stefan-jansen--machine-learning-for-trading
wehub-resource-sync
40 行
1.4 KiB
Markdown
40 行
1.4 KiB
Markdown
# Alternative Data (cross-asset third-party)
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Narrowed scope — datasets that are genuinely cross-asset or not tied to
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a single asset class live here. SEC filings and 13F now live under
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`data/equities/{fundamentals,positioning}/`; CFTC Commitment of Traders
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under `data/futures/positioning/`; on-chain (DefiLlama, CoinGecko) under
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`data/crypto/onchain/`.
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## Directory
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| Subdir | Dataset | Download script | Loader(s) |
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| --- | --- | --- | --- |
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| `news/` | Bloomberg news archive + FNSPID financial headlines | `news/bloomberg_download.py`, `news/fnspid_download.py` | `load_bloomberg_news`, `load_fnspid` |
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| `text/` | Reference / benchmark corpora (Financial Phrasebank) | — (HuggingFace cache) | `load_financial_phrasebank` |
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All loaders return Polars DataFrames. Each subdirectory has its own
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README with download instructions and storage footprint.
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## Quick Start
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```bash
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# FNSPID news (HuggingFace, ~1 GB)
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uv run python data/alternative/news/fnspid_download.py
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# Bloomberg news archive (HuggingFace mirror)
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uv run python data/alternative/news/bloomberg_download.py
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```
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## From Python
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```python
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from data import load_bloomberg_news, load_financial_phrasebank, load_fnspid
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news = load_fnspid(symbols=["AAPL", "MSFT"], start_date="2020-01-01")
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phrasebook = load_financial_phrasebank(agreement="100")
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```
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When data is missing, loaders raise `DataNotFoundError` with a pointer
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to the relevant subdirectory README and the exact download command.
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