The main stock market index of United States, the US500, rose to 6391 points on July 31, 2025, gaining 0.45% from the previous session. Over the past month, the index has climbed 3.12% and is up 17.34% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on July of 2025.

!!PLEASE NOTE!! When downloading the data, please select "File Geodatabase" to preserve long field names. Shapefile will truncate field names to 10 characters.This version of the Racial and Social Equity Index indexes all tracts in the remainder of King County against tracts in the city of Seattle. This index should only be used in direct consultation with the Office of Planning and Community Development, and is intended to be of use for comparing tracts in the remainder of King County within the context of percentiles set by tracts within the city of Seattle.Version: CurrentThe Racial and Social Equity Index combines information on race, ethnicity, and related demographics with data on socioeconomic and health disadvantages to identify where priority populations make up relatively large proportions of neighborhood residents. Click here for a User Guide.See the City of Seattle RSE Index in action in the Racial and Social Equity ViewerThe Composite Index includes sub-indices of: Race, English Language Learners, and Origins Index ranks census tracts by an index of three measures weighted as follows: Persons of color (weight: 1.0) English language learner (weight: 0.5) Foreign born (weight: 0.5)Socioeconomic Disadvantage Index ranks census tracts by an index of two equally weighted measures: Income below 200% of poverty level Educational attainment less than a bachelor’s degreeHealth Disadvantage Index ranks census tracts by an index of seven equally weighted measures: No leisure-time physical activity Diagnosed diabetes Obesity Mental health not good AsthmaLow life expectancy at birth Disability<div style='font-family:"Avenir Next W01"

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.

NASDAQ Composite Index NASDAQ Composite Index

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

Analysis of ‘Racial and Social Equity Composite Index’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/42acb6e8-d61a-4349-a916-e072d62ceced on 27 January 2022.

--- Dataset description provided by original source is as follows ---

Persons of color (weight: 1.0)
English language learner (weight: 0.5)
Foreign born (weight: 0.5)

Income below 200% of poverty level

Educational attainment less than a bachelor’s degree

No leisure-time physical activity

Diagnosed diabetes

Obesity

Mental health not good

Asthma

Low life expectancy at birth

Disability

!!PLEASE NOTE!! When downloading the data, please select "File Geodatabase" to preserve long field names. Shapefile will truncate field names to 10 characters.Version: CurrentThe Racial and Social Equity Index combines information on race, ethnicity, and related demographics with data on socioeconomic and health disadvantages to identify where priority populations make up relatively large proportions of neighborhood residents. Click here for a User Guide.See the layer in action in the Racial and Social Equity ViewerClick here for an 11x17 printable pdf version of the map.The Composite Index includes sub-indices of: Race, English Language Learners, and Origins Index ranks census tracts by an index of three measures weighted as follows: Persons of color (weight: 1.0) English language learner (weight: 0.5) Foreign born (weight: 0.5)Socioeconomic Disadvantage Index ranks census tracts by an index of two equally weighted measures:Income below 200% of poverty level Educational attainment less than a bachelor’s degreeHealth Disadvantage Index ranks census tracts by an index of seven equally weighted measures:No leisure-time physical activityDiagnosed diabetes ObesityMental health not good AsthmaLow life expectancy at birthDisabilityThe index does not reflect population densities, nor does it show variation within census tracts which can be important considerations at a local level.<div style='font-family:"Avenir Next W01", "Aven

Data on the climate-related financial policy index (CRFPI) - comprising the global climate-related financial policies adopted globally and the bindingness of the policy - are provided for 74 countries from 2000 to 2020. The data include the index values from four statistical models used to calculate the composite index as described in D’Orazio and Thole 2022. The four alternative statistical approaches were designed to experiment with alternative weighting assumptions and illustrate how sensitive the proposed index is to changes in the steps followed to construct it. The index data shed light on countries’ engagement in climate-related financial planning and highlight policy gaps in relevant policy sectors.

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.

Is Shanghai Composite Index stock expected to rise?

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

Hong Kong's main stock market index, the HK50, fell to 25524 points on July 29, 2025, losing 0.15% from the previous session. Over the past month, the index has climbed 6.03% and is up 50.12% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Hong Kong. Hong Kong Stock Market Index (HK50) - values, historical data, forecasts and news - updated on July of 2025.

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.

Shanghai Composite Index Stock Forecast, Price & Rating (Shanghai Composite Index)

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

In the dynamic landscape of financial markets, accurate forecasting of stock indices remains a pivotal yet challenging task, essential for investors and policymakers alike. This study is motivated by the need to enhance the precision of predicting the Shanghai Composite Index’s opening price spread, a critical measure reflecting market volatility and investor sentiment. Traditional time series models like ARIMA have shown limitations in capturing the complex, nonlinear patterns inherent in stock price movements, prompting the exploration of advanced methodologies. The aim of this research is to bridge the gap in forecasting accuracy by developing a hybrid model that integrates the strengths of ARIMA with deep learning techniques, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. This novel approach leverages the ARIMA model’s proficiency in linear trend analysis and the deep learning models’ capability in modeling nonlinear dependencies, aiming to provide a comprehensive tool for market prediction. Utilizing a comprehensive dataset covering the period from December 20, 1990, to June 2, 2023, the study develops and assesses the efficacy of ARIMA, LSTM, GRU, ARIMA-LSTM, and ARIMA-GRU models in forecasting the Shanghai Composite Index’s opening price spread. The evaluation of these models is based on key statistical metrics, including Mean Squared Error (MSE) and Mean Absolute Error (MAE), to gauge their predictive accuracy. The findings indicate that the hybrid models, ARIMA-LSTM and ARIMA-GRU, perform better in forecasting the opening price spread of the Shanghai Composite Index than their standalone counterparts. This outcome suggests that combining traditional statistical methods with advanced deep learning algorithms can enhance stock market prediction. The research contributes to the field by providing evidence of the potential benefits of integrating different modeling approaches for financial forecasting, offering insights that could inform investment strategies and financial decision-making.

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.

IDX Composite Index: The Definitive Measure of Market Health?

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

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.

Should You Buy Shanghai Composite Index Right Now? (Stock Forecast)

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

Spain's main stock market index, the ES35, rose to 14458 points on July 31, 2025, gaining 0.53% from the previous session. Over the past month, the index has climbed 3.36% and is up 33.20% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Spain. Spain Stock Market Index (ES35) - values, historical data, forecasts and news - updated on July of 2025.

The IMF-adapted ND-GAIN index is an adaptation of the original index, adjusted by IMF staff to replace the Doing Business (DB) Index, used as source data in the original ND-GAIN, because the DB database has been discontinued by the World Bank in 2020 and it is no longer allowed in IMF work. The IMF-adapted ND-GAIN is an interim solution offered by IMF staff until the ND-GAIN compilers will review the methodology and replace the DB index.Sources: ND-GAIN; Findex - The Global Findex Database 2021; Worldwide Governance Indicators; IMF staff calculations. Category: AdaptationData series: IMF-Adapted ND-GAIN IndexIMF-Adapted Readiness scoreReadiness score, GovernanceReadiness score, IMF-Adapted EconomicReadiness score, SocialVulnerability scoreVulnerability score, CapacityVulnerability score, EcosystemsVulnerability score, ExposureVulnerability score, FoodVulnerability score, HabitatVulnerability score, HeathVulnerability score, SensitivityVulnerability score, WaterVulnerability score, InfrastructureMetadata:The IMF-adapted ND-GAIN Country Index uses 75 data sources to form 45 core indicators that reflect the vulnerability and readiness of 192 countries from 2015 to 2021. As the original indicator, a country's IMF-adapted ND-GAIN score is composed of a Readiness score and a Vulnerability score. The Readiness score is measured using three sub-components – Economic, Governance and Social. In the original ND-GAIN database, the Economic score is built on the DB index, while in the IMF-adapted ND-GAIN, the DB Index is replaced with a composite index built using the arithmetic mean of “Borrowed from a financial institution (% age 15+)” from The Global Financial Index database (FINDEX_BFI) and “Government effectiveness” from the Worldwide Governance Indicators database (WGI_GE). The Vulnerability, Social and Governance scores do not contain any DB inputs and, hence, have been sourced from the original ND-GAIN database. Methodology:The procedure for data conversion to index is the same as the original ND-GAIN and follows three steps: Step 1. Select and collect data from the sources (called “raw” data), or compute indicators from underlying data. Some data errors (i.e., tabulation errors coming from the source) are identified and corrected at this stage. If some form of transformation is needed (e.g., expressing the measure in appropriate units, log transformation to better represent the real sensitivity of the measure etc.) it happens also at this stage. Step 2. At times some years of data could be missing for one or more countries; sometimes, all years of data are missing for a country. In the first instance, linear interpolation is adopted to make up for the missing data. In the second instance, the indicator is labeled as "missing" for that country, which means the indicator will not be considered in the averaging process. Step 3. This step can be carried out after of before Step 2 above. Select baseline minimum and maximum values for the raw data. These encompass all or most of the observed range of values across countries, but in some cases the distribution of the observed raw data is highly skewed. In this case, ND-GAIN selects the 90-percentile value if the distribution is right skewed, or 10-percentile value if the distribution is left skewed, as the baseline maximum or minimum. Based on this procedure, the IMF–Adapted ND-GAIN Index is derived as follows: i. Replace the original Economic score with a composite index based on the average of WGI_GE and cubic root of FINDEX_BFI1, as follows:IMF-Adapted Economic = ½ · (WGI_GE) + ½ · (FINDEX_BFI)1/3 (1) The IMF-adapted Readiness and overall IMF-adapted ND-GAIN scores are then derived as: IMF-Adapted ND-GAIN Readiness = 1/3 · ( IMF-Adapted Economic + Governance + Social) IMF-Adapted ND-GAIN = ½·( IMF-Adapted ND-GAIN Readiness+ND-GAIN Vulnerability) ii. In case of missing data for one of the indicators in (1), IMF-Adapted ND-GAIN Economic would be based on the value of the available indicator. In case none of the two indicators is available, the IMF-Adapted Economic score would not be produced but the IMF-Adapted ND-GAIN Readiness would be computed as average of the Governance and Social scores. This approach, that replicates the approach used to derive the original ND-GAIN indexes in case of missing data, ensures that the proposed indicator has the same coverage as the original ND-GAIN database.
1 Given that the FINDEX_BFI data are positively skewed, a cubic root transformation has been implemented to induce symmetry.

Context

The National Stock Exchange of India Ltd. (NSE) is an Indian stock exchange located at Mumbai, Maharashtra, India. National Stock Exchange (NSE) was established in 1992 as a demutualized electronic exchange. It was promoted by leading financial institutions on request of the Government of India. It is India’s largest exchange by turnover. In 1994, it launched electronic screen-based trading. Thereafter, it went on to launch index futures and internet trading in 2000, which were the first of its kind in the country.

With the help of NSE, you can trade in the following segments:

• Equities

• Indices

• Mutual Funds

• Exchange Traded Funds

• Initial Public Offerings

• Security Lending and Borrowing Scheme

https://cdn6.newsnation.in/images/2019/06/24/Sharemarket-164616041_6.jpg" alt="Stock image">

Companies on successful IPOs gets their Stocks traded over different Stock Exchnage platforms. NSE is one important platofrm in India. There are thousands of companies trading their stocks in NSE. But, I have chosen two popular and high rated IT service companies of India; TCS and INFOSYS. and the third one is the benchmark for Indian IT companies , i.e. NIFTY_IT_INDEX .

Content

The dataset contains three csv files. Each resembling to INFOSYS, NIFTY_IT_INDEX, and TCS, respectively. One can easily identify that by the name of CSV files.

Timeline of Data recording : 1-1-2015 to 31-12-2015.

Source of Data : Official NSE website.

Method : We have used the NSEpy api to fetch the data from NSE site. I have also mentioned my approach in this Kernel - "**WebScraper to download data for NSE**". Please go though that to better understand the nature of this dataset.

Shape of Dataset:

INFOSYS - 248 x 15 || NIFTY_IT_INDEX - 248 x 7 || **TCS - 248 x 15

• Colum Descriptors:

• Date: date on which data is recorded

• Symbol: NSE symbol of the stock

• Series: Series of that stock | EQ - Equity

OTHER SERIES' ARE:

EQ: It stands for Equity. In this series intraday trading is possible in addition to delivery.

BE: It stands for Book Entry. Shares falling in the Trade-to-Trade or T-segment are traded in this series and no intraday is allowed. This means trades can only be settled by accepting or giving the delivery of shares.

BL: This series is for facilitating block deals. Block deal is a trade, with a minimum quantity of 5 lakh shares or minimum value of Rs. 5 crore, executed through a single transaction, on the special “Block Deal window”. The window is opened for only 35 minutes in the morning from 9:15 to 9:50AM.

BT: This series provides an exit route to small investors having shares in the physical form with a cap of maximum 500 shares.

GC: This series allows Government Securities and Treasury Bills to be traded under this category.

IL: This series allows only FIIs to trade among themselves. Permissible only in those securities where maximum permissible limit for FIIs is not breached.

• Prev Close: Last day close point

• Open: current day open point

• High: current day highest point

• Low: current day lowest point

• Last: the final quoted trading price for a particular stock, or stock-market index, during the most recent day of trading.

• Close: Closing point for the current day

• VWAP: volume-weighted average price is the ratio of the value traded to total volume traded over a particular time horizon

• Volume: the amount of a security that was traded during a given period of time. For every buyer, there is a seller, and each transaction contributes to the count of total volume.

• Turnover: Total Turnover of the stock till that day

• Trades: Number of buy or Sell of the stock.

• Deliverable: Volumethe quantity of shares which actually move from one set of people (who had those shares in their demat account before today and are selling today) to another set of people (who have purchased those shares and will get those shares by T+2 days in their demat account).

• %Deliverble: percentage deliverables of that stock

Acknowledgements

I woul dlike to acknowledge all my sincere thanks to the brains behind NSEpy api, and in particular SWAPNIL JARIWALA , who is also maintaining an amazing open source github repo for this api.

Inspiration

I have also built a starter kernel for this dataset. You can find that right here .

I am so excited to see your magical approaches for the same dataset.

THANKS!

About this dataset

India's National Stock Exchange (NSE) has a total market capitalization of more than US$3.4 trillion, making it the world's 10th-largest stock exchange as of August 2021, with a trading volume of ₹8,998,811 crore (US$1.2 trillion) and more 2000 total listings.

NSE's flagship index, the NIFTY 50, is a 50 stock index is used extensively by investors in India and around the world as a barometer of the Indian capital market.

This dataset contains data of all company stocks listed in the NSE, allowing anyone to analyze and make educated choices about their investments, while also contributing to their countries economy.

How to use this dataset

• Create a time series regression model to predict NIFTY-50 value and/or stock prices.
• Explore the most the returns, components and volatility of the stocks.
• Identify high and low performance stocks among the list.

Highlighted Notebooks

• Your kernel can be featured here!
• Related Dataset: S&P 500 Stocks - daily updated
• More datasets

Acknowledgements

License

CC0: Public Domain

Splash banner

Stonks by unknown memer.

Top ten factors for composite indices.

Israel Index: TASE: Sector: Real Estate and Construction data was reported at 966.020 01Jan1984=94.57 in Sep 2018. This records an increase from the previous number of 963.490 01Jan1984=94.57 for Aug 2018. Israel Index: TASE: Sector: Real Estate and Construction data is updated monthly, averaging 462.510 01Jan1984=94.57 from Jan 2000 (Median) to Sep 2018, with 225 observations. The data reached an all-time high of 978.490 01Jan1984=94.57 in Dec 2017 and a record low of 164.950 01Jan1984=94.57 in Feb 2003. Israel Index: TASE: Sector: Real Estate and Construction data remains active status in CEIC and is reported by Tel Aviv Stock Exchange. The data is categorized under Global Database’s Israel – Table IL.Z001: Tel Aviv Stock Exchange: Index.

This dataset is used to calculate the Open Data Index for Schools (ODIS), which offers educators and researchers a resource for contextualizing student-learning outcomes through a set of school-neighborhood indicators as well as a composite index. The underlying repository is designed to consolidate data on communities surrounding the approximately 23,000 public high schools (including magnet schools, charter schools, and traditional public schools) across the United States and make them easier for users to access and use. The indicators and composite index represent the level of "stress" that school communities experience, measured across five domains Economics, Education, Health, Housing, and Crime. Each domain comprises several indicators, each with an associated measure.

The technical report provides detailed information about each variable in the dataset, along with methodological information on how to standardize, aggregate, and weight diverse data sources from census tracts, cities, and counties to create a composite index.

Motivation

This dataset was achieved in order to apply/implement the following attached Reserach paper.

Insight of Data

The columns are: - Symbol (representing the Stock Code) - t (timestamp when the following record was recorded) - o (opening price of stock) - c (closing price of the stock) - h (high price) - l (low price) - v (value of the stock)

Answers needed

• Decompose the closing/opening price into approximation/decomposition components using either Discrete Wavelet Analysis or implement Maximal Overlap Discrete Wavelet Transform.
• Build a pipeline script which can be tuned accordingly.
• Show business insights.
• Implement the research paper