Canada's main stock market index, the TSX, fell to 30943 points on December 2, 2025, losing 0.51% from the previous session. Over the past month, the index has climbed 2.21% and is up 20.70% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Canada. Canada Stock Market Index (TSX) - values, historical data, forecasts and news - updated on December of 2025.

This table contains 14 series, with data starting from 1953 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Stock market statistics (14 items: Toronto Stock Exchange; value of shares traded; United States common stocks; Dow-Jones industrials; high; United States common stocks; Dow-Jones industrials; low; Toronto Stock Exchange; volume of shares traded ...).

Context:

The provided dataset is extracted from yahoo finance using pandas and yahoo finance library in python. This deals with stock market index of the world best economies. The code generated data from Jan 01, 2003 to Jun 30, 2023 that’s more than 20 years. There are 18 CSV files, dataset is generated for 16 different stock market indices comprising of 7 different countries. Below is the list of countries along with number of indices extracted through yahoo finance library, while two CSV files deals with annualized return and compound annual growth rate (CAGR) has been computed from the extracted data.

Number of Countries & Index:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F90ce8a986761636e3edbb49464b304d8%2FNumber%20of%20Index.JPG?generation=1688490342207096&alt=media" alt="">

Content:

Unit of analysis: Stock Market Index Analysis

This dataset is useful for research purposes, particularly for conducting comparative analyses involving capital market performance and could be used along with other economic indicators.

There are 18 distinct CSV files associated with this dataset. First 16 CSV files deals with number of indices and last two CSV file deals with annualized return of each year and CAGR of each index. If data in any column is blank, it portrays that index was launch in later years, for instance: Bse500 (India), this index launch in 2007, so earlier values are blank, similarly China_Top300 index launch in year 2021 so early fields are blank too.

The extraction process involves applying different criteria, like in 16 CSV files all columns are included, Adj Close is used to calculate annualized return. The algorithm extracts data based on index name (code given by the yahoo finance) according start and end date.

Annualized return and CAGR has been calculated and illustrated in below image along with machine readable file (CSV) attached to that.

To extract the data provided in the attachment, various criteria were applied:

1. Content Filtering: The data was filtered based on several attributes, including the index name, start and end date. This filtering process ensured that only relevant data meeting the specified criteria.

2. Collaborative Filtering: Another filtering technique used was collaborative filtering using yahoo finance, which relies on index similarity. This approach involves finding indices that are similar to other index or extended dataset scope to other countries or economies. By leveraging this method, the algorithm identifies and extracts data based on similarities between indices.

In the last two CSV files, one belongs to annualized return, that was calculated based on the Adj close column and new DataFrame created to store its outcome. Below is the image of annualized returns of all index (if unreadable, machine-readable or CSV format is attached with the dataset).

Annualized Return:

As far as annualised rate of return is concerned, most of the time India stock market indices leading, followed by USA, Canada and Japan stock market indices.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F37645bd90623ea79f3708a958013c098%2FAnnualized%20Return.JPG?generation=1688525901452892&alt=media" alt="">

Compound Annual Growth Rate (CAGR):

The best performing index based on compound growth is Sensex (India) that comprises of top 30 companies is 15.60%, followed by Nifty500 (India) that is 11.34% and Nasdaq (USA) all is 10.60%.

The worst performing index is China top300, however this is launch in 2021 (post pandemic), so would not possible to examine at that stage (due to less data availability). Furthermore, UK and Russia indices are also top 5 in the worst order.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F58ae33f60a8800749f802b46ec1e07e7%2FCAGR.JPG?generation=1688490409606631&alt=media" alt="">

Geography: Stock Market Index of the World Top Economies

Time period: Jan 01, 2003 – June 30, 2023

Variables: Stock Market Index Title, Open, High, Low, Close, Adj Close, Volume, Year, Month, Day, Yearly_Return and CAGR

File Type: CSV file

Inspiration:

• Time series prediction model
• Investment opportunities in world best economies
• Comparative Analysis of past data with other stock market indices or other indices

Disclaimer:

This is not a financial advice; due diligence is required in each investment decision.

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 800 companies listed on the Canadian Securities Exchange (XCNQ) in Canada. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

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Top 5 used data fields in the End-of-Day Pricing Dataset for Canada:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

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Top 5 financial instruments with End-of-Day Pricing Data in Canada:

S&P/TSX Composite Index: The primary stock market index in Canada, tracking the performance of domestic companies listed on the Toronto Stock Exchange (TSX). It provides a comprehensive view of the Canadian equity market.

Canadian Dollar (CAD): The official currency of Canada, used for transactions and trade within the country. The Canadian Dollar is also widely traded in international foreign exchange markets.

Bank of Canada: Canada's central bank responsible for monetary policy, currency issuance, and overall financial system stability. It plays a critical role in managing the country's economic and financial well-being.

Royal Bank of Canada (RBC): One of the largest and most prominent banks in Canada, offering a wide range of financial services to individuals, businesses, and institutions. RBC is a key player in the Canadian banking sector.

Canadian Government Bonds: Debt securities issued by the Canadian government to finance its operations and projects. These bonds are considered relatively safe investments and play a significant role in the country's capital markets.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Canada, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

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Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Canada ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Canada?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Canada exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botswana, Techsalerator also provides data for other countries and international markets.

1. How do I pay for this dataset?

Techsalerator accepts various payment methods, including credit cards, direct tran...

This dataset is about stocks. It has 476 rows and is filtered where the exchange is Canadian Securities Exchange. It features 8 columns including stock name, company, exchange, and exchange symbol.

This table contains 25 series, with data for years 1956 - present (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Toronto Stock Exchange Statistics (25 items: Standard and Poor's/Toronto Stock Exchange Composite Index; high; Standard and Poor's/Toronto Stock Exchange Composite Index; close; Toronto Stock Exchange; oil and gas; closing quotations; Standard and Poor's/Toronto Stock Exchange Composite Index; low ...).

Global Stock Dataset

Overview

This dataset contains market data from various countries, organized into a hierarchical structure. It includes information such as share prices, trading volumes, market capitalization, and industry classifications.

Structure

The dataset is organized as follows:

• List of market data
  • Canada
  • China
  • India
  • Japan
  • Middle East
  • USA

Each country folder likely contains specific market data for companies within that region.

Data Fields

The dataset includes the following fields:

1. Share Price (CAD): The stock price in Canadian Dollars.
2. Volume: The trading volume of the stock.
3. Market Capitalization (CAD): The total market value of the company's outstanding shares in Canadian Dollars.
4. Industry: The sector or industry classification of the company.

Features

• Sorting: The data can be sorted by share price, volume, and market capitalization.
• Grid View: A 3x3 grid view is available for data visualization.
• Text Formatting: Volume and Market Capitalization data are formatted for easy reading.

Version Information

• Current Version: 1
• File Size: 1.72 MB

Usage

This dataset can be used for various purposes, including: - Market analysis - Comparative studies across different countries - Industry sector analysis - Investment research

Note

Please ensure you have the necessary permissions and comply with all relevant data usage regulations when using this dataset.

Updates

For the latest version and updates to this dataset, please check the source regularly.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

Canada CA: Stocks Traded: Turnover Ratio of Domestic Shares data was reported at 81.948 % in 2022. This records an increase from the previous number of 67.091 % for 2021. Canada CA: Stocks Traded: Turnover Ratio of Domestic Shares data is updated yearly, averaging 50.464 % from Dec 1977 (Median) to 2022, with 45 observations. The data reached an all-time high of 145.400 % in 2008 and a record low of 3.833 % in 1977. Canada CA: Stocks Traded: Turnover Ratio of Domestic Shares data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Canada – Table CA.World Bank.WDI: Financial Sector. Turnover ratio is the value of domestic shares traded divided by their market capitalization. The value is annualized by multiplying the monthly average by 12.;World Federation of Exchanges database.;Weighted average;Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

Canada Stock Symbols & Company Metadata

This dataset contains stock symbols and basic company metadata for all listed companies in Canada.It is updated weekly if new changes are there.

  📊 Dataset Contents

The dataset is provided as a CSV file with the following columns:

Column Description

name Full company name

ticker Stock ticker symbol (e.g., AAPL, MSFT)

market The exchange/market where the stock is listed

sector The primary business sector of the… See the full description on the dataset page: https://huggingface.co/datasets/ThunderDrag/Canada-Stock-Symbols-and-Metadata.

Canada CA: Market Capitalization: Listed Domestic Companies data was reported at 2,744.720 USD bn in 2022. This records a decrease from the previous number of 3,264.137 USD bn for 2021. Canada CA: Market Capitalization: Listed Domestic Companies data is updated yearly, averaging 910.231 USD bn from Dec 1977 (Median) to 2022, with 45 observations. The data reached an all-time high of 3,264.137 USD bn in 2021 and a record low of 173.942 USD bn in 1977. Canada CA: Market Capitalization: Listed Domestic Companies data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Canada – Table CA.World Bank.WDI: Financial Sector. Market capitalization (also known as market value) is the share price times the number of shares outstanding (including their several classes) for listed domestic companies. Investment funds, unit trusts, and companies whose only business goal is to hold shares of other listed companies are excluded. Data are end of year values converted to U.S. dollars using corresponding year-end foreign exchange rates.;World Federation of Exchanges database.;Sum;Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

This dataset is about stocks. It has 3 rows and is filtered where the company is National Bank Of Canada. It features 8 columns including stock name, company, exchange, and exchange symbol.

When there is a vast variety of metrics and tools available to gain market insight, Insider trading offers valuable clues to investors related to future share performance. We at Smart Insider provide global insider trading data and analysis on share transactions made by directors & senior staff in the shares of their own companies.

Monitoring all the insider trading activity is a huge task, we identify 'Smart Insiders' through specialist desktop and quantitative feeds that enable our clients to generate alpha.

Our experienced analyst team use quantitative and qualitative methods to identify the stocks most likely to outperform based on deep analysis of insider trades, and the insiders themselves. Using our easy-to-read derived data we help our clients better understand insider transactions activity to make informed investment decisions.

We provide full customization of reports delivered by desktop, through feeds, or alerts. Our quant clients can receive data in a variety of formats such as XML, XLSX or API via SFTP or Snowflake.

Sample dataset for Desktop Service has been provided with some proprietary fields concealed. Upon request, we can provide a detailed Quant sample.

Tags: Stock Market Data, Equity Market Data, Insider Transactions Data, Insider Trading Intelligence, Insider Trading Data, Investment Management, Alternative Investment, Asset Management, Equity Research, Market Analysis, United Sates of America, Canada, North America

Canada CA: Stocks Traded: Total Value: % of GDP data was reported at 104.060 % in 2022. This records a decrease from the previous number of 109.090 % for 2021. Canada CA: Stocks Traded: Total Value: % of GDP data is updated yearly, averaging 56.413 % from Dec 1975 (Median) to 2022, with 48 observations. The data reached an all-time high of 118.803 % in 2007 and a record low of 2.986 % in 1975. Canada CA: Stocks Traded: Total Value: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Canada – Table CA.World Bank.WDI: Financial Sector. The value of shares traded is the total number of shares traded, both domestic and foreign, multiplied by their respective matching prices. Figures are single counted (only one side of the transaction is considered). Companies admitted to listing and admitted to trading are included in the data. Data are end of year values.;World Federation of Exchanges database.;Weighted average;Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

Canada CA: Number of Listed Domestic Companies: Total data was reported at 3,534.000 Unit in 2022. This records an increase from the previous number of 3,455.000 Unit for 2021. Canada CA: Number of Listed Domestic Companies: Total data is updated yearly, averaging 1,834.500 Unit from Dec 1975 (Median) to 2022, with 48 observations. The data reached an all-time high of 3,922.000 Unit in 2020 and a record low of 1,124.000 Unit in 1980. Canada CA: Number of Listed Domestic Companies: Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Canada – Table CA.World Bank.WDI: Financial Sector. Listed domestic companies, including foreign companies which are exclusively listed, are those which have shares listed on an exchange at the end of the year. Investment funds, unit trusts, and companies whose only business goal is to hold shares of other listed companies, such as holding companies and investment companies, regardless of their legal status, are excluded. A company with several classes of shares is counted once. Only companies admitted to listing on the exchange are included.;World Federation of Exchanges database.;Sum;Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

Historical dataset of the Canada Stock Market Index (S&P/TSX Composite), covering values from 1979-07-01 to 2025-11-14, with the latest releases and long-term trends. Available for free download in CSV format.

🏦 Synthetic Loan Approval Dataset

A Realistic, High-Quality Dataset for Credit Risk Modelling

🎯 Why This Dataset?

Most loan datasets on Kaggle have unrealistic patterns where:

1. ❌ Credit scores don't matter
2. ❌ Approval logic is backwards
3. ❌ Models learn nonsense patterns

Unlike most loan datasets available online, this one is built on real banking criteria from US and Canadian financial institutions. Drawing from 3 years of hands-on finance industry experience, the dataset incorporates realistic correlations and business logic that reflect how actual lending decisions are made. This makes it perfect for data scientists looking to build portfolio projects that showcase not just coding ability, but genuine understanding of credit risk modelling.

📊 Dataset Overview

🔑 Key Features

Identifier:

-Customer ID (unique identifier for each application)

Demographics:

-Age, Occupation Status, Years Employed

Financial Profile:

-Annual Income, Credit Score, Credit History Length -Savings/Assets, Current Debt

Credit Behaviour:

-Defaults on File, Delinquencies, Derogatory Marks

Loan Request:

-Product Type, Loan Intent, Loan Amount, Interest Rate

Calculated Ratios:

-Debt-to-Income, Loan-to-Income, Payment-to-Income

💡 What Makes This Dataset Special?

1️⃣ Real-World Approval Logic The dataset implements actual banking criteria: - DTI ratio > 50% = automatic rejection - Defaults on file = instant reject - Credit score bands match real lending thresholds - Employment verification for loans ≥$20K

2️⃣ Realistic Correlations - Higher income → Better credit scores - Older applicants → Longer credit history - Students → Lower income, special treatment for small loans - Loan intent affects approval (Education best, Debt Consolidation worst)

3️⃣ Product-Specific Rules - Credit Cards: More lenient, higher limits - Personal Loans: Standard criteria, up to $100K - Line of Credit: Capped at $50K, manual review for high amounts

4️⃣ Edge Cases Included - Young applicants (age 18) building first credit - Students with thin credit files - Self-employed with variable income - High debt-to-income ratios - Multiple delinquencies

🎓 Perfect For - Machine Learning Practice: Binary classification with real patterns - Credit Risk Modelling: Learn actual lending criteria - Portfolio Projects: Build impressive, explainable models - Feature Engineering: Rich dataset with meaningful relationships - Business Analytics: Understand financial decision-making

📈 Quick Stats

Approval Rates by Product - Credit Card: 60.4% more lenient) - Personal Loan: 46.9 (standard) - Line of Credit: 52.6% (moderate)

Loan Intent (Best → Worst Approval Odds) 1. Education (63% approved) 2. Personal (58% approved) 3. Medical/Home (52% approved) 4. Business (48% approved) 5. Debt Consolidation (40% approved)

Credit Score Distribution - Mean: 644 - Range: 300-850 - Realistic bell curve around 600-700

Income Distribution - Mean: $50,063 - Median: $41,608 - Range: $15K - $250K

🎯 Expected Model Performance

With proper feature engineering and tuning: - Accuracy: 75-85% - ROC-AUC: 0.80-0.90 - F1-Score: 0.75-0.85

Important: Feature importance should show: 1. Credit Score (most important) 2. Debt-to-Income Ratio 3. Delinquencies 4. Loan Amount 5. Income

If your model shows different patterns, something's wrong!

🏆 Use Cases & Projects

Beginner - Binary classification with XGBoost/Random Forest - EDA and visualization practice - Feature importance analysis

Intermediate - Custom threshold optimization (profit maximization) - Cost-sensitive learning (false positive vs false negative) - Ensemble methods and stacking

Advanced - Explainable AI (SHAP, LIME) - Fairness analysis across demographics - Production-ready API with FastAPI/Flask - Streamlit deployment with business rules

⚠️ Important Notes

This is SYNTHETIC Data - Generated based on real banking criteria - No real customer data was used - Safe for public sharing and portfolio use

Limitations - Simplified approval logic (real banks use 100+ factors) - No temporal component (no time series) - Single country/currency assumed (USD) - No external factors (economy, market conditions)

Educational Purpose This dataset is designed for: - Learning credit risk modeling - Portfolio projects - ML practice - Understanding lending criteria

NOT for: - Actual lending decisions - Financial advice - Production use without validation

🤝 Contributing

Found an issue? Have suggestions? - Open an issue on GitHub - Suggest i...

This table contains 14 series, with data starting from 1953 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Stock market statistics (14 items: Toronto Stock Exchange; value of shares traded; United States common stocks; Dow-Jones industrials; high; United States common stocks; Dow-Jones industrials; low; Toronto Stock Exchange; volume of shares traded ...).

Explore the Redfin Canada Properties Dataset, available in CSV format and extracted in April 2022. This comprehensive dataset offers detailed insights into the Canadian real estate market, including property listings, prices, square footage, number of bedrooms and bathrooms, and more. Covering various cities and provinces, it’s ideal for market analysis, investment research, and financial modeling.

Key Features:

• Property Details: Includes crucial data such as listing price, property type, square footage, number of bedrooms and bathrooms, and more.
• Geo-Location Data: Provides geographical coordinates, allowing for spatial analysis and mapping.
• Market Trends: Offers historical data to analyze price trends and market fluctuations.

Who Can Use This Dataset:

• Real Estate Professionals: Evaluate market trends and property values to better advise clients or guide investment decisions.
• Investors: Analyze the Canadian housing market to identify investment opportunities and potential returns.
• Data Analysts and Researchers: Use this dataset to study market dynamics, urban development, or economic factors influencing the real estate sector.
• Financial Analysts: Incorporate the data into financial models to forecast market behavior and investment outcomes.

Download the Redfin Canada Properties Dataset to access valuable information on the Canadian housing market, perfect for anyone involved in real estate, finance, or data analysis.

This table contains 10 series, with data for years 1946 - 2010 (not all combinations necessarily have data for all years), and was last released on 2010-06-21. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...) Components (5 items: Total volume; Mining; Mining and oils; Industrials ...) Transactions (2 items: Shares traded; Value of shares traded ...).