mltrev23/gold-price

FINAL_USO Dataset

Overview

The FINAL_USO dataset is a comprehensive collection of financial data, including stock prices, volumes, and other relevant metrics for various market indices and individual securities. This dataset is particularly suited for financial analysis, time series forecasting, and market trend analysis.

Dataset Structure

The dataset is provided as a single CSV file named FINAL_USO.csv. It contains 1,718 entries and 80 columns, each representing different financial metrics over a period of time.

Columns

The dataset includes the following columns:

1. Date: The date for the corresponding entry.
2. Open: Opening price for the day.
3. High: Highest price reached during the day.
4. Low: Lowest price reached during the day.
5. Close: Closing price for the day.
6. Adj Close: Adjusted closing price after accounting for any corporate actions.
7. Volume: The number of shares traded during the day.

Additional columns include similar metrics for different indices and securities, such as:

• SP_open, SP_high, SP_low, SP_close, SP_Ajclose, SP_volume: Metrics for the S&P 500 index.
• DJ_open, DJ_high, DJ_low, DJ_close, DJ_Ajclose, DJ_volume: Metrics for the Dow Jones Industrial Average.
• EG_open, EG_high, EG_low, EG_close, EG_Ajclose, EG_volume: Metrics for a specified security or index.
• EU_Price, EU_open, EU_high, EU_low, EU_Trend: Metrics for European market indices or securities.
• OF_Price, OF_Open, OF_High, OF_Low, OF_Volume, OF_Trend: Metrics for other financial instruments or indices.
• USO_Open, USO_High, USO_Low, USO_Close, USO_Adj Close, USO_Volume: Metrics specific to the United States Oil Fund (USO).

Summary Statistics

• Date Range: The dataset spans multiple years, providing a comprehensive view of market movements.
• Volume: The number of shares traded, ranging from 1,035,100 to 110,265,700 for various securities.
• Price Metrics: Prices for different securities, with opening prices ranging from $7.82 to $41.60, and closing prices ranging from $7.96 to $42.01.

Usage

This dataset is suitable for a variety of financial analyses, including:

• Time Series Forecasting: Predict future prices or volumes based on historical data.
• Market Trend Analysis: Identify trends and patterns in financial markets over time.
• Correlation Analysis: Explore relationships between different market indices and securities.

Example Use Cases

• Predicting Stock Prices: Using machine learning models to forecast future stock prices based on historical data.
• Analyzing Market Volatility: Understanding the volatility of different securities and indices over time.
• Portfolio Management: Assisting in decision-making for portfolio allocation by analyzing past performance.

Requirements

To use this dataset, you will need the following Python libraries:

pip install pandas
pip install numpy
pip install matplotlib
pip install scikit-learn

Example Code

Loading the Dataset

You can load the dataset using the following code snippet:

import pandas as pd

# Load the dataset
df = pd.read_csv('FINAL_USO.csv')

# Display the first few rows
print(df.head())

Analyzing Trends

Here's how you might analyze price trends over time:

import matplotlib.pyplot as plt

# Plot the closing prices of the USO
plt.figure(figsize=(10, 6))
plt.plot(df['Date'], df['USO_Close'], label='USO Closing Price')
plt.title('USO Closing Prices Over Time')
plt.xlabel('Date')
plt.ylabel('Closing Price')
plt.legend()
plt.show()

Forecasting with Machine Learning

You can also use machine learning models to forecast future prices:

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

# Prepare the data
X = df[['Open', 'High', 'Low', 'Volume']]
y = df['Close']

# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train the model
model = LinearRegression()
model.fit(X_train, y_train)

# Predict future prices
predictions = model.predict(X_test)

# Evaluate the model
print(f"Model R^2 score: {model.score(X_test, y_test)}")

License

This dataset is provided under the [LICENSE] file included in the repository.