Explore short and medium-term Gold price forecast analysis and check long-term Gold predictions for 2026, 2030, and beyond.

Gold fell to 4,745.99 USD/t.oz on April 21, 2026, down 1.56% from the previous day. Over the past month, Gold's price has risen 7.66%, and is up 42.25% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gold - values, historical data, forecasts and news - updated on April of 2026.

Description for Kaggle Project

Title: Gold Price Prediction

Subtitle: Analysis and Forecasting Using Gold Price Data from Kaggle's goldstock.csv

Description This project aims to analyze and forecast gold prices using a comprehensive dataset spanning from January 19, 2014, to January 22, 2024. The dataset, sourced from Kaggle, includes daily gold prices with key financial metrics such as opening and closing prices, trading volume, and the highest and lowest prices recorded each trading day. Through this project, we perform time series analysis, develop predictive models, formulate and backtest trading strategies, and conduct market sentiment and statistical analyses.

Upload an Image - Choose a relevant image such as a graph of gold price trends, a gold bar, or an illustrative image related to financial data analysis.

Datasets - Source: Kaggle - File: goldstock.csv

Context, Sources, and Inspiration -Context: Understanding the dynamics of gold prices is crucial for investors and financial analysts. This project provides insights into historical price trends and equips users with tools to predict future prices. - Sources: The dataset is sourced from Kaggle and contains historical gold price data obtained from Nasdaq. Inspiration: The inspiration behind this project is to enable researchers, analysts, and data enthusiasts to make informed decisions, develop trading strategies, and contribute to a broader understanding of market behavior.

This dataset contains daily gold futures market data for the last five years, sourced from Yahoo Finance using the ticker GC=F.

It is designed specifically for time-series forecasting tasks, where the objective is to model and predict future gold prices using historical trends and technical indicators.

The dataset includes traditional OHLCV market data (Open, High, Low, Close, Volume) along with commonly used technical analysis indicators such as moving averages, volatility measures, RSI, MACD, and Bollinger Bands.

This makes it suitable for: • Financial time-series forecasting • Deep learning models (LSTM, GRU) • Statistical models (ARIMA, SARIMA) • Prophet forecasting • Feature engineering and EDA ⸻

📊 Columns Description

date – Trading date.

open – Opening price of gold futures for the day.

high – Highest price reached during the trading session.

low – Lowest price reached during the trading session.

close – Closing price of gold futures for the day.

volume – Trading volume for gold futures contracts.

ma_7 – 7-day moving average of closing price.

ma_30 – 30-day moving average of closing price.

ma_90 – 90-day moving average of closing price.

daily_return – Percentage change in closing price from the previous day.

volatility_7 – 7-day rolling standard deviation of daily returns.

volatility_30 – 30-day rolling standard deviation of daily returns.

rsi – Relative Strength Index, a momentum indicator measuring overbought/oversold conditions.

macd – Moving Average Convergence Divergence value.

macd_signal – Signal line for MACD.

bb_upper – Upper Bollinger Band.

bb_lower – Lower Bollinger Band.

⸻

🎯 Use Cases • Gold price forecasting • Financial trend analysis • Volatility modeling • Feature importance studies • Time-series ML projects

This statistic depicts the average annual prices for gold from 2014 to 2024 with a forecast until 2026. In 2024, the average price for gold stood at 2,388 U.S. dollars per troy ounce, the highest value recorded throughout the period considered. In 2026, the average gold price is expected to increase, reaching 3,200 U.S. dollars per troy ounce.

Monthly and long-term gold price data (US$/toz): historical series and analyst forecasts curated by FocusEconomics.

Gold Futures (GC=F) Price Prediction Dataset

This dataset contains cleaned historical Gold Futures (GC=F) price data along with predictions generated using a Long Short-Term Memory (LSTM) deep learning model.

The dataset is designed for financial time-series analysis, forecasting, and machine learning research.

Files Included

clean_price_data.csv

Daily OHLCV gold futures data with engineered technical indicators.

Columns: - Open — Opening price - High — Daily high price - Low — Daily low price - Close — Closing price - Volume — Trading volume - Returns — Daily percentage returns - Ma_7 — 7-day moving average - Ma_21 — 21-day moving average - Volatility — 21-day rolling volatility

price_predictions.csv

Model output generated using an LSTM neural network.

Columns: - Actual — Actual closing prices - Predicted — LSTM-predicted closing prices

Data Source

• Yahoo Finance
• Ticker: GC=F (Gold Futures)

Use Cases

• Financial time-series forecasting
• Deep learning model benchmarking
• Quantitative finance research
• Trading strategy experimentation
• Interactive dashboards and visualization

Disclaimer

This dataset is intended for educational and research purposes only. It does not constitute financial or investment advice.

Update Frequency

This dataset may be updated periodically with newer market data.

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Content

This dataset contains historical daily gold price data along with multiple technical indicators used for time series analysis and forecasting. It includes essential market features such as opening price, closing price, highest and lowest prices of the day, adjusted close price, and trading volume. In addition to raw price data, the dataset provides calculated indicators like moving averages (7-day, 30-day, 90-day), daily returns, short-term and long-term volatility, Relative Strength Index (RSI), MACD and its signal line, and Bollinger Bands (upper and lower). These engineered features make the dataset highly suitable for machine learning models, especially for price prediction and trend analysis tasks.

Context

The purpose of this dataset is to support gold price forecasting using statistical and machine learning techniques. Gold is a highly volatile and globally traded commodity, making it an important asset for financial analysis and investment strategies. By combining historical price movements with technical indicators, this dataset enables analysts and data scientists to study patterns, detect trends, measure momentum, and build predictive models. It is particularly useful for time series forecasting, regression modeling, feature engineering practice, and financial data analysis projects in data science and machine learning.

Comprehensive dataset of Wall Street analyst gold price forecasts for 2026, including institutional targets, sentiment analysis, and consensus metrics

Explore short and medium-term GOLD 24 Carat/Oz price prediction analysis and check long-term GOLD 24 Carat/Oz forecasts for 2026, 2030, and beyond.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Gold Price Outlook: S&P GSCI Gold index Faces Volatility Amidst Economic Uncertainties

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The Kuwaiti gold market soared to $X in 2021, jumping by 142% against the previous year. This figure reflects the total revenues of producers and importers (excluding logistics costs, retail marketing costs, and retailers' margins, which will be included in the final consumer price). In general, consumption showed prominent growth. As a result, consumption reached the peak level and is likely to continue growth in the immediate term.

In 2025, the price of platinum is forecast to hover around ***** U.S. dollars per troy ounce. Meanwhile, the cost of per troy ounce of gold is expected to amount to ***** U.S. dollars. Precious metals Precious metals are counted among the most valuable commodities worldwide. The most well known such metals are gold, silver and the platinum group metals. A precious metal can be used as an industrial commodity or as an investment. The major areas of application include the following sectors: technology, car-making, industrial manufacturing and jewelry making. Furthermore, gold and silver are used as coinage metals, and gold reserves are held by the central banks of many countries worldwide in order to store value or for use as a redemption medium. The idea behind this procedure is that gold reserves will help secure and stabilize the countries’ respective currencies. At ***** tons, the United States is the country with the most extensive stock of gold. It is kept in an underground vault at the New York Federal Reserve Bank. Russia, the United States, Canada, South Africa and China are the main producers of precious metals. Silver is the most abundant of the metals, followed by gold and palladium. Barrick Gold is the world’s largest gold mining company. The Toronto-based firm produced some **** million ounces of gold in 2020. The leading silver producers include Mexico-based Fresnillo, Poland’s KGHM Polska Miedž and the mining giant Glencore. Anglo Platinum and Impala are the key mining companies to produce platinum group metals. In 2023, Silver prices are expected to settle at around **** U.S. dollars per troy ounce. It is expected to remain the precious metal with the lowest value per ounce. The price of gold is forecast to drop to around ***** U.S. dollars per ounce, making it the most expensive precious metal in 2023.

Explore short and medium-term Meld Gold price prediction analysis and check long-term Meld Gold forecasts for 2026, 2030, and beyond.

This dataset contains the predicted prices of the asset GOLD over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

This comprehensive dataset contains 10 years of historical gold futures prices (2016-2026) sourced from Yahoo Finance, along with advanced machine learning predictions extending through December 2026. Perfect for financial analysis, time series forecasting, and machine learning enthusiasts.

Keywords: gold price prediction, time series forecasting, XGBoost, financial analysis, commodity trading, investment analysis, machine learning finance, gold futures, technical analysis, price forecasting

Historical Data Columns:** - Date (index) - Open, High, Low, Close prices (USD) - Volume - Adjusted Close Machine Learning Projects - Time series forecasting - Regression modeling - Feature engineering practice - Model comparison studies

✅ Financial Analysis - Investment strategy backtesting - Risk assessment - Trend analysis - Portfolio optimization

✅ Educational Purposes - Learning technical analysis - Understanding commodity markets - Practicing data visualization - Exploring EDA techniques

Model Performance

🤖 XGBoost Regression Model: - R² Score: 0.99+ (99%+ variance explained) - MAPE: <1% (prediction error) - RMSE: ~$15-20 (typical error range) - Training Set: 2,000+ samples - Test Set: 500+ samples

Data Source & Citation

Primary Source: Yahoo Finance (yfinance Python API) Ticker Symbol: GC=F (Gold Futures - COMEX) API: https://pypi.org/project/yfinance/ License: Educational and research purposes

Explore short and medium-term $GOLD price prediction analysis and check long-term $GOLD forecasts for 2026, 2030, and beyond.