metadata
annotations_creators:
- automated
language:
- en
license:
- mit
task_categories:
- time-series-forecasting
- reinforcement-learning
tags:
- finance
- stock-market
- technical-analysis
- yfinance
size_categories:
- 10K<n<100K
📊 Multi-Agent RL Trading System - Dataset
This dataset contains historical OHLCV (Open, High, Low, Close, Volume) data for AAPL, MSFT, and GOOGL, pre-processed for Reinforcement Learning based trading systems.
📁 Dataset Content
The dataset consists of CSV files downloaded via yfinance:
AAPL.csv: Apple Inc. daily data (Jan 2018 - Dec 2024).MSFT.csv: Microsoft Corp. daily data (Jan 2018 - Dec 2024).GOOGL.csv: Alphabet Inc. daily data (Jan 2018 - Dec 2024).
📝 Columns
| Column | Description |
|---|---|
| Date | Trading date (YYYY-MM-DD) |
| Open | Opening price |
| High | Highest price of the day |
| Low | Lowest price of the day |
| Close | Closing price (Adjusted for splits/dividends) |
| Volume | Number of shares traded |
⚙️ Usage
This data is designed to be fed into a Feature Engineering pipeline (calculating RSI, MACD, etc.) before being used by the TradingEnv.
import pandas as pd
# Load data
df = pd.read_csv("AAPL.csv", parse_dates=['Date'], index_col='Date')
print(df.head())
🔗 Related Models
- Trained Agents: AdityaaXD/Multi-Agent_Reinforcement_Learning_Trading_System_Models
- GitHub Repository: ADITYA-tp01/Multi-Agent-Reinforcement-Learning-Trading-System-Data
⚠️ Source
Data was sourced from Yahoo Finance API. Not intended for real financial advice or live trading decisions.
🛠️ Credits
Collected by Adityaraj Suman for the Multi-Agent RL Trading System project.