wise price ratio: data / -symbol2 data. Core primitive for pair trading — pair with fn-mean / fn-stdev / fn-zscore for deviation analysis.

Interquartile range (Q3 - Q1). Robust measure of dispersion that ignores outliers.

Mean after trimming -proportion from each tail (default 0.1 = 10%). Robust against extreme outliers.

Shannon entropy (nats) of the binned distribution. Measures uncertainty/information content. -bins controls resolution.

Equal-width histogram binning. Returns a table of bin ranges -> counts. -bins controls count (default 10).

Quantile-based binning — equal-count bins (default 4 = quartiles). Useful for rank/decile analysis.

Polynomial fit (default deg=1, linear). Returns coefficients, R², slope-per-bar, fit endpoints. Good for trendline analysis.

One-sample t-test: does the series mean differ from -popmean (default 0)? Returns t, p-value, df.

Two-sample Welch t-test (unequal variances) comparing means of two symbols. Returns t, p-value, means, difference.

Shapiro-Wilk normality test. H0: data is drawn from a normal distribution. Low p-value => reject normality.

Jarque-Bera normality test based on skewness and kurtosis. Returns JB stat, p-value, skewness, excess kurtosis.

Rolling minimum over -window bars (default 20). Warmup values are null.

Rolling maximum over -window bars (default 20). Warmup is null.

Rolling sum over -window bars (default 20). Useful for cumulative volume, total return, signal aggregation.

Rolling z-score: (x - rolling_mean) / rolling_std using ddof=1. Values beyond +/-2 are rare — useful for extension detection.

Percentile rank (0-100) of x_t within the last -window observations. 95+ = near-max, 5- = near-min.

Rolling Pearson correlation between two symbols (-symbol2) over -window bars. Reveals regime changes correlation alone hides.

Group values by weekday (Mon/Tue/Wed/Thu/Fri), return mean per day. Requires series source with date column (prices, volatility, earnings, dividends).

Group values by calendar month (Jan-Dec), return mean per month. Requires series source with dates. Use 500+ bars for stable monthly buckets.

How to use the ka commands

List all available KlickAnalytics CLI commands with descriptions and sample usage.

View the KlickAnalytics CLI changelog — lists all recent updates, new commands, and improvements.

Build and evaluate any options strategy from the live chain — single-leg (long call/put, covered call, naked put, married put), vertical spreads, protective collar, straddles & strangles, calendars/diagonals, butterflies and condors. Returns per-strike rows with breakevens, max profit/loss, risk-reward, net Greeks and probability of profit, computed with a leg-based payoff engine. Use -strategy to pick, -list to see all codes, -limit to cap rows and -width for spread/wing width.

Given a directional view (very bearish → very bullish, or 'best'), a target price and an optional budget, recommend the best-fit option strategies ranked by a Return-vs-Chance preference. Each recommendation shows the chosen strikes, return on risk/collateral, probability of profit, profit at target and capital at risk. -pref 0 maximizes return, 100 maximizes probability; -budget caps capital; -target sets the price objective.

Build any of 58 option strategies on a live chain and get the full position back: every leg with its strike and fill price, net debit or credit, max profit, max loss, breakevens, probability of profit and net greeks. Strategies are ATM-anchored and the width is expressed in strike steps, so it adapts to any chain. Use --list=1 to see all 58.

List, inspect, rename and delete the option strategies you saved in Options > Strategy Builder. Shows each position's legs at their saved entry prices plus net debit/credit, max profit, max loss and probability of profit. Owner-scoped: only your own strategies are visible.

Personal notebook CRUD. Create, list, read, update, append-to, and delete notebooks tied to your account. Notebooks store markdown text in a body field with optional title, pin status, and folder grouping. UUID ids returned on create; pass the first 8 chars or the full UUID to subsequent actions.

Watchlist CRUD. Manage symbol lists tied to your account. Integer ids (use 'list' to find). Supports adding and removing individual symbols on existing lists without recreating.

AI chat *session* CRUD. List, read, create, rename / star, archive, and restore chat sessions. Delete is SOFT — sets deleted_at timestamp; messages are preserved. Restore brings the session back. Ownership is verified on every operation. Use ai-chat to send new prompts. Session ids are UUIDs (8-char prefix accepted).

Live in-memory scan engine — query the latest market tick across all symbols with shorthand or raw SQL. Built on a Rust+Arrow daemon (kamd) that refreshes every 60s. Tier 1 columns: price action (gap_pct, chg_from_open, hl_spread_pct), volume (dollar_volume, vol_ratio_20d, vol_surge), session context (session_high_pct, session_low_pct). Shorthand grammar: gap_up:N, gap_down:N, chg:>N, vol:>N (suffix M/K), vol_surge, volatile, sector:tech, sort:FIELD, top:N. Use --sql for raw DataFusion SQL against the `snap` table. Use --feed in Phase 2 for SSE-streamed scan panels.

Server-Sent Events feed of 12 predefined market scans, pushed every 60s from kamd. Use directly with curl --no-buffer or via any EventSource-compatible client. Each event is a JSON object with scan_id, ts, tick_seq, results (rows). On connect kamd replays the latest snapshot of every subscribed scan so panels never start blank, then streams live updates. Per-user subscriptions controllable via GET/PUT /snap/subscriptions. Scans available: gap_ups, gap_downs, top_gainers, top_losers, unusual_volume, most_volatile, vol_surge, session_high, session_low, dollar_movers, gappers_with_volume, sector_leaders.