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 ...).

Canada's main stock market index, the TSX, rose to 26857 points on June 30, 2025, gaining 0.62% from the previous session. Over the past month, the index has climbed 1.77% and is up 22.33% 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 July of 2025.

Prices for Canada Stock Market Index (TSX) including live quotes, historical charts and news. Canada Stock Market Index (TSX) was last updated by Trading Economics this July 2 of 2025.

The S&P/TSX Composite index (CAD) closed at 24,727.94 points at the end of 2024. This was an increase over the past year. What is the S&P/TSX Composite index? The S&P/TSX Composite index is a Canadian index that measures stocks on the Toronto Stock Exchange, one of the largest stock exchanges worldwide. A stock market index tracks the development of a group of stock prices. It allows to get a quick idea of economic climate in a given region. Canadian stock market The size of a stock exchange is basically the sum of market capitalizations of companies being traded on this stock exchange. The largest companies in terms of market capitalization in Canada in 2024 were the Royal Bank of Canada, and Toronto Dominion Bank. The total market capitalization of listed domestic companies in Canada equaled to 2.74 trillion U.S. dollars in 2022.

Canada Toronto Stock Exchange (TSX): Market Capitalization data was reported at 5,063,989.423 CAD mn in Mar 2025. This records a decrease from the previous number of 5,145,344.969 CAD mn for Feb 2025. Canada Toronto Stock Exchange (TSX): Market Capitalization data is updated monthly, averaging 2,972,507.168 CAD mn from Dec 2012 (Median) to Mar 2025, with 148 observations. The data reached an all-time high of 5,145,344.969 CAD mn in Feb 2025 and a record low of 2,068,592.000 CAD mn in Jun 2013. Canada Toronto Stock Exchange (TSX): Market Capitalization data remains active status in CEIC and is reported by TMX Group Limited. The data is categorized under Global Database’s Canada – Table CA.Z002: TMX Group Limited: Market Capitalization. [COVID-19-IMPACT]

Explore LSEG's Toronto Stock Exchange (TSX) Market Data, representing a broad range of businesses from Canada and abroad.

Prices for Canada TSX 60 Stock Market Index including live quotes, historical charts and news. Canada TSX 60 Stock Market Index was last updated by Trading Economics this July 3 of 2025.

Stock market return (%, year-on-year) in Canada was reported at 23.7 % in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Canada - Stock market return (%, year-on-year) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Canada TSX: Volume data was reported at 7,139.548 Unit mn in Feb 2019. This records a decrease from the previous number of 7,990.357 Unit mn for Jan 2019. Canada TSX: Volume data is updated monthly, averaging 7,020.077 Unit mn from May 2002 (Median) to Feb 2019, with 202 observations. The data reached an all-time high of 12,193.068 Unit mn in Oct 2008 and a record low of 2,971.922 Unit mn in Aug 2002. Canada TSX: Volume data remains active status in CEIC and is reported by Toronto Stock Exchange. The data is categorized under Global Database’s Canada – Table CA.Z002: Toronto Stock Exchange: Turnover.

Canada TSX/Venture Exchange: Volume data was reported at 3,524.439 Unit mn in Feb 2019. This records an increase from the previous number of 3,313.127 Unit mn for Jan 2019. Canada TSX/Venture Exchange: Volume data is updated monthly, averaging 3,310.682 Unit mn from May 2002 (Median) to Feb 2019, with 202 observations. The data reached an all-time high of 8,895.611 Unit mn in Feb 2011 and a record low of 469.141 Unit mn in Aug 2002. Canada TSX/Venture Exchange: Volume data remains active status in CEIC and is reported by Toronto Stock Exchange. The data is categorized under Global Database’s Canada – Table CA.Z002: Toronto Stock Exchange: Turnover.

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.

Where Will CAE:TSX Stock Be in 6 Month?

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 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 ...).

Canada Toronto Stock Exchange (TSX): Equity Capital Raised data was reported at 2,585.648 CAD mn in Mar 2025. This records an increase from the previous number of 1,894.288 CAD mn for Feb 2025. Canada Toronto Stock Exchange (TSX): Equity Capital Raised data is updated monthly, averaging 16,943.339 CAD mn from Dec 2012 (Median) to Mar 2025, with 148 observations. The data reached an all-time high of 57,751.832 CAD mn in Dec 2016 and a record low of 217.719 CAD mn in Jan 2024. Canada Toronto Stock Exchange (TSX): Equity Capital Raised data remains active status in CEIC and is reported by TMX Group Limited. The data is categorized under Global Database’s Canada – Table CA.Z005: TMX Group Limited: Equity Capital Raised: Year to Date. [COVID-19-IMPACT]

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.

CM:TSX Stock Forecast: A Buy For The Next 6 Month

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

Key information about Canada S&P/TSX Composite

• Canada S&P/TSX Composite closed at 25,393.5 points in Feb 2025, compared with 25,533.1 points at the previous month end
• Canada Equity Market Index: Month End: TMX: S&P/TSX Composite data is updated monthly, available from May 2002 to Feb 2025, with an average number of 13,818.0 points
• The data reached an all-time high of 25,648.0 points in Nov 2024 and a record low of 6,180.4 points in Sep 2002

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.

Where Will OR:TSX Stock Be in 1 Year?

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.

Will the TSX Index Soar or Stall?

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.

AII:TSX Stock: Set a stop-loss order

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

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...

Canada TSX/Venture Exchange: Value data was reported at 2,028.545 CAD mn in Feb 2019. This records an increase from the previous number of 1,675.675 CAD mn for Jan 2019. Canada TSX/Venture Exchange: Value data is updated monthly, averaging 1,399.576 CAD mn from May 2002 (Median) to Feb 2019, with 202 observations. The data reached an all-time high of 6,916.844 CAD mn in Jan 2018 and a record low of 139.198 CAD mn in Aug 2002. Canada TSX/Venture Exchange: Value data remains active status in CEIC and is reported by Toronto Stock Exchange. The data is categorized under Global Database’s Canada – Table CA.Z002: Toronto Stock Exchange: Turnover.