---
license: mit
---

## 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

day_of_week, month

## Market Indicators (parsed from context)

open, close, high, low, adj_close, volume, pct_change

Technical signals: macd, rsi, rsi_30, cci_30, dx_30, boll_ub, boll_lb, close_30_sma, close_60_sma

## Derived Financial Signals

daily_return = (close-open)/open

volatility = high-low

is_overbought (RSI>70), is_oversold (RSI<30)

## NLP Enrichments

news_embedding → 384-dim semantic vector (MiniLM)

finance_sentiment_scores (lexicon-based per-headline)

avg_finance_sentiment → aggregate sentiment per day

total_positive_hits, total_negative_hits

## Event Tags (regex-based)

mentions_policy, mentions_merger, mentions_earnings, mentions_commodity

## Rolling & Cross Features

rolling_close_3d, rolling_close_5d

rolling_volatility_5d

sma_crossover (30SMA vs. 60SMA)

sentiment_aligned_return = sentiment × pct_change

## Example Row
{
  "date": "2010-01-26",
  "open": 110.12,
  "close": 109.77,
  "volume": 147680200,
  "macd": 0.8312,
  "rsi_30": 59.84,
  "daily_return": -0.0031,
  "volatility": 1.12,
  "is_overbought": 0,
  "is_oversold": 0,
  "avg_finance_sentiment": 0.007,
  "mentions_policy": 1,
  "mentions_merger": 0,
  "mentions_earnings": 1,
  "mentions_commodity": 1,
  "rolling_close_3d": 110.95,
  "rolling_close_5d": 112.31,
  "sma_crossover": 1,
  "sentiment_aligned_return": -2.1e-05,
  "news_embedding": [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 this dataset, please also cite the original work:

NIFTY Dataset: Raeid Saqur, NIFTY: News-Informed Financial Trend Yield Dataset (2024)

@article{saqur2024nifty,
  title={NIFTY: News-Informed Financial Trend Yield Dataset},
  author={Saqur, Raeid and others},
  journal={arXiv preprint arXiv:2405.09747},
  year={2024}
}

## 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.