GenAIDevTOProd/NIFTY-feature-enhanced

NIFTY-Feature-Enhanced

Dataset Summary

NIFTY-Feature-Enhanced is a multi-modal, finance-focused dataset built on top of raeidsaqur/NIFTY . We enrich the original dataset with structured financial indicators, derived signals, temporal features, sentiment scores, embeddings, and event tags.

This makes it suitable for:

Predictive ML models (e.g., XGBoost, LSTMs, Transformers)

Financial NLP tasks (sentiment, RAG, semantic search)

Multi-modal research (numeric + textual features combined)

Enrichments Added

Temporal Features

dayofweek, month

Market Indicators (parsed from context)

open, close, high, low, adjclose, volume, pctchange

Technical signals: macd, rsi, rsi30, cci30, dx30, bollub, bolllb, close30sma, close60_sma

Derived Financial Signals

daily_return = (close-open)/open

volatility = high-low

isoverbought (RSI>70), isoversold (RSI<30)

NLP Enrichments

news_embedding → 384-dim semantic vector (MiniLM)

financesentimentscores (lexicon-based per-headline)

avgfinancesentiment → aggregate sentiment per day

totalpositivehits, totalnegativehits

Event Tags (regex-based)

mentionspolicy, mentionsmerger, mentionsearnings, mentionscommodity

Rolling & Cross Features

rollingclose3d, rollingclose5d

rollingvolatility5d

sma_crossover (30SMA vs. 60SMA)

sentimentalignedreturn = sentiment × pct_change

Example Row

{ "date": "2010-01-26", "open": 110.12, "close": 109.77, "volume": 147680200, "macd": 0.8312, "rsi30": 59.84, "dailyreturn": -0.0031, "volatility": 1.12, "isoverbought": 0, "isoversold": 0, "avgfinancesentiment": 0.007, "mentionspolicy": 1, "mentionsmerger": 0, "mentionsearnings": 1, "mentionscommodity": 1, "rollingclose3d": 110.95, "rollingclose5d": 112.31, "smacrossover": 1, "sentimentalignedreturn": -2.1e-05, "newsembedding": [0.036, -0.041, 0.082, ...] # 384-dim vector }

Use Cases

Financial prediction: Build ML models using enriched market + sentiment signals.

Financial NLP: Benchmark sentiment models, retrieval tasks, RAG pipelines.

Multi-modal ML: Combine embeddings + structured features for hybrid models.

Explainability studies: Investigate interactions between news tone and market moves.

Citation

If you use the NIFTY Financial dataset in your work, please consider citing our paper:

@article{raeidsaqur2024NiftyLM, title = {NIFTY-LM Financial News Headlines Dataset for LLMs}, author = {Raeid Saqur}, year = 2024, journal = {ArXiv}, url = {https://arxiv.org/abs/2024.5599314} }

Acknowledgements

Original dataset: raeidsaqur/NIFTY

Enrichments by Naga Adithya Kaushik (GenAIDevTOProd)

This makes NIFTY-Feature-Enhanced one of the most feature-rich financial datasets on Hugging Face, bridging numeric markets + NLP headlines for ML + GenAI research.