---
license: mit
dataset_info:
  features:
  - name: id
    dtype: string
  - name: context
    dtype: string
  - name: news
    dtype: string
  - name: conversations
    list:
    - name: role
      dtype: string
    - name: value
      dtype: string
  - name: label
    dtype: string
  - name: pct_change
    dtype: float64
  - name: day_of_week
    dtype: int64
  - name: month
    dtype: int64
  - name: open
    dtype: float64
  - name: close
    dtype: float64
  - name: rsi
    dtype: 'null'
  - name: macd
    dtype: float64
  - name: volume
    dtype: float64
  - name: high
    dtype: float64
  - name: low
    dtype: float64
  - name: adj_close
    dtype: float64
  - name: boll_ub
    dtype: float64
  - name: boll_lb
    dtype: float64
  - name: rsi_30
    dtype: float64
  - name: cci_30
    dtype: float64
  - name: dx_30
    dtype: float64
  - name: close_30_sma
    dtype: float64
  - name: close_60_sma
    dtype: float64
  - name: daily_return
    dtype: float64
  - name: volatility
    dtype: float64
  - name: is_overbought
    dtype: int64
  - name: is_oversold
    dtype: int64
  - name: date
    dtype: timestamp[us]
  - name: news_embedding
    list: float64
  - name: mentions_policy
    dtype: int64
  - name: mentions_merger
    dtype: int64
  - name: mentions_earnings
    dtype: int64
  - name: mentions_commodity
    dtype: int64
  - name: finance_sentiment_scores
    list: float64
  - name: avg_finance_sentiment
    dtype: float64
  - name: total_positive_hits
    dtype: int64
  - name: total_negative_hits
    dtype: int64
  - name: rolling_close_3d
    dtype: float64
  - name: rolling_close_5d
    dtype: float64
  - name: rolling_volatility_5d
    dtype: float64
  - name: sma_crossover
    dtype: int64
  - name: sentiment_aligned_return
    dtype: float64
  splits:
  - name: train
    num_bytes: 47360695
    num_examples: 1477
  - name: test
    num_bytes: 2527791
    num_examples: 317
  - name: valid
    num_bytes: 3603858
    num_examples: 317
  download_size: 31109624
  dataset_size: 53492344
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
  - split: valid
    path: data/valid-*
---

## 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 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.