Update README.md

README.md

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-license: mit
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+---
+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.