Datasets:
File size: 2,322 Bytes
dbc519a 6c8fcc3 dbc519a 6c8fcc3 dbc519a 6c8fcc3 dbc519a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 |
---
license: mit
language:
- en
tags:
- trading
- stock
- market
pretty_name: sunny thakur
size_categories:
- 10K<n<100K
---
LLM Trading Instruction Dataset – 2025 Edition
```
Filename: llm_trading_dataset_20250629_115540.jsonl
Size: 20,306 entries
Format: JSON Lines (.jsonl)
Created: June 29, 2025
Task Type: Instruction Tuning for Financial Signal Prediction
Target Use: Finetuning Instruction-Following LLMs (e.g., LLaMA, Mistral, GPT-NeoX)
```
🧠 Dataset Overview
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.
Each entry follows a structured instruction format, making it suitable for instruction-tuned or reinforcement-aligned LLMs.
📁 Format
Each line contains a JSON object with the following fields:
```
{
"instruction": "Given technical indicators, predict if it's a Buy or Sell signal.",
"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",
"output": "Buy"
}
```
🔍 Fields:
instruction: Standardized prompt for LLMs
input: Stock ticker, date, and key technical indicators
output: Target label – "Buy" or "Sell"
🛠️ Use Cases
Finetuning instruction-following models for financial forecasting
Training agents to act as trading advisors
Simulating AI portfolio decision-making
Building autonomous trading copilots
Researching interpretable signal generation
📉 Indicators Included
EMA20, EMA50 (Exponential Moving Averages)
BB_upper, BB_lower (Bollinger Bands)
MACD, MACD_signal (Trend Momentum)
RSI (Relative Strength Index)
CCI (Commodity Channel Index)
STOCH_K, STOCH_D (Stochastic Oscillator)
```
import json
with open("llm_trading_dataset_20250629_115540.jsonl") as f:
for line in f:
sample = json.loads(line)
print("Prompt:", sample["instruction"])
print("Input:", sample["input"])
print("Target:", sample["output"])
```
📜 License
MIT License (customizable)
Attribution recommended for academic or commercial use.
🤝 Contact
For collab, model finetuning, or tool integrations:
📧 [email protected] |