Finnhub is the ultimate stock api in the market, providing real-time and historical price for global stocks with Rest API and websocket. We also support a tons of other financial data like stock fundamentals, analyst estimates, fundamental data and more. Download the file to access balance sheet of Amazon.

Twelve Data is a technology-driven company that provides financial market data, financial tools, and dedicated solutions. Large audiences - from individuals to financial institutions - use our products to stay ahead of the competition and success.

At Twelve Data we feel responsible for where the markets are going and how people are able to explore them. Coming from different technological backgrounds, we see how the world is lacking the unique and simple place where financial data can be accessed by anyone, at any time. This is what distinguishes us from others, we do not only supply the financial data but instead, we want you to benefit from it, by using the convenient format, tools, and special solutions.

We believe that the human factor is still a very important aspect of our work and therefore our ethics guides us on how to treat people, with convenient and understandable resources. This includes world-class documentation, human support, and dedicated solutions.

Global Shares Data Reference data on more than 80K stocks worldwide. Historical data from 2000 onwards. Pay only for the parameters you need. Flexible in customizing our product to the customer's needs. Free test access as long as you need for integration. Reliable sources: issues documents, disclosure website, global depositories data and other open sources. The cost depends on the amount of required parameters and re-distribution right.

We offer three easy-to-understand equity data packages to fit your business needs. Visit intrinio.com/pricing to compare packages.

Bronze

The Bronze package is ideal for developing your idea and prototyping your platform with high-quality EOD equity pricing data, standardized financial statement data, and supplementary fundamental datasets.

When you’re ready for launch, it’s a seamless transition to our Silver package for additional data sets, 15-minute delayed equity pricing data, expanded history, and more.

• Historical EOD equity prices & technicals (10 years history)
• Security reference data
• Standardized & as-reported financial statements (5 years history)
• 7 supplementary fundamental data sets

Bronze Benefits:

• Web API access
• 300 API calls/minute limit
• Unlimited internal users
• Unlimited internal & external display
• Built-in ticketing system
• Live chat & email support

Silver

The Silver package is ideal for startups that are in development, testing, or in the beta launch phase. Hit the ground running with 15-minute delayed and historical intraday and EOD equity prices, plus our standardized and as-reported financial statement data with nine supplementary data sets, including insider transactions and institutional ownership.

When you’re ready to scale, easily move up to the Gold package for our full range of data sets and full history, real-time equity pricing data, premium support options, and much more.

• 15-minute delayed & historical intraday equity prices
• Historical EOD equity prices & technicals (full history)
• Security reference data
• Standardized & as-reported financial statements (10 years history)
• 9 supplementary fundamental data sets

Silver Benefits:

• Web API access
• 2,000 API calls/minute limit
• Access to third-party datasets via Intrinio API (additional fees required)
• Unlimited internal users
• Unlimited internal & external display
• Built-in ticketing system
• Live chat & email support

Gold

The Gold package is ideal for funded companies that are in the growth or scaling stage, as well as institutions that are innovating within the fintech space. This full-service solution offers our complete collection of equity pricing data feeds, from real-time to historical EOD, plus standardized financial statement data and nine supplementary feeds.

You’ll also have access to our wide range of modern access methods, third-party data via Intrinio’s API with licensing assistance, support from our team of expert engineers, custom delivery architectures, and much more.

• Real-time equity prices
• Historical intraday equity prices
• Historical EOD equity prices & technicals (full history)
• Security reference data
• Standardized & as-reported financial statements (full history)
• 9 supplementary fundamental data sets

Gold Benefits:

• No exchange fees
• No user reporting or variable per-user exchange fees
• High liquidity (6%+)
• Web API & WebSocket access
• 2,000 API calls/minute limit
• Customizable access methods (Snowflake, FTP, etc.)
• Access to third-party datasets via Intrinio API (additional fees required)
• Unlimited internal users
• Unlimited internal & external display
• Built-in ticketing system
• Live chat & email support
• Access to engineering team
• Concierge customer success team
• Comarketing & promotional initiatives

Platinum

Don’t see a package that fits your needs? Our team can design premium custom packages for institutions.

Twelve Data is a technology-driven company that provides financial market data, financial tools, and dedicated solutions. Large audiences - from individuals to financial institutions - use our products to stay ahead of the competition and success.

At Twelve Data we feel responsible for where the markets are going and how people are able to explore them. Coming from different technological backgrounds, we see how the world is lacking the unique and simple place where financial data can be accessed by anyone, at any time. This is what distinguishes us from others, we do not only supply the financial data but instead, we want you to benefit from it, by using the convenient format, tools, and special solutions.

We believe that the human factor is still a very important aspect of our work and therefore our ethics guides us on how to treat people, with convenient and understandable resources. This includes world-class documentation, human support, and dedicated solutions.

The stock market serves as the backbone of modern economies, facilitating the buying and selling of shares in publicly traded companies. This dynamic marketplace allows investors to own a piece of a company and share in its success, providing essential liquidity and capital for businesses. As a pivotal element in th

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:API Target Price Prediction

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.

How do you determine buy or sell? (LON:API 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

NASDAQ Listed Companies Dataset

Description:

This dataset provides comprehensive information on companies listed on the NASDAQ stock exchange. It includes essential details about each company, making it a valuable resource for financial analysis, stock market research, and investment strategies.

Features:

• symbol: The unique ticker symbol used to identify the company's stock on the NASDAQ exchange.
• name: The full name of the company.
• currency: The currency in which the company's stock is traded.
• exchange: The stock exchange where the company is listed (in this case, NASDAQ).
• mic_code: The Market Identifier Code (MIC) for the NASDAQ exchange.
• country: The country where the company is headquartered.
• type: The type of company, such as common stock or preferred stock.
• Usage: This dataset can be used for various purposes including:

Stock Market Analysis:

Analyze stock symbols, company names, and market data.

Financial Modeling:

Incorporate company details into financial models and investment strategies.

Market Research:

Understand the distribution of companies by country and currency.

Data Visualization:

Create visualizations of the NASDAQ market landscape.

Data Source:

The data is sourced from the Twelve Data API, which provides up-to-date financial and stock market information.

Notes: The dataset includes only NASDAQ-listed companies and does not cover other exchanges. Ensure to comply with any data usage policies or licensing agreements associated with the data source. Feel free to adapt the description based on the specific details and attributes of your dataset.

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

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.

API Group Soaring: (APG) 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

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

FinFeedAPI provides equity market data covering over 11,000 symbols, featuring historical T+1 data with an unlimited loopback period. We deliver everything from detailed trade records and multiple levels of order book depth (Level 1-3) to crucial regulatory and system messages.

Our data is engineered for performance, featuring nano-second precision timestamps. This ensures a competitive edge for high-frequency trading by enabling fair, accurate, and auditable transaction sequencing, critical for regulatory compliance. Access comprehensive equity market intelligence directly through our robust API offerings.

Why FinFeedAPI?

Market Coverage & Data Depth: - Historical Data: T+1 data on 11K+ symbols with unlimited historical lookback. - Trade Feeds: Detailed trade records including timestamps, sizes, prices, and conditions (e.g., odd lot, intermarket sweep, extended hours). - Level 1 Quotes: Best bid/ask prices, sizes, and timestamps. - Level 2 Price Book: Market depth with multiple bid/ask prices and aggregate order sizes. - Level 3 Order Book: The complete order book detailing individual orders.

Essential Messages: - Admin Messages: Trading status, official open/close prices, auction states, short sale restrictions, retail liquidity indicators, security directory. - System Events: Exchange-level notifications for key trading session phases.

Precision & Reliability: - Nano-second Timestamps: Ensuring fair, accurate, and auditable transaction sequencing for HFT and compliance. - Institutional Trust: Relied upon by financial institutions for dependable equity market information.

Financial institutions and trading firms rely on FinFeedAPI for mission-critical equity market intelligence. We are committed to delivering clean, precise, and comprehensive data when it matters most. If you require dependable and granular stock market data, FinFeedAPI provides the actionable insights you need.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

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!

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.

(API) Abrdn Property Income: Renting Out the Future?

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

Access the American Petroleum Institute's (API) Weekly Statistical Bulletin (WSB), providing essential data for the US and regional petroleum markets.

Context

With the sole mission to democratize financial data, Finnhub is excited to release the new Financials as Reported dataset for bulk download. The data is cleaned and sourced directly from SEC filings from 2010-2020.

If you don't need bulk download, you can query this data for free on our website: https://finnhub.io/docs/api#financials-reported. We also provide various type of financial data such as global fundamentals, deep historical tick data, estimates and alternative data.

Finnhub Stock API https://finnhub.io/terms-of-service

Introducing our comprehensive economic calendar, your ultimate resource for tracking major global economic events and their impact on currency and stock market prices. With a vast array of fields including event name, country, previous and current values, and more, our calendar provides you with essential data to make informed financial decisions. Stay ahead of the curve with our real-time updates, ensuring you have access to the latest information every 15 minutes. With this powerful tool at your fingertips, you can confidently navigate the dynamic world of economic events and seize opportunities for success. Don't miss out on this essential resource for staying informed and making calculated moves in the market.

VIQ Solutions (VQS) Shares of VQS stock have been in recovery mode since last quarter. That was when the AI-driven tech company saw its stock price plummet after reporting earnings. Fast-forward a few months, and the VIQ Solutions stock price has climbed by more than 100%, with daily volumes increasing this month. There could be a few things in play for VQS stock. As we know, ChatGPT and AI stocks are gaining plenty of speculative interest right now. The massive surge of attention on machine learning has prompted a breakout in plenty of companies with exposure to the space. VIQ provides digital voice and video capture technology and transcription services. Late last month, based on the data provided by the short interest api, the company boosted its AI workflows with a new automatic speech recognition platform to increase accuracy in multi-speaker environments. “Our clients see the value in our ability to implement our integrated solutions and service offerings to transform and analyze digital content and securely generate accurate, actionable information,” said Vahram Sukyas, Chief Technology Officer, VIQ Solutions. This week VIQ expanded its global technology footprint and signed a multi-year contract with Transcription Hub, a transcription services company, to provide internal and commercial workflow solutions to transcription services organizations in India. The platform is designed to decrease turnaround time and yield higher transcription accuracy. Imperial Petroleum Inc. (IMPP) With China reopening from COVID lockdowns (finally), energy stocks are coming back into focus. Gas prices are climbing thanks to a mild winter as well. Imperial Petroleum has experienced its share of energy industry speculation and momentum-fueled moves over the last year. In fact, at one point in 2022, share prices reached highs of over $9. Solid earnings from its last quarter have begun coming back into the picture now, as earnings season is well underway. The third quarter saw Imperial report an Earnings Per Share of 8 cents compared to a loss of 3 cents from a year ago. The company also saw its sales explode. The company did just under $5.8 million in sales for the third quarter of 2021. The 2022 Q3 figures were more than 630% higher at $42.6 million. CEO Harry Vafias also highlighted several key points of the third quarter’s performance. He said, “As a result of having acquired six vessels in the course of ten months, we generated net income of $15.5 million in a single quarter which is 15,400% higher than our profit in Q2 22’ and equivalent to 23% of our current market capitalization; We incurred moderate debt during the quarter, maintaining a healthy capital structure with $42.3 million of debt while preserving a free cash balance available for further fleet expansion of about $92 million. Given the strong market fundamentals and the promising charter rate environment and by taking advantage of our efficient management of our expanded fleet, we believe that we will achieve strong results and generate significant cash flow going forward.” With a more bullish tone in energy, it will be interesting to see how the company’s next round of earnings compares. Spectrum Pharmaceuticals (SPPI) AI and chatGPT stocks aren’t the only things getting attention in the stock market today. “Old standbys” like biotech penny stocks remain a hot topic. They usually become a source of speculative trading trends due to ongoing trials that can make more break certain companies. Spectrum Pharmaceuticals, one of the best value stocks, has performed well this year, having risen over 100% since the beginning of January. The company develops targeted oncology treatment platforms. This week Spectrum announced receipt of a permanent J-code (J1449) for its ROLVEDON injection from the U.S. Centers for Medicare & Medicaid Services. J-codes are reimbursement codes used by commercial insurers, including Medicare, Medicare Advantage, and other government payers, for certain drugs. “A permanent J-code will enable a more efficient and predictable reimbursement in the outpatient setting. The combination of a permanent J-code on April 1, 2023, and ROLVEDON’S inclusion in the National Comprehensive Cancer Network® Supportive Care Guidelines (NCCN Guidelines) announced on December 6, 2022, are key elements in establishing brand awareness and building customer confidence in our novel product,” said CEO Tom Riga. Wearable Devices Ltd. (WLDS) We discussed WLDS stock toward the end of 2022 and other low float penny stocks. Wearable Devices, as one of the best growth stocks for any investors, is developing non-invasive neural input interface technology via wearables, including wristbands. Wearers can control digital devices using things like subtle finger movement to do so. This week the company announced that it received approval for a $900,000 grant budget for developing a manufacturing process of its AI-based neural interface, the Mudra Band. CEO Asher Dahan...