CFTC · positioning
Weekly leveraged-fund exposure and changes. The existing COT collector supplies the Home read.
Reports ↗Create your Firebase project and deploy the backend package first.
9:30 AM → noon · New York time
Automatic preparation at 5:45 PM and 9:00 AM while open.
Waiting for manual session preparation.
Input agreement · probability not calibrated. Score —.
Explore all evidence ↗Charts, observations and source details, organized in one place.
An economic reading guide. These concepts explain competing channels; they do not add scoring votes.
Stronger employment can support earnings and also increase expectations of tighter monetary policy. Compare actual releases with a sourced forecast captured before publication. An expected policy decision is different from a policy surprise.
Separate real growth from price increases, and inflation slowing from prices falling. Read unemployment together with participation, payroll revisions and hours. Keep monthly, annual and annualized growth measures distinct.
Real consumption, disposable income and private investment describe demand. Headline GDP also includes inventories, government and net exports; its composition matters. Monthly and quarterly trends are background, not automatic session catalysts.
A yield change can reflect policy expectations, inflation, growth or risk premiums. Lower yields do not guarantee stronger equities; higher yields do not identify a tightening shock. The Fed sets its policy stance, not every Treasury yield. The classroom IS–LM model is a simplified economic framework, not an NQ return model.
Based on the supplied MIT lectures 1, 2, 3 and 5. Historical examples describe Spring 2023, not today’s economy. MIT lecture library ↗
Research links, not connected live feeds or extra directional votes.
Weekly leveraged-fund exposure and changes. The existing COT collector supplies the Home read.
Reports ↗Check 8-K earnings releases and guidance, then 10-Q/10-K financials for index-heavy companies. A filing needs a material change and timing before it can support a session case.
EDGAR data ↗Competition and merger actions can affect specific companies. Confirm the company, legal stage and economic impact; a headline alone has no fixed market direction.
Official feeds ↗Global liquidity, credit and debt-service statistics describe the broader financial regime. Respect each series’ publication lag; quarterly changes are not fresh intraday shocks.
Statistics ↗Public market aggregates help study activity and settlement. Gross volume is not net buying; detailed analytics may require a subscription.
Public data ↗Related inputs are averaged within groups. Bullish rates support is halved when eligible VIX and credit-spread inputs both show deterioration on comparable observation dates. This is an experimental conflict rule. Headlines, old Fed language and unsourced monthly surprises receive no directional votes.
Official agencies publish actual values. Consensus needs a separate survey source. Lock the expectation before a release, then record the official actual. Both numbers and timestamps stay visible.
User-entered sourced values; the app checks timing, not the contents of the source. Trading Economics provides actual, consensus and revised fields through an optional licensed integration. Set TRADING_ECONOMICS_API_KEY on the server to enable scheduled provider collection. No subscription is required for this manual workflow. Values captured after publication are not automatically certified as first-release vintages.
Compare the original bias with what the market did during that exact window.
Market-price collection is off. Review each completed call manually as right, wrong or mixed. Remove duplicates to exclude them from the scoreboard; Restore is available.
Results for the selected research session.
Keep old and revised scoring results separate before judging a change in accuracy.
Win rate = right ÷ (right + wrong). Mixed and pending are excluded. Category direction is compared with the direction implied by your overall outcome. These descriptive results are not a calibrated probability.
Each window keeps one original call. Refresh or Check results after a window finishes to grade it when complete prices are available.
Open session history ↗Appearance and Home-page preferences save in this browser.
Loads automatically after sign-in. Keys stay out of hosted HTML, history and exports. Requests published inflation and labor data; calendar schedules and consensus forecasts are separate.
Save once to your private Firebase account for automatic loading after sign-in. The key is sent through your Cloudflare service to EIA. It is excluded from history, exports and hosted HTML. Browser storage is used if private sync is unavailable.
Save once to your private Firebase account for automatic loading after sign-in. Keys are excluded from history, exports and hosted HTML. Browser storage is used if private sync is unavailable. Alpha Vantage market-price collection is off for this macro-only dashboard. Your key remains saved for a future supported integration.
Checking private history…
Already connected. Only change this address if you move your data service.
9:00 AM and 5:45 PM New York time. Opening during a session catches up if its preparation is missing. Collection pauses when the page is closed.
Fresh daily rates, risk appetite and dollar readings are grouped. Related indicators share one vote. Monthly levels and weekly positioning remain context. Release surprises require a sourced expectation saved before the event.
Direction, evidence and the ±0.05% flat band freeze at the first save. Human reviews can override an automatic grade. Removed records remain recoverable and cannot auto-save again in the same window.
Published directions are Bullish or Bearish. Conviction and opposing evidence are shown separately. Missing, stale, single-group or exactly balanced evidence stays Not ready. No win-probability percentage is claimed without forward calibration.
Checking usable training records…
Training requires session features saved before the open and verified subsequent returns. We do not train a text model to sound more confident or tune weights to the same records used for evaluation.