I am hereby sharing the dataset used in the paper titled 'Beyond Tradition: A Hybrid Model Unveiling News Impact on Exchange Rates'. The dataset comprises the following components: Taylor Rule Fundamentals: - Inflation - Industrial production index (as a high-frequency proxy of GDP) - Money market rate spanning from 2000 to 2018. Textual Information: - Economic Policy Uncertainty Index from https://www.policyuncertainty.com/index.html (as of November 9, 2023). - Time series of entropies calculated for U.S. Dollar-related news topics extracted from the Nexis-Uni database. Note: To acquire the textual data from the Nexis-Uni database, we conducted the following steps: We entered "U.S. Dollar" as a keyword in the search for news, resulting in over 15 million non-duplicate news items. Subsequently, we cleaned the news data and selected relevant news items using the following criteria: (i) The U.S. Dollar appears in the title of news items, (ii) The term "U.S. Dollar" is repeated several times in the news, (iii) The first paragraph of the news contains the word "U.S. Dollar", (iv) The news items are automatically selected by the Nexis-Uni database with the U.S. Dollar as the subject. Subsequently, we identified the topics related to the US Dollar from the news using LDA and calculated the Shannon entropies over time for each topic.

Hereby I am sharing the data used in the paper: "The words have power: the impact of news on exchange rates". The dataset includes: Taylor Rule Fundamentals: - inflation, - industrial production index (as a high-frequency proxy of GDP), - money market rate from 2000 until 2018. Textual information: - Entropies of news items about the U.S. Dollar from Nexis-Uni database. This is how we get the textual data from Nexis-Uni database: We enter “U.S. Dollar” as a keyword in searching for the news, which gives over 15 Million non-duplicate news. Next, we clean data news and select the relevant news items as follows. We select news about U.S. Dollar with the following criteria: (i) the U.S. Dollar appears in the title of news items, (ii) U.S. Dollar is repeated several times in the news, (iii) the first paragraph of news contains the word “U.S. Dollar”, (iv) U.S. Dollar is the subject of news items which are automatically selected by Nexis-Uni database. - economic policy uncertainty index from https://www.policyuncertainty.com/index.html

Dataset Description Overview

This dataset contains historical daily exchange rates between the US Dollar (USD) and the Indonesian Rupiah (IDR), sourced from Yahoo Finance. Each row represents one trading day, making it suitable for time-series analysis, forecasting, and financial research. Context

The USD/IDR exchange rate is widely used for:

Monitoring currency risk and hedging USD–IDR exposure
Evaluating Indonesia’s macroeconomic and financial conditions
Backtesting FX trading strategies
Teaching time-series and financial modeling

Because Indonesia is an emerging market, USD/IDR often exhibits notable volatility, driven by global interest rates, commodity prices, and domestic policy changes. Source and Collection

Data provider: Yahoo Finance
Instrument: USD/IDR exchange rate (e.g., ticker USDIDR=X on Yahoo Finance)
Frequency: Daily (one record per trading day)
Fields: Standard Yahoo Finance OHLC data (Open, High, Low, Close, Adjusted Close, Volume)
Collection method: Downloaded programmatically via a Python library that wraps Yahoo Finance data (e.g., yfinance)
Retrieval: Data was fetched in Python and then exported to CSV for this dataset
Coverage period: From the earliest available date on Yahoo Finance for USD/IDR up to the download date (please add the exact start and end dates if you want, e.g., YYYY‑MM‑DD to YYYY‑MM‑DD)

Please check Yahoo Finance’s terms of use before using the dataset in commercial or production settings. Possible Use Cases

Time-series forecasting models (ARIMA, Prophet, LSTM, etc.)
Volatility and risk analysis (e.g., rolling volatility, VaR)
Studying the impact of macroeconomic news or events on IDR
Feature in multi-asset or macroeconomic research datasets
Educational projects in finance, econometrics, and data science

Notes

This dataset contains no personal or sensitive information.
Values are provided as-is from Yahoo Finance; minor discrepancies may exist compared with other FX data vendors.

It is forecast that the global online trading market will increase at a global compound annual growth rate of *** percent per year, increasing to an estimated **** billion U.S. dollars in 2026. This is from a base of around ***** billion U.S. dollars in 2022. Following the coronavirus pandemic beginning in 2020, online trading activity increased among millennial investors. Many online brokers, including Robinhood, experienced notable growth in the number of platform users from the second quarter of 2020 through to 2021. A low-cost business model, paired with technological integration and social media promotion were contributing factors to the popularity of online trading. What is an online trading platform? The online trading market is typically accessed through an online market broker, providing a platform for users to track market prices and execute buy and sell orders on financial securities. The user typically holds their portfolio through an online broker. The number of monthly downloads for leading online trading apps spiked in early 2021. While this was influenced by media attention to popular news stories such as the increase in the price of GameStop shares, online trading is expected to continue as an alternative to traditional investment methods. Factors driving online trading The integration of technology has improved investing activities. From a global survey, most respondents stated technology made investing easier, cheaper, and more efficient. The use of technology allowed information such as real-time data, industry and firm reports, and trading notifications to be more accessible directly to the investor. Online platforms had experienced an increase in the number of trades placed per day, in 2019, interactive brokers had an average of 1,380 trades placed per day. This number steadily increased to 3,905 trades per day in 2021. Technological integration allowed trading via online platforms to be an alternative to traditional methods of relying on an in-person full-service broker.

This dataset consists of seven columns and 2740 rows collected from thirteen different sources for digital currencies. The dataset includes information on the opening price, closing price, highest price, lowest price, and volume, as well as the percentage change and the currencies collected in March 2024.

Here's a description of the contents based on the available columns in the data:

Last Price: The most recent recorded price of Bitcoin. Open Price: The opening price of Bitcoin at the start of the specified time period. Max: The maximum price of Bitcoin during the specified time period. Min: The minimum price of Bitcoin during the specified time period. Size: This may refer to the trading volume of Bitcoin during the specified time period, but requires further clarification to confirm its meaning. Change Persent: The percentage change in the price of Bitcoin compared to the previous time period, it seems there's a typographical error and it might mean "Change Percent". Class: The classification of the currency, in this context, all the data is classified under "Bitcoin". This data could be useful in financial market analytics, especially for those interested in cryptocurrencies and the dynamics of Bitcoin prices. It can be used to study price changes, market fluctuations, or even to develop models for predicting cryptocurrency prices.

Applications in Machine Learning and Beyond This dataset, focusing on Bitcoin prices and their fluctuations, has a wide range of applications, especially within the realm of machine learning and financial analysis:

Price Prediction: Utilizing historical data to train models that can predict future Bitcoin prices. Techniques like time series analysis, regression models, and more sophisticated neural networks (e.g., LSTM) could be applied. Volatility Modeling: Analyzing the variability in Bitcoin prices over time. Machine learning models can help understand patterns in price fluctuations, potentially leading to insights for investors about risk and volatility. Trend Analysis: Identifying long-term trends in Bitcoin's market performance. Machine learning algorithms can detect underlying patterns and trends, helping investors make informed decisions. Anomaly Detection: Spotting unusual patterns or outliers in Bitcoin prices that could indicate market manipulation, fraud, or significant market events. Machine learning models, especially unsupervised algorithms, are adept at detecting anomalies. Sentiment Analysis: By integrating this dataset with social media and news sentiment data, models can assess how public sentiment impacts Bitcoin prices. This involves natural language processing (NLP) techniques to gauge sentiment and correlate it with price movements. Portfolio Management: In the broader scope of financial management, machine learning models can use such datasets to optimize cryptocurrency portfolios, balancing risk and return based on historical performance. Risk Assessment: Analyzing the data to evaluate the financial risk associated with Bitcoin investments. Machine learning can provide probabilistic estimates of future price drops or gains, aiding in risk management strategies. Overall, the detailed data on Bitcoin's pricing and trading volume offers a rich foundation for various analytical and predictive modeling efforts in both academic research and practical financial applications. ​

Collected and Preprocessing: Wisam Abdullah , Dr. Modhar , and Dr. Ahmed Alsardly are lecturers in Tikrit University.

Copy Trading Platform Market Outlook

According to our latest research, the global copy trading platform market size reached USD 4.27 billion in 2024. The market is experiencing robust growth, driven by the increasing adoption of automated trading technologies and social trading trends. With a projected compound annual growth rate (CAGR) of 17.8% from 2025 to 2033, the market is forecasted to reach USD 15.42 billion by 2033. This impressive trajectory is primarily fueled by a convergence of factors such as the democratization of financial markets, enhanced accessibility through digital platforms, and a surge in retail investor participation worldwide.

One of the most significant growth drivers for the copy trading platform market is the rising demand for user-friendly investment solutions among retail investors. As financial literacy improves and access to trading platforms becomes more widespread, individuals with limited trading experience are increasingly seeking ways to participate in the markets. Copy trading platforms allow these users to replicate the strategies of experienced traders, reducing the barrier to entry and minimizing the learning curve. The proliferation of mobile applications and web-based platforms has further simplified the process, enabling investors to monitor and manage their portfolios in real-time from anywhere in the world. This trend is particularly pronounced among millennials and Gen Z, who prefer digital-first financial tools and are more inclined to explore innovative investment avenues.

Technological advancements are playing a pivotal role in shaping the future of the copy trading platform market. The integration of artificial intelligence, machine learning, and big data analytics has significantly enhanced the efficiency and reliability of these platforms. Automated risk management tools, real-time analytics, and customizable trading signals are now standard features, empowering users to make more informed decisions. Additionally, the growing popularity of cryptocurrencies and alternative assets has expanded the scope of copy trading beyond traditional stocks and forex, attracting a broader user base. Platform providers are also focusing on robust security measures and regulatory compliance to build trust and ensure the safety of user funds, further strengthening market growth.

Another critical factor contributing to the expansion of the copy trading platform market is the increasing collaboration between fintech companies and traditional financial institutions. Banks, brokerage firms, and wealth management companies are recognizing the value of integrating copy trading functionalities into their service offerings to attract and retain tech-savvy customers. Strategic partnerships, mergers, and acquisitions are becoming more common as established players seek to enhance their digital capabilities and tap into new revenue streams. This collaborative ecosystem is fostering innovation, driving competition, and accelerating the adoption of copy trading platforms across various segments of the financial services industry.

In the realm of digital trading, the role of a Forex Trading Platform is becoming increasingly pivotal. These platforms provide traders with the necessary tools to engage in the foreign exchange market, which is known for its high liquidity and 24-hour trading opportunities. Forex trading platforms offer a range of features, including real-time currency quotes, charting tools, and news feeds, enabling traders to make informed decisions. As the demand for forex trading grows, platforms are continuously enhancing their offerings with advanced analytics, automated trading capabilities, and user-friendly interfaces to cater to both novice and experienced traders. The integration of these platforms with copy trading functionalities further broadens their appeal, allowing users to replicate successful forex strategies with ease.

From a regional perspective, Asia Pacific is emerging as a key growth engine for the global copy trading platform market. The region's rapidly expanding middle class, increasing internet penetration, and favorable regulatory environment are creating a fertile ground for digital investment solutions. Countries such as China, India, and Singapore are witnessing a surge in retail trading activity, supported by government initiatives to

Context

The script used to acquire all of the following data can be found in this GitHub repository. This repository also contains the modeling codes and will be updated continually, so welcome starring or watching!

Stock market data can be interesting to analyze and as a further incentive, strong predictive models can have large financial payoff. The amount of financial data on the web is seemingly endless. A large and well structured dataset on a wide array of companies can be hard to come by. Here provided a dataset with historical stock prices (last 12 years) for 29 of 30 DJIA companies (excluding 'V' because it does not have the whole 12 years data).

   ['MMM', 'AXP', 'AAPL', 'BA', 'CAT', 'CVX', 'CSCO', 'KO', 'DIS', 'XOM', 'GE',

   'GS', 'HD', 'IBM', 'INTC', 'JNJ', 'JPM', 'MCD', 'MRK', 'MSFT', 'NKE', 'PFE',

   'PG', 'TRV', 'UTX', 'UNH', 'VZ', 'WMT', 'GOOGL', 'AMZN', 'AABA']

In the future if you wish for a more up to date dataset, this can be used to acquire new versions of the .csv files.

Content

The data is presented in a couple of formats to suit different individual's needs or computational limitations. I have included files containing 13 years of stock data (in the all_stocks_2006-01-01_to_2018-01-01.csv and corresponding folder) and a smaller version of the dataset (all_stocks_2017-01-01_to_2018-01-01.csv) with only the past year's stock data for those wishing to use something more manageable in size.

The folder individual_stocks_2006-01-01_to_2018-01-01 contains files of data for individual stocks, labelled by their stock ticker name. The all_stocks_2006-01-01_to_2018-01-01.csv and all_stocks_2017-01-01_to_2018-01-01.csv contain this same data, presented in merged .csv files. Depending on the intended use (graphing, modelling etc.) the user may prefer one of these given formats.

All the files have the following columns: Date - in format: yy-mm-dd

Open - price of the stock at market open (this is NYSE data so all in USD)

High - Highest price reached in the day

Low Close - Lowest price reached in the day

Volume - Number of shares traded

Name - the stock's ticker name

Inspiration

This dataset lends itself to a some very interesting visualizations. One can look at simple things like how prices change over time, graph an compare multiple stocks at once, or generate and graph new metrics from the data provided. From these data informative stock stats such as volatility and moving averages can be easily calculated. The million dollar question is: can you develop a model that can beat the market and allow you to make statistically informed trades!

Acknowledgement

This Data description is adapted from the dataset named 'S&P 500 Stock data'. This data is scrapped from Google finance using the python library 'pandas_datareader'. Special thanks to Kaggle, Github and the Market.

Gold fell to 4,199.97 USD/t.oz on December 2, 2025, down 0.75% from the previous day. Over the past month, Gold's price has risen 4.93%, and is up 58.92% 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 December of 2025.

Corporate Profits in the United States increased to 3259.41 USD Billion in the second quarter of 2025 from 3252.44 USD Billion in the first quarter of 2025. This dataset provides the latest reported value for - United States Corporate Profits - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.