Macro Trading
面试准备全景

"I'd approach this across five systematic dimensions: fundamental supply/demand, the futures term structure, macro and cross-asset linkages, positioning and sentiment, and price-based statistical signals. For crude oil specifically, the fundamental layer is most important — starting with the EIA weekly inventory surprise, Baker Hughes rig count for supply trajectory, and OPEC compliance rates. On the curve side, the WTI 1-12 month backwardation/contango structure encodes collective supply-demand judgment. For signal combination, I'd start with equal-weight as baseline, then use Sharpe-ratio-based dynamic weighting, with strict horizon matching between the signal evaluation window and the execution holding period."

"Copper is demand-dominated, with China accounting for ~55% of global consumption — so unlike oil where supply (OPEC) is the primary driver, here the demand-side signals matter most. Three must-mention signals: First, China Credit Impulse — the change in new credit as a share of GDP — leads copper prices by roughly two quarters and is the strongest medium-term signal. Second, LME Cancelled Warrants Ratio — when this spikes, it means physical holders are pulling metal from warehouses, a real-time signal of spot tightness that leads the inventory figure by 1-5 days. Third, TC/RC charges — when treatment charges compress, it signals copper concentrate scarcity at the smelter level, a supply-side signal most people overlook. Additionally, the SHFE/LME ratio tracks Chinese import arbitrage, and CFTC Managed Money net positioning gives you crowded trade reversal signals."

"Three key migrations. First, the alpha structure shifts from cross-sectional to time-series — with only 30 macro assets, statistical power comes from time-series variation rather than cross-section, so the L/S ranking approach doesn't directly apply. Second, models must be much simpler: macro has two to three orders of magnitude fewer data points than equity, so I'd shift focus from feature engineering to regime detection and economic-prior constraints — preferring a 10-feature model that passes the stationarity test over a 200-feature LGBM that fits the training set. Third, risk management transitions from VaR and factor neutralization to scenario analysis and dynamic correlation management, because macro tail events are discontinuous and fat-tailed. The horizon-matching discipline from vol systematic work at Millennium actually transfers well to macro — mixing monthly fundamental signals with daily curve signals without alignment is exactly the kind of latent look-ahead bias that kills Sharpe."

"First, data diagnostics — coverage, frequency, latency, and critically whether there's backfill bias or survivorship bias in the panel. For credit card data, the key caveat is that coverage skews toward certain consumer demographics. Then the economic intuition: CC data directly measures consumer spending, making it a coincident indicator of income and a leading indicator of retail revenue by 2-8 weeks before earnings. For features, I'd build: one, a company-level revenue forecast versus consensus using the transaction panel; two, market share momentum within categories — capturing relative competitive dynamics; three, discretionary/staples spending ratio as a real-time consumer confidence gauge. The decay analysis is critical: CC signals tend to peak around week 2-4 before the earnings date and decay rapidly after release, so the rebalancing frequency should match. Most directly applicable to retail, restaurant, and travel equities for pre-earnings alpha."