Real questions. Full answers.

Fourteen data examples, plus an agent workflow. Open a section to see the prompt, routes, SDK calls and dated output.

Real-day examples — see for yourself what you get.

Single-name research

one company · filing, regulator, exchange, central bank
01

The filing, the regulator, the exchange and the central bank

3 domains · 4 vendors · 4 routes · fmp · finra · cboe · ecb

Prompt
Apple: last four quarters of margin, whether anyone is short, what expiries I could hedge into — and what the quarter is worth in euros.
equity.fundamentals.get_income_statementequity.shorting.get_short_interestderivatives.options.get_options_expirationsfx.rates.get_ecb_reference_rates
Show the SDK calls
d = QuantJourneyAPI.from_env().domains

inc = d.equity.fundamentals.get_income_statement(symbol="AAPL", period="quarter", limit=4)
si  = d.equity.shorting.get_short_interest(symbol="AAPL")
exp = d.derivatives.options.get_options_expirations(symbol="AAPL")
fx  = d.fx.rates.get_ecb_reference_rates()

usd_per_eur = next(r.rate for r in fx.data if r.currency == "USD")
eur_revenue = inc.data[0].revenue / usd_per_eur
Answer

Margin expanded 290bp in four quarters while shorts grew 20% in a single month — and if you want to act on that, 24 listed expiries run out to January 2029. A data vendor, a US regulator, an options exchange and a central bank, in one call shape. No one of them sells the other three.

Two of the four numbers came from institutions that do not sell data at all — FINRA and the ECB publish, they do not license. That is the part no vendor contract replaces.

Fiscal quarterRevenue fmpIn EUR ecbGross marginNet marginDiluted EPSFiled
Q3 FY2026$109.42bn€95.48bn50.1%27.2%$2.0331 Jul 2026
Q2 FY2026$111.18bn€97.02bn49.3%26.6%$2.0101 May 2026
Q1 FY2026$143.76bn€125.44bn48.2%29.3%$2.8430 Jan 2026
Q4 FY2025$102.47bn€89.41bn47.2%26.8%$1.8531 Oct 2025

Statements from fmp with fiscalYear, period and filingDate attached, so “Q3” is never silently a calendar quarter. EUR column at the ECB reference rate for 18 Sep 2026 (EUR/USD 1.1460) — the rate a European IC pack is obliged to use, taken from the central bank that sets it.

Gross margin, 4q change+290bp47.2% → 50.1%
Short interest finra139.7M+20.1% in one month
Days to cover finra3.53settlement 31 Aug
Listed expiries cboe24next 21 Sep · longest Jan 2029
02

A capex story only rates can finish

3 domains · 3 vendors · 5 routes · fmp · fred · finra

Prompt
Quality read on Microsoft — returns, what the AI build-out is doing to cash, and whether the free cash flow yield still clears the risk-free rate.
equity.fundamentals.get_key_metrics_ttmequity.shorting.get_short_interestbenchmarks.treasury.get_treasury_ratesmacro.series.get_cpi
Show the SDK calls
km  = d.equity.fundamentals.get_key_metrics_ttm(symbol="MSFT")
si  = d.equity.shorting.get_short_interest(symbol="MSFT")
ust = d.benchmarks.treasury.get_treasury_rates(start_date="2026-09-01", end_date="2026-09-18")
cpi = d.macro.series.get_cpi(start_date="2026-01-01", end_date="2026-08-31")

ten_year = ust.data.rates[0].year10
spread   = km.data.freeCashFlowYieldTTM * 100 - ten_year
Answer

The AI-capex story sits in two numbers — 18.2× EBITDA but 56.3× free cash flow, because capex absorbs 63% of operating cash flow at 2.7× depreciation. The question that finishes it lives in another domain entirely: a 1.83% free cash flow yield against a 5.01% ten-year is minus 318 basis points. You are paid in growth or not at all. An equity-only API cannot ask that question.

Capex at 2.7× depreciation cannot hold indefinitely: either depreciation catches up and the multiple compresses, or revenue absorbs it. Either way the spread to the risk-free rate is the number to track — not the EBITDA multiple.

Return on equity fmp33.2%ROIC 20.6%
EV / EBITDA fmp18.2×EV/Sales 11.4×
Capex / revenue fmp34.9%2.7× depreciation
FCF yield fmp1.83%EV/FCF 56.3×
10-year Treasury fmp5.01%18 Sep close
Spread to risk-free−318bpFCF yield minus 10-year
CPI y/y fred3.35%real 10-year ≈ +166bp
Short interest finra74.5Munder 1% of shares
03

A valuation argument, both sides

2 domains · 4 vendors · 5 routes · fmp · eod · finra · cboe

Prompt
Is NVIDIA actually expensive here? Bull and bear case from the numbers — and tell me if anyone is betting against it.
equity.fundamentals.get_key_metrics_ttmequity.ratios.get_quarterly_revenue_growth_yoyequity.estimates.get_price_target_summaryequity.shorting.get_short_interestderivatives.options.get_options_expirations
Show the SDK calls
km  = d.equity.fundamentals.get_key_metrics_ttm(symbol="NVDA")
g   = d.equity.ratios.get_quarterly_revenue_growth_yoy(symbol="NVDA")
pt  = d.equity.estimates.get_price_target_summary(symbol="NVDA")
si  = d.equity.shorting.get_short_interest(symbol="NVDA")
exp = d.derivatives.options.get_options_expirations(symbol="NVDA")

upside = pt.data.lastMonthAvgPriceTarget / quote.data.price - 1
Answer

Bull: 28× earnings, +105.9% revenue, 59.5% ROIC, 2.4% capex intensity. Bear: income quality of 0.70 and 150 days of inventory say cash is arriving slower than earnings. The tell: at 2.14 days to cover almost nobody is positioned for the bear case, even though 24 listed expiries make it trivial to express. Four institutions built that argument; the agent didn't get to choose which one flattered the thesis.

For a desk the actionable line is the last one: with 24 expiries and 2.14 days to cover, hedging the bear case costs option premium, not liquidity.

P/E, trailing fmp28.0×vs 38.4× for Apple
Revenue y/y eod+105.9%the route's own provider
Return on capital fmp59.5%ROE 110%
Capex / revenue fmp2.4%fabless — MSFT is 34.9%
Income quality fmp0.70cash below earnings
Days of inventory fmp150dcash cycle 154 days
Days to cover finra2.14298.3M short · 1.2% of shares
Expiries to 2029 cboe24the bear case is expressible

Screens, calendars & ownership

cross-sectional · events · 13F flows
04

Valuation from the vendor, positioning from the regulator

1 domain · 3 vendors · 4 methods · fmp · eod · finra

Prompt
Screen semis above $100bn. Who's cheapest relative to growth — and where are the shorts actually sitting?
equity.reference.get_stock_screenerequity.ratios.get_pe_ratio_ttmequity.ratios.get_quarterly_revenue_growth_yoyequity.shorting.get_short_interest
Show the SDK calls
rows = d.equity.reference.get_stock_screener(
           industry="Semiconductors", marketCapMoreThan=100_000_000_000, limit=12)

for r in rows.data:
    pe = d.equity.ratios.get_pe_ratio_ttm(symbol=r.symbol)
    g  = d.equity.ratios.get_quarterly_revenue_growth_yoy(symbol=r.symbol)
    si = d.equity.shorting.get_short_interest(symbol=r.symbol)
    print(r.symbol, pe.data, g.data, si.data[0].days_to_cover)
Answer

The valuation ranking and the positioning ranking disagree, which is the whole point of joining them: Micron is the cheapest name on the fastest growth and the least shorted at 1.16 days to cover, while shorts grew 12.2% in a month on Intel — the only name in the group with no trailing earnings. One vendor ranked them; a regulator said who was actually leaning.

Positioning is the column that changes the trade. Cheap-and-unshorted (Micron) and expensive-and-shorted (AMD) are different setups, even when the valuation screen ranks them side by side.

NameMarket capP/E ttmRevenue y/yShort interest finraDays to cover finra
Micron · MU$1.15tn22.7×+345.7%29.7M · −1.0%1.16
NVIDIA · NVDA$5.38tn28.0×+105.9%298.3M · +4.3%2.14
Broadcom · AVGO$1.70tn44.4×+85.5%not in FINRA file—
TSMC · TSM$2.25tn28.5×+36.0%ADR — not covered—
AMD$0.91tn142.1×+50.1%41.7M · +4.1%2.49
Intel · INTC$0.55tnn/m+25.4%152.2M · +12.2%1.69

The screen itself is one vendor's job and we don't pretend otherwise — fmp returns the universe, the multiple and the market cap in three calls. The column FMP cannot produce is the last two: FINRA's consolidated short-interest file, published by the regulator on a settlement cycle. Blank cells are blank on purpose — ADRs and some issues are not in that file, and we would rather show the gap than invent a number.

05

The next two weeks, from four institutions

3 domains · 4 vendors · 4 routes · fmp · eod · fred · polymarket

Prompt
Map the next two weeks: who reports, what macro prints, what the street expects — and what the market already prices for the Fed.
equity.calendar.get_earnings_calendarmacro.calendar.get_economic_eventsmacro.series.get_jobless_claimspredictions.markets.search_markets
Show the SDK calls
win = dict(from_date="2026-09-21", to_date="2026-10-05")

earn   = d.equity.calendar.get_earnings_calendar(**win)
events = d.macro.calendar.get_economic_events(**win)
claims = d.macro.series.get_jobless_claims(start_date="2026-07-01", end_date="2026-09-18")
odds   = d.predictions.markets.search_markets(query="Fed rate", limit=5)

us = [e for e in events.data if e.country == "US"]
Answer

Four institutions, one calendar: a data vendor for earnings, a macro provider for prints, a Federal Reserve bank for the realised claims series, and a prediction market for what is already in the price. The street expects durable goods to swing from +1.1% to −0.5% and payrolls to fall by a quarter — while real money pays 54.5% for a hike. One of those two views is wrong, and you can see both in a single response.

The Micron print on 30 September lands the same morning as core PCE. If both surprise in the same direction, the curve moves before the equity does.

DateEventTypePreviousEstimate
23 SepS&P Global Composite PMImacro eod56.055.2
24 SepCostco · COSTearnings fmp—$6.53 EPS
24 SepInitial Jobless Claimsmacro eod196k202k
25 SepDurable Goods Ordersmacro eod+1.1%−0.5%
29 SepCB Consumer Confidencemacro eod89.4—
30 SepMicron · MUearnings fmp—$31.27 EPS
30 SepCore PCE Price Index y/ymacro eod3.3%—
30 SepChicago PMImacro eod47.148.9
01 OctISM Manufacturing PMImacro eod54.654.0
01 OctNike · NKE / Accenture · ACNearnings fmp—$0.44 / $3.19
02 OctNonfarm Payrolls, privatemacro eod127k95k

1,352 scheduled earnings and 1,000 macro events across 111 countries in the window; 150 of the macro prints are US. Shown: what actually moves a book.

Claims, latest fred196k12 Sep · from 217k in July
Claims, next estimate eod202ktwo vendors, same series
Payrolls estimate eod95kfrom 127k — a 25% step down
Oct hike priced polymarket54.5%before any of it prints
06

A 170KB filing payload, reduced to seven numbers

2 domains · 2 vendors · 4 routes · fmp · finra

Prompt
How crowded is NVDA in 13F land, is the sell-side still raising targets, and is anyone short?
regulatory.ownership.get_institutional_holdersequity.estimates.get_price_target_summaryequity.market.get_quoteequity.shorting.get_short_interest
Show the SDK calls
h  = d.regulatory.ownership.get_institutional_holders(symbol="NVDA")
pt = d.equity.estimates.get_price_target_summary(symbol="NVDA")
q  = d.equity.market.get_quote(symbol="NVDA")
si = d.equity.shorting.get_short_interest(symbol="NVDA")

top10 = sum(x.sharesNumber for x in sorted(h.data, key=lambda x: -x.sharesNumber)[:10])
breadth = sum(1 for x in h.data if x.changeInSharesNumber > 0) / len(h.data)
Answer

Institutions hold 14.47bn shares and shorts hold 298M — a 48-to-1 long-to-short ratio with breadth on a knife edge at 51%. The vendor gave you the crowd; the regulator gave you the absence of a counterweight. You never touched an EDGAR parser.

Read with rung 03: a 48:1 long-to-short ratio on 51% breadth is the profile of a name that gaps on flow, not on fundamentals.

Q2 2026 filings, quarter-end price $200.09. One call returned 100 filers across 4,816 lines of JSON.

Institutional shares fmp14.47bnacross 100 filers
Top-10 concentration56.3%BlackRock alone 8.0%
Breadth51%51 added / 49 cut
Net share change+829M3 new, 0 sold out
Short interest finra298.3M2.1% of institutional holdings
Days to cover finra2.14on 139M average volume
Consensus target fmp$341.9522 targets, last 30 days
Implied upside+53.8%vs $222.27 spot

Macro, rates & the Fed

bls · fred · treasury · cftc · polymarket
07

The same series from two agencies

1 domain · 2 vendors · 4 routes · bls · fred

Prompt
Is US inflation re-accelerating? Cross-check CPI against a second source and show me the labour market underneath it.
macro.us.get_cpimacro.us.get_unemployment_ratemacro.series.get_cpimacro.series.get_jobless_claims
Show the SDK calls
cpi_a  = d.macro.us.get_cpi(start_year=2023, end_year=2026)
un     = d.macro.us.get_unemployment_rate(start_year=2024, end_year=2026)
cpi_b  = d.macro.series.get_cpi(start_date="2026-01-01", end_date="2026-08-31")
claims = d.macro.series.get_jobless_claims(start_date="2026-07-01", end_date="2026-09-18")

cpi_a.data[-1].cpi_all == cpi_b.data[-1].CPIAUCSL
cpi_b.data[-1].realtime_start
Answer

CPI bottomed at 2.33% (Apr 2025), peaked at 4.17% (May 2026) and sits at 3.35%, while unemployment fell from 4.5% to 4.1% and claims dropped to 196k. Two independent agencies agree on the index to the third decimal — and FRED carries realtime_start on every row, so a backtest can ask what was known on the day rather than what is believed today.

For a backtest this matters more than the level: the FRED vintage field reconstructs what an analyst could have known on any given day, with no look-ahead.

CPI, year over yearUnemployment, U-3

Line break at October 2025: BLS published no observation. The gap is left open rather than interpolated — a silent fill is how a hole enters a backtest.

Aug CPI index bls334.131via macro.us.get_cpi
Aug CPI index fred334.131CPIAUCSL — identical
Vintage fred11 Seprealtime_start on every row
Jobless claims fred196k12 Sep · from 217k in July
08

Rates and volatility, one question

2 domains · 2 vendors · 2 routes · fmp · cboe

Prompt
What did the Treasury curve do this month, and did volatility notice?
benchmarks.treasury.get_treasury_ratesderivatives.vol.get_vix_data
Show the SDK calls
ust = d.benchmarks.treasury.get_treasury_rates(start_date="2026-09-01", end_date="2026-09-18")
vix = d.derivatives.vol.get_vix_data(start_date="2026-08-18", end_date="2026-09-18")

curve = ust.data.rates[0]
twos_tens = curve.year10 - curve.year2
Answer

The front end sold off nearly twice as hard as the long end, and equity vol gave the whole move back inside a week.

Bear flattening with vol collapsing is the market saying the hike is priced and not feared. Whether that is right is a separate question — see rung 12.

1 Sep 202618 Sep 2026
2-year fmp+37bp4.39 → 4.76
10-year fmp+22bp4.79 → 5.01
2s10s fmp+25bpfrom +40bp — bear flattening
VIX cboe14.81spiked to 17.84 on 10 Sep
09

What real money thinks the Fed will do

2 domains · 2 vendors · 2 routes · polymarket · fmp

Prompt
Pull the Polymarket odds on the October Fed meeting. Has the market moved this week — and does the curve agree?
predictions.markets.search_marketsbenchmarks.treasury.get_treasury_rates
Show the SDK calls
mk  = d.predictions.markets.search_markets(query="Fed rate", limit=5)
ust = d.benchmarks.treasury.get_treasury_rates(start_date="2026-09-11", end_date="2026-09-18")

hike = next(m for m in mk.data
            if "increase" in m.question and "25 bps" in m.question)
hike.yes_price, hike.one_week_change, hike.volume
Answer

In seven days the market flipped from 62/38 in favour of holding to 55/44 in favour of hiking, on $7.1m of combined volume and $2.3m of resting liquidity — while the 2-year sold off 37bp in the same window. A prediction market and the Treasury curve, two institutions, telling the same story independently. That corroboration is the trade; assembling it by hand is an afternoon.

The move is large relative to liquidity: $2.3m of resting orders absorbed a 17-point swing. Worth knowing before treating the probability as a consensus.

One week ago (implied)19 Sep 2026

Four outcomes on the October 2026 meeting, priced at 05:06 UTC. Week-ago levels derived from each market's own reported one-week change. The four probabilities sum to 99.5% — near arbitrage-free.

Hike 25bp polymarket54.5%+17pt in a week
No change polymarket43.5%−18pt in a week
Cut priced polymarket0.55%effectively ruled out
2-year yield fmp+37bpsame window, same direction

Credit & crypto

fdic · defillama · coingecko · binance
10

Counterparty diligence: the regulator and the market

2 domains · 2 vendors · 3 routes · fdic · fmp

Prompt
We're picking a partner bank. Give me the regulatory profile of the large US institutions — and what the equity market pays for each.
regulatory.banking.get_institutionsequity.ratios.get_pb_ratio_ttm
Show the SDK calls
banks = d.regulatory.banking.get_institutions(limit=5, filters="ACTIVE:1")

for b, ticker in zip(banks.data, ["JPM", "BAC", "C", "WFC", "GS"]):
    pb = d.equity.ratios.get_pb_ratio_ttm(symbol=ticker)
    print(b.NAMEHCR, b.ROA, b.ROE, pb.data)
Answer

JPMorgan earns 159bp on assets against Citi's 106bp — and the market pays 2.60× book versus 1.06× for that gap. The regulator and the market agree on the ranking. FDIC data needs no vendor contract, no licence and no key; it is simply in the catalog.

The pairing is the point: the regulator's ROA says how the bank earns; the market's P/B says what investors make of it. When the two disagree, that is the diligence question.

InstitutionAssets fdicDeposits fdicROA fdicROE fdicP/B fmp
JPMorgan Chase$4,091bn$2,820bn1.59%18.55%2.60×
Bank of America$2,655bn$2,122bn1.13%12.31%—
Citigroup$1,976bn$1,548bn1.06%11.69%1.06×
Wells Fargo$1,908bn$1,564bn1.38%14.88%—
Goldman Sachs$759bn$499bn1.21%13.72%—

FDIC reports the insured depository institution; the ratio route reports the listed holding company. Same brand, different legal entity — which is exactly why the envelope labels each number with the source that produced it, so the two never get averaged by accident.

11

Stablecoin float is a bill bid

2 domains · 4 vendors · 4 routes · defillama · coingecko · fmp · ccxt

Prompt
How big is stablecoin float now, what share of crypto is it, what does its collateral earn at the front end — and what is the perp carry paying?
crypto.stablecoins.get_stablecoinscrypto.market.get_global_databenchmarks.treasury.get_treasury_ratescrypto.funding.get_funding_rate
Show the SDK calls
sc  = d.crypto.stablecoins.get_stablecoins()
glb = d.crypto.market.get_global_data()
ust = d.benchmarks.treasury.get_treasury_rates(start_date="2026-09-18", end_date="2026-09-18")
fr  = d.crypto.funding.get_funding_rate(symbol="BTC/USDT:USDT", exchange="binance")

float_usd = sum(s.circulating.peggedUSD for s in sc.data)
carry     = float_usd * ust.data.rates[0].month3 / 100
perp_apr  = fr.data.fundingRate * 3 * 365
Answer

A $311.9bn float sitting mostly in short-dated Treasuries earns roughly $12.9bn a year at a 4.14% three-month bill — which is why the front end and stablecoin supply are now the same trade seen from two sides. And with perp funding at 8.04%, the leveraged long pays almost double what the collateral earns. Four vendors: an on-chain analytics firm, a market aggregator, a rates source and an exchange connector. No equity data vendor sells any of this.

It also means the front end is now partly a crypto-demand market — a $30bn change in stablecoin float is a $30bn change in bill demand, visible through these same routes.

Total pegged float defillama$311.9bnacross 427 issuers
USDT defillama$183.3bnon 130 chains
USDC defillama$74.4bnon 157 chains
Share of crypto cap coingecko9.2%of $2.78tn total
3-month bill fmp4.14%18 Sep · +22bp in Sep
Implied annual carry~$12.9bnfloat × front-end yield
BTC perp funding ccxt+8.04%annualised, 8h basis
BTC dominance coingecko58.3%ETH 11.5%

Volatility regime & cross-asset

cboe · one question across six domains
12

Identifying the volatility regime

4 domains · 4 vendors · 6 methods · cboe · fmp · bls · polymarket

Prompt
Classify the current volatility regime. Is the market cheap or complacent — and does anything else agree?
macro.us.get_cpiderivatives.vol.get_vix_dataderivatives.vol.get_vvix_dataderivatives.vol.get_skew_index_databenchmarks.treasury.get_treasury_ratespredictions.markets.search_markets
Show the SDK calls
cpi = d.macro.us.get_cpi(start_date="2026-01-01", end_date="2026-08-31")
win  = dict(start_date="2026-08-18", end_date="2026-09-18")

vix  = d.derivatives.vol.get_vix_data(**win)
vvix = d.derivatives.vol.get_vvix_data(**win)
skew = d.derivatives.vol.get_skew_index_data(**win)
ust  = d.benchmarks.treasury.get_treasury_rates(start_date="2026-09-01", end_date="2026-09-18")
odds = d.predictions.markets.search_markets(query="Fed rate", limit=5)

ratio = float(vvix.data[-1].close) / float(vix.data[-1].close)
Answer

The regime is cheap spot vol with expensive tails. VIX and VVIX both sit below where August started, but SKEW is 3% above it and set its high on 11 September — after the VIX spike had passed. A VVIX/VIX ratio of 5.90 is the month's highest: convexity is bid even though the level is not. Put that beside a front end repricing 37bp and prediction markets flipping to a hike, and the classification writes itself — this is not calm, it is unhedged.

Practically: protection here is cheap on level and expensive on skew, so the efficient hedge is a spread rather than an outright put.

VIXVVIXSKEW

All three rebased to 100 at 18 August 2026, so they share one axis — three different scales never belong on two y-axes. Cboe publishes all three; the gateway returns them in the same shape.

VIX cboe14.816.5% below the Aug base
VVIX cboe87.45.9% below the Aug base
SKEW cboe148.13.1% above the Aug base
VVIX / VIX5.90the month's high ratio
SKEW peak154.511 Sep — after the VIX spike
2s10s fmp+25bpflattened 15bp in Sep
CPI y/y bls3.35%accelerating since March
Oct hike polymarket54.5%+17pt in a week
13

One question across six domains

6 domains · 7 vendors · 9 calls · bls · fmp · cftc · cboe · cnnf · ccxt · polymarket

Prompt
Build me a cross-asset regime packet: inflation, the curve, rates positioning, equity vol, sentiment, crypto carry and what prediction markets price for the Fed — and tell me what they say together.
macro.us.get_cpimacro.us.get_unemployment_rate benchmarks.treasury.get_treasury_ratesmacro.positioning.get_net_positioning derivatives.vol.get_vix_datasentiment.fear_greed.get_summary crypto.funding.get_funding_ratepredictions.markets.search_markets
Show the SDK calls
packet = {}
for route, kw in [
    ("macro.us.get_cpi",                       dict(start_year=2023, end_year=2026)),
    ("macro.us.get_unemployment_rate",         dict(start_year=2024, end_year=2026)),
    ("benchmarks.treasury.get_treasury_rates", dict(start_date="2026-09-01", end_date="2026-09-18")),
    ("macro.positioning.get_net_positioning",  dict(symbol="T_NOTES_10Y")),
    ("derivatives.vol.get_vix_data",           dict(start_date="2026-08-18", end_date="2026-09-18")),
    ("sentiment.fear_greed.get_summary",       {}),
    ("crypto.funding.get_funding_rate",        dict(symbol="BTC/USDT:USDT", exchange="binance")),
    ("predictions.markets.search_markets",     dict(query="Fed rate", limit=5)),
]:
    r = api.call(route, kw)
    packet[route] = (r.data, r.meta.provider, r.meta.request_id)
Answer

Read together: prices re-accelerating into a tightening labour market, a front end repricing hawkishly, speculators structurally short duration, real money paying 54.5% for an October hike — and equity vol asleep at 14.8 while sentiment sits in fear. That is a regime where the surprise is not the hike; it is that nothing is hedged for it. Assembling it from seven institutions is a sprint. Here it is one prompt, ~11 seconds, one key — and every figure still names the source that produced it.

Every figure above carries its request_id, so the packet can be re-run next week and diffed line by line.

Non-commercial net position, UST 10Y futures

CFTC legacy report, 52 weeks to 15 Sep 2026. Negative means speculators are net short.

CPI y/y3.35%bls · from 2.39% in Jan
Unemployment4.1%bls · down from 4.5%
2s10s+25bpfmp · flattened 15bp
Specs, 10Y−812.5kcftc · short all 52 weeks
VIX14.81cboe · low, post-spike
Fear & Greed29cnnf · “fear”
BTC perp funding+8.0%ccxt · annualised
Oct hike priced54.5%polymarket · +17pt/wk
Agent workflow

One prompt. A repeatable research note.

The skill supplies the research method. MCP supplies the routes and source context.

Prompt
Use qj-equity-deep-dive on Micron ahead of the 30 September print.

1. Follow the skill

Start with the research question and the skill's method: profile, prices, fundamentals, estimates, events and ownership context.

2. Discover and call routes

Use the MCP route catalog to describe the required inputs, call the routes and retain provider context and request IDs.

3. Write the note

Separate facts, interpretation and open questions. State coverage, caveats and the tools used.

QuantJourney API

Share product feedback

✓

Thank you.

Your note is now in the QuantJourney inbox.