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--- |
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license: mit |
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language: |
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- en |
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tags: |
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- stockmarket |
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- trading |
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pretty_name: sunny thakur |
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size_categories: |
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- 100K<n<1M |
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--- |
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📈 LLM Trading Instruction Dataset – V2 (2023–2025) |
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``` |
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Dataset Version: 2 |
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Filename: llm_trading_dataset_20250629_115817.jsonl |
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Entries: 157k |
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Period Covered: 2023–2025 |
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Format: JSON Lines (.jsonl) |
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Task: Instruction Tuning for Financial Signal Classification |
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Target Models: LLaMA, Mistral, GPT-J, Falcon, Zephyr, DeepSeek, Qwen |
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``` |
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🧠 Overview |
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This second version of the dataset expands the time horizon and depth of training data for instruction-tuned LLMs by covering real-world market indicators from 2023 through 2025. It enables financial models to learn patterns, sentiment, and timing in Buy/Sell signal generation. |
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📁 Dataset Format |
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Each entry follows the instruction tuning schema: |
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``` |
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{ |
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"instruction": "Given technical indicators, predict if it's a Buy or Sell signal.", |
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"input": "AAPL on 2025-03-20 with indicators: EMA20=224.55, EMA50=232.09, BB_upper=254.87, BB_lower=203.31, MACD=-6.81, MACD_signal=-4.88, RSI=33.61, CCI=-85.31, STOCH_K=15.95, STOCH_D=15.7", |
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"output": "Buy" |
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} |
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``` |
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📌 Fields: |
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``` |
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instruction – Prompt for LLM task (uniform for all entries) |
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input – Date, stock symbol, and associated technical indicators |
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output – Predicted trading signal: "Buy" or "Sell" |
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``` |
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🔍 Technical Indicators Used |
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``` |
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Indicator Description |
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EMA20 / EMA50 Short and medium-term exponential MA |
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BB_upper/lower Bollinger Bands – price volatility zones |
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MACD, MACD_sig Momentum crossover indicators |
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RSI Overbought/Oversold indicator (0–100) |
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CCI Momentum-based deviation indicator |
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STOCH_K / D Stochastic oscillator %K/%D lines |
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``` |
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🔧 Example Usage |
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``` |
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import json |
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with open("llm_trading_dataset_20250629_115817.jsonl") as f: |
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for line in f: |
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ex = json.loads(line) |
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print("Prompt:", ex["instruction"]) |
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print("Indicators:", ex["input"]) |
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print("Decision:", ex["output"]) |
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``` |
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🧪 Use Cases |
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Finetune instruction-tuned LLMs for trading automation |
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Evaluate transformer models for financial decision tasks |
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Build explainable AI advisors using LLM-based logic |
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Backtest models on realistic multi-year indicators |
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Create copilot assistants for traders & hedge funds |
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📌 Version Info |
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Version Range Covered Notes |
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v1 2025 only Initial dataset release |
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v2 2023–2025 Extended multi-year training set |
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📜 License |
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MIT License – Open for use, distribution, and modification. |
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Attribution recommended for research and commercial tools. |
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🤝 Contact |
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📧 [email protected] |
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🧠 AI/Trading Collab: DM for finetuning support or strategy model help |
