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.