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--- |
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license: mit |
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--- |
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## NIFTY-Feature-Enhanced |
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## Dataset Summary |
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NIFTY-Feature-Enhanced is a multi-modal, finance-focused dataset built on top of raeidsaqur/NIFTY |
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We enrich the original dataset with structured financial indicators, derived signals, temporal features, sentiment scores, embeddings, and event tags. |
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This makes it suitable for: |
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Predictive ML models (e.g., XGBoost, LSTMs, Transformers) |
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Financial NLP tasks (sentiment, RAG, semantic search) |
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Multi-modal research (numeric + textual features combined) |
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## Enrichments Added |
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## Temporal Features |
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day_of_week, month |
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## Market Indicators (parsed from context) |
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open, close, high, low, adj_close, volume, pct_change |
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Technical signals: macd, rsi, rsi_30, cci_30, dx_30, boll_ub, boll_lb, close_30_sma, close_60_sma |
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## Derived Financial Signals |
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daily_return = (close-open)/open |
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volatility = high-low |
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is_overbought (RSI>70), is_oversold (RSI<30) |
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## NLP Enrichments |
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news_embedding → 384-dim semantic vector (MiniLM) |
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finance_sentiment_scores (lexicon-based per-headline) |
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avg_finance_sentiment → aggregate sentiment per day |
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total_positive_hits, total_negative_hits |
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## Event Tags (regex-based) |
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mentions_policy, mentions_merger, mentions_earnings, mentions_commodity |
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## Rolling & Cross Features |
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rolling_close_3d, rolling_close_5d |
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rolling_volatility_5d |
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sma_crossover (30SMA vs. 60SMA) |
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sentiment_aligned_return = sentiment × pct_change |
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## Example Row |
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{ |
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"date": "2010-01-26", |
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"open": 110.12, |
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"close": 109.77, |
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"volume": 147680200, |
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"macd": 0.8312, |
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"rsi_30": 59.84, |
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"daily_return": -0.0031, |
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"volatility": 1.12, |
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"is_overbought": 0, |
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"is_oversold": 0, |
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"avg_finance_sentiment": 0.007, |
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"mentions_policy": 1, |
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"mentions_merger": 0, |
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"mentions_earnings": 1, |
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"mentions_commodity": 1, |
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"rolling_close_3d": 110.95, |
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"rolling_close_5d": 112.31, |
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"sma_crossover": 1, |
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"sentiment_aligned_return": -2.1e-05, |
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"news_embedding": [0.036, -0.041, 0.082, ...] # 384-dim vector |
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} |
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## Use Cases |
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Financial prediction: Build ML models using enriched market + sentiment signals. |
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Financial NLP: Benchmark sentiment models, retrieval tasks, RAG pipelines. |
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Multi-modal ML: Combine embeddings + structured features for hybrid models. |
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Explainability studies: Investigate interactions between news tone and market moves. |
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## Citation |
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If you use this dataset, please also cite the original work: |
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NIFTY Dataset: Raeid Saqur, NIFTY: News-Informed Financial Trend Yield Dataset (2024) |
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@article{saqur2024nifty, |
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title={NIFTY: News-Informed Financial Trend Yield Dataset}, |
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author={Saqur, Raeid and others}, |
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journal={arXiv preprint arXiv:2405.09747}, |
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year={2024} |
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} |
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## Acknowledgements |
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## Original dataset: raeidsaqur/NIFTY |
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## Enrichments by Naga Adithya Kaushik (GenAIDevTOProd) |
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## 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. |
