| | --- |
| | license: mit |
| | language: |
| | - en |
| | tags: |
| | - stockmarket |
| | - trading |
| | pretty_name: sunny thakur |
| | size_categories: |
| | - 100K<n<1M |
| | --- |
| | |
| | 📈 LLM Trading Instruction Dataset – V2 (2023–2025) |
| | ``` |
| | Dataset Version: 2 |
| | Filename: llm_trading_dataset_20250629_115817.jsonl |
| | Entries: 157k |
| | Period Covered: 2023–2025 |
| | Format: JSON Lines (.jsonl) |
| | Task: Instruction Tuning for Financial Signal Classification |
| | Target Models: LLaMA, Mistral, GPT-J, Falcon, Zephyr, DeepSeek, Qwen |
| | ``` |
| |
|
| | 🧠 Overview |
| |
|
| | 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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| |
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| |
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| | 📁 Dataset Format |
| |
|
| | Each entry follows the instruction tuning schema: |
| | ``` |
| | { |
| | "instruction": "Given technical indicators, predict if it's a Buy or Sell signal.", |
| | "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", |
| | "output": "Buy" |
| | } |
| | ``` |
| | 📌 Fields: |
| | ``` |
| | |
| | instruction – Prompt for LLM task (uniform for all entries) |
| | |
| | input – Date, stock symbol, and associated technical indicators |
| | |
| | output – Predicted trading signal: "Buy" or "Sell" |
| | ``` |
| |
|
| |
|
| |
|
| | 🔍 Technical Indicators Used |
| | ``` |
| | Indicator Description |
| | EMA20 / EMA50 Short and medium-term exponential MA |
| | BB_upper/lower Bollinger Bands – price volatility zones |
| | MACD, MACD_sig Momentum crossover indicators |
| | RSI Overbought/Oversold indicator (0–100) |
| | CCI Momentum-based deviation indicator |
| | STOCH_K / D Stochastic oscillator %K/%D lines |
| | ``` |
| | 🔧 Example Usage |
| | ``` |
| | import json |
| | |
| | with open("llm_trading_dataset_20250629_115817.jsonl") as f: |
| | for line in f: |
| | ex = json.loads(line) |
| | print("Prompt:", ex["instruction"]) |
| | print("Indicators:", ex["input"]) |
| | print("Decision:", ex["output"]) |
| | ``` |
| |
|
| |
|
| | 🧪 Use Cases |
| |
|
| | Finetune instruction-tuned LLMs for trading automation |
| | |
| | Evaluate transformer models for financial decision tasks |
| | |
| | Build explainable AI advisors using LLM-based logic |
| | |
| | Backtest models on realistic multi-year indicators |
| | |
| | Create copilot assistants for traders & hedge funds |
| | |
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|
| | 📌 Version Info |
| | Version Range Covered Notes |
| | v1 2025 only Initial dataset release |
| | v2 2023–2025 Extended multi-year training set |
| |
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| | 📜 License |
| |
|
| |
|
| | MIT License – Open for use, distribution, and modification. |
| |
|
| | Attribution recommended for research and commercial tools. |
| |
|
| | 🤝 Contact |
| |
|
| | 📧 sunny48445@gmail.com |
| |
|
| | 🧠 AI/Trading Collab: DM for finetuning support or strategy model help |
