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.

You can reach us also on slack, twitter, or discord.

#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!

Follow Alpha Vantage on Slack, Twitter, Discord, Medium, and Linkedin for updates, new announcements, competitions, tutorials, and more!

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.

This is going to be your equivalent of “hello world” but for blockchain!

You’ve probably heard about the blockchain, right? You can write your first application in minutes with this tutorial. If you don’t know anything about blockchain, here are some of the main benefits:

• Decentralized: Meaning it’s open source to the max, no one owns the blockchain.
• Transparent: Every transaction or use is viewable by everyone.
• Immutable: The past can’t be changed or tampered with.
• Automatic transactions without third party intervention: It’s like having an agreement that the entire world is the enforcer of, to make sure it’s enacted.
• Security: It removes the risk of duplicate entry or fraud.

Our favorite concept of something you could build on the blockchain:

• A transparent stock exchange where all data is public, transactions are not through a brokerage, and every transaction ever made is public information. Reducing settlement periods, fraud, HFT abuse, and more.

And some people are working on that now.

Anyways, here is how you can get started:

1. Get MetaMask.io for your browser

You can learn more about MetaMask from their site, for our purposes, it allows you to create a test blockchain account and mainnet account. You’ll learn more about those later.

Here’s a video on MetaMask. You can also skip it and just go to https://metamask.io and download it for your browser. It’s currently supported by Chrome Firefox Opera Brave iOS (beta) Android (beta)

2. Open up the Remix Etherium IDE

Located here

This is your workspace for the moment. We will dive into it in a second.

You will be asked to log into your MetaMask account, if not, go ahead and do so. When you log into MetaMask, you’ll want to change to the “Ropstean Test Network”. This is where you can test all your code on a real blockchain! You will also get some “paper” LINK and ETH to play with

3. Compile and deploy your contract

The link should auto-populate, but if it doesn’t, copy and paste the below gist into a file. Hit “start to compile” and then go to the “run” tab.

You’ll want to make sure next to “environment” you see:

• Injected Web3 and “Ropsten” grayed out.

Then hit the “deploy” button, and a “deployed contracts” entry will show up.

You have just deployed your first contract!

But it’s not active quite yet. Now you’ll have to fund your contract. Contracts only work when they are pre-funded. We will have to get you Ropsten ETH and Ropsten LINK.

4. Fund your contract

Get your MetaMask address by copying it.

Then paste that address in each of these links to get your test funding.

Ropsten ETH link

Ropsten LINK link

In a minute or so you’ll see that you have some ETH now, and if you hit the hamburger in the top left, you’ll see you have some LINK. We need this, since smart contracts auto-execute, so they need to store the payment in them prior to running.

Copy the contract’s address from the remix IDE, open MetaMask, switch to “LINK”, hit “send”, place the address in the top, set a LINK value (5 or more should be fine), hit “next” and then “confirm.”

Now, you can test it! You’ll see at the bottom right there are some blue and pink buttons.

5. Run a function!

“requestTickerPrice” is a simple function that grabs data from the Alpha Vantage API, and puts it on the blockchain. We can input in a ticker and a function (the only function this specific method supports is “GLOBAL_QUOTE”) to get the EOD price of a ticker.

You can see the result of the function from the “ticker_price” button.

Right now, if you hit the blue “ticker_price” button, it will show 0, since we have no data for it now.

Let’s try it! Enter:

“TSLA”,“GLOBAL_QUOTE”

next to where it says “requestTickerPrice” and then hit the pink button. A pop-up will ask you to confirm. You are now running the method on the blockchain!

Now, after the blockchain confirms (may take a little longer than MetaMask tells you), you’ll be able to see the EOD price of Tesla by hitting that “ticker_price” button!

6. Spice it up!

This was just to get you started. This code from this sample explains how each of the steps work, and how you can modify it to get any data from Alpha Vantage that you want.

A Common Pitfall

If you see an error like:

gas required exceeds allowance (8000000) or always failing transaction

This generally means you haven’t funded your contract properly, go back to step 4!

Keep going….

Want to learn more? Check out Chainlink’s documentation to learn more!

Follow Alpha Vantage on Medium and see the tutorials that are coming out soon, with content such as; more in-depth Alpha Vantage-Chainlink documentation, how to build a node yourself, and solidity competitions. As well as our non-blockchain related content!

You can reach us also on slack, twitter, or discord.

#blockchain #chainlink #stockapi #fintech #finance #investing

Write your first blockchain application in minutes — with Alpha Vantage! was originally published in Alpha Vantage on Medium, where people are continuing the conversation by highlighting and responding to this story.