This is not going to be an article or Op-Ed about Michael Jordan. Since 2009 we've been in the longest bull-market in history, that's 11 years and counting. However a few metrics like the stock market P/E, the call to put ratio and of course the Shiller P/E suggest a great crash is coming in-between the levels of 1929 and the dot.com bubble. Mean reversion historically is inevitable and the Fed's printing money experiment could end in disaster for the stock market in late 2021 or 2022. You can read Jeremy Grantham's Last Dance article here. You are likely well aware of Michael Burry's predicament as well. It's easier for you just to skim through two related videos on this topic of a stock market crash. Michael Burry's Warning see this YouTube. Jeremy Grantham's Warning See this YouTube. Typically when there is a major event in the world, there is a crash and then a bear market and a recovery that takes many many months. In March, 2020 that's not what we saw since the Fed did some astonishing things that means a liquidity sloth and the risk of a major inflation event. The pandemic represented the quickest decline of at least 30% in the history of the benchmark S&P 500, but the recovery was not correlated to anything but Fed intervention. Since the pandemic clearly isn't disappearing and many sectors such as travel, business travel, tourism and supply chain disruptions appear significantly disrupted - the so-called economic recovery isn't so great. And there's this little problem at the heart of global capitalism today, the stock market just keeps going up. Crashes and corrections typically occur frequently in a normal market. But the Fed liquidity and irresponsible printing of money is creating a scenario where normal behavior isn't occurring on the markets. According to data provided by market analytics firm Yardeni Research, the benchmark index has undergone 38 declines of at least 10% since the beginning of 1950. Since March, 2020 we've barely seen a down month. September, 2020 was flat-ish. The S&P 500 has more than doubled since those lows. Look at the angle of the curve: The S&P 500 was 735 at the low in 2009, so in this bull market alone it has gone up 6x in valuation. That's not a normal cycle and it could mean we are due for an epic correction. I have to agree with the analysts who claim that the long, long bull market since 2009 has finally matured into a fully-fledged epic bubble. There is a complacency, buy-the dip frenzy and general meme environment to what BigTech can do in such an environment. The weight of Apple, Amazon, Alphabet, Microsoft, Facebook, Nvidia and Tesla together in the S&P and Nasdaq is approach a ridiculous weighting. When these stocks are seen both as growth, value and companies with unbeatable moats the entire dynamics of the stock market begin to break down. Check out FANG during the pandemic. BigTech is Seen as Bullet-Proof me valuations and a hysterical speculative behavior leads to even higher highs, even as 2020 offered many younger people an on-ramp into investing for the first time. Some analysts at JP Morgan are even saying that until retail investors stop charging into stocks, markets probably don’t have too much to worry about. Hedge funds with payment for order flows can predict exactly how these retail investors are behaving and monetize them. PFOF might even have to be banned by the SEC. The risk-on market theoretically just keeps going up until the Fed raises interest rates, which could be in 2023! For some context, we're more than 1.4 years removed from the bear-market bottom of the coronavirus crash and haven't had even a 5% correction in nine months. This is the most over-priced the market has likely ever been. At the night of the dot-com bubble the S&P 500 was only 1,400. Today it is 4,500, not so many years after. Clearly something is not quite right if you look at history and the P/E ratios. A market pumped with liquidity produces higher earnings with historically low interest rates, it's an environment where dangerous things can occur. In late 1997, as the S&P 500 passed its previous 1929 peak of 21x earnings, that seemed like a lot, but nothing compared to today. For some context, the S&P 500 Shiller P/E closed last week at 38.58, which is nearly a two-decade high. It's also well over double the average Shiller P/E of 16.84, dating back 151 years. So the stock market is likely around 2x over-valued. Try to think rationally about what this means for valuations today and your favorite stock prices, what should they be in historical terms? The S&P 500 is up 31% in the past year. It will likely hit 5,000 before a correction given the amount of added liquidity to the system and the QE the Fed is using that's like a huge abuse of MMT, or Modern Monetary Theory. This has also lent to bubbles in the housing market, crypto and even commodities like Gold with long-term global GDP meeting many headwinds in the years ahead due to a...

Interactive chart of the S&P 500 stock market index since 1927. Historical data is inflation-adjusted using the headline CPI and each data point represents the month-end closing value. The current month is updated on an hourly basis with today's latest value.

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.

RNR^F Stock: Is This the End of the Bull Market?

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.

LON:RTW Stock: Is This the End of the Bull Market?

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

Browse BULL (BULL) market data. Get instant pricing estimates and make batch downloads of binary, CSV, and JSON flat files.

Nasdaq TotalView-ITCH is the proprietary data feed that provides full order book depth for Nasdaq market participants.

Origin: Directly captured at Equinix NY4 (Secaucus, NJ) with an FPGA-based network card and hardware timestamping. Synchronized to UTC with PTP.

Supported data encodings: DBN, CSV, JSON Learn more

Supported market data schemas: MBO, MBP-1, MBP-10, BBO-1s, BBO-1m, TBBO, Trades, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, Definition, Statistics, Status, Imbalance Learn more

Resolution: Immediate publication, nanosecond-resolution timestamps

Silver Bull Resources net worth as of July 04, 2025 is $0.01B. Interactive chart of historical net worth (market cap) for Silver Bull Resources (SVBL) over the last 10 years. How much a company is worth is typically represented by its market capitalization, or the current stock price multiplied by the number of shares outstanding.

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.

IAE Stock: Is This the End of the Bull Market?

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 2024, Red Bull held a carbonated soft drink (CSD) market share of almost *** percent in the United States. The company’s share has grown significantly in the past years. The Red Bull GmbH is an Austrian beverage company, mostly known for the energy drink Red Bull. The company is headquartered in Fuschl am See, Austria. The leading energy drink brand In 2024, Red Bull was the leading brand of energy drink in the United States, based on generated sales of close to ***** billion U.S. dollars. Monster Energy, one of Red Bull’s main competitors, ranked second that year, with just around *** billion U.S. dollars’ worth of sales. Energy drinks in the U.S. In 2024, total energy drink sales reached over ** billion U.S. dollars in the United States. This type of beverage has become considerably more popular when compared to previous years; however, it remains low on the list of favorites among Americans - energy drinks accounted for *** percent of the beverages consumed by U.S. consumers in 2024. Other beverages, such as bottled water and CSD, were more popular: each of these two segments accounted for a consumption share of approximately ** percent that year.

If I were to boil the thesis down to a few bullets, I’d say: Uranium is an essential input for nuclear reactors with no substitute. Following the Fukushima disaster, there was a massive supply glut as reactors were taken offline due to safety concerns Now a supply crunch is looming, with a current market deficit of ~40m lbs Nuclear power plants usually contract uranium supplies several years out before their inventory gets run down. Due to the oversupply coming out of the previous cycle, however, they have been purchasing additional supply needs in the spot market instead of contracting years in advance. 13f filings indicate that the power plants’ coverage rates (contracted lbs of uranium supply / lbs of uranium required) are beginning to trend below 100%, indicating utilities have less locked-in supply than they need to keep running their reactors, at a time when market supply is tightening (note utilities typically look to maintain coverage ratios well above 100% to ensure no unforeseen shortfalls) Global demand for uranium is increasing, with ~56 new reactors under construction an a further 99 in planning currently. Nuclear power currently generates ~10% of the world’s electricity but with the closure of coal and fossil fuel power plants due to ESG considerations, nuclear energy is increasingly being seen as the only viable way to make up up the lost energy capacity. Putting all of this together, a fundamental supply/demand imbalance for an essential commodity with price insensitive buyers and ESG tailwinds makes the bull case extremely compelling. But a picture is worth a thousand words, so some historic charts probably best provide a sense of the future upside expected in the next cycle. Using the data of form 8k, at the peak of the previous uranium bull market in 2007 (when there was no supply deficit) the uranium spot price reached ~$136/lb after a run up from ~$15/share at the start of 2004 (~9x increase). Today the current price is ~$42/lb with the view that the price will reach new highs in this coming cycle: Many uranium investors, based on the majority of form 10q, focus on the miners rather than the commodity as being the way to play the new uranium bull market, as these are more levered to price increases in the underlying commodity. The share price for Canadian-based Cameco Corporation (CCO / CCJ, the second largest uranium producer in the world) increased from USD $3/share to $55/share ( ~18x bagger) during the previous bull market from ~2004 – 2007: While Cameco’s performance was impressive, it was not the biggest winner during the previous uranium bull market. Australian miner Paladin Energy ($PALAF) went from AUD $0.01 to AUD $10.70 (~1000x! ) between late 2003 and the market peak in Q1 2007, according to their stock price in Google Sheets: Similar multibagger returns for uranium stocks will be seen again if a new bull market in uranium materializes in the coming 2-3 years when utilities’ uranium supply falls to inoperable levels & they begin contracting again for new supplies. Based on SEC form 4, Paladin in particular is expected to be big winner in any new bull market, as it operates one of the lowest cost uranium mines in the world, the Langer Heinrich mine in Namibia, which was a fully producing mine before being idled in the last bear market. As such, it is a ready-to-go miner rather than a speculative prospect, and so is in a position to immediately capitalise on an uptick in uranium prices and a new contracting cycle with utilities. Given the extent of the structural supply/demand imbalance (which again wasn’t present during the previous bull market) combined with utilities likely becoming forced purchasers of uranium at almost any price, market commentators are forecasting the uranium spot price to reach highs of up to $150/lb, thus enabling the producers to contract at price levels 3x+ the current spot price, driving a massive increase in profitability and cash flows. With some very interesting dynamics and the sprott uranium trust acting as a catalyst, I think the uranium market has the potential to offer a really unique and asymmetric return over the next 2 years. To reproduce this analysis, use this guide on how to get stock price in Excel. You will also need high-quality stock data, I recommend you check out Finnhub Stock Api Cheers!

Direxion Daily Financial Bull 3X Shares net worth as of June 22, 2025 is $1.99B. Interactive chart of historical net worth (market cap) for Direxion Daily Financial Bull 3X Shares (FAS) over the last 10 years. How much a company is worth is typically represented by its market capitalization, or the current stock price multiplied by the number of shares outstanding.

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.

S32 Stock: Is This the End of the Bull Market?

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

The popular energy drink Red Bull sold 12.7 billion cans in 2024 worldwide, up from just over four billion cans in 2011. Red Bull in the U.S. Red Bull is one of the most popular energy drinks in the United States, controlling about 37.4 percent of the market. In 2023, Red Bull energy drinks generated sales of over 1.7 billion U.S. dollars. Energy Drink Consumption Energy drinks are great for boosting energy and keeping awake. Many contain large amounts of caffeine, as well Taurine and B-vitamins. A survey of American consumers found that among those who consume energy drinks, about half drink them several times a week, and a quarter drink them on a daily or almost daily basis. Perhaps to stave off post-lunch drowsiness, the most popular time to drink energy drinks is between 2:00 and 4:00 in the afternoon.

This study focuses on factors supportive of gold’s bull run. In the light of the increasing complexity of the bullion market, we try to use a comprehensive robust framework to examine the importance of each factor in determining the prices. The random tree regression results show that macro factors such as central bank buying and inflows into gold ETFs, the Fed’s policy tilts and U.S. government debt, have played a crucial role while short-term factors, like 10-Year Treasury Inflation-Indexed Security Rate, S&P 500, gold to silver ratio, USD Index, employment report, and CPI, have taken a back seat. Gold acts as a quintessential hedge against inflation and uncertainties.

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.

NMTC Stock: Is This the End of the Bull Market?

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

It is estimated that the cumulative market cap of cryptocurrencies increased in early 2023 after the downfall in November 2022 due to FTX. That value declined in the summer of 2023, however, as international uncertainty grew over a potential recession. Bitcoin's market cap comprised the majority of the overall market capitalization. What is market cap? Market capitalization is a financial measure typically used for publicly traded firms, computed by multiplying the share price by the number of outstanding shares. However, cryptocurrency analysts calculate it as the price of the virtual currencies times the number of coins in the market. This gives cryptocurrency investors an idea of the overall market size, and watching the evolution of the measure tells how much money is flowing in or out of each cryptocurrency. Cryptocurrency as an investment The price of Bitcoin has been erratic, and most other cryptocurrencies follow its larger price swings. This volatility attracts investors who hope to buy when the price is low and sell at its peak, turning a profit. However, this does little for price stability. As such, few firms accept payment in cryptocurrencies. As of June 25, 2025, the cumulative market cap of cryptocurrencies reached a value of ******.

Bitcoin dominance steadily declined in April 2024 to below ** percent, amid rumors of central banks halting or potentially lowering interest rates in the future. Within the crypto world, this so-called "dominance" ratio is one of the oldest and most investigated metrics available. It measures the coin's market cap relative to the overall crypto market — effectively showing how strong Bitcoin compared to all the other cryptocurrencies that are not BTC, called "altcoins". Why dominance matters is because market caps of any crypto can change relatively quickly, either due to sudden price changes or a change of recorded trading volume. Essentially, the figure somewhat resembles a trading sentiment, revealing whether Bitcoin investors are responding to certain events or whether Bitcoin is losing out on functions offered by, for example, stablecoins or NFT tokens. "Dominance" criticism: Ethereum and stablecoin The interpretation of the Bitcoin metric is not without its criticism. When first conceived, Bitcoin was the first cryptocurrency to be created and had a substantial market share within all cryptocurrencies? The overall share of stablecoins, such as Tether, as well as Ethereum increasingly start to resemble that of Bitcoin, however. Some analysts argue against this comparison. For one, they point towards the large influence of trading activity between Bitcoin and Ethereum in the dominance metric. Second, they argue that stablecoins can be traded in for Bitcoin and Ethereum, essentially showing how much investors are willing to engage with "regular" cryptocurrency. A rally around Bitcoin in late 2023? By December 2023, the Bitcoin price reached roughly 41,000 U.S. dollars — the first time in 20 months such a value was reached. A weaker U.S. dollar, speculation on decreasing interest rates, and a potential Bitcoin ETF approval are believed to be at the heart of this price increase. Whether this will hold in 2024 is unclear: The monthly interest rate from the U.S. Fed is speculated to decrease in 2024, despite a vow of "higher for longer". In December 2023, the thought of decreasing interest rates and the potential of a Bitcoin ETF fuelled market sentiment towards riskier assets.

The behavior recognition market is experiencing robust growth, driven by increasing demand for advanced surveillance systems, improved safety measures, and the rising adoption of AI-powered solutions across various sectors. The market's expansion is fueled by several key trends, including the development of more accurate and efficient algorithms, the miniaturization of sensor technologies, and the increasing availability of large datasets for training machine learning models. Applications span diverse industries, from healthcare (monitoring patient behavior for early disease detection) and transportation (enhancing driver safety and autonomous vehicle navigation) to retail (analyzing customer behavior to optimize store layout and marketing strategies). While challenges remain, such as ensuring data privacy and addressing ethical concerns surrounding bias in algorithms, the market is poised for significant growth. The substantial investment in research and development, coupled with the decreasing cost of hardware and software, is expected to propel market expansion over the coming years. Despite the promising outlook, certain restraints are influencing market growth. Concerns around data privacy and security are paramount, requiring robust data anonymization and encryption techniques. Regulatory hurdles in different regions also present challenges to market penetration. Additionally, the need for high-quality data for training accurate models and the potential for algorithmic bias represent ongoing obstacles. Overcoming these challenges will require a collaborative approach involving technology developers, regulators, and ethical experts. However, the long-term potential of behavior recognition is undeniable, promising transformative improvements in various sectors and leading to the development of more intelligent and responsive systems. Assuming a conservative CAGR of 15% (a reasonable estimate considering the rapid technological advancements in AI), the market, valued at approximately $2 billion in 2025, is projected to surpass $5 billion by 2033.

By 2025, the Bitcoin market cap had grown to over ***** billion USD as the cryptocurrency kept growing. Market capitalization is calculated by multiplying the total number of Bitcoins in circulation by the Bitcoin price. The Bitcoin market capitalization increased from approximately *** billion U.S. dollars in 2013 to several times this amount since its surge in popularity. Dominance The Bitcoin market cap takes up a significant portion of the overall cryptocurrency market cap. This is referred to as "dominance". Within the crypto world, this so-called "dominance" ratio is one of the oldest and most investigated metrics available. It measures the coin's market cap relative to the overall crypto market — effectively showing how strong Bitcoin compared to all the other cryptocurrencies that are not BTC, called "altcoins". The Bitcoin dominance was above ** percent. Maximum supply and scarcity Bitcoin is unusual from other cryptocurrencies in that its maximum supply is getting closer. By 2025, well over ** million out of all 21 million possible Bitcoin had been created. Bitcoin's supply is expected to reach its maximum around the year 2140, likely making mining more energy-intensive.

Browse Direxion Daily TSLA Bull 1.5X Shares (TSLL) market data. Get instant pricing estimates and make batch downloads of binary, CSV, and JSON flat files.

Nasdaq TotalView-ITCH is the proprietary data feed that provides full order book depth for Nasdaq market participants.

Origin: Directly captured at Equinix NY4 (Secaucus, NJ) with an FPGA-based network card and hardware timestamping. Synchronized to UTC with PTP.

Supported data encodings: DBN, CSV, JSON Learn more

Supported market data schemas: MBO, MBP-1, MBP-10, BBO-1s, BBO-1m, TBBO, Trades, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, Definition, Statistics, Status, Imbalance Learn more

Resolution: Immediate publication, nanosecond-resolution timestamps

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.

SBI:TSX Stock: Is This the End of the Bull Market?

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