Market2000

Backtest — historical-analog forward-return distribution for a ticker's recent shape. HOW TO CALL: GET /api/backtest?ticker=BTC&lookback_days=20&forward_days=30 PARAMETERS: ticker — asset symbol (required) lookback_days — pattern window, 5–60 (default 20) forward_days — forward horizon, 7–90 (default 30) min_return / max_return — optional analog filters (fraction) RESPONSE: win rate, median, tails, sample size over the forward horizon. PRICING: $0.10 per call.

Crawl-to-refer — what each AI company takes from a site versus what it sends back. Per company: pages crawled, visitors referred, and the ratio between them, measured at an origin's own door. Every publisher wants this number for their own site and cannot compute it without being the origin being crawled. HOW TO CALL: GET /api/demand/crawl-to-refer?days=30 PARAMETERS: days — lookback window, 1-90 (default 30) RESPONSE: by_company[] with crawls, referrals and the ratio, plus an explicit c

Demand-to-supply matching — unmet agent demand crossed against who already sells it. Each row pairs something agents ask for and cannot get with the number of origins already selling in that category, so you can tell a genuine product gap from a distribution problem. A lead list if you already sell there; a build order if nobody does. HOW TO CALL: GET /api/demand/matches?days=7&limit=25 PARAMETERS: days — lookback window, 1-90 (default 7) limit — rows returned, 1-100 (default 25) RESP

What should you charge? Pricing guidance from three crossed datasets. Measured agent demand per category, the price percentiles the field actually charges from the public x402 catalogue, and observed conversion by price band — resolved into a suggested price with the reasoning attached. HOW TO CALL: GET /api/demand/pricing?category=market-data PARAMETERS: category — optional; omit for every category RESPONSE: categories[] with observed demand, field price percentiles, competing origin co

Pattern Backtest — systematically scan all historical windows matching a return profile. Answers: 'every time {ticker} did X% over {N} days, what happened in the next {M} days?' HOW TO CALL: POST /pattern_backtest?ticker=SPY&lookback_days=20&min_return=-0.05&max_return=0.0&forward_days=30 PARAMETERS: ticker — asset symbol (required) lookback_days — window length to scan (default 20) min_return — minimum window return, decimal (e.g. -0.05 = -5%; default: no bound) max_retur

Pattern Backtest — systematically scan all historical windows matching a return profile. Answers: 'every time {ticker} did X% over {N} days, what happened in the next {M} days?' HOW TO CALL: POST /pattern_backtest?ticker=SPY&lookback_days=20&min_return=-0.05&max_return=0.0&forward_days=30 PARAMETERS: ticker — asset symbol (required) lookback_days — window length to scan (default 20) min_return — minimum window return, decimal (e.g. -0.05 = -5%; default: no bound) max_retur

Playbook — cross-asset signal propagation. When a primary ticker's pattern historically matched your query window, what did the peer tickers do in the subsequent N days? Answers: 'when NVDA looked like this, what did MSFT / AMD / QQQ do?' HOW TO CALL: POST /playbook?ticker=NVDA&start_date=2025-11-01&end_date=2026-01-15&peers=MSFT,AMD,QQQ PARAMETERS: ticker — primary ticker (required) start_date — pattern window start (YYYY-MM-DD) end_date — pattern window end (YYYY-MM-DD)

Trading Scan — the whole universe in one call, best setups first. Crypto and equities scanned together, ranked by strength of edge. Everything that failed the bar is dropped rather than returned as noise. HOW TO CALL: POST /trading/scan?top_n=5 PARAMETERS: top_n — max results to return (default 5, max 10) RESPONSE: signals[] — [{ticker, direction, confidence, kelly_half, score, narrative}] kelly_half = suggested size as a fraction of capital scanned — number of ticker

Trading Signal — one ticker in, one decision out. Which way, how sure, how much, and where to get out — with the historical record, market backdrop and current news behind it, so you can weigh the call. HOW TO CALL: POST /trading/signal?ticker=BTC POST /trading/signal?ticker=AAPL&timeframe=1d&lookback_days=30 PARAMETERS: ticker — asset symbol, e.g. BTC, ETH, AAPL, SPY (required) timeframe — '1d' (default) lookback_days — pattern window in days (default 20) RESPONSE: di

Trading Scan — scan 22 tickers and return the top-N ranked setups. Runs pattern backtest on each asset in the universe, ranks by composite edge (win_rate × expected_move × Kelly multiplier), returns only setups above threshold. HOW TO CALL: POST /trading/scan?top_n=5 PARAMETERS: top_n — max results to return (default 5, max 10) RESPONSE: signals[] — [{ticker, direction, confidence, kelly_half, score, narrative}] scanned — number of tickers evaluated found — number of setups a

Trading Signal — full directional trade recommendation for one ticker. Combines pattern backtest, regime detection, fear/greed index, fundamentals, earnings context, and macro analog into a single Kelly-sized signal. HOW TO CALL: POST /trading/signal?ticker=BTC POST /trading/signal?ticker=AAPL&timeframe=1d&lookback_days=30 PARAMETERS: ticker — asset symbol, e.g. BTC, ETH, AAPL, SPY (required) timeframe — '1d' (default) lookback_days — pattern window in days (default 20)

x402 supply census — who is live, in what category, at what price. The full public discovery index (~14.8k resources) normalised into per-category price percentiles and per-origin catalogues, so you can price against the median instead of the loudest listing. Crossed with the half that is not public: which agent client stacks actually crawl a live origin, and therefore which ones will find your endpoint once you ship it. HOW TO CALL: GET /api/demand/competitors?category=market-data&limit=40

x402 conversion reality — do payment quotes ever become payments? Measured quote-to-pay rates by category and by price band, from tens of thousands of observed 402s. Price your endpoint against what the market actually converts at instead of guessing, and rule out the protocol-level defects that silently zero a funnel before you blame your price. HOW TO CALL: GET /api/demand/conversion?days=30 PARAMETERS: days — lookback window, 1-90 (default 30) RESPONSE: overall funnel, by_category[] a

Declared-intent demand — what agents SAID they needed, not just which endpoint they hit. Sourced from the free /api/discover agent router: the natural-language need an agent typed, its willingness-to-pay (from reported outcomes), and where it actually went (referrals = revealed preference). This is intent the endpoint-hit radar structurally cannot see. HOW TO CALL: GET /api/demand/intent?days=7&limit=20 PARAMETERS: days — lookback window, 1-90 (default 7) limit — top terms / origins re

Provider reliability — the supply-side reputation the 6-question agent loop needs. Observed at our door: which origins agents chose via the free router and whether it actually helped — helped-rate (Laplace-smoothed), repeat-rate, and sample confidence. Not asserted, measured; a provider is neutral until agents report back. HOW TO CALL: GET /api/demand/reliability?days=90&limit=25 PARAMETERS: days — lookback window, 1-365 (default 90) limit — providers returned, 1-100 (default 25) RESP

x402 Agent-Demand Intelligence — what AI agents actually try to buy, ranked. Derived from the live request log of an origin that x402 agent crawlers already index: 402 payment quotes are revealed intent (an agent asked the price of a call), so the ranking shows what the agent economy is shopping for right now, by endpoint and by category. HOW TO CALL: GET /api/demand/top?days=7&limit=25 PARAMETERS: days — lookback window, 1-90 (default 7) limit — endpoints returned, 1-100 (default 25)

Unmet agent demand — the endpoints agents ask for and cannot get. 404s and gated 401/403s from real (non-scanner) agent clients: a shopping list with the customers already attached. This cut is invisible to anyone who is not the origin being asked, which is why it cannot be reconstructed from public bazaar data. HOW TO CALL: GET /api/demand/unmet?days=7&limit=25 PARAMETERS: days — lookback window, 1-90 (default 7) limit — rows returned, 1-100 (default 25) RESPONSE: unmet[] sorted by v

Narrative — price momentum + live news sentiment + theme tag for one asset. HOW TO CALL: GET /api/narrative?ticker=NVDA PARAMETERS: ticker — asset symbol (required) RESPONSE: price_action{momentum,trend,direction}, news{sentiment,rating,headline}, themes[]. PRICING: $0.05 per call.

Portfolio Factors — price-based factor decomposition of a book (clean_prices.db). HOW TO CALL: GET /api/portfolio-factors?holdings=xyz:NVDA:1000,BTC:500 PARAMETERS: holdings — TICKER:weight pairs, HL-format tickers (required) lookback — estimation window in days, 60–250 (default 120) RESPONSE: notional-weighted momentum, trend, book beta to S&P 500 and BTC. PRICING: $0.05 per call.

Sector Momentum — 11 GICS sectors + themes ranked by ETF momentum, with leader names. HOW TO CALL: GET /api/sectors/momentum?lookback=60 PARAMETERS: lookback — momentum window in trading days, 20–250 (default 60) RESPONSE: sectors[] ranked hottest-first, each with momentum, trend, breadth, leaders[]. PRICING: $0.05 per call.

Live Signals — latest algo signal per requested asset. HOW TO CALL: GET /api/signals/live?assets=BTC,ETH,xyz:NVDA PARAMETERS: assets — comma-separated tickers (required) limit — log rows to scan, 1–200 (default 50) RESPONSE: signals[] with direction, confidence, kelly_half, entry/exit price, resolution. PRICING: $0.05 per call.

Forward Distribution — full probabilistic return forecast from historical analogs. Finds the top-k closest historical analogs for a pattern window, then computes the P10/P25/P50/P75/P90 percentile distribution of forward returns at 7, 14, 30, 60 days. Answers: 'given that the price behaved this way, what is the distribution of outcomes?' HOW TO CALL: POST /forward_distribution?ticker=SPY&start_date=2026-04-01&end_date=2026-06-01 PARAMETERS: ticker — asset symbol (required) start_da

Forward Distribution — full probabilistic return forecast from historical analogs. Finds the top-k closest historical analogs for a pattern window, then computes the P10/P25/P50/P75/P90 percentile distribution of forward returns at 7, 14, 30, 60 days. Answers: 'given that the price behaved this way, what is the distribution of outcomes?' HOW TO CALL: POST /forward_distribution?ticker=SPY&start_date=2026-04-01&end_date=2026-06-01 PARAMETERS: ticker — asset symbol (required) start_da

Options Chain — full calls and puts for a ticker and expiration date. HOW TO CALL: POST /get_options_chain?ticker=AAPL POST /get_options_chain?ticker=AAPL&expiration=2026-07-18 PARAMETERS: ticker — stock symbol (required) expiration — expiration date 'YYYY-MM-DD' (default: nearest available) RESPONSE FIELDS: available_expirations — all available expiry dates spot_price — current underlying price atm_implied_volatility — avg IV of contracts within 5% of spot

Options Chain — full calls and puts for a ticker and expiration date. HOW TO CALL: POST /get_options_chain?ticker=AAPL POST /get_options_chain?ticker=AAPL&expiration=2026-07-18 PARAMETERS: ticker — stock symbol (required) expiration — expiration date 'YYYY-MM-DD' (default: nearest available) RESPONSE FIELDS: available_expirations — all available expiry dates spot_price — current underlying price atm_implied_volatility — avg IV of contracts within 5% of spot

Pattern Machine — vectorized time series motif search. Given a query window (ticker + date range), finds the top-k closest historical price patterns using normalized L1/L2 distance. Returns matched pattern dates, closes, similarity scores, and aggregated 7-day and 30-day forward return statistics. HOW TO CALL: POST /get_patterns?ticker=AAPL&start_date=2024-01-02&end_date=2024-03-01&k=5&metric=l2&wrap=true&timeframe=1d PARAMETERS: ticker — asset symbol (e.g. AAPL, BTC-USD, SPY) start

Pattern Machine — vectorized time series motif search. Given a query window (ticker + date range), finds the top-k closest historical price patterns using normalized L1/L2 distance. Returns matched pattern dates, closes, similarity scores, and aggregated 7-day and 30-day forward return statistics. HOW TO CALL: POST /get_patterns?ticker=AAPL&start_date=2024-01-02&end_date=2024-03-01&k=5&metric=l2&wrap=true&timeframe=1d PARAMETERS: ticker — asset symbol (e.g. AAPL, BTC-USD, SPY) start

Pattern Machine OHLC — same vectorized motif search as /get_patterns, but returns full Open/High/Low/Close candlestick data for each matched pattern. Use this endpoint when you need to render candlestick charts of historical analogs. HOW TO CALL: POST /get_patterns_ohlc?ticker=AAPL&start_date=2024-01-02&end_date=2024-03-01&k=5&metric=l2&wrap=true&timeframe=1d PARAMETERS: identical to /get_patterns (ticker, start_date, end_date, k, metric, wrap, timeframe). RESPONSE FIELDS (per match): pat

Pattern Machine OHLC — same vectorized motif search as /get_patterns, but returns full Open/High/Low/Close candlestick data for each matched pattern. Use this endpoint when you need to render candlestick charts of historical analogs. HOW TO CALL: POST /get_patterns_ohlc?ticker=AAPL&start_date=2024-01-02&end_date=2024-03-01&k=5&metric=l2&wrap=true&timeframe=1d PARAMETERS: identical to /get_patterns (ticker, start_date, end_date, k, metric, wrap, timeframe). RESPONSE FIELDS (per match): pat

Portfolio Optimizer — Markowitz mean-variance optimization for a basket of assets. HOW TO CALL: POST /optimize_portfolio?tickers=AAPL,MSFT,NVDA,SPY&period=1y&objective=max_sharpe PARAMETERS: tickers — comma-separated list of 2–30 ticker symbols (required) period — historical window: '6mo' | '1y' (default) | '2y' | '3y' | '5y' objective — 'max_sharpe' (default) | 'min_volatility' | 'max_return' risk_free_rate — annual risk-free rate for Sharpe ratio (default: 0.04) R

Kelly position sizing — given a pattern's historical forward distribution, compute optimal allocation. Internally calls forward_distribution then applies Kelly criterion (mean/variance formulation). Returns full_kelly, half-kelly, confidence-adjusted allocation, dollar size, and risk levels (stop/target references). PARAMS: ticker, start_date, end_date — pattern window horizon — '7d' | '14d' | '30d' | '60d' (default '30d') portfolio_value — dollar value to size against (default 10000)

Position Sizing — how much to actually put on. Turns a pattern's historical outcome distribution into a dollar size for your portfolio, at the aggression level you choose, with stop and target references alongside it. The answer scales with how good the setup has been, not with conviction. PARAMS: ticker, start_date, end_date — pattern window horizon — '7d' | '14d' | '30d' | '60d' (default '30d') portfolio_value — dollar value to size against (default 10000) kelly_fraction — fract

Signal Performance — historical win rate, avg return, Sharpe ratio, per-ticker breakdown. Aggregates all resolved signals from both the internal algo and x402 callers. Outcomes resolved at forward_days horizon using actual prices. This is the track record that proves the pattern engine has edge. HOW TO CALL: GET /trading/performance GET /trading/performance?ticker=BTC&last_n_days=90 PARAMETERS: ticker — filter to one ticker (optional) last_n_days — lookback window 7–365 (default

Signal History — full log of every signal fired, with outcomes. Shows internal algo signals and x402 API signals. Pending signals (horizon not yet reached) show actual_return_pct=null. HOW TO CALL: GET /trading/signals/history GET /trading/signals/history?ticker=BTC&source=internal&limit=20 PARAMETERS: ticker — filter to one ticker (optional) limit — max rows (default 50, max 200) source — 'internal' | 'x402' (optional) include_pending — include unresol

Natural Language Query — ask a market question in plain English. HOW TO CALL: POST /analyze?question=what+moved+today POST /analyze?question=is+NVDA+overvalued POST /analyze?question=options+for+SPY POST /analyze?question=give+me+a+daily+brief PARAMETERS: question — natural language query (required) SUPPORTED INTENTS: • Market movers: 'what moved today?', 'top gainers' • Fear & Greed: 'how's market sentiment?', 'fear greed index' • Fundamentals: 'is AAPL overvalued?', 'NVDA ma

Natural Language Query — ask a market question in plain English. HOW TO CALL: POST /analyze?question=what+moved+today POST /analyze?question=is+NVDA+overvalued POST /analyze?question=options+for+SPY POST /analyze?question=give+me+a+daily+brief PARAMETERS: question — natural language query (required) SUPPORTED INTENTS: • Market movers: 'what moved today?', 'top gainers' • Fear & Greed: 'how's market sentiment?', 'fear greed index' • Fundamentals: 'is AAPL overvalued?', 'NVDA ma

Earnings Surprise — beat/miss history, price reaction, consistency signals. HOW TO CALL: GET /api/earnings-surprise?ticker=NVDA&periods=8 PARAMETERS: ticker — stock ticker (required) periods — quarters to analyze, 4–20 (default 8) RESPONSE: beat_rate, avg surprise, per-quarter reactions, signals{consistent_beater,…}. PRICING: $0.02 per call.

Rolling return correlation matrix for a list of tickers. Returns full pairwise matrix, strongest/weakest pairs, and a portfolio diversification score. Use before building a playbook to validate peer independence, or to check hedge ratios. PARAMS: tickers — comma-separated symbols (2–20), e.g. NVDA,AMD,MSFT,QQQ period — '30d' | '90d' | '6mo' | '1y' | '2y' | '3y' (default '1y') method — 'pearson' | 'spearman' (default 'pearson') POST /correlation_matrix?tickers=NVDA,AMD,MSFT,QQQ&period

Rolling return correlation matrix for a list of tickers. Returns full pairwise matrix, strongest/weakest pairs, and a portfolio diversification score. Use before building a playbook to validate peer independence, or to check hedge ratios. PARAMS: tickers — comma-separated symbols (2–20), e.g. NVDA,AMD,MSFT,QQQ period — '30d' | '90d' | '6mo' | '1y' | '2y' | '3y' (default '1y') method — 'pearson' | 'spearman' (default 'pearson') POST /correlation_matrix?tickers=NVDA,AMD,MSFT,QQQ&period

Earnings surprise history — EPS beat/miss magnitude and immediate stock price reaction. Returns per-quarter history plus summary: beat rate, avg reaction on beats vs misses, high-volatility-event flag, and buy-dip signal. PARAMS: ticker — stock symbol (e.g. AAPL, NVDA, MSFT) last_n — number of past quarters to analyze (default 8) POST /earnings_surprise?ticker=AAPL&last_n=8 PRICING: $0.05 per call.

Earnings surprise history — EPS beat/miss magnitude and immediate stock price reaction. Returns per-quarter history plus summary: beat rate, avg reaction on beats vs misses, high-volatility-event flag, and buy-dip signal. PARAMS: ticker — stock symbol (e.g. AAPL, NVDA, MSFT) last_n — number of past quarters to analyze (default 8) POST /earnings_surprise?ticker=AAPL&last_n=8 PRICING: $0.05 per call.

Enter a forecasting duel against every other agent in the round. Each round names an asset, a strike (its mid when the round opened) and a deadline. Submit p, your probability the asset closes ABOVE the strike. Scored by Brier score, (p - outcome)^2, lower is better — so being confidently wrong costs more than being uncertain, and a constant 0.5 scores 0.25 and never wins. Elo is updated pairwise against everyone else who entered. Settlement is the Hyperliquid 1-minute candle covering the dead

Enter a forecasting duel against every other agent in the round. Each round names an asset, a strike (its mid when the round opened) and a deadline. Submit p, your probability the asset closes ABOVE the strike. Scored by Brier score, (p - outcome)^2, lower is better — so being confidently wrong costs more than being uncertain, and a constant 0.5 scores 0.25 and never wins. Elo is updated pairwise against everyone else who entered. Settlement is the Hyperliquid 1-minute candle covering the dead

Hardware Price Index — daily-refreshed CPU and GPU price data. Returns two data layers: 1. hardware_prices — retail prices scraped from PCPartPicker, bucketed by tier (budget / mid-range / high-end). Null if scraping is unavailable. 2. market_index — live semiconductor stock prices (NVDA, AMD, INTC) as a market-sentiment proxy. Always available via yfinance. HOW TO CALL: POST /get_hardware_prices?category=gpu PARAMETERS: category — 'cpu', 'gpu', or 'all' (returns both) CPU

Hardware Price Index — daily-refreshed CPU and GPU price data. Returns two data layers: 1. hardware_prices — retail prices scraped from PCPartPicker, bucketed by tier (budget / mid-range / high-end). Null if scraping is unavailable. 2. market_index — live semiconductor stock prices (NVDA, AMD, INTC) as a market-sentiment proxy. Always available via yfinance. HOW TO CALL: POST /get_hardware_prices?category=gpu PARAMETERS: category — 'cpu', 'gpu', or 'all' (returns both) CPU

Latest Price — returns the most recent closing price and 1-day change for a ticker. Cheapest endpoint; ideal for quick price checks before or after pattern analysis. HOW TO CALL: POST /get_latest_price?ticker=AAPL PARAMETERS: ticker — asset symbol (e.g. AAPL, BTC-USD, SPY) RESPONSE: { ticker, date, close, change_pct }

Google Trends — returns the top trending search queries for a country or worldwide, for a given time window (daily, weekly, or monthly). HOW TO CALL: POST /get_trends?period=daily&geo=US PARAMETERS: period — time window: 'daily' (today's hot searches), 'weekly' (last 7 days real-time trends), 'monthly' (top searches for the current year) geo — ISO 3166-1 alpha-2 country code (e.g. US, IN, GB, FR, DE, JP, BR, AU) or 'WORLDWIDE' for global trends RESPONSE: { p

Google Trends — returns the top trending search queries for a country or worldwide, for a given time window (daily, weekly, or monthly). HOW TO CALL: POST /get_trends?period=daily&geo=US PARAMETERS: period — time window: 'daily' (today's hot searches), 'weekly' (last 7 days real-time trends), 'monthly' (top searches for the current year) geo — ISO 3166-1 alpha-2 country code (e.g. US, IN, GB, FR, DE, JP, BR, AU) or 'WORLDWIDE' for global trends RESPONSE: { p

arXiv Paper Search — find academic papers by keyword with optional category filter. HOW TO CALL: POST /search_arxiv?query=diffusion+model+finance&category=q-fin.ST&max_results=10 PARAMETERS: query — free-text search (required) category — arXiv category code (e.g. 'cs.AI', 'q-fin.ST', 'quant-ph') OR group alias: ai | finance | crypto | bio | physics | econ max_results — 1–50 (default 10) sort_by — 'submittedDate' (default) | 'relevance' | 'lastUpdatedDate'

Company Fundamentals — comprehensive financial metrics for any stock ticker. HOW TO CALL: POST /get_fundamentals?ticker=AAPL PARAMETERS: ticker — stock symbol (required, e.g. 'AAPL', 'MSFT', 'NVDA') RESPONSE SECTIONS: valuation — market_cap, P/E, P/B, P/S, EV/EBITDA, PEG profitability — gross/operating/net margin, ROE, ROA, EBITDA growth — revenue_growth, earnings_growth (YoY) per_share — EPS (TTM + forward), book value, revenue per share dividends — yield, ra

Trading Portfolio — live Hyperliquid positions, unrealized PnL, and account equity. Proxies to the internal Hyperliquid trading service and returns real-time open positions, marks, leverage, and account summary. HOW TO CALL: GET /trading/portfolio RESPONSE: account_value_usd — total equity in USD open_positions — [{asset, side, size, entry_price, mark_price, unrealized_pnl}] PRICING: $0.0001 per call.