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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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- trading |
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- stock |
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- market |
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pretty_name: sunny thakur |
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size_categories: |
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- 10K<n<100K |
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--- |
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LLM Trading Instruction Dataset – 2025 Edition |
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``` |
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Filename: llm_trading_dataset_20250629_115540.jsonl |
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Size: 20,306 entries |
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Format: JSON Lines (.jsonl) |
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Created: June 29, 2025 |
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Task Type: Instruction Tuning for Financial Signal Prediction |
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Target Use: Finetuning Instruction-Following LLMs (e.g., LLaMA, Mistral, GPT-NeoX) |
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``` |
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🧠 Dataset Overview |
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This dataset is crafted to train and evaluate large language models for predicting Buy/Sell signals based on technical indicators in real-world market conditions. |
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Each entry follows a structured instruction format, making it suitable for instruction-tuned or reinforcement-aligned LLMs. |
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📁 Format |
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Each line contains a JSON object with the following fields: |
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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 2025-03-14 with indicators: EMA20=229.85, EMA50=235.28, BB_upper=257.98, BB_lower=212.55, MACD=-5.63, MACD_signal=-2.33, RSI=30.81, CCI=-176.99, STOCH_K=12.19, STOCH_D=7.04", |
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"output": "Buy" |
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} |
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``` |
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🔍 Fields: |
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instruction: Standardized prompt for LLMs |
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input: Stock ticker, date, and key technical indicators |
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output: Target label – "Buy" or "Sell" |
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🛠️ Use Cases |
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Finetuning instruction-following models for financial forecasting |
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Training agents to act as trading advisors |
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Simulating AI portfolio decision-making |
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Building autonomous trading copilots |
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Researching interpretable signal generation |
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📉 Indicators Included |
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EMA20, EMA50 (Exponential Moving Averages) |
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BB_upper, BB_lower (Bollinger Bands) |
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MACD, MACD_signal (Trend Momentum) |
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RSI (Relative Strength Index) |
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CCI (Commodity Channel Index) |
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STOCH_K, STOCH_D (Stochastic Oscillator) |
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``` |
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import json |
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with open("llm_trading_dataset_20250629_115540.jsonl") as f: |
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for line in f: |
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sample = json.loads(line) |
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print("Prompt:", sample["instruction"]) |
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print("Input:", sample["input"]) |
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print("Target:", sample["output"]) |
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``` |
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📜 License |
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MIT License (customizable) |
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Attribution recommended for academic or commercial use. |
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🤝 Contact |
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For collab, model finetuning, or tool integrations: |
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📧 [email protected] |
