Gold prices in , July, 2025 For that commodity indicator, we provide data from January 1960 to July 2025. The average value during that period was 603.55 USD per troy ounce with a minimum of 34.94 USD per troy ounce in January 1970 and a maximum of 3352.66 USD per troy ounce in June 2025. | TheGlobalEconomy.com

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.

The United States recorded a trade deficit of 60.18 USD Billion in June of 2025. This dataset provides the latest reported value for - United States Balance of Trade - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The jewelry industry accounted for a 43.68 percent share of global gold demand in 2024, which amounted to about 2,012 metric tons. Jewelry was the largest gold demanding industry worldwide in most recent years, although in 2020 it was the second after investment demand. What is gold used for? Gold is a transition metal that has characteristically bright yellow coloring. Besides its most famous use, in jewelry, it has many useful applications. Because it is a very efficient conductor that can carry tiny electrical charges, gold is found in cell phones and televisions. In 2024, gold used for technology accounted for around seven percent of global gold demand. It is also used in dentistry, as it is the best material for fillings and crowns since it is easy to insert. Gold used for investing was the second-largest demand sector in 2024, accounting for over one-quarter of global gold demand. Gold mining worldwide The world production of gold mines has increased steadily over the years since 2005. In 2005, about 2,470 metric tons of gold was produced. In 2024, about 3,300 metric tons were produced. That was a slight decrease in production compared to the previous three years. The countries that produced the most gold in 2024 were China, Russia, and Australia. China produced an estimated 380 metric tons, while Russia produced an estimated 310 metric tons and Australia's production trailed at an estimated 290 metric tons.

Context

Gold, the yellow shiny metal, has been the fancy of mankind since ages. From making jewelry to being used as an investment, gold covers a huge spectrum of use cases. Gold, like other metals, is also traded on the commodities indexes across the world. For better understanding time series in a real-world scenario, we will work with gold prices collected historically and predict its future value.

Content

Metals such as gold have been traded for years across the world. Prices of gold are determined and used for trading the metal on commodity exchanges on a daily basis using a variety of factors. Using this daily price-level information only, our task is to predict future price of gold.

Data

For the purpose of implementing time series forecasting technique , i will utilize gold pricing from Quandl. Quandl is a platform for financial, economic, and alternative datasets. To access publicly shared datasets on Quandl, we can use the pandas-datareader library as well as quandl (library from Quandl itself). The following snippet shows a quick one-liner to get your hands on gold pricing information since 1970s.

import quandl gold_df = quandl.get("BUNDESBANK/BBK01_WT5511")

The time series is univariate with date and time feature

Starter Kernel(s)

-Start with Fundamentals: TSA & Box-Jenkins Methods

This notebook is an overview of TSA and traditional methods

Acknowledgements

For this dataset and tasks, i will depend upon Quandl. The premier source for financial, economic, and alternative datasets, serving investment professionals. Quandl’s platform is used by over 400,000 people, including analysts from the world’s top hedge funds, asset managers and investment banks.

Inspiration

• Forecast gold price

*If you find the data useful your upvote is an explicit feedback for future works, Have fun exploring data!*

#

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Gold Reserves in the United States remained unchanged at 8133.46 Tonnes in the second quarter of 2025 from 8133.46 Tonnes in the first quarter of 2025. This dataset provides - United States Gold Reserves - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Gold Reserves in Philippines increased to 129.70 Tonnes in the second quarter of 2025 from 127.48 Tonnes in the first quarter of 2025. This dataset provides - Philippines Gold Reserves - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The euro and U.S. dollar made up more than ***** of 10 SWIFT payments worldwide in 2025, outperforming many other currencies. This is according to a monthly report meant to track the market share of China's yuan renminbi within the international bank transfer system SWIFT. Although China holds the largest forex reserves in the world, the yuan ranked as the ******-used currency in international payments. The figures concern customer-initiated and institutional payments and exclude trade. Discussions on the potential weakening role of the U.S. dollar especially touch world trade and forex. For example, the share of the USD in forex reserves declined visibly against the euro and Japanese yen in 2024. What sparked this de-dollarization trend, and will it continue? Trade sanctions and de-dollarization De-dollarization in 2024 is mentioned mostly alongside trade and the BRICS countries - an informal name given to Brazil, Russia, India, China, and South Africa. The combined GDP of BRICS is about ** percent of the world's economy. After the start of the Ukraine war and Russia received economic sanctions, the BRICS slowly evolved into a trading bloc. The group increasingly wanted its own currency to settle payments within the trade bloc, to avoid using the U.S. dollar. In August 2024, BRICS will gather in South Africa to discuss the creation of such a new joint currency. Additionally, ** countries - including Argentina, Algeria, Egypt, Saudi Arabia, Turkey, and Yemen - expressed interest in joining the BRICS group. CBDC, or projects into a digital payment settlement A factor of future uncertainty for the U.S. dollar is how central bank digital currencies (CBDC) develop in emerging countries. Several projects exist between individual countries that specifically target cross-border interbank payments. A cooperation between Thailand and Hong Kong, Inthanon-Lionrock, ranks as the most advanced of these projects. CBDC does not require the U.S. dollar to function. Tangible such as commodities or gold can back them. The value of transactions processed with CBDC is to grow by ******* percent between 2024 and 2030.

Historically, gold and silver have played distinct roles in traditional monetary systems. While gold has primarily been revered as a superior store of value, prompting individuals to hoard it, silver has commonly been used as a medium of exchange. As the financial world evolves, the emergence of cryptocurrencies has introduced a new paradigm of value and exchange. However, the store-of-value characteristic of these digital assets remains largely uncharted. Charlie Lee, the founder of Litecoin, once likened Bitcoin to gold and Litecoin to silver. To validate this analogy, our study employs several metrics, including UTXO, STXO, WAL, CoinDaysDestroyed (CDD), and public on-chain transaction data. Furthermore, we've devised trading strategies centered around the Price-to-Utility (PU) ratio, offering a fresh perspective on crypto-asset valuation beyond traditional utilities. Our back-testing results not only display trading indicators for both Bitcoin and Litecoin but also substantiate Lee's metaphor, underscoring Bitcoin's superior store-of-value proposition relative to Litecoin. We anticipate that our findings will drive further exploration into the valuation of crypto assets. For enhanced transparency and to promote future research, we've made our datasets available on Harvard Dataverse and shared our Python code on GitHub as open source.

Currently, the stock market is attractive, and it is challenging to develop an efficient investment model with high accuracy due to changes in the values of the shares for political, economic, and social reasons. This paper presents an innovative proposal for a short-term, automatic investment model to reduce capital loss during trading, applying a reinforcement learning (RL) model. On the other hand, we propose an adaptable data window structure to enhance the learning and accuracy of investment agents in three foreign exchange markets: crude oil, gold, and the Euro. In addition, the RL model employs an actor-critic neural network with rectified linear unit (ReLU) neurons to generate specialized investment agents, enabling more efficient trading, minimizing investment losses across different time periods, and reducing the model's learning time. The proposed RL model obtained a reduction average loss of 0.03% in Euro, 0.25% in Gold, and 0.13% in Crude Oil in the test phase with varying initial conditions.

Gold Reserves in Nepal remained unchanged at 7.99 Tonnes in the second quarter of 2024 from 7.99 Tonnes in the first quarter of 2024. This dataset provides - Nepal Gold Reserves - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Gold was the main export from Tanzania in 2020, reaching an export value of nearly three billion U.S. dollars. Exports of manufactured goods amounted to 908.6 million U.S. dollars, while cashewnuts exports added 360 million U.S. dollars to the Tanzanian merchandise trade balance.

Effective methods for simultaneously measuring viral, bacterial, protozoan, and fungal pathogens in wastewater are needed. Here, we investigate how the sample type and nucleic acid extraction protocols affect broad-range pathogen detection. We compared methods for analyzing wastewater solids and whole influent by dPCR detection of spiked and endogenous targets including DNA and RNA viruses (mpox, norovirus, SARS-CoV-2), bacteria (Clostridium difficile, Campylobacter jejuni), protozoa (Cryptosporidium spp.), fungi (Candida auris), and antibiotic resistance genes. Using selected methods, we then analyzed date-matched (1) solid, (2) centrifuged influent, and (3) whole influent samples collected from 11 facilities at three time points. We demonstrate that one workflow can be used to simultaneously detect all of the targets. Comparing normalization of targets by concentration of PMMoV, carjivirus, and 16S rRNA, we show that using different controls together can complicate the interpretation of concentrations across targets. We observed similar detection rates and nucleic acid concentrations for the targets examined in this study across all three sample types with slightly higher concentrations in centrifuged influent. These results suggest that centrifuged influent is a viable option and can circumvent the limitation of varying residence times for primary settled solids.

Gold Reserves in India increased to 880 Tonnes in the second quarter of 2025 from 879.60 Tonnes in the first quarter of 2025. This dataset provides - India Gold Reserves - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Gold Reserves in Canada remained unchanged at 0 Tonnes in the second quarter of 2025 from 0 Tonnes in the first quarter of 2025. This dataset provides - Canada Gold Reserves - actual values, historical data, forecast, chart, statistics, economic calendar and news.

In 2024, ** percent of adults in the United States invested in the stock market. This figure has remained steady over the last few years, and is still below the levels before the Great Recession, when it peaked in 2007 at ** percent. What is the stock market? The stock market can be defined as a group of stock exchanges, where investors can buy shares in a publicly traded company. In more recent years, it is estimated an increasing number of Americans are using neobrokers, making stock trading more accessible to investors. Other investments A significant number of people think stocks and bonds are the safest investments, while others point to real estate, gold, bonds, or a savings account. Since witnessing the significant one-day losses in the stock market during the Financial Crisis, many investors were turning towards these alternatives in hopes for more stability, particularly for investments with longer maturities. This could explain the decrease in this statistic since 2007. Nevertheless, some speculators enjoy chasing the short-run fluctuations, and others see value in choosing particular stocks.

The largest portion of India's imports originated from China, representing more than 15 percent of the total imports in the financial year 2024, with Russia following at over nine percent. Conversely, during the same period, India's primary exports were directed towards the United States. India’s import mix India's imports encompass a wide range of goods, with some key commodities dominating the landscape. Among these critical imports are crude oil, gold, and petroleum products. Crude oil, constituting approximately 20 percent of total imports, is critical due to the nation’s reliance on energy sources. In addition, India, as a major textile producer, has witnessed a significant rise in textile imports over the years to meet the diverse consumer and industrial demands. Trade balance In the recent past, the country experienced a notable change in its trade balance-to-GDP ratio. This ratio, which reflects the relationship between the country's trade activities and the size of its economy, saw a significant decrease of 8.5 percent in fiscal year 2023. In other words, the country was importing more goods and services than it was exporting during the period.

About This Dataset: Copper Prices and Market Trends

    This dataset provides **insights into copper prices**, including current rates, historical trends, and key factors affecting price fluctuations. Copper is essential in **construction**, **electronics**, and **transportation** industries. Investors, traders, and analysts use accurate copper price data to guide decisions related to **trading**, **futures**, and **commodity investments**.

    ### **Key Features of the Dataset**

    #### **Live Market Data and Updates**
    Stay updated with the latest **copper price per pound** in USD. This data is sourced from exchanges like the **London Metal Exchange (LME)** and **COMEX**. Price fluctuations result from **global supply-demand shifts**, currency changes, and geopolitical factors.

    #### **Interactive Copper Price Charts**
    Explore **dynamic charts** showcasing real-time and historical price movements. These compare copper with **gold**, **silver**, and **aluminium**, offering insights into **market trends** and inter-metal correlations.

    ### **Factors Driving Copper Prices**

    #### **1. Supply and Demand Dynamics**
    Global copper supply is driven by mining activities in regions like **Peru**, **China**, and the **United States**. Disruptions in production or policy changes can cause **supply shocks**. On the demand side, **industrial growth** in countries like **India** and **China** sustains demand for copper.

    #### **2. Economic and Industry Trends**
    Copper prices often reflect **economic trends**. The push for **renewable energy** and **electric vehicles** has boosted long-term demand. Conversely, economic downturns and **inflation** can reduce demand, lowering prices.

    #### **3. Impact of Currency and Trade Policies**
    As a globally traded commodity, copper prices are influenced by **currency fluctuations** and **tariff policies**. A strong **US dollar** typically suppresses copper prices by increasing costs for international buyers. Trade tensions can also disrupt **commodity markets**.

    ### **Applications and Benefits**

    This dataset supports **commodity investors**, **traders**, and **industry professionals**:

    - **Investors** forecast price trends and manage **investment risks**. 
    - **Analysts** perform **market research** using price data to assess **copper futures**. 
    - **Manufacturers** optimize supply chains and **cost forecasts**.

    Explore more about copper investments on **Money Metals**:

    - [**Buy Copper Products**](https://www.moneymetals.com/buy/copper) 
    - [**95% Copper Pennies (Pre-1983)**](https://www.moneymetals.com/pre-1983-95-percent-copper-pennies/4) 
    - [**Copper Buffalo Rounds**](https://www.moneymetals.com/copper-buffalo-round-1-avdp-oz-999-pure-copper/297)

    ### **Copper Price Comparisons with Other Metals**

    Copper prices often correlate with those of **industrial** and **precious metals**:

    - **Gold** and **silver** are sensitive to **inflation** and currency shifts. 
    - **Iron ore** and **aluminium** reflect changes in **global demand** within construction and manufacturing sectors.

    These correlations help traders develop **hedging strategies** and **investment models**.

    ### **Data Variables and Availability**

    Key metrics include:

    - **Copper Price Per Pound:** The current market price in USD. 
    - **Copper Futures Price:** Data from **COMEX** futures contracts. 
    - **Historical Price Trends:** Long-term movements, updated regularly. 

    Data is available in **CSV** and **JSON** formats, enabling integration with analytical tools and platforms.

    ### **Conclusion**

    Copper price data is crucial for **monitoring global commodity markets**. From **mining** to **investment strategies**, copper impacts industries worldwide. Reliable data supports **risk management**, **planning**, and **economic forecasting**.

    For more tools and data, visit the **Money Metals** [Copper Prices Page](https://www.moneymetals.com/copper-prices).

Forecasting stock returns is a vital and at the same time challenging task in the financial arena, given the market’s susceptibility to abrupt swings. In this paper, we propose a strategy that adapts to different volatility regimes: during periods of high volatility, we employ the copper-gold ratio (CGR) as a leading indicator for the S&P 500 (SPY), while in periods of normal volatility, we introduce a differential long-term memory (DLSTM) neural network. The CGR combines the properties of copper (which reflects industrial and economic activity) and gold, a traditional safe-haven asset. In four major economic events, our analysis reveals that sharp movements in the CGR often precede corresponding changes in the SPY, suggesting the ratio’s potential as an early warning signal. For more stable markets, we introduce the DLSTM, which extends the standard LSTM architecture through a loss function designed to exploit differences between consecutive price steps. This design increases predictive power and achieves 82% directional accuracy on daily SPY forecasts, outperforming both a baseline LSTM and a binary classification approach. Finally, we validate the trading utility of the DLSTM by simulating intraday trading over one- and three-month periods, demonstrating consistent gains that highlight the practical value of the method. By synthesizing CGR analysis and DLSTM modeling, our approach offers a versatile framework to address diverse market environments and provide new insights for both researchers and practitioners.