The dataset of INR to Dollar exchange rates from 2003 to 2024 downloaded from Yahoo Finance likely contains historical exchange rate data for the Indian Rupee (INR) against the US Dollar (USD) over the specified time period. Here's a general description of what you might find in such a dataset:

1. Date: Each entry in the dataset likely includes a date or timestamp indicating when the exchange rate was recorded.

2. Exchange Rate: The dataset should include the exchange rate value, representing the number of Indian Rupees equivalent to one US Dollar on the corresponding date.

3. Time Period: The dataset should cover exchange rate data for each trading day or a specified frequency (e.g., weekly, monthly) from 2003 to 2024.

4. Additional Information: Depending on the source and format of the dataset, it may include additional information such as opening, high, low, and closing exchange rates for each day, as well as volume and adjusted closing prices.

5. Currency Pair: The dataset focuses specifically on the exchange rate between the Indian Rupee (INR) and the US Dollar (USD), allowing users to analyze trends and fluctuations in the value of the Indian Rupee relative to the US Dollar over time.

6. The dataset of INR to Dollar exchange rates from 2003 to 2024 downloaded from Yahoo Finance likely contains historical exchange rate data for the Indian Rupee (INR) against the US Dollar (USD) over the specified time period. Here's a general description of what you might find in such a dataset:

7. Date: Each entry in the dataset likely includes a date or timestamp indicating when the exchange rate was recorded.

8. Exchange Rate: The dataset should include the exchange rate value, representing the number of Indian Rupees equivalent to one US Dollar on the corresponding date.

9. Time Period: The dataset should cover exchange rate data for each trading day or a specified frequency (e.g., weekly, monthly) from 2003 to 2024.

10. Additional Information: Depending on the source and format of the dataset, it may include additional information such as opening, high, low, and closing exchange rates for each day, as well as volume and adjusted closing prices.

11. Currency Pair: The dataset focuses specifically on the exchange rate between the Indian Rupee (INR) and the US Dollar (USD), allowing users to analyze trends and fluctuations in the value of the Indian Rupee relative to the US Dollar over time.

12. Data Quality: It's important to consider the reliability and accuracy of the data. Ensure that the dataset is sourced from a reputable financial data provider like Yahoo Finance and that any missing or erroneous data points are appropriately handled.

Overall, this dataset can be used for various analytical purposes, including trend analysis, forecasting, and risk management in the context of currency exchange markets and international finance.: It's important to consider the reliability and accuracy of the data. Ensure that the dataset is sourced from a reputable financial data provider like Yahoo Finance and that any missing or erroneous data points are appropriately handled.

Overall, this dataset can be used for various analytical purposes, including trend analysis, forecasting, and risk management in the context of currency exchange markets and international finance.

This dataset contains daily FX rates for the major currencies provided by Yahoo finance; the naming convention should be self-explanatory, for convenience the codes of individual currencies:

USD: US dollar EUR: Euro JPY: Japanese yen GBP: British pound AUD: Australian dollar NZD: New Zealand dollar CAD: Canadian dollar SEK: Swedish krona CHF: Swiss franc HUF: Hungarian forint CNY: Chinese yuan (renminbi) HKD: Hong Kong dollar SGD: Singapore dollar INR: Indian rupee MXN: Mexican peso PHP: Philippine peso THB: Thai baht MYR: Malaysian ringgit ZAR: South African rand RUB: Russian ruble

Note from KB2: Please leave an upvote if you download the dataset:-)

Daily USD/IDR Dataset

  Dataset Description

This dataset contains daily historical exchange rate data for USD/IDR (US Dollar to Indonesian Rupiah). It includes daily price information such as opening rate, high, low, and closing rate.

  Dataset Summary

Homepage: Yahoo Finance USD/IDR Repository: Hugging Face Dataset Point of Contact: Gareth Aurelius Harrison

  Supported Tasks and Leaderboards

This dataset is suitable for:

Time series analysis Foreign… See the full description on the dataset page: https://huggingface.co/datasets/theonegareth/daily-usd-idr.

Cotton futures showed resilience with gains despite early weakness, influenced by dollar and oil trends, CFTC positioning, and ICE stock changes.

Data Source: This dataset was sourced from Yahoo Finance (finance.yahoo.com). Historical Time Period: The dataset covers historical currency exchange rate data for the USD/EUR currency pair from January 1, 2021, to September 30, 2023. Currency Pair: The currency pair being tracked in this dataset is USD/EUR, where USD represents the United States Dollar and EUR represents the Euro. Columns in the Dataset:

Date: This column represents the date when the currency exchange rate data was recorded. It covers the entire time period from January 1, 2021, to September 30, 2023.

Open: This column displays the opening exchange rate at the beginning of each trading day within the specified time frame. It indicates the starting rate for USD/EUR.

High: This column records the highest exchange rate observed during each trading day within the time frame. It shows the peak value reached by USD/EUR on each day.

Low: The "Low" column indicates the lowest exchange rate observed during each trading day within the given time period. It represents the lowest value of USD/EUR on each day.

Close: This column represents the closing exchange rate at the end of each trading day within the specified date range. It shows the final rate for USD/EUR on each day.

Adj Close: "Adj Close" represents the adjusted closing price for stocks, accounting for factors like dividends and stock splits.

Volume: The "Volume" column tracks the trading volume, indicating the total number of USD and EUR units traded on each trading day during the time period. It provides insights into the level of trading activity for the USD/EUR currency pair.

This dataset is valuable for analyzing historical trends and fluctuations in the USD/EUR exchange rate over the specified time frame, which can be useful for various financial and analytical purposes.

This statistic gives information on Yahoo!'s net income from 2004 to 2016. In the last reported year, the internet company's GAAP net loss was *** million US dollars, down from a net income of *** billion US dollars in 2014.

Yahoo has had its share of financial troubles, in part due to Google’s almost complete domination of market sectors where Yahoo used to be an important player, such as the search engine market. For example, as of April 2015, just under * percent of worldwide internet users search the web using Yahoo’s service, while more than ** percent use Google Search. But despite its ups and downs, the company has remained one of the most relevant multinational technology companies in the world. In 2014, Yahoo’s net income was a reported *** billion U.S. dollars, up from *** billion in the previous year. That same year, the company’s yearly revenue however was the second-lowest in the past decade – *** billion U.S. dollars. Especially the second quarter of 2014 displays lower than ever revenues for the company, as compared to previous years – just slightly over * billion U.S. dollars. According to the most recent report regarding Yahoo’s quarterly net income, the company generated a **** billion U.S. dollars profit in the third quarter of 2014, as a result the company's sale of Alibaba shares, but also a net loss of ***** million U.S. dollars in the second quarter of 2015. Yahoo was founded in the mid ***** in California, in the midst of the Silicon Valley technological boom. It is mostly known for its search engine, Yahoo Search, and the Yahoo web portal, featuring such services as Yahoo Finance, Yahoo News, Yahoo Answers and most notably Yahoo Mail. The company, which has made a lot of acquisitions since its modest beginnings, also provides advertising services, online mapping and video sharing. Since it acquired Tumblr in 2013, the company has also started to move into the social media sector. As of 2015, Yahoo is the second-most popular website in the United States, after Google, with more than *** million unique visitors per month on all of its properties combined.

Cryptocurrency historical datasets from January 2012 (if available) to October 2021 were obtained and integrated from various sources and Application Programming Interfaces (APIs) including Yahoo Finance, Cryptodownload, CoinMarketCap, various Kaggle datasets, and multiple APIs. While these datasets used various formats of time (e.g., minutes, hours, days), in order to integrate the datasets days format was used for in this research study. The integrated cryptocurrency historical datasets for 80 cryptocurrencies including but not limited to Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Cardano (ADA), Tether (USDT), Ripple (XRP), Solana (SOL), Polkadot (DOT), USD Coin (USDC), Dogecoin (DOGE), Tron (TRX), Bitcoin Cash (BCH), Litecoin (LTC), EOS (EOS), Cosmos (ATOM), Stellar (XLM), Wrapped Bitcoin (WBTC), Uniswap (UNI), Terra (LUNA), SHIBA INU (SHIB), and 60 more cryptocurrencies were uploaded in this online Mendeley data repository. Although the primary attribute of including the mentioned cryptocurrencies was the Market Capitalization, a subject matter expert i.e., a professional trader has also guided the initial selection of the cryptocurrencies by analyzing various indicators such as Relative Strength Index (RSI), Moving Average Convergence/Divergence (MACD), MYC Signals, Bollinger Bands, Fibonacci Retracement, Stochastic Oscillator and Ichimoku Cloud. The primary features of this dataset that were used as the decision-making criteria of the CLUS-MCDA II approach are Timestamps, Open, High, Low, Closed, Volume (Currency), % Change (7 days and 24 hours), Market Cap and Weighted Price values. The available excel and CSV files in this data set are just part of the integrated data and other databases, datasets and API References that was used in this study are as follows: [1] https://finance.yahoo.com/ [2] https://coinmarketcap.com/historical/ [3] https://cryptodatadownload.com/ [4] https://kaggle.com/philmohun/cryptocurrency-financial-data [5] https://kaggle.com/deepshah16/meme-cryptocurrency-historical-data [6] https://kaggle.com/sudalairajkumar/cryptocurrencypricehistory [7] https://min-api.cryptocompare.com/data/price?fsym=BTC&tsyms=USD [8] https://min-api.cryptocompare.com/ [9] https://p.nomics.com/cryptocurrency-bitcoin-api [10] https://www.coinapi.io/ [11] https://www.coingecko.com/en/api [12] https://cryptowat.ch/ [13] https://www.alphavantage.co/ This dataset is part of the CLUS-MCDA (Cluster analysis for improving Multiple Criteria Decision Analysis) and CLUS-MCDAII Project: https://aimaghsoodi.github.io/CLUSMCDA-R-Package/ https://github.com/Aimaghsoodi/CLUS-MCDA-II https://github.com/azadkavian/CLUS-MCDA

Dataset

This dataset was created by Muhammed Aboseif

Contents

Data was acquired from yahoo finance

In 2025 there were almost over 39,000 dollar stores in the United States. This was an increase of approximately 570 in comparison to the previous year, and more than 4,700 since 2021. Profitable years for dollar stores While many industries struggled in 2020 and 2021 due to the impact of the coronavirus (COVID-19) pandemic and difficult financial circumstances, dollar stores in the United States experienced significant growth. Discount department stores experienced falling sales as an industry from 2007 to 2020, but saw a massive rebound since 2021 when sales reached the highest levels since 2016. Dollar stores have fared better; revenues are estimated to have steadily grown since 2016. Dollar General sales The sales of Dollar General, one of the market leaders, reflect this pattern. In the fiscal year 2024, Dollar General's net sales amounted to approximately 40.6 billion U.S. dollars. The company’s sales have consistently grown since 2007, seeing the most significant year-on-year growth in 2020. This growth is understandable, considering that in 2023, Dollar General was the retailer with the most U.S. stores, followed by rival Dollar Tree.

The average market risk premium in the United States remained at *** percent in 2025. This suggests that the returns that investors expected for their investrments remained the same as the previous year in that country, in exchange for the risk they are exposed to. This premium has hovered between *** and *** percent since 2011. What causes country-specific risk? Risk to investments come from two main sources. First, inflation causes an asset’s price to decrease in real terms. A 100 U.S. dollar investment with three percent inflation is only worth ** U.S. dollars after one year. Investors are also interested in risks of project failure or non-performing loans. The unique U.S. context Analysts have historically considered the United States Treasury to be risk-free. This view has been shifting, but many advisors continue to use treasury yield rates as a risk-free rate. Given the fact that U.S. government securities are available at a variety of terms, this gives investment managers a range of tools for predicting future market developments.

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.

Forecasting speculative stock prices is essential for effective investment risk management and requires innovative algorithms. However, the speculative nature, volatility, and complex sequential dependencies within financial markets present inherent challenges that necessitate advanced techniques. In this regard, a novel framework, ACB-XDE (Attention-Customized BiLSTM-XGB Decision Ensemble), is proposed for predicting the daily closing price of speculative stock Bitcoin-USD (BTC-USD). The proposed ACB-XDE framework integrates the learning capabilities of a customized Bi-directional Long Short-Term Memory (BiLSTM) model with a novel attention mechanism and the XGBoost algorithm. The customized BiLSTM leverages its learning capabilities to capture complex sequential dependencies and speculative market trends. Meanwhile, the new attention mechanism dynamically assigns weights to influential features based on volatility patterns, thereby enhancing interpretability and optimizing effective cost measures and volatility forecasting. Moreover, XGBoost handles nonlinear relationships and contributes to the proposed ACB-XDE framework’s robustness. Furthermore, the error reciprocal method improves predictions by iteratively adjusting model weights based on the difference between theoretical expectations and actual errors in the individual attention-customized BiLSTM and XGBoost models. Finally, the predictions from both the XGBoost and attention-customized BiLSTM models are concatenated to create a varied prediction space, which is then fed into the ensemble regression framework to improve the generalization capabilities of the proposed ACB-XDE framework. Empirical validation of the proposed ACB-XDE framework involves its application to the volatile Bitcoin market, utilizing a dataset sourced from Yahoo Finance (Bitcoin-USD, 10/01/2014 to 01/08/2023). The proposed ACB-XDE framework outperforms state-of-the-art models with a MAPE of 0.37%, MAE of 84.40, and RMSE of 106.14. This represents improvements of approximately 27.45%, 53.32%, and 38.59% in MAPE, MAE, and RMSE respectively, over the best-performing attention-BiLSTM. The proposed ACB-XDE framework presents a technique for informed decision-making in dynamic financial landscapes and demonstrates effectiveness in handling the complexities of BTC-USD data.

This dataset contains daily historical data of major financial instruments and indexes from January 1, 2015, to August 15, 2025 . It includes the following columns:

SPX – S&P 500 Index daily closing prices.

GLD – SPDR Gold Shares ETF daily adjusted closing prices.

USO – United States Oil Fund ETF daily adjusted closing prices.

SLV – iShares Silver Trust ETF daily adjusted closing prices.

EUR/USD – Daily Euro to US Dollar exchange rate.

The data was collected from Yahoo Finance using the yfinance Python library. The dataset is intended for research, analysis, and educational purposes.

Forecasting speculative stock prices is essential for effective investment risk management and requires innovative algorithms. However, the speculative nature, volatility, and complex sequential dependencies within financial markets present inherent challenges that necessitate advanced techniques. In this regard, a novel framework, ACB-XDE (Attention-Customized BiLSTM-XGB Decision Ensemble), is proposed for predicting the daily closing price of speculative stock Bitcoin-USD (BTC-USD). The proposed ACB-XDE framework integrates the learning capabilities of a customized Bi-directional Long Short-Term Memory (BiLSTM) model with a novel attention mechanism and the XGBoost algorithm. The customized BiLSTM leverages its learning capabilities to capture complex sequential dependencies and speculative market trends. Meanwhile, the new attention mechanism dynamically assigns weights to influential features based on volatility patterns, thereby enhancing interpretability and optimizing effective cost measures and volatility forecasting. Moreover, XGBoost handles nonlinear relationships and contributes to the proposed ACB-XDE framework’s robustness. Furthermore, the error reciprocal method improves predictions by iteratively adjusting model weights based on the difference between theoretical expectations and actual errors in the individual attention-customized BiLSTM and XGBoost models. Finally, the predictions from both the XGBoost and attention-customized BiLSTM models are concatenated to create a varied prediction space, which is then fed into the ensemble regression framework to improve the generalization capabilities of the proposed ACB-XDE framework. Empirical validation of the proposed ACB-XDE framework involves its application to the volatile Bitcoin market, utilizing a dataset sourced from Yahoo Finance (Bitcoin-USD, 10/01/2014 to 01/08/2023). The proposed ACB-XDE framework outperforms state-of-the-art models with a MAPE of 0.37%, MAE of 84.40, and RMSE of 106.14. This represents improvements of approximately 27.45%, 53.32%, and 38.59% in MAPE, MAE, and RMSE respectively, over the best-performing attention-BiLSTM. The proposed ACB-XDE framework presents a technique for informed decision-making in dynamic financial landscapes and demonstrates effectiveness in handling the complexities of BTC-USD data.

1) name - The full name of the company or stock listed in the dataset.Example: NVIDIA Corporation. dtype -- object

2) symbol - The stock ticker symbol, which is a unique identifier for the company in the stock exchange. Example: NVDA (NVIDIA). dtype -- object

3) price - The current trading price of the stock in USD.Example: 131.29. dtype -- float64

4) change - The net change in the stock price during the last trading session, expressed in USD. Positive values indicate an increase, while negative values indicate a decrease in price. Example: -1.54. dtype -- flaot64

5) volume - The total number of shares traded for the stock during the trading session.Represented in millions (e.g., 197.102M = 197,102,000 shares). Example: 197.102M. dtype -- object

6) market_cap - The market capitalization of the company, calculated as the total number of outstanding shares multiplied by the stock's price.Represented in trillions (T), billions (B), or other notations.Example: 3.202T. dtype -- object

7) pe_ratio - The Price-to-Earnings ratio, a financial metric to evaluate a company's profitability relative to its stock price.A value of -- indicates that the P/E ratio is unavailable, often because the company is not profitable.Example: 44.66. dtype -- float

View monthly updates and historical trends for US M2 Money Supply. from United States. Source: Federal Reserve. Track economic data with YCharts analytics.

Forecasting speculative stock prices is essential for effective investment risk management and requires innovative algorithms. However, the speculative nature, volatility, and complex sequential dependencies within financial markets present inherent challenges that necessitate advanced techniques. In this regard, a novel framework, ACB-XDE (Attention-Customized BiLSTM-XGB Decision Ensemble), is proposed for predicting the daily closing price of speculative stock Bitcoin-USD (BTC-USD). The proposed ACB-XDE framework integrates the learning capabilities of a customized Bi-directional Long Short-Term Memory (BiLSTM) model with a novel attention mechanism and the XGBoost algorithm. The customized BiLSTM leverages its learning capabilities to capture complex sequential dependencies and speculative market trends. Meanwhile, the new attention mechanism dynamically assigns weights to influential features based on volatility patterns, thereby enhancing interpretability and optimizing effective cost measures and volatility forecasting. Moreover, XGBoost handles nonlinear relationships and contributes to the proposed ACB-XDE framework’s robustness. Furthermore, the error reciprocal method improves predictions by iteratively adjusting model weights based on the difference between theoretical expectations and actual errors in the individual attention-customized BiLSTM and XGBoost models. Finally, the predictions from both the XGBoost and attention-customized BiLSTM models are concatenated to create a varied prediction space, which is then fed into the ensemble regression framework to improve the generalization capabilities of the proposed ACB-XDE framework. Empirical validation of the proposed ACB-XDE framework involves its application to the volatile Bitcoin market, utilizing a dataset sourced from Yahoo Finance (Bitcoin-USD, 10/01/2014 to 01/08/2023). The proposed ACB-XDE framework outperforms state-of-the-art models with a MAPE of 0.37%, MAE of 84.40, and RMSE of 106.14. This represents improvements of approximately 27.45%, 53.32%, and 38.59% in MAPE, MAE, and RMSE respectively, over the best-performing attention-BiLSTM. The proposed ACB-XDE framework presents a technique for informed decision-making in dynamic financial landscapes and demonstrates effectiveness in handling the complexities of BTC-USD data.

This dataset includes 6 months data from yahoo finance for the following assets: GBP/USD Euro/USD JPY/USD CNY/USD INR/USD Gold Futures Crude Oil Bitcoin/USD Ethereum/USD

It includes important metrics such as Date, ticker, asset_name,Open, High, Low, Close, return. The data can be used for analysis and projects such as vizualisation and dashboards.

This is a Dataset for US to INR currency change This dataset start from 2023 to 2021 . It was collected from Yahoo Finance. You can perform Time Series Analysis and EDA on data.