API marketplace

The money-market spreads that signal whether US dollar funding is calm or seizing up, computed live from the Federal Reserve Bank of New York's public rates API — no key, nothing stored. The headline overnight rates all sit within a few basis points of each other when markets are healthy; it is the spreads between them, and their spikes, that reveal stress. The most-watched is SOFR minus EFFR: SOFR is the cost of secured (collateralised, repo) borrowing and EFFR the cost of unsecured fed-funds borrowing, so when SOFR climbs above EFFR it means collateral is suddenly expensive — the classic repo-stress signal that blew out in September 2019 and around quarter-ends. This API computes that and the other key spreads — SOFR vs the Overnight Bank Funding Rate, SOFR vs the Broad General Collateral Rate, and the general-vs-tri-party collateral spread — in basis points, with a funding-stress regime read. The spreads endpoint returns the live rate board and every spread; the distribution endpoint returns SOFR's intraday percentile spread (99th minus 1st), a within-day dispersion gauge that widens when funding is segmented; the history endpoint returns the time series of any spread and counts the stress days. This is the funding-stress / money-market-spread cut — distinct from the raw NY-Fed rate-level feed (which lists the rates but not the spreads or the stress signal), the central-bank-policy and the yield-curve APIs. It is the gap between the rates, which is where the stress lives.

Uptime

100.0%

Latency

163ms

Subs

3,765

How much more volatility the options market is pricing in than the market has actually delivered — the carry that every short-volatility strategy harvests — computed live from Yahoo Finance, no key, nothing stored. Implied volatility (the VIX and its cousins) is almost always richer than the volatility that subsequently shows up: investors pay up for protection, and that gap, the variance risk premium, is one of the most persistent paid-for risks in markets. This API measures it directly across the major asset classes that publish an implied-vol index: for the S&P 500 (VIX), the Nasdaq 100 (VXN), crude oil (OVX) and gold (GVZ), it takes the live implied-vol index and subtracts the realised volatility actually delivered by the underlying over the matching ~30-day window (annualised standard deviation of daily log returns), and returns the premium in volatility points, the implied/realised ratio and a rich/cheap read. A large positive VRP means options are expensive relative to what the market has been doing (sellers are well paid); a negative VRP — implied below realised — is rare and flags that options are cheap, often during or right after a stress event. The premium endpoint returns all four markets ranked; the asset endpoint returns one market with 21- and 30-day realised legs; the history endpoint returns the VRP time series. This is the implied-minus-realised / variance-risk-premium cut for equities and commodities — distinct from the implied-vol level board (no realised leg), the realised-volatility dashboard (no implied leg) and the crypto-only DVOL/VRP API.

Uptime

100.0%

Latency

138ms

Subs

3,963

The shape of the equity volatility curve — the single most-watched regime signal in the options world — computed live from Yahoo Finance, no key, nothing stored. A VIX level tells you how scared the market is right now; the term structure tells you whether that fear is short-term panic or a calm, persistent state, and which way it is rolling. This API reads the S&P 500 implied-volatility curve across four tenors — the 9-day VIX, the headline 30-day VIX, the 3-month VIX and the 6-month VIX — and turns it into a regime. When the curve slopes up (VIX < VIX3M < VIX6M) the market is in contango: calm, with near-term vol cheaper than far, the state short-vol strategies harvest. When it inverts to backwardation (VIX above VIX3M) the front end is bid above the back: acute stress, fear spiking, historically near capitulation. The structure endpoint returns the live curve, the contango ratio (VIX / VIX3M), the short-end ratio (VIX9D / VIX), the roll yield a short-vol position would earn, the slope classification and a regime read, with VVIX (the vol of the VIX) for context. The history endpoint returns the daily time series of the contango ratio and flags every backwardation day. The percentile endpoint places today's contango ratio in its one-year range. This is the volatility term-structure / contango-backwardation cut — distinct from the cross-asset VIX-family level board, the crypto DVOL index and the realised-volatility APIs. It is the shape of fear, not its level.

Uptime

100.0%

Latency

133ms

Subs

3,276

A formal statistical test of whether a market follows a random walk, or whether its returns carry tradeable momentum or mean-reversion that is real rather than noise — the Lo-MacKinlay variance ratio test, computed live from Yahoo Finance daily closes, no key, nothing stored. Most persistence tools give you a single descriptive number; this gives you a hypothesis test with a verdict. The variance ratio compares the variance of multi-day returns to the variance of one-day returns scaled up: under a true random walk the ratio is 1 at every horizon. A ratio above 1 means returns positively autocorrelate (trends persist — momentum); below 1 means they reverse (mean-reversion). Crucially it attaches a heteroskedasticity-robust z-statistic and a p-value at each horizon, so you know whether the deviation from a random walk is statistically significant or just sampling noise — the thing a point estimate cannot tell you. The asset endpoint runs the test at horizons of 2, 4, 8 and 16 days and returns each ratio, z-statistic, p-value and a reject/fail-to-reject verdict, plus an overall read. The screener endpoint ranks the cross-asset universe by their 2-day variance ratio, separating the statistically momentum-like markets from the mean-reverting ones. This is the random-walk hypothesis-test cut — distinct from the Hurst-exponent regime API (a point estimate with no significance), the momentum and the price APIs. It is the test, with the p-value attached.

Uptime

100.0%

Latency

213ms

Subs

3,388

The two best-documented calendar anomalies in equities — the day-of-week effect and the turn-of-month effect — measured live across a cross-asset universe from Yahoo Finance daily history, no key, nothing stored. Decades of research show returns are not spread evenly through the week or the month: the turn-of-month effect — the cluster of the last trading day of a month and the first few of the next — has historically captured the bulk of the entire month's gain while the rest of the month drifts; and the day-of-week effect (the old "Monday effect" and its kin) shows some weekdays running persistently stronger than others. This API quantifies both directly. The turnofmonth endpoint splits an instrument's history into the turn-of-month window (the last trading day plus the first three of each month) versus the rest, and returns the average daily return and win-rate of each, the spread between them, and the share of the total return earned inside that handful of days. The dayofweek endpoint returns, for each weekday, the average daily return, win-rate and sample size, with the best and worst day. The screener endpoint ranks the cross-asset universe by the strength of the turn-of-month effect, so you can see where the calendar edge is biggest. This is the day-of-week / turn-of-month calendar-anomaly cut — distinct from the month-of-year seasonality APIs (equity-index, FX, commodity) and the crypto-only intraday/day-of-week seasonality API. Patterns are descriptive, not predictive.

Uptime

100.0%

Latency

336ms

Subs

4,164

Which markets are trading on abnormal volume right now — the first scan a day-trader runs to find what is "in play" — computed live from Yahoo Finance daily volume, no key, nothing stored. Price tells you where a market is; volume tells you whether anyone cares. A stock drifting on half its normal volume is noise; the same stock on three times its average is a market reacting to something — earnings, news, a breakout — and that is where the opportunity and the risk live. Relative volume (RVOL) is today's volume divided by its recent average: 1.0 is a normal day, 2.0 is double, and anything above signals unusual participation. For each instrument this API returns today's volume, its 20- and 50-day average volume, the RVOL against each, where today's volume sits as a percentile of the window, the dollar (notional) volume for liquidity, and whether volume is trending up or down. The asset endpoint returns one instrument's full volume profile; the screener endpoint ranks the universe by RVOL, putting the names trading on the most unusual volume — the ones in play — at the top. This is the relative-volume / unusual-activity cut — distinct from the bring-your-own-series volume-indicator tools (OBV, MFI), the crypto volume-by-price profile, the order-flow tape and the price APIs. It is the volume that is out of the ordinary.

Uptime

100.0%

Latency

294ms

Subs

3,956

Where each market closes inside its daily range, and what that says about who is in control into the bell, computed live from Yahoo Finance daily OHLC — no key, nothing stored. The close is the most important price of the day: a market that runs up but closes back near its low was sold into all afternoon (distribution), while one that closes on its highs has buyers in firm control (accumulation), even if the headline change is the same. The Close Location Value (CLV) captures this on a -1 to +1 scale — +1 is a close exactly on the high, -1 exactly on the low, 0 the middle of the range. This API turns it into a conviction gauge. For each instrument it returns today's CLV, the average CLV over the window (a positive average means closes persistently in the upper half — accumulation; negative means distribution), the recent 20-day CLV as the current pressure reading, the share of days that closed in the upper third versus the lower third of their range, and a plain-language read. The asset endpoint returns one instrument's full closing-strength profile; the screener endpoint ranks the cross-asset universe from strongest accumulation to heaviest distribution, so you can see where buyers are quietly winning the close. This is the close-location / accumulation-distribution-pressure cut, price-only and no volume — distinct from the candlestick-pattern API (named shapes on the last bar), the volume-indicator tools and the price feeds. It is who won the day.

Uptime

100.0%

Latency

356ms

Subs

4,737

The volatility-coiling setups breakout traders hunt, computed live from Yahoo Finance daily OHLC — no key, nothing stored. Markets do not trend or chop at random: tight-range days cluster and precede expansion, and the classic edge — Toby Crabel's NR7 (the narrowest daily range of the last seven), the inside day (a bar wholly inside the prior one) and the outside day (a bar that engulfs it) — is that a coiled spring releases. This API measures the coil and the release. For each instrument it returns today's range as a percentile of its recent range (low = contracted/coiling, high = already expanded), whether today is an NR7, NR4, inside or outside day, the average daily range, and the historical frequency of each setup. Crucially it also returns the follow-through: after an NR7, how often the next day broke the NR7 day's high or low and how often its range expanded — the base rate that tells you whether the coil is worth trading. The asset endpoint returns one instrument's full range profile; the screener endpoint ranks the universe by contraction (most coiled, lowest current range percentile — the breakout candidates) or by realised range. This is the range-contraction / NR7 breakout-setup cut — distinct from the candlestick-pattern API (named reversal/continuation shapes, not range size), the volatility dashboard (level, not the coil), and the gap and price APIs. It is the squeeze before the move.

Uptime

100.0%

Latency

344ms

Subs

4,860

The consecutive up- and down-day runs swing-traders fade, with the historical probability that a run reverses, computed live from Yahoo Finance daily closes — no key, nothing stored. "It has gone up five days in a row, it is due a pullback" is a guess until you put a number on it. This API counts every up- and down-day run in an instrument's history and measures, for each run length, how often the very next day reversed it — turning a gut feeling into a base rate. For each instrument it returns the current streak (its direction and length), the longest up and down streaks in the window, the average run length, the full distribution of run lengths, and the reversal table: after k consecutive up (or down) days, the share of times the next day went the other way, with the sample size behind each figure. If a name is currently on a streak it also returns the historical odds that tomorrow reverses it — the one number a mean-reversion trader wants. The asset endpoint returns one instrument's full streak profile; the screener endpoint ranks the universe by how stretched each is right now (current streak length), so you can see what is most extended. This is the consecutive-run / reversal-odds cut — distinct from the Hurst persistence-regime API, the multi-timeframe momentum API, the candlestick-pattern API and the price feeds. It is the runs, counted, with the odds attached.

Uptime

100.0%

Latency

327ms

Subs

3,407

The overnight-gap behaviour day-traders actually trade, computed live from Yahoo Finance daily OHLC — no key, nothing stored. A gap is the jump between yesterday's close and today's open — the move that happens while the market is shut, on overnight news and futures drift. Traders live and die on two questions: how often does a name gap, and does the gap fill (price retraces to yesterday's close) or run (it keeps going). This API answers both with hard frequencies. For each instrument it returns how often it gaps up and down beyond a configurable threshold, the average size of up- and down-gaps, the gap-fill rate (the share of gaps where price traded back through the prior close intraday — for an up-gap, the day's low reaching the prior close), and the continuation rate (how often the day closes in the direction of the gap rather than fading it), plus the largest recent gaps. The asset endpoint returns one instrument's full gap profile with its biggest recent gaps; the screener endpoint ranks a universe of liquid stocks and ETFs by gappiness or gap-fill rate, surfacing the names that gap most and the ones whose gaps reliably fill. This is the opening-gap / overnight-jump microstructure cut — distinct from the price, candlestick-pattern, volatility and risk APIs in the catalogue. It is what happens between the close and the open.

Uptime

100.0%

Latency

218ms

Subs

4,756

Measures the thing that destroys portfolios: correlations that look comfortably low in calm markets but spike toward 1 exactly when the market crashes, so the diversifiers you were counting on all fall together — computed live from Yahoo Finance daily closes, no key, nothing stored. A normal full-sample correlation hides this by averaging the calm days with the crisis days; this API instead conditions on the benchmark's extremes. For each asset it returns the ordinary correlation to the benchmark, the crash correlation (measured only on the benchmark's worst days — its lower tail), the rally correlation (on its best days), and the breakdown: how much the correlation rises in a crash versus normal. A bond, gold or commodity position with a low normal correlation but a high crash correlation is a false diversifier; one whose correlation stays low or falls in the tail is a genuine hedge. The asset endpoint returns one instrument's full tail-correlation profile; the screener endpoint ranks the cross-asset universe by crash correlation, surfacing which holdings actually fail when you need them. This is the conditional / tail-correlation cut — distinct from the unconditional cross-asset, sector and FX correlation matrices (which average all days together), the up/down capture API (magnitudes, not co-movement) and the price APIs. It is correlation when it matters: in the crash.

Uptime

100.0%

Latency

190ms

Subs

3,023

Measures the asymmetry every allocator actually cares about: how much of a benchmark's gains an asset captures when the market rises, versus how much of its losses it suffers when the market falls — computed live from Yahoo Finance daily closes, no key, nothing stored. A single beta assumes a market moves the same up and down, but the assets worth owning do not: they participate in rallies and cushion sell-offs, and the ones to avoid do the opposite. This API splits the benchmark's history into up-days and down-days and measures each side separately. The upside capture is the asset's average gain on the benchmark's up-days relative to the benchmark (above 100 = it gains more than the market in rallies); the downside capture is the same on down-days (below 100 = it loses less in sell-offs — defensive). Their ratio, the capture ratio, is the headline: above 1 means a favourable asymmetry. It also returns the downside beta and upside beta — the asset's beta measured only on the benchmark's down- and up-days — whose gap reveals whether the asset is more exposed in crashes than in rallies. The asset endpoint returns one instrument's full asymmetry profile; the screener endpoint ranks the cross-asset universe by capture ratio, downside capture or downside beta. This is the conditional / up-down asymmetry cut — distinct from the single unconditional beta screener, the correlation matrix, and the total-risk and tail-risk APIs. It separates the up market from the down market.

Uptime

100.0%

Latency

214ms

Subs

4,245

Ranks the major markets by how brutal their bad days are, computed live from Yahoo Finance daily closes — no key, nothing stored. Volatility and the Sharpe ratio assume returns are symmetric and well-behaved, but the losses that actually blow up a book live in the left tail — the rare, deep down-days a standard-deviation number smooths away. This API measures that tail directly. For each market it returns Value-at-Risk (the daily loss not exceeded on 95% / 99% of days, both the historical percentile and the normal-distribution parametric estimate), the Conditional VaR / Expected Shortfall (the average loss on the worst days, beyond VaR — how bad the bad days really are), and the shape of the return distribution: skewness (negative = crash-prone, a long left tail) and excess kurtosis (high = fat-tailed, outlier-prone). The asset endpoint returns one instrument's full tail-risk profile; the screener endpoint ranks the cross-asset universe (equities, sectors, commodities, bonds, FX and crypto; filterable by class) from the most tail-risky to the safest. This is the cross-asset distribution-tail / VaR-CVaR cut — distinct from the bring-your-own-series risk-metrics engine, the crypto-only coin risk scorecard, the drawdown-pain (Ulcer) screener and the volatility APIs. It is the left tail, measured across the whole book.

Uptime

100.0%

Latency

163ms

Subs

4,383

Tells you whether each market is trending, behaving like a random walk, or mean-reverting — the single most important thing to know before choosing a strategy — computed live from Yahoo Finance daily closes, no key, nothing stored. A trend-following system bleeds money in a mean-reverting market, and a fade-the-move system gets run over in a trending one; the Hurst exponent (via rescaled-range R/S analysis) measures which world you are in. A Hurst above ~0.55 means the series is persistent — moves tend to continue, so it trends and trend-following fits; near 0.5 it is a random walk with no edge either way; below ~0.45 it is anti-persistent — moves tend to reverse, so it mean-reverts and fading extremes fits. Alongside it the API returns the Kaufman Efficiency Ratio (net move divided by the total path travelled, 0 = pure noise, 1 = a perfectly straight trend), a second intuitive read on how cleanly a market is trending. The asset endpoint returns one instrument's Hurst, efficiency ratio and a regime label; the screener endpoint ranks the cross-asset universe (equities, sectors, commodities, bonds, FX and crypto; filterable by class) from most trending to most mean-reverting. This is the persistence / trend-versus-mean-reversion regime cut — distinct from the z-score stretch gauges (how far a price is from its average right now, not the structure of its moves), the multi-timeframe momentum-alignment API and the price APIs. It tells you which kind of strategy the market is paying for.

Uptime

100.0%

Latency

186ms

Subs

4,825

Where the leveraged funds and the asset managers are positioned in the financial futures — currencies, stock indices and interest rates — read live from the CFTC Traders in Financial Futures (TFF) report, no key. For financial futures the CFTC publishes a dedicated breakdown the commodity-style reports do not: Dealer/Intermediary (the sell-side banks), Asset Manager/Institutional (pension funds, mutual funds and insurers — the real-money long-term side), Leveraged Funds (hedge funds and CTAs — the fast speculative money) and Other Reportables. The split between Leveraged Funds and Asset Managers is the one macro traders watch: in the Treasury complex, leveraged funds run the famous cash-futures basis trade short while asset managers sit long, and the gap is a systemic-risk gauge. The positioning endpoint returns, for a market, the full four-group breakdown — each group's long, short and net contracts, share of open interest, trader count and week-over-week change — with a leveraged-funds bias read. The screener endpoint ranks a curated set of 17 FX, equity-index and interest-rate futures by where the leveraged funds (or the asset managers) are net positioned, surfacing the most crowded macro bets. This is the financial-futures TFF positioning cut — distinct from the legacy COT feed, the normalised COT-Index, the commodity Managed-Money report and the price APIs. It is who the hedge funds and the real money are, in the markets that move macro.

Uptime

100.0%

Latency

431ms

Subs

3,945

How the major currency pairs move together, computed live from Yahoo Finance daily closes — no key, nothing stored. Correlation is the input every FX desk needs before sizing a book: going long EUR/USD and long GBP/USD is not two bets but one, because the pairs move almost in lockstep; shorting USD/JPY against long EUR/USD doubles the same dollar view. This API turns the majors and key crosses into the pairwise correlation grid traders use to avoid stacking the same risk and to find genuine diversifiers. The matrix endpoint returns the full correlation matrix across ~14 pairs over a chosen window. The pair endpoint returns one pair's correlation to every other, ranked — its closest co-movers and its best hedges (the most negatively correlated). The highlights endpoint surfaces the most correlated and most inversely correlated pairs across the whole grid, the actionable extremes. Correlation is computed on daily log returns aligned over common trading days. This is the FX-pair correlation cut — distinct from the cross-asset-class correlation matrix (stocks/bonds/gold/oil/crypto/dollar), the currency-strength meter, the FX heat-map (which shows the day's move, not co-movement) and the price APIs in the catalogue.

Uptime

100.0%

Latency

185ms

Subs

4,097

Ranks a cross-asset universe by how painful each market's drawdowns have been, and how much return it paid for that pain, computed live from Yahoo Finance daily closes — no key, nothing stored. Volatility treats an up-move and a down-move as equally risky, but investors only lose sleep over the downside: the depth of the fall from the last high and how long it drags on before recovering. The Ulcer Index (Peter Martin) captures exactly that — the root-mean-square of every day's percentage drawdown from the running peak, so a deep, long drawdown is penalised far more than a brief dip and a market that keeps making new highs scores near zero. From it comes the Martin ratio (the Ulcer Performance Index) — annualised excess return divided by the Ulcer Index — the return earned per unit of drawdown pain, a downside-only cousin of the Sharpe ratio. The asset endpoint returns one instrument's full pain profile: Ulcer Index, maximum, average and current drawdown, longest time underwater, the Martin ratio and the pain ratio. The screener endpoint ranks the 21-instrument universe (equities, sectors, commodities, bonds, crypto; filterable by class) by Martin ratio (best pain-adjusted return) or by Ulcer Index (smoothest ride). This is the drawdown-pain / Ulcer-Index cut — distinct from a current-drawdown monitor (a point-in-time snapshot of how far below peak each market is), the Sharpe/Sortino/Calmar screener (Calmar uses only the single worst drawdown) and the price APIs. It scores the whole shape of the pain, not one point of it.

Uptime

100.0%

Latency

209ms

Subs

3,451

Where the hedge funds are positioned in commodity futures, read live from the CFTC Disaggregated Commitments-of-Traders report — no key. The legacy COT report lumps every speculator into one "non-commercial" bucket; the Disaggregated report, introduced in 2009 precisely because that was too crude, splits the market into four real groups — Managed Money (the trend-following hedge funds and CTAs, the speculative flow everyone watches), Producer/Merchant (the physical hedgers who make and use the commodity), Swap Dealers (the banks intermediating index and OTC exposure) and Other Reportables. The positioning endpoint returns, for a commodity, the full four-group breakdown — each group's long, short and net contracts, its share of open interest, the number of traders and the week-over-week change — with a managed-money bias read: Managed Money net long in gold of +112,179 contracts (34% of open interest, 74 funds long) tells you the funds are crowded long. The screener endpoint ranks a curated set of 20 metals, energy, grain, soft and livestock futures by where Managed Money is positioned (net as a share of open interest), surfacing the most crowded long and short hedge-fund bets. This is the disaggregated hedge-fund-positioning cut — distinct from the legacy raw COT-report feed, the normalised COT-Index, and the price and open-interest APIs. It is who the smart speculative money is, by the report traders actually read.

Uptime

100.0%

Latency

412ms

Subs

3,455

Ranks a cross-asset universe by beta to a benchmark, so you can see at a glance which markets amplify the benchmark's moves and which dampen or hedge them, computed live from Yahoo Finance daily closes — no key, nothing stored. Beta is the single number that says how much an asset moves for each 1% the market moves: a beta of 1.3 rises ~1.3% when the benchmark rises 1% (and falls harder when it drops), a beta near 0 is decoupled, a negative beta moves against the market (a hedge). The screener endpoint ranks the 21-instrument universe (equities, sectors, commodities, bonds, crypto; filterable by class) by beta to a chosen benchmark (the S&P 500 by default), each with its correlation and R-squared so you know how reliable the beta is. The asset endpoint returns one instrument's full beta profile against the benchmark. The dispersion endpoint returns the spread of betas across the universe — the high-beta-minus-low-beta gap, the mean beta and the share of risk-on names — a read on how much the market is rewarding risk-taking right now. This is the systematic-risk / market-sensitivity ranking cut — distinct from a bring-your-own-series CAPM/beta calculator, the total-risk Sharpe/Sortino screener, the correlation matrix and the price APIs. It ranks live assets by how much market risk they carry.

Uptime

100.0%

Latency

279ms

Subs

3,612

The normalised Commitments-of-Traders positioning signal traders actually act on, computed live from the US CFTC public reporting API — no key. A raw COT net-position number means little on its own: "large speculators are +176,020 contracts net long gold" tells you nothing until you know whether that is high or low versus history. The COT Index fixes that by normalising each trader group's current net futures position to a 0-100 percentile over a lookback window (the classic Larry Williams 156-week / three-year COT Index): 100 = the most net-long that group has been in the window, 0 = the most net-short. Above 80 marks a crowded long extreme (contrarian bearish), below 20 a crowded short extreme (contrarian bullish). The index endpoint returns one market's COT Index for both the large speculators (non-commercials) and the commercial hedgers, with the current net, the window min/max, the week-over-week change and an extreme flag. The screener endpoint computes the index across a curated set of 17 FX, stock-index, metal, energy and grain futures and ranks them, surfacing which markets sit at a positioning extreme right now. This is the normalised positioning-signal cut — distinct from the raw COT-report feed (which serves the weekly long/short contract counts), and from the price, open-interest and options-positioning APIs. It turns the report into the signal.

Uptime

100.0%

Latency

340ms

Subs

3,388

Ranks a cross-asset universe by how much return each asset delivers per unit of risk, live from Yahoo Finance daily closes — no key, nothing stored. A raw return tells you nothing about how much risk you took to earn it: two assets up 12% are not equal if one rode a calm trend and the other whipsawed through deep drawdowns. This screener turns each asset's price history into the three risk-adjusted ratios allocators actually rank on — the Sharpe ratio (excess return per unit of total volatility), the Sortino ratio (excess return per unit of downside volatility only), and the Calmar ratio (annualised return per unit of worst peak-to-trough drawdown) — and sorts the whole universe (21 instruments across equities, sectors, commodities, bonds and crypto) so you can see in one call which markets pay the most for the risk you bear. The screener endpoint ranks the universe (filterable by asset class) by the metric you choose; the asset endpoint returns one instrument's full risk-adjusted profile with plain-language reads. This is the risk-adjusted-return / reward-per-risk ranking cut — distinct from a bring-your-own-series Markowitz optimiser, the CAPM/beta calculator, the momentum and the price APIs. It ranks live assets by efficiency, not raw performance.

Uptime

100.0%

Latency

197ms

Subs

4,229

Where each S&P 500 sector sits on the rotation map versus the market, computed live from Yahoo Finance (no key, nothing stored). The Relative Rotation Graph is how professional allocators visualise sector rotation: it plots each sector on two axes — relative strength (is it out- or under-performing the S&P 500) and relative momentum (is that relative strength improving or fading) — and the combination lands each sector in one of four quadrants that rotate clockwise over time: Leading (strong and getting stronger), Weakening (strong but losing steam), Lagging (weak and getting weaker) and Improving (weak but turning up). Money rotates Improving to Leading to Weakening to Lagging, so the quadrant tells you not just who is winning but who is next. This computes each of the eleven SPDR sectors' RS-Ratio and RS-Momentum against the S&P 500 and places it in its quadrant. The rrg endpoint returns the whole rotation map; the sector endpoint returns one sector's coordinates and quadrant; the sectors endpoint lists what is covered. The sector-rotation RRG / quadrant cut — distinct from the relative-strength ranking (a one-dimensional list), the sector price/performance feed and the correlation APIs. It shows the rotation, not just the ranking.

Uptime

100.0%

Latency

168ms

Subs

3,650

Which markets are breaking out of their volatility-adjusted trend channel, computed live from Yahoo Finance (no key, nothing stored). Keltner Channels wrap a 20-day exponential average in bands set at two Average-True-Ranges above and below it — and unlike Bollinger Bands, whose width is statistical standard deviation, Keltner's width is the market's actual trading range. A close above the upper Keltner band is a trend-following breakout (riding strength), below the lower a breakdown, and price hugging a band signals a powerful, persistent trend. For a cross-asset, cross-sector universe — equity indices and sectors, gold, oil, commodities, bonds and crypto — this computes each asset's Keltner upper, middle and lower bands, where price sits inside the channel, and flags fresh breakouts. The screener endpoint returns the upside and downside Keltner breakouts across the board. The asset endpoint returns one market's Keltner card. The universe endpoint lists what is covered. The cross-asset Keltner-channel / volatility-trend screener cut — distinct from the Bollinger-Bands screener (standard-deviation width, mean-reversion), the bring-your-own-candle ATR API and the other indicator screeners.

Uptime

100.0%

Latency

277ms

Subs

4,110

Which markets are stretched to an overbought or oversold extreme on the Commodity Channel Index, computed live from Yahoo Finance (no key, nothing stored). The CCI measures how far price has run from its statistical average relative to normal volatility: above +100 a market is in a strong up-move (and, when it unwinds, overbought), below -100 a strong down-move (or oversold), and the swing through zero frames trend and reversal trades. For a cross-asset, cross-sector universe — equity indices and sectors, gold, oil, commodities, bonds and crypto — this computes each asset's 20-period CCI from its typical price (high+low+close over three) and tags it overbought, bullish, bearish or oversold, then ranks the whole board. The screener endpoint returns the overbought (>+100) and oversold (<-100) markets right now. The asset endpoint returns one market's CCI card. The universe endpoint lists what is covered. The cross-asset CCI / extension screener cut — distinct from the bring-your-own-candle oscillator API, the RSI screener (a different oscillator), the OBV/volume and Bollinger screeners. It finds the over-extended markets across every asset class at once.

Uptime

100.0%

Latency

195ms

Subs

3,072