Economic Frontiers
Economic questions the platform can investigate with the data and formulas already wired in. Readiness says how strong the research path is; status says whether the platform already has a working solved artifact.
When do macro fundamentals actually matter for EUR/USD?
Turn the exchange-rate disconnect puzzle into a state-conditioned rule: macro matters only when confidence, spread, and market sensitivity line up.
Operationalized as a conditional macro-to-FX gate: macro evidence is allowed to influence EUR/USD only when relative macro spread and confidence clear the threshold.
relative-bias engine and market-expression gate
- relative_macro_bias = (US signal - EA signal) * confidence_gate
- active_fx_signal = relative_macro_bias if abs(relative_macro_bias) >= signal_threshold else 0
- expression_state = active when macro_bias, confidence, and market sensitivity align
Prevents the EUR/USD page from showing empty or generic commentary by turning prior macro analysis into a visible final FX bias and explanation chain.
- Is macro currently relevant for EUR/USD?
- Which side has the stronger macro impulse?
- Is the signal strong enough to use or should it be suppressed?
- US/EA regional macro signals
- EUR/USD relative bias
- market expression gate
- DXY beta policy
A EUR/USD truth ladder that separates active macro signal from random-walk noise.
How does today’s macro state evolve into 1M/3M/6M bill value over six months?
Connect macro pressure to bill-rate paths, then use deterministic bill pricing and ladder math for the value path.
Operationalized as a hybrid structural Treasury bill engine: live bill yields anchor the start, macro/FX/rate features forecast the bill-rate path, and TreasuryDirect pricing converts rates into value.
Treasury value forecast API /v1/research/treasury-value-forecast
- policy_pressure = 0.35*prices + 0.15*labour + 0.15*activity + 0.15*money_financial + 0.10*sovereign_liquidity + 0.10*eurusd_relative_bias
- y_hat(t+h, tenor) = y_t + beta_mom*momentum + beta_policy*macro_impulse + beta_curve*curve_slope + beta_fx*fx_bias + residual_correction
- bill_price = face * (1 - discount_rate * days_to_maturity / 360)
- ladder_value_next = current_value / bill_price
Turns macro analysis into an inspectable six-month Treasury value path instead of a disconnected rates chart.
- What is the expected 6M value of a short bill ladder?
- How much return is carry versus repricing?
- Which macro drivers are pushing bill yields up or down?
- 1M/3M/6M Treasury yields
- Fed funds, 2Y, 10Y
- macro pressure
- walk-forward residuals
A carry-vs-repricing dashboard for short-term Treasury value.
What is the economy’s actual state before GDP and inflation data are revised?
Use freshness, revision risk, confidence, and pyramid compression to distinguish current truth from stale truth.
Operationalized as release-aware regional scoreboards and pyramid layers where every macro state carries coverage, freshness, confidence, and regime diagnostics.
regional scoreboards, pyramid builder, freshness policies
- confidence = base_confidence * freshness_score * coverage_score * revision_quality
- regional_signal = weighted_pillar_score adjusted by coverage and imbalance penalties
- regime = threshold(regional_signal, duration, diffusion, extreme_flags)
Makes stale or partial macro readings visible, so downstream FX, beta, and Treasury modules do not treat weak data as hard truth.
- What does the platform currently believe the economy is doing?
- How much of that belief is fresh?
- Which macro pillars are carrying the regime classification?
- indicator lineage
- freshness policies
- regional scoreboards
- macro pyramid
A current-regime confidence meter with explicit stale-data haircuts.
Which stress signals arrive before financial strain becomes obvious?
Rank macro, monetary, funding, leverage, sovereign, and digital systemic stress by coverage and driver concentration.
Operationalized as a risk catalogue and overview that turns many heterogeneous risk metrics into comparable domain scores, top drivers, and source-quality diagnostics.
risk catalogue and risk overview pages
- domain_score = weighted_average(metric_risk_scores)
- metric_weight = source_quality * coverage * freshness_decay * frequency_reliability
- freshness_decay = exp(-lambda * update_lag_days)
Moves risk from scattered pages into a comparable stress surface that can feed macro, Treasury, and transmission models.
- Which risk domain is most stressed?
- Which metrics are driving that stress?
- Is the signal broad or one-metric fragile?
- 73 risk metrics
- 10 risk domains
- risk driver ranking
- macro confidence
A stress ladder that flags rising risk before it becomes a headline outcome.
When is a market beta estimate stale because the macro regime changed?
Let macro instability determine the beta window, benchmark horizon, and signal threshold.
Operationalized as a deterministic beta policy that adapts the estimation window, benchmark horizon, and threshold from macro stress and confidence.
frontend beta policy module
- macro_instability = f(abs(signal), dispersion, freshness, confidence)
- window_obs = base_window - instability_shortening + confidence_extension
- signal_threshold = base_threshold + stress_haircut + low_confidence_haircut
Keeps beta estimates from looking precise when the macro regime has shifted underneath them.
- Should beta use a long stable window or a shorter crisis window?
- Is the current beta reliable enough to act on?
- What macro condition changed the beta settings?
- macro-conditioned beta policy
- regional confidence
- pillar dispersion
- market benchmark moves
A beta reliability score beside every benchmark signal.
Which risk domains actually precede macro deterioration?
Promote a domain-pillar edge only when it improves point-in-time, walk-forward prediction after macro, risk, market, quality, placebo, and reverse-direction controls.
Operationalized as a validated transmission edge, not a universal causal proof: a risk domain is admitted only when it improves out-of-sample prediction of later macro deterioration versus strict baselines.
risk-to-macro transmission API /v1/research/risk/transmission
- macro_damage(p,t,h) = macro_score(p,t) - macro_score(p,t+h)
- G = max(0, 1 - MAE_augmented / MAE_baseline)
- S = 2 * max(share(beta > 0), share(beta < 0)) - 1
- V = 1 if q <= threshold and placebo, reverse, coverage, and uniqueness tests pass else 0
- T = V * G^0.7 * effect_size * sign_stability * quality_weight
Connects risk pages to macro outcomes, so the platform can explain which stress channels historically improved prediction instead of showing parallel dashboards.
- Which risk domains precede macro pillar deterioration?
- At what lag does the edge validate?
- Is the edge unique after controlling for other risks?
- Did markets already price it?
- What are the failure cases?
- risk overview
- regional score history
- market proxies
- walk-forward loss tests
A validated transmission map with formula trace, OOS gain, q-value, falsification tests, and failure cases.
What should we believe when macro, FX, Treasury, beta, and risk disagree?
Do not force one master score. Rank concurrent truths and show conflict flags beside the evidence.
Operationalized as an overview ladder that ranks multiple validated outputs side by side and flags conflicts rather than collapsing them into one overconfident truth.
overview headlines and concurrent outlook ranking
- outlook_score = signal_strength * confidence * validation_quality
- conflict_flag = sign(disagreement across macro, FX, Treasury, beta, risk)
- rank = sort(outlooks by confidence-adjusted evidence strength)
Lets the dashboard say “these truths disagree” while preserving the evidence chain behind each one.
- Which platform output deserves attention first?
- Where do macro, FX, Treasury, beta, and risk disagree?
- Which evidence chain is strongest today?
- overview ladder
- formula traces
- confidence gates
- validation errors
A concurrent outlook engine on the overview page with a ranked ladder below it.
When do debt, rates, interest burden, and liquidity become binding?
Treat sovereign liquidity as a path problem, not a single debt/GDP snapshot.
Partially operationalized through sovereign risk, liquidity-cover scorecards, and Treasury rates; the full path model still needs a dedicated trigger and projection layer.
sovereign risk, interest-burden, liquidity-cover, and Treasury data surfaces
- liquidity_pressure = debt_service_burden + refinancing_rate_pressure - liquidity_cover
- interest_burden_path = debt_stock * projected_rate / fiscal_capacity
- binding_flag = liquidity_pressure >= trigger_threshold
Would connect fiscal capacity, rates, refinancing pressure, and market liquidity into an early warning path.
- When does rate pressure become fiscally binding?
- Which threshold is closest to being breached?
- Is liquidity improving or deteriorating?
- debt/GDP
- interest outlays
- liquidity cover
- Treasury rates
- sovereign risk
A sovereign-liquidity pressure path with trigger thresholds.
When is positive carry real value versus compensation for hidden repricing risk?
Decompose carry, repricing, validation error, and macro pressure into a quality score.
Operationalized from the Treasury value engine: carry is separated from repricing and penalized by validation error and adverse macro pressure.
Treasury value forecast ladder and price decomposition
- carry_quality = carry_contribution - repricing_risk - validation_error_penalty - adverse_macro_pressure
- carry_contribution = maturity_value - purchase_price
- repricing_contribution = mark_to_market_price - purchase_price
Prevents positive yield from being mistaken for high-quality value when forecast error or repricing risk is high.
- Is the carry actually attractive?
- Is value coming from income or mark-to-market movement?
- How much does model error reduce confidence?
- Treasury ladder value
- carry contribution
- repricing contribution
- walk-forward MAE
A carry-quality label beside the Treasury value forecast.
Do stablecoin, crypto-wrapper, and digital infrastructure stresses leak into macro or Treasury truth?
Use digital systemic risk as an overlay, not a standalone drama button.
Not solved yet: the data exists as risk surfaces, but the spillover equation into macro or Treasury outcomes has not been validated.
digital systemic risk surfaces
- candidate_spillover = digital_systemic_stress * exposure_weight * transmission_gate
- promotion requires OOS gain versus macro, market, and all-risks baselines
Would test whether digital financial stress adds information beyond conventional funding and market stress.
- Does digital systemic stress lead macro deterioration?
- Does it affect Treasury value directly or through risk channels?
- Is the signal unique or redundant?
- digital systemic risk
- digital policy readiness
- infrastructure coverage
- risk overview drivers
A digital-systemic overlay in the stress ladder.