Most stock API comparisons tell you what each provider offers. This article starts somewhere different: with the question of what it actually costs to get it wrong.

A systematic trading team at a mid-sized hedge fund spent eight months building a multi-factor equity strategy on top of a data provider that looked, from the outside, like a perfectly reasonable choice. The API was clean, the documentation was good, and the historical data went back fifteen years. What the team didn’t discover until they were deep into validation was that the provider’s historical options data was reconstructed from secondary sources rather than sourced directly from OPRA. The implied volatility surface they had been using to calibrate their options overlay was, in places, mathematically inconsistent with what had actually traded. The strategy was rebuilt from scratch on a properly licensed source. Eight months, gone.

A fintech startup built a real-time portfolio dashboard on a provider whose terms of service permitted their own research use of the data but not redistribution to end users. They found out when they received a letter from an exchange’s licensing division six weeks after launch. The product was pulled while legal negotiated a retroactive license. The cost was not just the licensing fee. It was the customer churn, the engineering time, and the trust damage with their early adopters.

These are not edge cases. They are the predictable consequences of treating the choice of a stock API as a commodity decision.

This article takes a different approach from most comparisons. Rather than simply listing features, we examine what each provider is actually like to build with, what each one costs across its full lifecycle, and where each one fits and doesn’t fit in the landscape of applications being built in 2026. The evaluation spans five providers: Alpha Vantage, Interactive Brokers, QuoteMedia, Bloomberg API, and Xignite.

The Infrastructure Decision Nobody Talks About: What Exchange Licensing Actually Costs

Before evaluating any specific provider, it is worth understanding something about the financial data supply chain that most developers and even many quants never fully reckon with: obtaining and maintaining direct exchange licenses is extraordinarily expensive.

NASDAQ, OPRA, CBOE, and S&P Global do not grant data distribution licenses for free. Each relationship involves annual licensing fees that run into hundreds of thousands of dollars, technical compliance audits, ongoing reporting requirements, and contractual restrictions on how data can be used, latency-stamped, and redistributed. OPRA licensing alone, which covers consolidated US options market data, is a substantial operational undertaking. Most data providers simply do not pursue it.

This is why the market is structured the way it is. Providers with full, direct exchange licensing are rare. Most of the market consists of providers who source data through intermediaries, reconstruct it from secondary sources, carry only partial licensing that covers display use but not redistribution, or offer data that is technically exchange-delayed regardless of how it is labeled. The price differential between a fully licensed provider and a partially licensed one often looks modest on a per-API-call basis. It looks very different when you account for the legal, operational, and trust costs of building on an improperly licensed data source.

The reason this matters for a comparison article is that it explains why not all stock APIs are competing on the same terms. A provider with complete exchange licensing, covering equities, options, indices, and ETFs across the major authorities, is offering something that is structurally different from a provider without it, regardless of how similar their endpoints and pricing tiers appear.

Keep this framing in mind as we evaluate each provider below.

#1: Alpha Vantage, the Best Stock API for Quants and Developers Alike

What the Licensing Actually Means

Alpha Vantage is one of the very few data providers that holds direct licenses from all four of the most important market data authorities: NASDAQ, OPRA, CBOE Global Markets, and S&P Global. It is worth being specific about what each of those covers, because the combination is rarer than the list of names might suggest.

NASDAQ licensing covers equities across the full range of NASDAQ market tiers, from the Global Select Market where the largest technology companies trade to the Capital Market where smaller growth companies are listed. OPRA licensing covers consolidated US options market data aggregated from every participating options exchange, which includes CBOE, NYSE Arca Options, Nasdaq PHLX, MIAX, and others. This is the license that most providers lack entirely, because OPRA data is expensive to source and technically demanding to distribute. CBOE licensing covers CBOE-specific derivatives and the VIX family of volatility indices that options traders and volatility strategists depend on. S&P Global licensing covers S&P indices, including the S&P 500, S&P MidCap 400, and S&P SmallCap 600, along with structured fundamental financial data.

Together, these four licensing relationships give Alpha Vantage the ability to distribute legally clean, exchange-direct data across equities, ETFs, options, and indices. For a fintech developer, this means you can build a redistribution-facing product without carrying unlicensed data exposure. For a quant, it means the data your strategy consumes is what actually traded, not a reconstruction.

What Building With It Actually Looks Like

The Alpha Vantage REST API follows a simple and consistent pattern. Every endpoint accepts a function parameter that specifies the data type, a symbol parameter, and an API key. A request for the daily adjusted price series of a stock, for example, is a single GET request with three parameters. The response is a structured JSON object with consistent key naming across endpoints. There are no session handshakes, no subscription protocols, and no persistent connections required for most use cases.

For a quant building a Python-based research pipeline, getting from API key to a clean pandas DataFrame of adjusted historical prices takes roughly a dozen lines of code with the community-maintained alphavantage Python wrapper, or a few more lines using the requests library directly. The data comes back split-adjusted and dividend-adjusted by default, which means the time series is ready for backtesting use without a separate adjustment step.

For a fintech developer integrating live prices into a web application, the live quote endpoints return JSON with timestamp, open, high, low, price, volume, and extended hours data. Polling at the interval appropriate for the application is straightforward, and the response structure is consistent enough to build a reliable data layer around without extensive defensive coding.

The MCP integration changes the access pattern fundamentally for AI-powered applications. Rather than writing code that calls the API and passes the result to a language model, an LLM agent with Alpha Vantage configured as an MCP tool can query financial data directly as a step in its reasoning process. The agent frames a question, the MCP layer translates it into an API call, and the result is returned as structured context. This means building an AI research assistant that can answer questions like “what has the implied volatility of AAPL at-the-money options done in the week before each of the last six earnings releases” requires no custom data pipeline code at all. The agent handles the query composition, the API call, and the interpretation.

Data Quality in Practice

Alpha Vantage’s corporate action handling is applied retroactively across the full historical dataset. The implication for backtesting is that you can pull a twenty-year price series for a stock that has undergone multiple splits and special dividends and use it directly in a factor model without a separate adjustment pipeline. For a quant team, eliminating that pipeline reduces both engineering complexity and the risk of introducing adjustment errors.

The fundamental data, including quarterly earnings, revenue, gross margin, operating income, free cash flow, and balance sheet items, is normalized consistently across reporting periods. This matters for factor model construction: if the definition of a metric shifts between reporting periods because of a provider’s inconsistent normalization, factors built on that metric will have a structural instability that only appears when you dig into the raw numbers.

The One Constraint

The free tier is genuinely useful for prototyping, research validation, and individual developer workflows, but it is not designed for bulk historical data extraction across large universes or for high-frequency polling across hundreds of symbols simultaneously. Teams building production systems at scale will need a paid plan, and teams with redistribution or enterprise requirements should engage Alpha Vantage directly for a commercial license. Factor this into your infrastructure budget from the beginning rather than discovering it mid-build.

#2: Interactive Brokers API, Where Data and Execution Come Together

Interactive Brokers is a brokerage that happens to have an extremely capable API, not a data provider that happens to enable trading. That distinction is the key to understanding where it belongs in your infrastructure stack.

What Building With It Actually Looks Like

The Interactive Brokers API comes in two primary forms. The Trader Workstation API, known as TWS API, connects to the desktop trading application and is suitable for algorithmic trading and live data consumption in environments where the workstation is running. The Client Portal API is a lighter REST-based interface that covers a broader range of brokerage operations and does not require the desktop application. For systematic traders, there is also the FIX protocol interface for order routing.

The TWS API, which is what most algorithmic traders use for market data, requires a running instance of Trader Workstation or IB Gateway as a local middleware layer. This architecture works well for a single trader running strategies on a personal workstation. It requires more careful thinking for cloud-based deployments, because IB Gateway needs to be run as a persistent process on the server, requires periodic re-authentication, and has session management characteristics that reflect its origins as a desktop trading tool rather than a cloud API service. Engineering teams that have built cloud-deployed systematic trading systems on Interactive Brokers have solved these problems, but the solutions require custom infrastructure work that would not be necessary with a purpose-built data API.

Market data access through the Interactive Brokers API requires market data subscriptions managed through the brokerage account. Exchange data fees apply, and subscribing to real-time data for multiple asset classes and exchanges requires configuring and paying for each data subscription separately within the Interactive Brokers account management interface. This is a different cost model from a data API provider that packages exchange data into a tier-based subscription.

Where It Wins

For a systematic trader who is already an Interactive Brokers client and is trading through the platform, the integration between market data and order execution is a genuine operational advantage. The same connection that feeds your strategy its live market data is the connection through which orders are routed. There is no latency introduced by translating between a data API’s data model and a brokerage API’s order model, and there is no risk of inconsistency between the price your strategy sees and the price at which your orders execute.

Interactive Brokers also provides access to a remarkably broad range of global markets from a single account, covering equities, options, futures, forex, and fixed income across dozens of exchanges worldwide. For a strategy that needs to trade or monitor markets across multiple geographies, this breadth is difficult to replicate through a pure data provider.

Where It Doesn’t Fit

Interactive Brokers is not suitable as a backend data provider for fintech products serving a general audience. Users of your application would each need their own Interactive Brokers account and would need to grant your application access to their data through Interactive Brokers’ OAuth-based authorization flow. This architecture is appropriate for tools built specifically for the Interactive Brokers user base, not for consumer applications that need to serve any user regardless of their brokerage relationship.

The historical data available through the Interactive Brokers API is adequate for near-term backtesting but is not the deep, decades-long, research-grade historical archive that serious quantitative research demands. The platform was built for trading, and its historical data access reflects that priority.

AI readiness is minimal. The TWS API and Client Portal API were designed for programmatic trading workflows, and there is no MCP integration or native compatibility with LLM-based agent frameworks. Building an AI-powered research or execution assistant on top of Interactive Brokers data requires substantial custom adapter engineering.

#3: QuoteMedia, Purpose-Built for Publishers and Display-Oriented Fintech

QuoteMedia occupies a specific and genuinely well-served niche that the other providers on this list mostly do not target: the financial publisher, the market data portal, and the fintech product where the primary use case is displaying market data to end users rather than computing on it programmatically.

What Building With It Actually Looks Like

QuoteMedia’s product stack is explicitly designed around display and distribution use cases. In addition to a REST API for programmatic data access, QuoteMedia offers a library of embeddable widgets: stock tickers, charts, options chains, financial summary tables, and earnings calendars that can be dropped into a web page or application with minimal integration work. For a financial news publisher or a content platform that needs to display market data alongside editorial content, the widget path dramatically reduces the engineering cost of adding market data to a product.

The REST API is well-documented and returns consistent, predictable responses. Authentication is straightforward, and the data model is designed to be easily consumed by frontend applications without requiring a sophisticated backend transformation layer. Rate limits and usage tiers are clearly defined in the documentation, which makes infrastructure planning more predictable.

QuoteMedia’s licensing model is specifically designed to accommodate redistribution to end users, which is a meaningful differentiator for publishers and display-focused fintech applications. The company has structured its licensing around the needs of organizations that are putting market data in front of audiences rather than using it exclusively for internal computation.

Where It Wins

For a financial media company, a news publisher adding market data to their editorial platform, or a fintech product where the primary value is a clean, well-displayed view of market data rather than systematic analysis of it, QuoteMedia is well-suited. The widget library reduces time-to-market significantly for these use cases. The publisher-friendly licensing structure means redistribution is built into the product model rather than requiring a separate legal negotiation. Canadian market coverage, including TSX-listed equities, is a particular strength that some competitors handle less gracefully.

Where It Falls Short

QuoteMedia’s strengths for publishers become limitations for quantitative users. The historical data depth is adequate for chart display and near-term context but is not designed for multi-decade backtesting. The corporate action adjustment pipeline is suitable for displaying correct current prices but has not been built to the standards that systematic traders require for research-grade historical accuracy. Options data at the precision and licensing depth that derivatives traders need is not QuoteMedia’s core offering.

AI readiness is limited. There is no MCP integration, and the API was designed for human-facing display use cases rather than machine-to-machine reasoning workflows. For a fintech developer building a product where AI agents need to query and reason about financial data, QuoteMedia’s architecture requires substantial bridging work.

The platform also does not provide the kind of programmatic fundamental data access that factor investors need. Financial statement data is available primarily in a display-ready format rather than as a structured, normalized dataset optimized for systematic computation.

#4: Bloomberg API, the Institutional Standard With Institutional Constraints

A complete comparison of the best stock APIs has to include Bloomberg, not because it is the right choice for most of the people reading this article, but because it defines the ceiling of what financial data infrastructure can be. Understanding Bloomberg’s position on this list requires separating what the platform is from what it costs and what it is like to work with.

What the Data Actually Is

Bloomberg Terminal and its associated API, BLPAPI, represent the most comprehensively curated financial data asset ever assembled. The breadth is not matched by any other provider on this list or, in most dimensions, by any provider in the market at all. Fixed income data covering government bonds, corporate bonds, municipal bonds, and structured products across global markets. Equity data spanning global exchanges with corporate action handling backed by decades of institutional curation. Derivatives coverage across listed and OTC markets. Macroeconomic data across hundreds of countries and thousands of series. Real-time news with relevance scoring. Credit ratings and credit risk data. Alternative data sets of numerous types.

For an institutional asset manager or a large hedge fund that needs to operate across all of these dimensions, Bloomberg is not a vendor relationship; it is infrastructure. The Terminal is the tool that the industry’s most sophisticated financial professionals use as their primary research environment, and BLPAPI allows that data to be accessed programmatically in the same way that a Bloomberg Terminal user accesses it interactively.

What Building With It Actually Looks Like

BLPAPI is not a REST API. It uses a proprietary subscription-based protocol that requires installing a Bloomberg-provided SDK and maintaining an active connection to Bloomberg’s data network. This connection is typically provided through a Bloomberg Terminal subscription for desktop use, or through a Bloomberg Server API arrangement for server-side programmatic access.

A basic BLPAPI request to retrieve the current price and daily change of a set of securities in Python requires initializing a session, creating a service, composing a request object, sending the request, and then processing a response that arrives through an event-driven callback mechanism. The resulting code is significantly more verbose than the equivalent REST API call in any other provider on this list. The Bloomberg-provided API documentation is thorough but reflects the complexity of the underlying system, and developers who come from a background of modern REST API integration typically experience a meaningful learning curve.

Data schema conventions in BLPAPI reflect the Terminal’s origins as a product for financial professionals rather than for software developers. Field names are Bloomberg’s proprietary mnemonics, such as PX_LAST for last price and EPS_DILUTED for diluted earnings per share. Translating between Bloomberg’s field namespace and the normalized data models that modern applications use is a mapping exercise that requires maintaining a reference dictionary or using one of the community-maintained wrapper libraries that abstract this complexity.

The Cost Reality

A Bloomberg Terminal subscription is approximately $24,000 per user per year. Server-side API access is priced separately and typically requires an enterprise agreement. For a team of five quants each needing Terminal access, the annual data cost approaches or exceeds the fully-loaded compensation cost of a junior engineer. This is not a complaint about Bloomberg’s pricing; it reflects the genuine cost of maintaining the infrastructure and curation depth that Bloomberg provides. But it is a real constraint that places Bloomberg outside the practical reach of individual systematic traders, early-stage fintech startups, and any team that has not yet reached institutional scale.

Redistribution licensing for third-party applications is complex and restrictive. Bloomberg protects its data relationships carefully, and building a consumer-facing product that surfaces Bloomberg data to end users requires a specific licensing arrangement that is neither straightforward to obtain nor inexpensive.

AI readiness, measured against the standard of native MCP integration, is currently limited. Bloomberg has invested significantly in its own AI products, including Bloomberg GPT, which is trained on financial data and integrated into the Terminal’s interactive experience. However, BLPAPI as a programmatic interface does not have MCP support, and connecting Bloomberg data to open-source LLM frameworks for external AI application development requires custom adapter engineering.

Bloomberg earns its ranking at number four on this list, rather than lower, because of what it actually is. The data quality and breadth are unmatched. The institutional track record is definitive. For the organizations that can afford it and have the engineering capacity to work with its complexity, it remains the standard against which other providers are measured.

#5: Xignite, the “OG” Enterprise Solution

Xignite has been part of the financial data market for long enough that many teams evaluating it in 2026 are not asking “should we use Xignite” for the first time. They are asking whether they should continue to use it, or whether the cost of switching to a more modern alternative has become justified. That is a meaningfully different question, and it deserves a different kind of analysis.

The Case for Continuing With Xignite

If your organization already has a Xignite integration in production, the data is flowing reliably, and the products built on top of it are stable, the case for maintaining that relationship is real. Xignite’s uptime record is strong. The API is consistent and well-understood by teams that have been working with it. The enterprise SLA structure provides the kind of contractual reliability commitments that procurement and legal teams require. The data quality for major US and European equity markets is solid. These are not trivial advantages.

Xignite’s cloud-native API architecture, which was genuinely forward-thinking when it was introduced, means that traditional REST-based integrations built on it continue to function predictably. If the products you are maintaining are built around standard request-response data flows rather than AI agent workflows, Xignite’s architecture remains fit for purpose.

The True Cost of Staying

The argument for continuing with Xignite changes when you start accounting for what the platform does not provide and what it will cost your team to work around those gaps.

The most concrete gap is AI readiness. Xignite has no MCP integration. This means that any AI-powered financial application your team builds will require a custom data adapter that translates between your AI agent framework and Xignite’s API. That adapter is not a one-time engineering cost. It is a maintenance cost: every time your AI framework updates its tool-calling conventions, every time Xignite changes an endpoint, and every time you add a new AI feature that requires accessing a different data type, the adapter layer needs to be updated. Over the lifecycle of an AI-powered product, this ongoing maintenance burden is a real and recurring engineering cost that a provider with native MCP integration eliminates.

The options data gap is similarly real for any team whose product scope is expanding into derivatives. Xignite’s options coverage and its licensing depth across the OPRA and CBOE domains do not match what Alpha Vantage provides. If your product roadmap includes options analytics, options pricing, or volatility surface display, building that capability on Xignite requires either accepting a licensing and data quality ceiling or introducing a second data provider for the derivatives layer, which adds integration complexity and operational overhead.

Xignite’s pricing is enterprise-oriented, which suited the customer profile it was built for. For teams whose data needs are growing and whose engineering resources are finite, the question of whether that pricing is justified relative to more accessible alternatives with stronger AI and derivatives capabilities becomes more pressing over time.

Who Should Be Looking at Alternatives

Teams that are building new AI-powered financial products from a greenfield starting point have little reason to choose Xignite. The absence of MCP integration means you are building technical debt into the foundation of the product from day one. Teams whose product scope is expanding to include options or volatility data will find the licensing and data quality ceiling a genuine constraint. Teams that are cost-sensitive and evaluating their infrastructure spend against the alternatives on this list will find that Xignite’s enterprise pricing is harder to justify given the feature gaps.

Teams with stable, non-AI-native products built on Xignite that are not planning to expand into derivatives should evaluate their switching costs honestly before making a change. The case for staying is real when the integration is working, the product scope is not changing, and the engineering cost of a migration exceeds the operational cost of the current gaps.

Total “Cost of Ownership” Across All Five Providers

Feature comparisons tell you what each provider offers at a point in time. Total cost of ownership captures what each provider actually costs across the full lifecycle of a product built on top of it. These are meaningfully different numbers, and the gap between them tends to be larger than most teams estimate before they start building.

Alpha Vantage: The free tier carries zero direct cost and provides meaningful functionality for development and validation phases. Paid plans scale in a predictable, published way. The primary TCO advantage is what you avoid: no legal exposure from licensing gaps, no data cleaning pipeline for adjustment errors, and no custom adapter engineering for AI integration thanks to MCP. For teams building AI-powered financial products, the engineering time saved by native MCP support alone can represent weeks of development work that does not need to be done.

Interactive Brokers: Direct API access is included with a funded brokerage account, making the nominal data cost very low for traders already using the platform. The true TCO includes the engineering cost of building and maintaining cloud-deployable infrastructure around a desktop-first API architecture, the ongoing management of per-exchange market data subscriptions through the brokerage account interface, and the custom adapter engineering required for any AI-powered application. For teams not already in the Interactive Brokers ecosystem, the setup cost is substantial relative to a purpose-built data API.

QuoteMedia: Licensing is explicitly structured for redistribution use cases, which means the base cost is the published subscription rather than a base cost plus a separately negotiated redistribution license. For publisher and display-oriented use cases, this predictability is a genuine TCO advantage. For teams whose needs extend into quantitative computation, the TCO includes the cost of supplementing QuoteMedia with a second provider for research-grade historical data and fundamental data, which adds both direct cost and integration complexity.

Bloomberg: The headline cost of $24,000 per Terminal per year is the starting point, not the ceiling. Server API access, additional data modules, and the engineering cost of maintaining BLPAPI integrations all add to the total. For a five-person quant team, the annual data cost can reach six figures before accounting for engineering time. The TCO is justified at institutional scale by data breadth and quality that no other provider matches. Below that scale, it is difficult to make the economics work relative to the alternatives.

Xignite: The enterprise pricing reflects a customer profile of established financial institutions, and the published rates are generally higher than Alpha Vantage’s at comparable data volumes. The less visible TCO component is the ongoing maintenance cost of working around the gaps: custom adapter engineering for AI features, potential supplementation with a second provider for options data, and the compounding burden of maintaining integrations that were not designed for modern AI-native development patterns. For teams that are growing their product scope and their AI capabilities, these hidden costs accumulate.

Choosing the Right Provider for Your Use Case

Rather than a matrix, the most useful decision guide organizes around the primary question your team is actually trying to answer.

“I need a data foundation I can build anything on, including AI-powered features, without hitting licensing or data quality walls.” Alpha Vantage. The combination of full exchange licensing across equities, ETFs, options, and indices, institutional-grade data quality, decades of clean historical data, and native MCP integration makes it the only provider on this list that satisfies this requirement without caveats.

“I am a systematic trader who executes through Interactive Brokers and I want my data and execution to be the same system.” Interactive Brokers API. The execution-data integration is a genuine operational advantage that no purpose-built data provider can replicate. Accept the engineering complexity of the API in exchange for a unified trading infrastructure.

“I am building a financial content or news platform and I need market data I can display to users with minimal integration work.” QuoteMedia. The widget library and publisher-oriented licensing model are designed specifically for this use case. It will get you to market faster than any other provider on this list for display-oriented applications.

“I run an institutional asset management operation and I need the broadest possible data universe across every asset class globally.” Bloomberg API. The data breadth and quality justify the cost and complexity at institutional scale. No other provider matches it across the full range of asset classes and geographies.

“My organization has an existing Xignite integration and I am deciding whether to stay or switch.” If your product is stable, non-AI-native, and not expanding into options: stay and evaluate switching costs carefully. If your product roadmap includes AI features or derivatives data: start building the business case for a migration to Alpha Vantage, and plan the transition around a specific product milestone where the new data layer can be introduced cleanly.

Final Verdict

The best stock API in 2026 is the one that matches where your product is going, not just where it is today.

For most teams, that means Alpha Vantage. Its exchange licensing from NASDAQ, OPRA, CBOE, and S&P Global removes the legal and data quality risks that have derailed real products. Its institutional-grade data quality and decades of clean historical depth provide the foundation that serious quantitative work requires. Its native MCP integration means that as AI capabilities become central to financial products, the data layer is already compatible rather than requiring a retrofit.

Interactive Brokers is the right answer for execution-integrated systematic trading within its own ecosystem. QuoteMedia is the right answer for display-oriented financial publishing and content platforms. Bloomberg is the right answer for institutions where data breadth across every asset class justifies the cost. Xignite is the right answer for organizations with stable, established integrations whose switching costs genuinely outweigh the benefits of moving to a more modern platform.

The cost of getting this decision wrong is not just the switching cost when you eventually change providers. It is the legal exposure, the data quality errors, the engineering debt, and the product delays that accumulate in the meantime. The opening examples in this article were not hypothetical. Treat the infrastructure decision accordingly.

This article evaluates stock data APIs from the perspectives of quantitative traders and fintech developers as of 2026. API capabilities, licensing terms, and pricing are subject to change; verify current details directly with each provider before making infrastructure decisions.

Best Stock Market APIs in 2026 was originally published in Alpha Vantage on Medium, where people are continuing the conversation by highlighting and responding to this story.

Financial data represents the lifeblood of markets, providing critical information required to make informed investment decisions. This data encompasses a wide array of quantitative and qualitative information pertinent to the performance of various asset classes such as such as stocks, bonds, currencies, commodities, and derivatives. Markets are complex systems that rely heavily on the constant flow of this data to function efficiently; in its absence, investors are akin to wanderers in a desert without a compass.

For decades, large enterprises such as Reuters, Dow Jones, and Bloomberg dominated the financial data industry, gatekeeping access to institutions and the wealthy. When Bloomberg launched its renowned ‘Terminal’ back in the early ’80s, it cost a staggering $1,000 per month. For reference, the average monthly mortgage in 1980 was approximately $580 per month, or roughly 40% less than a Bloomberg subscription. This cost barrier gave institutions a major advantage over individuals.

By the 2000s, the dawn of the internet era democratized access to financial information. Online brokerages, financial news websites, and data services emerged, offering real-time market data and analysis tools to a broader audience. The emergence of financial data APIs transformed the landscape, allowing seamless integration and sharing of financial data across platforms and applications. The advent of these financial data APIs has had a transformative impact on how data can be used in the real world, such as algorithmic trading, portfolio management, market research and analysis, risk management, and powering various fintech applications.

A quick look at some of the best financial data APIs:

1. Stock Market API
2. News & Sentiment API
3. Fundamental Data API
4. Forex + Crypto API
5. Technical Indicator APIs

Best for Stock Prices: Core Stock API

The Core Stock API at Alpha Vantage provides all you need for your trading algorithm or market research; it allows you to retrieve data for a ticker’s open, high, low, close, and volume (OHLCV) at daily, weekly, monthly, or intraday intervals. This data can be returned either raw (as-traded) or as split and dividend-adjusted. Alpha Vantage boasts one of the largest financial data libraries in the industry, covering over 200K stock tickers from more than 20 global exchanges, with historical data dating back over 20 years. Being exchange-licensed, Alpha Vantage allows you to retrieve end-of-day, 15-minute delayed, or real-time data, while also ensuring complete exchange compliance.

Link: https://www.alphavantage.co/documentation/#time-series-data

Best for News: News & Sentiment API

The News & Sentiment API aggregates unstructured news data from prominent and trustworthy global sources and transforms it into structured business insights. This LLM-powered technology analyzes the text of each story and attaches ticker-level sentiment and relevance scores to equities, forex, and crypto symbols. For instance, if an article about Ford recalling 25,000 SUVs breaks, this endpoint not only returns the relevant article data (story’s title, link, author, etc.) but also attaches a negative sentiment score, reflecting the story’s potential negative impact on the company.

Link: https://www.alphavantage.co/documentation/#news-sentiment

Best for Fundamentals: Fundamental API

The Fundamental Data API can be an extremely useful tool when conducting market research or fundamental analysis. It can return a wide variety of essential information about any listed company, including their industry sector, market capitalization, PE ratio, and dividend yield, along with details financial statements like the balance sheet, income statement, and cash flow statement. This data is typically updated on the same day a company reports its earnings.

Link: https://www.alphavantage.co/documentation/#fundamentals

Best for Currencies: Forex & Crypto API

The Crypto & Forex API at Alpha Vantage covers all majorly traded cryptos and forex pairs and updates in realtime, allowing you can build a trading bot, alert system, and much more. Looking to perform an analysis or backtest a strategy? The endpoint also can pull historical data at daily, weekly, or monthly intervals.

Link: https://www.alphavantage.co/documentation/#fx

Best for Trading: Technicals API

The Technicals API empowers tech-savvy traders to access over 50 different technical indicators for any listed ticker, including cryptocurrencies and forex pairs. Popular indicators such as Simple Moving Average (SMA), Exponential Moving Average (EMA), Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI), Volume Weighted Average Price (VWAP), and Bollinger Bands (BBANDs) are widely used. By integrating these real-time indicators into your programs, you can gain a competitive edge in the market.

Link: https://www.alphavantage.co/documentation/#technical-indicators

Stock market APIs offer developers a powerful way to programmatically access, analyze, and employ stock market data. This opens up opportunities for creating advanced financial applications, research tools, and trading systems. These APIs are vital in making stock market information more accessible, thereby empowering individuals and businesses to engage more effectively in the financial markets.

Best Stock Market APIs was originally published in Alpha Vantage on Medium, where people are continuing the conversation by highlighting and responding to this story.

Backdrop

On November 19th, 2021, The Nasdaq Composite Index exceeded the $16,000 mark for the first time in history. The index rallied 130% over the course of 18 months, coming out of a global pandemic that kept billions of people around the world locked inside their homes. During those 18 months, the US economy was injected with more fiscal and monetary support than ever before. The US Congress outlaid $13.8T of fiscal spending in response to the pandemic, which represents nearly 65% of the United State’s GDP. How did they finance this spending? By issuing bonds. Who bought these bonds? Its own central bank, the Federal Reserve (Fed). The Fed’s balance sheet grew from $4.17T at the start of 2020, to over $8.75T by the end of 2021. They absorbed over $4.5T of treasuries and mortgage-backed securities in just two years.

Both consumers & the financial markets loved it. Consumers were receiving thousands in stimulus checks every few months and watched the balance in their savings accounts pile up. Meanwhile, financial markets were soaking up the excess liquidity being dumped into the economy. The Dow Jones Industrial Average rallied 90% from the COVID bottom, the S&P 500 rallied 105%, and the Nasdaq Composite Index rallied 130%. The Shiller-Cape PE ratio for the S&P500 jumped to over 35x, a level seen just once in the last century; the dot-com bust.

In the spirit of this pending asset bubble, here is a great quote from 2007:

“As long as the music is playing, you’ve got to get up and dance. We’re still dancing.” — Chuck Prince, Ex-Citi CEO, July 2007

The US economy had a really fun party with lots of dancing and lots of drinks, but now it’s the morning after and it’s time to wake up and smell the roses; there’s a hangover coming. This unprecedented financial support from Congress & the Fed didn’t go without consequence. The economy is now suffering from soaring inflation to the tune of 9.1% year-over-year prints on the Consumer Price Index (CPI).

The Fed has reversed course, and fast. With the goal of taming inflation, the Fed is raising rates while also allowing up to $95B of assets(bonds) to run off their balance sheet every month, a corrective action we haven’t seen in decades.

Outline

With the goal of keeping this review as objective as possible, we will discuss a few popular indicators for the US economy, examine how accurate they’ve been in the past, and break down where those same indicators stand today. We will go through both leading & lagging indicators, so bear that in mind.

But first, let’s make sure we have the fundamentals down. What even is a recession? A recession is typically recognized as two consecutive quarters of negative real GDP growth. It can be as simple as that. However, different entities such as the National Bureau of Economic Research (NBER) break it down even further and define it as a “significant decline in economic activity, lasting more than a few months, normally visible in real GDP, real income, employment, industrial production, and wholesale-retail sales.” But for the sake of simplicity, let’s just say two-quarters of real negative GDP growth.

Q1’s GDP release in April 2022 shocked most as it came in at -1.9%, much lower than the 1% gain analysts were expecting. That means one more quarter of negative GDP growth for the US economy and it will technically be in a recession. So that brings us to the indicators…

The indicators. First leading, then lagging.

#1 — Yield Curve Inversion

A yield curve inverts when longer-term yields drop below shorter-term yields for debt with the same risk profile. This happens because investors expect shorter-term rates to decline soon, due to a poor economic outlook, or in some cases, a recession. For this indicator, we will observe the spread between the 2-year and 10-year treasury yields.

In the last forty years, the 10Y yield has dipped below the 2Y yield on just five occasions, of which the US experienced a recession 80% of the time within 18 months. Today, the spread is -0.2% and has been negative since the first week of July 2022. This indicator is waving a red flag for recession.

#2 — Monetary Tightening

Tightening monetary conditions restricts the flow of capital and makes financing new ventures more expensive. This indicator tracks the rate of change in the Fed Funds Rate. The trigger on this indicator is 2%, or a Fed Funds Rate that is 200 basis points (bps) higher than the year prior. Over the last 65 years, we’ve hit this trigger on ten occasions, of which the US experienced a recession 80% of the time within 24 months. Today, the Federal Funds Rate is 1.5% higher than a year ago today. The CME FedWatch tool predicts another 75 bps rate hike at the Federal Reserve’s July meeting in one week's time. If this hike comes to fruition, it will raise this indicator beyond its 2% trigger and wave the red flag for recession.

#3 — Conference Board Leading Economic Index (LEI)

The Conference Board is a non-profit research organization. They’ve created a Leading Economic Index (LEI) that tracks ten different leading indicators and aggregates them into one index. A positive year-over-year change in the index signals the underlying indicators are improving, and vice versa. Over the last forty years, this indicator has gone negative on six occasions, of which a recession had occurred 83% of the time within 18 months. Today, the current reading is +1.7% YoY. This signal is not raising a red flag just yet.

#4 — Brave Butters Kelley Index

The Brave-Butters-Kelley Index (BBKI) is a Federal Reserve Bank of Chicago research project. They use hundreds of dynamic factors to forecast the strength of US economic activity. The data is measured in standard deviation (std) units away from the real GDP growth trend. Since the 1970s, the BBKI has dropped 1.5 standard deviations below trend on just five occasions, each of which the US economy was in a recession, or soon to be in one. Today, the reading of the BBKI is 1.5 standard deviations below the trend. This signal is waving a red flag for recession.

Lagging

#5 — Consumer Sentiment

The University of Michigan tracks sentiment among consumers through monthly surveys and aggregates the responses into an index score. The results are important to consider as consumer spending makes up over two-thirds of US GDP. Therefore, a strong consumer is vital to US economic growth. Over the last 40 years, consumer sentiment has dropped 15% year-over-year on eight occasions, of which the US economy has been in a recession 63% of the time. Currently, consumer confidence is down 30% YoY, a level only seen in the recession of 1990 & the Great Financial Crisis of 2008. This indicator is waving the red flag for recession.

#6 — Energy Prices

This indicator tracks Brent Crude Oil’s price deviation above or below its trend. Energy is the #1 input cost of doing business. Whether you’re an airline paying for jet fuel, an eCommerce brand paying for freight, or a manufacturer running heavy machinery, energy is a huge cost driver for your business. Over the last fifty years, the price of crude oil has jumped 50% above its trend on six occasions, of which the US has been in a recession 100% of the time. Today, energy prices are 65% above trend, well above the trigger. This indicator is waving the red flag for recession.

#7 — ISM PMI

The Purchasing Managers’ Index (PMI) is a monthly indicator for US economic activity based on surveys from hundreds of purchasing managers at manufacturing firms. A PMI reading above 50 signals expansion relative to the previous month. A reading below 50 suggests contraction. Over the last sixty years, the PMI has broken below the 45 level on eight occasions, each of which the US was in a recession. This indicator is certainly “lagging”, but it’s worth noting that the current reading is 47.5, only slightly above the trigger. No red flag for recession yet, but getting really close.

#8 — Stock Market

This indicator tracks S&P500 Index Performance on a year-over-year basis. Markets are forward-looking and there may be some truth to be told if investors begin to price in a recession or a significant drop in corporate earnings. Over the last forty years, the S&P500 has dropped below the -15% YoY threshold on seven occasions, of which the US has been in a recession 71% of the time. On July 14th, the S&P500 index was down -13.4% YoY, just above the trigger, so no red flag for a recession just yet.

#9 — CEO Confidence Index

The Conference Board also has an indicator called the CEO Confidence Index. It is a proxy for the predicted strength of the US economy from the perspective of US-based CEOs. A reading above 50 signals a majority of outlooks being positive, while a reading under 50 signals the majority of outlooks being negative. Since the 1970s, the index has fallen below the 40-level on five occasions, each of which the US was on the way into, or already in a recession. Currently, the index is at 43, just barely above the trigger. This indicator is not waving the red flag yet, but the index is deteriorating quite rapidly.

#10 — Labor Market

The labor market is the backbone of US economic growth & prosperity. If less people are employed, there is less disposable income to be spent on goods and services. Since the 1940s, every single time the unemployment rate ticked up 2% year-over-year, the US was already in a recession. Currently, the US labor market remains strong with unemployment down 2.3% from last year as it bounces around 3.6%. Definitely no recession warning out of the labor market so far. A few things to note: the labor force participation rate dropped from 63.4% pre-COVID to just about 60% during COVID, and people have been slow to rejoin the workforce. Currently, the participation rate is at 62.2% and is trending back to pre-COVID levels, but just another thing to keep your eye on.

#11 — Durable Goods Orders

Durable Goods Orders is a monthly survey conducted by the US Census Bureau that measures current industrial activity. It tracks the number of new orders for durable goods. In the last thirty years, durable goods orders have dropped 20% YoY on four occasions, of which the US was in a recession 75% of the time. The current reading is at +10.8% YoY, which is quite healthy.

Atlanta Fed GDPNow Estimate

That’s it for indicators, but what does the Fed think will happen in Q2? GDPNow is an estimate from the Atlanta Federal Reserve branch for the upcoming quarter’s GDP growth. The estimate constantly changes with new incoming data points. Currently, the GDPNow estimate is -1.6% real GDP growth for Q2. If true, this would technically put the United States in a recession.

Final Scorecard

It’s certainly not the prettiest picture for the next twelve to eighteen months, but it’s always good to know where the fundamental economic data is pointing. I’d recommend keeping track of these indicators as we progress through this turbulent time in the market. Here is the final scorecard:

Recession Report Card was originally published in Alpha Vantage on Medium, where people are continuing the conversation by highlighting and responding to this story.

We’d like to introduce you to a simple tool to receive email and Slack alerts on your cryptos and keep an eye on your new prospective investment. Using Rows template Track crypto with email and Slack alerts and a built-in Alpha Vantage integration you can build the tool within minutes. Rows is the only true spreadsheet with built-in integrations and a beautiful sharing experience. Thanks to its 50+ built-in integrations Rows instantly exports data from the tools of your choice into a spreadsheet, reducing manual work and copy&paste.

This step-by-step guide will show you how to set up cryptocurrencies tracking and notifications via email and Slack:

1. Sign up for your free account at https://rows.com/

2. After activating your account head to the Templates page. It’s an internal Rows library that contains all spreadsheet templates you can use to connect for data export/import and automate your workflows.

3. Search for the Track crypto with email and Slack alerts template. The template is essentially a spreadsheet that already contains a few formulas to help you quickly customise your crypto tracker.

4. Hit “Use Template” and follow the tutorial to learn how to use the tool and connect your spreadsheet with Alpha Vantage API. In a matter of minutes you will be ready to customize this crypto tracker.

5. In the setup table enter your email address or your Slack name depending on where you would like to activate the notifications. Now you can set up how often you would like to get notified. Hit “SCHEDULE” and correct the frequency and time. The tracking is set to daily notifications at 9:05 am by default.

6. In the Tracker table are already listed 20 most popular cryptos, their current exchange rate and the date/time of the latest rate update. You can easily modify this list by adding cryptos of your interest and removing irrelevant ones. Any time you can go back and make changes in your settings and cryptocurrencies list.

7. All set? Check boxes to activate email or Slack tracking (or both) and instantly start receiving your alerts. From now on you will receive regular alerts according to the schedule you set up.

Use the Track crypto with email and Slack alerts template to easily set up your personal tracker and alerts and stay on top of your crypto game.

Disclaimer: This a guest article by an employee of Rows GmbH. The views and opinions expressed in this article are those of the author and do not necessarily reflect the official position of Alpha Vantage, or any of its affiliates or subsidiaries. The accuracy, completeness and validity of any statements made within this guest article are not guarantees and we accept no liability for any errors, omissions or representations.

Receive email and Slack alerts on crypto prices in a spreadsheet was originally published in Alpha Vantage on Medium, where people are continuing the conversation by highlighting and responding to this story.

From May 29 — May 31, nearly 100 software engineers, financial advocates, beginners and experienced alike, entered the Alpha Vantage first virtual hackathon.

It was a fast-paced 3 days, where beginners and experienced engineers alike threw themselves into the world of fintech, looking to build something innovative.

With valuable prizes up for grabs, and a ton of workshops to help the engineers, there was a lot of support to give them the motivation and tools they needed. Some worked tirelessly without any sleep for the weekend, to build an interesting use case for themselves and the judges.

Here were the winners of each category!

Blockchain track winner: Blockaxis

They also won a judges award!

BlockAxis

BlockAxis is a blockchain-based hybrid trading platform that enables users around the globe to gain access and trade in the foreign equity markets. The economic signal for investing in equity (stock) markets is a strong one in the COVID-19 era. A recent economic review article observes that “The COVID-19 pandemic has created the greatest economic crisis since the Great Depression. Unsurprisingly, 2020 has so far been a rollercoaster ride for investors in the public equity markets. Despite the higher volatility, it is still as important as ever for individuals to be invested in the public equity markets for their long-term financial health.” Under the macroeconomic backdrop, BlockAxis mixes the elements of both centralized and decentralized exchanges and reaps their benefits. Instead of using fiat currency as the medium of stock purchases, it uses stablecoins such as DAI which are primarily designed to maintain its stable price and avoid market volatility unlike traditional cryptocurrencies like Bitcoin or Ethereum.

Along with a beautiful UI, the team of Saffat Aziz, Thomas Hai Li from the University of Ottawa, High school student Mihir Kachroo, and Arianne Ghislaine Rull of L’Amoreaux Collegiate Institute worked day and night to build this project that won both a grand prize and judges pick award, bringing their total prize up to 75LINK token. Congratulations to all!

Fintech Track Winner: Nick’s mega-cap momentum trading strategy

Stock Data Trade Simulator and Basic Strategy Implementation

This is a trading simulator with one strategy built-in. Other strategies could be added and run against Alpha Vantage data to find more patterns and learn more about the markets. The strategy uses a reverse head-and-shoulders approach to predict the momentum of securities and assigns alpha to them based off their movements.

Nick came into the hackathon knowing little about fintech and how to code it with python, and ended up leaving winning a 6 month 120 API call/minute key! The judges were impressed with a backtested (albeit a relatively short timeframe) trading strategy built from scratch in just a few days.

Built with python, his code is on Github if you want to try it out yourself, suggest improvements, or fork it to do whatever you’d like.

Judge’s Pick: SmartHealth

Also the rookie award!

SmartHealth - Health Insurance Smart Contract

SmartHealth is a smart contract in which its owner can create a mapping for both doctors and people of the country. Respectively, a specific doctor will be assigned to a specific person. Built entirely with solidity, this ETH based medical system could be used to store patient information on-chain, solving the issue of losing patient data and making patient data easier to share with other hospitals, while still keeping it private.

The most robust solidity contract in the competition, Sumit Banik of the Siliguri Institute of Technology proudly presents one of his earliest dives into the Ethereum and blockchain world and happily won two prizes over a thrilling weekend.

Judges Pick: VC Links

VCLinks

VCLinks connects entrepreneurs to venture capitalists looking to provide financing and expertise for projects. Entrepreneurs can easily create listings that describe their venture and their areas of interest, along with a recorded pitch and contact details. Additional details such as the amount of funding, type of funding, and financial details are provided upon further request. Venture capitalists can also express interest in certain industries or projects and will be required to complete a profile to facilitate our advanced tag searching features.

VCLinks can empower VC firms and entrepreneurs to meet in an easy, fun, interactive way. Powered by Edward Lee, Eli-Henry Dykhne, Kelvin Chen, and Ashmita Rajkumar, this is their team's first hackathon win and they are so proud of it!

I’d be sure to watch out for all these engineers in the future, as they have some great ideas and a lot of hard work to get them places.

Congratulations to all again!

AlphaVHack Winners and hackathon! was originally published in Alpha Vantage on Medium, where people are continuing the conversation by highlighting and responding to this story.

One of the cool features in the Office 365 API is the ability to set up streaming functions in Excel that update themselves at a predetermined interval. Today we are going to demonstrate how to set up a “live” updating chart for a forex series. Users should be aware that unless you have a premium membership with Alpha Vantage, this example will burn through your data allowance quickly. The intra-day series are updated at the same frequency as the series. For example, a 1-minute series will be updated every minute.

Alpha Vantage does not offer tick by tick data. However, we do offer intra-day forex series that update every minute. That’s more than enough data to create a chart that updates during the day. We’re assuming here that you already have the Alpha Vantage Market Data Add-in installed and have entered your API key.

The first thing we note are the numbers in the Date column. No worries! This is just because dates in Excel are actually floating numbers and we have to format them to look like we want. Excel has a few standard date formats you can choose.

If you want, you can customize your format any way you like. For longer series of intraday forex data, I like seeing a datetime. There is a pre-defined format under “Custom” in the Format dialog that gives me the date time to the minute.

However, for the purposes of this example where we are only charting the current day’s data, I can format the Date column as a time of day.

When using Alpha Vantage routines with an output size of “compact” the series is limited to the most recent 100 points. This is fine for many charting applications but sometimes we want to view fewer than 100 points.

We can then apply a filter to limit data to the last 30 data points using the FILTER function.

Here we are saving the FILTER result to a separate location in the worksheet. In principle you could nest a query formula within the FILTER formula (replace E2# with the appropriate Alpha Vantage formula). However, as we’ll see later, the flexibility will help when trying to use Excel’s financial chart types.

At this moment we’ll take a short aside to explain the E2# notation. There is a formula returning a dynamic array to cell E2. The notation E2# says take the entire output range of that formula. Pretty cool, right?

So, in our spreadsheet the filtered output formula is in L5. So just select that individual cell and choose to insert an area chart from the Excel Insert tab.

Let’s start by selecting the specific series to chart. Right click on the chart and choose “Select Data…”

Then you can choose the series you want in this chart.

I only want to look at the price at the end of each interval, so I uncheck all the series except close.

Let’s adjust the time axis. Right click in the area of the times and select “Format Axis…”

We select “Categories in reverse order.”

Those of you who have used Excel’s financial chart types know that the input data must be in a specific format. For example, the first column header must be the name of the entry (instead of “Date” which is returned by the time series query). Since our data is almost in the right format, we only need to modify the FILTER function so that the header row is not reproduced. This way we can enter our own values for the header values.

Remember from previous formulas that the cell N2 contains the number of points to be charted. Now we select all the columns except Volume including the customized headers. While this region is selected, we then choose from the Insert tab to make an Open/High/Low/Close chart. As with the previous example you will have to choose to reverse the x axis so that later times are on the right.

Both of the charts we’ve built will update every minute.

Look what you can build in a few minutes with Alpha Vantage and Excel!

Questions? Comments? Feel free to leave us a message below! You can also reach out to us for spreadsheet-specific topics here.

Live Charts with the Alpha Vantage Excel 365 Add-in was originally published in Alpha Vantage on Medium, where people are continuing the conversation by highlighting and responding to this story.

First of all the Add-in is for Office 365 only. This means that if you don’t have a subscription you will not be able to use the Add-in. On the other hand, if you do have a subscription you’ll be able to use the Add-in on desktop Windows, desktop Mac and browser versions of Excel.

Requirements and Caveats

If you have an Office 365 subscription you’ll still have to be on a Monthly or Insider channel to use this Add-in properly. The Add-in depends on a feature called dynamic arrays to return blocks of data from a function. At the time of this writing the functionality is not available on the Semi-Annual channel though the expectation is that the next update in July should contain the necessary functionality. Microsoft seems to generally recommend the Monthly channel unless your company has sensitive applications running in Excel. If you’re stuck on the Semi-Annual channel it is possible to use the Add-in but you’ll have to enter formulas as array formulas (CTRL+SHIFT+ENTER). It’s much more cumbersome than working with the dynamic arrays.

Setting your Office 365 Channel

On a Windows machine there are a couple ways to set the channel. Not as simple as it should be. Some helpful links

• Switch Office 2016 to Monthly Targeted Channel
• Switching to the Office 365 Monthly Update Channel

On a Mac OSX machine you can set the channel in the Microsoft AutoUpdate application. Insider/Slow is the same as Monthly.

Error Handling

The other main issue with the current JavaScript api is that it’s difficult (impossible?) to return useful error information from a failed function invocation. We’re waiting for wider release of error handling code (see :https://docs.microsoft.com/en-us/office/dev/add-ins/excel/custom-functions-errors). Our Add-in is pretty cool but don’t expect much information at the moment if something fails.

Installing the Add-in!

The Office 365 web Add-ins cannot be directly downloaded. The Add-in must be installed directly from Microsoft using an account with an active Office 365 subscription.

To download the Add-in from either a desktop or browser based version of Excel go to the Insert ribbon interface. On a desktop version of Excel this should look something like:

In the browser version of Excel this should look something like:

This will bring up an online store run by Microsoft. You can search for “Alpha Vantage” and install the Add-in from there.

After installation you should see a ribbon tab named Alpha Vantage(Web) added to Excel.

The actual ribbon will look like

In order to use this Add-in you will need a valid Alpha Vantage API key. You can request one here if you don’t already have one. On the Alpha Vantage(Web) ribbon tab select either the Info or Tasks button on the left. This will open a taskpane on your page. Scroll to the bottom of the pane and find the text box to update your API key at the bottom. If you have previously entered your API key you should see it here.

We’ll be publishing new blog posts soon on ways you can get the most out of the Add-in.

Enjoy!

Install the Alpha Vantage Office 365 Excel Add-in was originally published in Alpha Vantage on Medium, where people are continuing the conversation by highlighting and responding to this story.

Chainlink has quickly become one of our favorite cryptocurrency projects here at Alpha Vantage. They easily allow external data to be placed onto the blockchain for ETH developers to create smart contracts. Smart contracts have seemingly limitless applications, and getting stock market, cryptocurrency, forex, and other data are essential for creating substantial financial and investing applications.

The Chainlink team has put A LOT of work into making this process simple and easy for node operators to do, however the infrastructure is up to you. This how-to will give you a basic rundown of how to get one up.

Things that are beyond the scope of this article include:

• Monitoring scripts and tools
• Deployment and maintenance best practices
• Password protection and job creation
• How to generate traffic to your node

What it does give is a good idea of how simple it is to at least start.

We made a video on it, check it out here! (updated)

Before you get into the instructions from the CL team themselves here, there are just a few things to set up on the Google Cloud Platform.

1. A Virtual Machine
2. A Database
3. Gcloud command line on your local machine

Why would you want to run a CL node?

• Help the decentralization community
• Collect rewards in LINK token
• Learn more about how Chainlink and the Ethereum developers work

For those with some devops experience or technical background, this will all seem straightforward. A more sophisticated tutorial can be found here, which helps set up kubernetes to help maintain the docker containers.

#blockchain #chainlink #GCP #tutorial

How to build a simple Chainlink node on the GCP was originally published in Alpha Vantage on Medium, where people are continuing the conversation by highlighting and responding to this story.

There are a few steps in every machine learning / AI project (and also algorithmic trading/investing!), these are:

1. Get data
2. Play with the data and discover insights through visualization
3. Clean and prepare the data
4. Train a model
5. Fine-tune it
6. Run real-time, monitor, and maintain
7. Repeat with new insights (important!)

For this tutorial, we are going to focus on how python/scikit can help us with steps 1–4.

Let’s say we think that there might be some relationship between daily percent change with TSLA, GOOGL, and SPY. Luckily, with Alpha Vantage the first step to getting this data is easy. You may need pip install a few things!

from alpha_vantage.timeseries import TimeSeries
import pandas as pd
from datetime import datetime

ts = TimeSeries(output_format = 'pandas', key = "XXX")
# If you have your ALPHAVANTAGE_API_KEY you can just use:
# ts = TimeSeries(output_format = 'pandas')
# Get a free key at https://www.alphavantage.co/support/#api-key

spy, spy_meta_data = ts.get_daily_adjusted(symbol = 'SPY', outputsize = 'full')
spy.insert(0, "ticker", 'SPY', True)
spy = spy.reset_index()

tsla, tsla_meta_data = ts.get_daily_adjusted(symbol = 'TSLA', outputsize = 'full')
tsla.insert(0, "ticker", 'TSLA', True)
tsla = tsla.reset_index()

spy = spy[spy['date'] >= tsla.min().date]

This will give you 2 dataframes, one of Tesla and the S&P500 index, which is great! We can start some initial screening of the data… Although you may quickly find out that in this format, there isn’t much to explore.

import matplotlib.pyplot as plt
tsla.hist(bins=50, figsize=(20,15))
plt.show()

This doesn’t tell us much of anything. Let’s add some columns and merge the two dataframes.

spy.insert(10, "9. % change", spy['5. adjusted close'].pct_change(), True)
spy.insert(11, "10. weekday", spy['date'].dt.dayofweek, True)
tsla.insert(10, "9. % change", tsla['5. adjusted close'].pct_change(), True)
tsla.insert(11, "10. weekday", tsla['date'].dt.dayofweek, True)
from functools import reduce
dfs = [tsla, spy]
tickers = reduce(lambda left,right: pd.merge(left,right,how = 'outer'), dfs)
tickers = tickers.sort_values(by = ['date', 'ticker'])
tickers

Click here to learn more about reduce, and here to learn about lambda functions in python.

Now that we have our data in a better format, we can start using some tools to look for trends, here are some common ones:

tickers.info()
tickers["ticker"].value_counts()
tickers.describe()

Of course, this original data isn’t really that helpful, so we have to dig a little deeper. By setting our strings to integers, we can visualize comparisons between tickers and dates. (But only temporarily, we’ll touch on this more later)

test_tickers = tickers.copy()
test_tickers.loc[(test_tickers['ticker'] == 'SPY'), 'ticker'] = 0
test_tickers.loc[(test_tickers['ticker'] == 'TSLA'), 'ticker'] = 1

test_tickers.plot(kind='scatter', x='weekday', y='% change', alpha=0.5, s=test_tickers["6. volume"]/100000, figsize=(10,7), label = "volume", c = 'ticker', cmap=plt.get_cmap('jet'), colorbar = True)

This gives us a little more meaningful comparison of data, and we can use the scatter_matrix object to get another visualization of correlations between variables.

from pandas.plotting import scatter_matrix

attributes = ["6. volume", "5. adjusted close",
              "1. open", 'weekday', '% change']
scatter_matrix(tickers[attributes], figsize=(12, 8))

Or more appropriately with test_tickers.corr()

The correlations go from -1 to 1, where 1 means they are directly proportional, -1 is inversely proportional, and the closer to 0 they are, the less of any sort of correlation they have.

Sadly, it doesn’t look like we’ve found much for trends here, but it can still be fun to see what you can do. Let’s try to incorporate this data into a machine learning algorithm. We first need to clean the data. You’ll notice above we set weekdays and ticker symbols to numbers. This was OK just to visualize the data, but it may skew the results, offering some sort of linear correlation between the workdays. The better way to do this is to set a bunch of binary variables, for example monday = True, tuesday = False, etc..

However, this can be really annoying, and eventually we’d have a massive array of data! An easier way to accomplish this is using the OneHotEncoder package. We also want to scale features, we do a really simple version of that here.

from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer

tickers = tickers.sort_values(by = ['ticker', 'date'])
tickers_list = tickers[['ticker']]
tickers['date'] = tickers['date'].astype(int)
category_encoder = OneHotEncoder()
tickers_1hot = category_encoder.fit_transform(tickers_list)

num_attribs = list(tickers.drop('ticker', axis = 1))
category_attribs = ['ticker']

This makes the data easier for the ML model to digest. For cleaning data, there are often a lot of steps. Sklearn has a pipeline package to help ease the standardization of pipelining data, and you can add methods to be enacted on all data transformations.

import numpy
from sklearn.pipeline import Pipeline

num_pipeline = Pipeline([
        ('std_scaler', StandardScaler()),
    ])

full_pipeline = ColumnTransformer([
        ("num", num_pipeline, num_attribs),
        ("cat", OneHotEncoder(), category_attribs),
    ])
tickers_prepped = full_pipeline.fit_transform(tickers)

The last thing to do is fit it on a ML model. We are going to use a regression model again.

from sklearn.linear_model import LinearRegression
tickers = tickers.dropna()
tickers_labels = tickers['% change'].dropna().copy()
lin_reg = LinearRegression()
lin_reg.fit(tickers_prepped[1:], tickers_labels[1:])

Since we trained our model on the entire dataset, our model may be a bit overfitted. We are going to ignore that at the moment, but in the future, you really want to train you model on a subset and then test it on a different subset. Sklearn has some tools for that next time too!

from sklearn.model_selection import train_test_split

train_set, test_set = train_test_split(tickers, test_size=0.2, random_state=2, shuffle = False)

This will give you a test set and and a training set to do exactly that. Anyways, now that we have our model trained, we can start making predictions, including on what we have already tested.

some_data = tickers.iloc[:5]
some_labels = tickers_labels.iloc[:5]
some_data_prepared = full_pipeline.transform(some_data)

print("Predictions:", lin_reg.predict(some_data_prepared))
print("Labels:", list(some_labels))

Returns:

Predictions: [ 0.00959096  0.00447642  0.00547957 -0.00651265 -0.03053395]
Labels: [0.009590964598126028, 0.004476416039214115, 0.005479565662968255, -0.006512645101450443, -0.03053395044822982]

We can see that the predictions match pretty close to what the actually were.

These were just some quick tips on how to start playing with data and some of the awesome stuff you can do in python. To get even MORE in depth, check out the ageron/handson-ml github repo, which walks through the Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

GitHub - ageron/handson-ml: A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

What are some tools you use to clean and visualize data? Share with us in the comments below!

Want to learn more?

Follow Alpha Vantage on Medium and see the tutorials that are coming out soon, with content like blockchain applications, machine learning with python, hackathons, and a ton of other helpful content.

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#investing #machinelearning #AI #stockapi #fintech

Adding artificial intelligence to your investing strategy; part 2 was originally published in Alpha Vantage on Medium, where people are continuing the conversation by highlighting and responding to this story.

At the end of this article will be an example of how to build a simple model that predicts the prices of stocks in the future, integrated with some AI tools.

With all the advances in artificial intelligence (AI), it’s easier than ever to get started building a strategy that uses machine learning and AI right out of the box. There are a plethora of reasons why individuals and institutions choose to have strategies involving AI techniques.

Robo-advisors like Betterment, SoFi, and Wealthfront stake the majority of their existence on their AI-powered strategies.

It can sound daunting to get started, but their are a lot of reasons why a lot of these companies have popped up in the last few years, and there are a lot of tools for you to start integrating AI into your investment strategies right now.

Before we get started, first a little bit of a clarification.

AI vs Machine Learning

AI and machine learning are often used interchangeably, but they mean slightly different things. Simply put, AI is the execution of learnt information while machine learning is the process of gaining insight from data.

In order to have AI, you need to learn or be learning first. Only then can your system actually make decisions. We want to first teach a system, and then have it make decisions. It’s the same as how a human works to build strategies:

1. Learn and train your decision making (Machine learning)
2. Make decisions/predictions (AI)

Tools

One of the main reasons it has become so easy to get started in AI is the massive amount of tools available. We are going to look at the python ones for this article, since python has recently become the most popular language in the world, and its library of AI tools.

To get started, we’ve recently become fans of the book Hands-On Machine Learning with Scikit-Learn and TensorFlow. It has all of its examples online that you can try out right now.

Python also has some additional open source and out-of-the box tools to play with:

• Pandas/Numpy for easy data manipulation.
• Keras for deep learning. Here is GitHub user driemworks’ implementation of Keras on Alpha Vantage data. A little humor thrown in there too :)
• Matplotlib for data visualization.
• Theano so you can define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.
• Scikit-image for image processing.
• PyBrain for neural networks, unsupervised and reinforcement learning.

Now as I mentioned above, with so many tools, it’s easy to feel overwhelmed. You can start however, with something as simple as a linear regression.

Linear Regression -> Sophisticated AI strategy

A linear regression is a simple type of predictive analysis. It is the attempt to model the behavior of two variables in a linear way. Or put more simply, get a straight line on a graph that somewhat accurately shows the relationship.

For example, if we want to look at the variables time (x axis) and price (y axis) and see if there is a relationship for a ticker (TSLA).

As stated above, the two steps are:

1. Learn/train
2. Make decisions/predictions

Let’s start with TSLA. Here is a graph of the TSLA’s stock price dating back to its IPO:

We can get this graph pretty easily with this code:

# Code example
import matplotlib.pyplot as plt
import pandas as pd
from alpha_vantage.timeseries import TimeSeries

# Remember to have an environment variable named:
#ALPHAVANTAGE_API_KEY
# otherwise, use:
# ts = TimeSeries(output_format = "pandas", key = <key_here>)
ts = TimeSeries(output_format = "pandas")

tsla_data, meta_data = ts.get_daily_adjusted(symbol = 'TSLA', outputsize = 'full')

# Visualize the data
tsla_data = tsla_data.reset_index() # Make the index a column
tsla_data.plot(x = 'date', y = '5. adjusted close')
plt.show()

This is the dataset that we want to train on.

Remember we are going to make a linear regression, so we are looking for a simple straight line. I’m sure many of you know a number of ways you could do it yourself, but python tools make it easier so you can save your brainpower and time elsewhere.

Using the sklearn.linear_model python package, we can simply tell your computer to train on this dataset, and then use that data to make a linear prediction. Add the following code to the code above:

import sklearn.linear_model
# The prediction package doesn't work with dates
# So we convert all the dates in the index to floats
tsla_data['date'] = tsla_data['date'].values.astype(float)

# # We can go over what .c_ does later
X = np.c_[tsla_data['date']]
Y = np.c_[tsla_data['5. adjusted close']]

# Select a linear model
model = sklearn.linear_model.LinearRegression()

# Train the model
model.fit(X, Y)

# Make a prediction
date = [[1736208000000000000.0]] # This is the float value of 2025-01-07
print(model.predict(date))

With this package, you simply tell it what variables to look for a relationship on, create the model, and then make the predictions.

As you can see, the model.predict method takes a list of lists, so you can pass in as many dates as you’d like for the model to predict.

With a little extra credit, we can even see what the model would predict in the next few years:

You will notice it’s a straight line! Perfect.

Now it’s safe to say a model like this probably would not perform all that well (although… maybe you can make some tweaks to make it perform amazingly), but this is the first step to becoming stronger at creating your AI strategy and backtesting.

What do you think? Is a linear regression model something that you think could be added to your investing tool kit? What did you learn from here?

Share your implementation of Alpha Vantage with Scikit and Linear regressions!

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Adding artificial intelligence to your investing strategy; part 1 was originally published in Alpha Vantage on Medium, where people are continuing the conversation by highlighting and responding to this story.