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개요데이터 지표학습 센터지식 베이스블로그
빠른 접근TGA BalanceVIXSOFR-IORB
Policy / Reserves
Fed Balance Sheet (Total Assets)Treasury General Account (TGA)Overnight Reverse Repo (ON RRP)
Funding / Plumbing
SOFR–IORB SpreadStanding Repo Facility (SRF)
Credit / Intermediation
Bank Cash Buffer (Cash Assets / Total Assets)High Yield Spread (ICE BofA HY OAS)
Risk / Price
VIX Volatility IndexBroad Dollar Index10-Year Real Yield (TIPS)
Broader Liquidity
Net Liquidity IndexM2 Money Supply

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개요데이터 지표학습블로그

Research Framework

Methodology

Computation Pipeline

The DLI Liquidity Score synthesizes structural data from the Federal Reserve system and capital markets through a four-stage quantitative pipeline:

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1Multi-Source Ingestion

Direct integration with FRED, US Treasury Fiscal Data, and NY Fed Markets APIs — automated ingestion with cross-frequency alignment

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2Robust Statistical Normalization

Robust z-scores via 10-year rolling median and MAD (Median Absolute Deviation) — resilient to outliers and structural breaks, winsorized to [-4, +4]

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3Tiered Sub-Index Construction

The 10 scoring indicators are classified into 4 transmission tiers: Policy/Reserves, Funding/Plumbing, Credit/Intermediation, and Risk/Price, then equal-weighted within each tier.

4Composite Scoring & Adaptive Classification

Supply-side weighted 60%, price feedback 40% — regime classified via rolling 5-year percentile thresholds that adapt across macro cycles

Sub-Index Structure & Weights

The site tracks 12 indicators. The DLI score uses 10 core indicators grouped into 4 tiers. Supply-side (Policy + Funding) carries 60% total weight so the score reflects liquidity substance rather than sentiment.

추적 대상 2개 지표(순유동성 및 M2)는 참고용으로 제공되며 DLI 종합 점수에는 포함되지 않습니다.

TierWeightIndicatorTightening Signal
정책 / 준비금30%Fed 총자산↓ Falling = Tighter liquidity
TGA 잔액↑ Rising = Tighter liquidity
ON RRP 잔액↑ Rising = Tighter liquidity
자금조달 / 배관30%SOFR-IORB 스프레드↑ Rising = Tighter liquidity
SRF 이용량↑ Rising = Tighter liquidity
신용 / 중개20%현금 버퍼↓ Falling = Tighter liquidity
HY 신용 스프레드↑ Rising = Tighter liquidity
리스크 / 가격20%VIX 변동성↑ Rising = Tighter liquidity
달러 인덱스↑ Rising = Tighter liquidity
10Y 실질금리↑ Rising = Tighter liquidity

DLI Liquidity Score Classification

The DLI Score uses adaptive rolling 5-year percentile thresholds to classify liquidity into four tiers, keeping signals stable and comparable across macro cycles.

PercentileLiquidity ConditionRisk Bias (Secondary)Interpretation
≤ P20AbundantRisk-seeking tiltExtremely loose liquidity — strongly favorable for risk assets
P20 – P50SupportiveMild risk-seeking tiltConditions still positive but moderating — monitor momentum shifts
P50 – P80RestrictiveMild defensive tiltConditions tightening — risk assets face moderate headwinds
≥ P80TightDefensive tiltSignificantly tight liquidity — risk assets face strong headwinds

Additional Metrics

  • 7-Day Momentum: Difference between today's DLI and 7 days ago, measuring direction and pace. Classified as Improving / Stable / Deteriorating.
  • Signal Concentration: Share of total contribution from Top 3 drivers — higher concentration means a cleaner, more interpretable signal
  • Contribution Decomposition: Daily change decomposed by indicator, showing which factors are driving DLI movement

Historical State Consistency Check (Not a Trading Backtest)

This section checks directional consistency between DLI liquidity states and subsequent asset behavior. It is not a direct return-forecasting model.

1) State First

Read liquidity state from percentile bands: supportive / neutral / tight.

2) Then Statistics

Check 20-trading-day average return and win rate under that state.

3) Validation, Not Signal

Use as model interpretability evidence, not as a standalone trading instruction.

유동성 완화≤ P20
SPX 20D Avg+2.1%SPX Win Rate68%BTC 20D Avg+5.4%BTC Win Rate63%

역사적으로 위험자산의 전반적 성과가 더 강하지만 변동성은 여전히 클 수 있습니다.

유동성 중립P20 – P80
SPX 20D Avg+0.7%SPX Win Rate54%BTC 20D Avg+1.2%BTC Win Rate52%

방향성이 약하며 다른 거시 변수에 더 민감합니다.

유동성 긴축≥ P80
SPX 20D Avg-1.3%SPX Win Rate41%BTC 20D Avg-3.8%BTC Win Rate38%

역사적으로 위험자산에 대한 하방 압력이 더 강하며 하락 확률이 높습니다.

* Sample window: 2020-2025; method: conditional 20-trading-day distributions grouped by DLI state. Historical results do not guarantee future outcomes.

Model Limitations

  • Weights are based on historical analysis and expert judgment, not ML optimization — they may not perfectly adapt to future structural market changes
  • Robust z-scores (median + MAD) substantially reduce outlier distortion compared to mean/std, but extreme tail events may still temporarily affect scores
  • The model uses daily frequency data and cannot capture intraday liquidity events (flash crashes, intraday VIX spikes)
  • Score signals have inherent lag — they are not real-time trading signals, and are better suited for strategic allocation than short-term timing

Data Sources

  • FRED — Federal Reserve Economic Data (St. Louis Fed)
  • Treasury — US Treasury Fiscal Data API
  • NY Fed — Federal Reserve Bank of New York Markets

Disclaimer

This site is for informational and educational purposes only. It does not constitute financial advice. All data comes from public sources; we do not guarantee completeness or timeliness. Investment decisions should be based on personal research and professional consultation.