README.md

metadata

title: P2-ETF-CNN-LSTM-ALTERNATIVE-APPROACHES
emoji: 🧠
colorFrom: green
colorTo: blue
sdk: streamlit
sdk_version: 1.32.0
python_version: '3.10'
app_file: app.py
pinned: false

P2-ETF-CNN-LSTM-ALTERNATIVE-APPROACHES

Macro-driven ETF rotation using three augmented CNN-LSTM variants.
Winner selected by highest raw annualised return on the out-of-sample test set.

---

Architecture Overview

Approach	Core Idea	Key Addition
1 — Wavelet	DWT decomposes each macro signal into frequency subbands before the CNN	Separates trend / cycle / noise
2 — Regime-Conditioned	HMM detects macro regimes; one-hot regime label concatenated into the network	Removes non-stationarity
3 — Multi-Scale Parallel	Three CNN towers (kernels 3, 7, 21 days) run in parallel before the LSTM	Captures momentum + cycle + trend simultaneously

---

ETF Universe

Ticker	Description
TLT	20+ Year Treasury Bond
TBT	20+ Year Treasury Short (2×)
VNQ	Real Estate (REIT)
SLV	Silver
GLD	Gold
CASH	3m T-bill rate (from HF dataset)

Benchmarks (chart only, not traded): SPY, AGG

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Data

All data sourced exclusively from:
P2SAMAPA/fi-etf-macro-signal-master-data (HuggingFace Dataset)
File: master_data.parquet

No external API calls (no yfinance, no FRED).
The app checks daily whether the prior NYSE trading day's data is present in the dataset.

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Project Structure

├── .github/
│   └── workflows/
│       └── sync.yml            # Auto-sync GitHub → HF Space on push to main
│
├── app.py                      # Streamlit orchestrator (UI wiring only)
│
├── data/
│   └── loader.py               # HF dataset load, freshness check, column validation
│
├── models/
│   ├── base.py                 # Shared: sequences, splits, scaling, callbacks
│   ├── approach1_wavelet.py    # Wavelet CNN-LSTM
│   ├── approach2_regime.py     # Regime-Conditioned CNN-LSTM
│   └── approach3_multiscale.py # Multi-Scale Parallel CNN-LSTM
│
├── strategy/
│   └── backtest.py             # execute_strategy, metrics, winner selection
│
├── signals/
│   └── conviction.py           # Z-score conviction scoring
│
├── ui/
│   ├── components.py           # Banner, conviction panel, metrics, audit trail
│   └── charts.py               # Plotly equity curve + comparison bar chart
│
├── utils/
│   └── calendar.py             # NYSE calendar, next trading day, EST time
│
├── requirements.txt
└── README.md

---

Secrets Required

Secret	Where	Purpose
HF_TOKEN	GitHub + HF Space	Read HF dataset · Sync HF Space

Set in:

• GitHub: Settings → Secrets → Actions → New repository secret
• HF Space: Settings → Repository secrets

---

Deployment

Push to main → GitHub Actions (sync.yml) automatically syncs to HF Space.

Local development

pip install -r requirements.txt
export HF_TOKEN=your_token
streamlit run app.py

---

Output UI

1. Data freshness warning — alerts if prior NYSE trading day data is missing
2. Next Trading Day Signal — date + ETF from the winning approach
3. Signal Conviction — Z-score gauge + per-ETF probability bars
4. Performance Metrics — Annualised Return, Sharpe, Hit Ratio, Max DD
5. Approach Comparison Table — all three approaches side by side
6. Equity Curves — all three approaches + SPY + AGG benchmarks
7. Audit Trail — last 20 trading days for the winning approach