Dollar Hegemony

Quantifying USD Dominance Across BRICS vs G7 with DSI, XGBoost, and LSTM

An interactive macro-financial ML research system that tracks USD strength, estimates stress transmission across BRICS/G7 economies, and supports scenario-based currency depreciation analysis.

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Live Demo

• Hugging Face Space: https://huggingface.co/spaces/Ayush0826/dollar-hegemony

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Research Objective

This project quantifies how USD regime shifts (strength, momentum, liquidity pressure) propagate into country-level currency stress, especially for BRICS economies relative to G7 context.

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Key Contributions

• Dollar Stress Index (DSI): composite USD pressure signal
• Country-level depreciation analytics: 12-month impact vs USD
• Hybrid model layer: XGBoost + LSTM
• Scenario simulator: user-controlled DXY level + momentum
• Crisis diagnostics: country alert states and stress timelines
• Macro context integration: fundamentals + live BRICS-focused news

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Dashboard Modules

1) Macro

• Timeline coverage: 2000–2024
• DXY historical chart with crisis markers (e.g., GFC, COVID)
• DSI gauge and stress-zone view
• BRICS vs G7 depreciation comparison
• Correlation/stress exploration panels

2) Models

• Country selector: Brazil, Russia, India, China, South Africa
• LSTM actual vs predicted panel (3-month ahead)
• Metrics table: MAE, RMSE, Directional Accuracy
• Directional accuracy comparison chart by country

3) Scenario Sim

• DXY level slider
• DXY 12-month momentum slider
• Quick presets: Strong Dollar / Current / Weak Dollar
• Projected 12M depreciation cards (country-wise)
• Sensitivity analysis and scenario bar chart

4) World Map

• Global choropleth of currency stress/depreciation
• Color mode toggle (Latest Depreciation / Alert Level)
• Period selector + country click interaction

5) Crisis Alerts

• Regime counters: Critical / Warning / Watch / Stable
• Country status board with depreciation + crisis-period behavior
• DSI timeline for crisis windows
• Worst depreciation events ranking
• Country crisis-history selector

6) Fundamentals

• Indicator selector (example: GDP Growth)
• Multi-country BRICS trend view
• Country summary cards
• BRICS stock index panel
• Cross-indicator correlation scatter analysis

7) News Feed

• Live BRICS/emerging-market/USD-relevant headlines
• Refresh workflow for updates
• Macro snapshot: DXY, DSI, WTI, Fed Rate, US CPI YoY

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Header KPIs

The app top bar displays:

• DXY Index
• DSI Score
• Coverage (2000–2024)
• Alerts count

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Dollar Stress Index (DSI)

Conceptual Form

The DSI is a normalized composite of USD-linked macro-financial stress factors.

DSI(t) = Σ (w_i × z_i(t)), for i = 1 to n

Where:

• z_i(t) = standardized value of factor i at time t
• w_i = weight of factor i (fixed or calibrated)
• DSI is scaled into regime bands (low / moderate / high stress)

Typical Factor Families

• USD broad strength proxies (e.g., DXY-linked signals)
• Global rates/liquidity environment
• Inflation and commodity stress proxies
• Risk-off behavior and FX volatility clusters
• Country-specific depreciation response terms

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Modeling Framework

XGBoost

• Handles nonlinear interactions in macro + market tabular signals
• Strong baseline for country-level depreciation estimation

LSTM

• Learns temporal dependencies in FX stress transmission
• Used for short-horizon directional and level prediction workflows

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Data Sources

As shown in the app and footer:

• Yahoo Finance
• FRED
• World Bank
• Live financial news feeds (News tab)

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Tech Stack

• Language: Python
• Dashboard/UI: Plotly Dash
• Backend/API: FastAPI
• ML: XGBoost, LSTM
• Containerization: Dockerfile

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High-Level Architecture

Data Sources (Yahoo/FRED/World Bank/News)
            │
            ▼
   Data Processing + Feature Engineering
            │
            ├── DSI Computation
            ├── XGBoost Pipeline
            └── LSTM Pipeline
            │
            ▼
     Inference + Metrics + Alerts
            │
            ▼
 Plotly Dash Interface + FastAPI Endpoints

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Local Setup

1) Clone

git clone https://github.com/ayushcmd/dollar-hegemony.git
cd dollar-hegemony

2) Install dependencies

pip install -r requirements.txt --break-system-packages

3) Run (based on your repo entrypoint)

python app.py

or

uvicorn app.main:app --reload

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Reproducibility Notes

• Use fixed time-split train/validation/test windows
• Version processed datasets and model artifacts
• Track metrics per country and horizon
• Record scenario assumptions when reporting outputs

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Limitations

• Regime shifts can reduce historical model reliability
• Output quality depends on data freshness and retraining
• Research/analytics tool only — not financial advice

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Roadmap

• Add uncertainty bands (quantile/interval forecasts)
• Add explainability layer (feature attribution diagnostics)
• Extend benchmark suite and ablation reporting
• Automate retraining + monitoring workflows

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Author

Ayush Raj
BSc CSDA, IIT Patna (Grad: Aug 2027)

• GitHub: https://github.com/ayushcmd
• LinkedIn: https://www.linkedin.com/in/ayush08iitp
• Portfolio: https://ayushcmd.me

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Citation

If you use this project in research/content, cite:

Ayush Raj — Dollar Hegemony (GitHub + Hugging Face Space)

• Repo: https://github.com/ayushcmd/dollar-hegemony
• Demo: https://huggingface.co/spaces/Ayush0826/dollar-hegemony

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License

MIT (or as specified in LICENSE)