Intermarket analysis studies the correlations and divergences between asset classes — equities, bonds, commodities, currencies, and crypto — to gain a macro edge. When relationships that normally hold start breaking down, it often foreshadows major market regime shifts. Traders use these cross-market signals to confirm or contradict setups in their primary market.
Stock-bond correlation and risk parity. Dollar index (DXY) inverse relationship with commodities and crypto. Yield curve signals (2Y-10Y spread). Gold vs real yields relationship. BTC correlation with Nasdaq/SPX. Copper-gold ratio as growth indicator. VIX as equity sentiment barometer.
DXY breaking down while BTC and gold diverge upward. Yield curve steepening after inversion (risk-on rotation). VIX spike above 30 following by rapid decline (volatility reset). Copper-gold ratio turning up (growth expectations rising). Bond yields falling while equities rising (Goldilocks).
Intermarket correlation breaking from expected behavior. DXY strengthening into risk-off with all correlations aligning bearish. Major divergence closing (confirmation complete, edge expired). Correlation regime shift detected.
Daily, Weekly — intermarket signals are most reliable on higher timeframes. Use for directional bias, not intraday timing.
Correlations are not static — they shift across market regimes. Always check whether the current correlation regime is risk-on, risk-off, or transitioning. Don't over-rely on a single intermarket relationship; use confluence across multiple cross-market signals.
Event-driven trading capitalises on price dislocations caused by scheduled and unscheduled catalysts — earnings announcements, FOMC meetings, governance votes, protocol upgrades, token unlocks, mergers, and geopolitical shocks. The edge comes from correctly anticipating the event's impact or exploiting the volatility expansion around it.
Macro trading uses macroeconomic data — GDP, inflation, interest rates, employment, and central bank policy — to establish directional biases across currencies, equities, bonds, and commodities. Traders position for large economic regime shifts rather than intraday noise, using fundamental catalysts as the primary edge.
On-chain fundamental analysis examines blockchain data to assess the health, adoption, and valuation of crypto assets. Unlike traditional fundamental analysis that uses corporate financial statements, on-chain analysis leverages transparent blockchain data — active addresses, transaction volumes, exchange flows, holder distribution, and network fees — to evaluate whether a crypto asset is accumulating value or showing signs of weakness.
Qualitative factor analysis evaluates the non-numerical attributes of an asset or company that influence long-term value — management quality, competitive moats, brand strength, regulatory positioning, and industry trends. While quantitative metrics measure what has happened, qualitative analysis assesses the strategic factors that will drive future performance, providing context that numbers alone cannot capture.