darkknight25/trading_dataset_v2
📈 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… See the full description on the dataset page: https://huggingface.co/datasets/darkknight25/trading_dataset_v2.
📈 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.
📁 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
📌 Version Info Version Range Covered Notes v1 2025 only Initial dataset release v2 2023–2025 Extended multi-year training set
📜 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