Every day all around the globe money is changing hands in the hope of turning it into more and more money.

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The most popular vehicle? Stock markets.

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Stock markets used to be localized to countries or cities. Today, they are global intertwining networks of activity.

The New York Stock Exchange (NYSE) trades by far the most volume. The New York Stock Exchange trades upwards of $169B worth of securities and 6.29 billion shares every day.

But traded between who?

Your father might tell you that these shares trade between brokers and wealthy clients, but Wall Street has changed over the past decade. Today, more shares are traded through automated algorithms than by anything else.

Yet despite what many have called the over-complication of the stock market, trading has become more democratized. More people than ever have the ability to take part in the greatest wealth creation machine of a capitalist economy. This applies to the rise in algorithmic trading as well. Even the average retail investor now has the power to get in on the trading action from their laptops through various platforms and APIs.

Here, we’ll discuss one of the many APIs available for python programmers to dip their toes in the world of quantitative trading research.

In this article, I’ll also go through plenty of topics in the area of python trading. This article will cover topics such as “Can algorithms I write predict stock prices?” and “Do day traders make any money?”

Let’s get started.

Stock Market & Finance APIs

Once a trader or investor decides they’re interested in the research opportunity APIs can provide to them, how do they decide which one is the best fit? /employee-relations-management-by-p-n-singh-pdf.html.

There are many free and reputable API services out there to give historical information for your algorithms and trading decisions.

The most important questions start broad, and then narrow as you’ll get closer to whittling down the choices to a final decision.

If you’re trading only in cryptocurrencies, then yahoo finance might not be the best fit for your needs.

If day trading is your game of choice, then ease of use and plenty of data points is what’s most important.

RapidAPI has put together a list of some of the best finance APIs out there. As a day trader dealing in U.S. stocks, I’ve chosen to use only one API.

What is the Alpha Vantage API?

One of the most popular and free financial market data providers out there has been Alpha Vantage Stock API.

As a popular stock market API on RapidAPI and other fintech platforms, Alpha Vantage is a community of researchers and engineers who provide an API for real-time and historical time-series data.

https://newgym193.weebly.com/blog/office-365-outlook-mac-download. The data accessed can be from stocks, forex, and even cryptocurrencies.

What I found from using their API for stock research was that it was super easy to set up and use even without world-class programming skills.

Here, I’ll provide a short walkthrough of how to get started with their technology from the rapidapi.com.

How To Use the Alpha Vantage API

Python code for stock market prediction

First, head over to the Alpha Vantage API page to claim your free API key.

Next, open up your terminal and pip install Alpha Vantage like so…

Once that’s installed, go ahead and open a new python file and enter in your given API key where I’ve put “XXX”. This is an example to show you how simple it is to get some basic time-series data from stock (in this case, I’ve chosen Apple).

As you can see, anyone can get started with using python for the stock market. But python code for stock market prediction? That’s not so simple. For meaningful data that will influence trading decisions, technical indicators can be helpful. Let’s go through a simple example with Microsoft (ticker: MSFT).

In Alpha Vantage, many of the top technical indicators used by professional traders can be accessed with a basic structure. Alpha Vantage requires the specification of “function” (the indicator used), “symbol” (the stock’s ticker), “interval” (the time interval of the data requested), “time_period” (the amount of intervals requested), “series_type” (The desired price in the series. This can be close, open, high, or low), and “apikey” which is unique to the user but can also be “demo”. So, in our example with Microsoft, let’s look at the weekly simple moving average (SMA) data over ten weeks looking at the opening price each week…

Wonderful! Now you see what can be done with this API. Other technical indicators supported include exponential moving averages (EMA), weighted moving averages (WMA), volume-weighted moving averages, moving average convergence divergence (MACD), and more!

Data is great, but raw data in an HTML file with no ease of manipulation is not going to yield much profit. For that reason, Alpha Vantage has easy integration with the excel add-on which we’ll go over next.

First, head over to https://www.alphavantage.co/excel_installer/# for the free installation guide and link to download the add-on.

After installing the Excel add-on, each excel workbook will have the Alpha Vantage tab at the top of the page. This tab has technical indicators, symbol searches, and other helpful functions for research using a Python API!

But number crunching is not the only form of data analysis traders use. Stock charting platforms are ever popular in the trading community and everyone has their own opinion about which is the best. But, if using python to trade stocks is what you’re looking for, then creating your own visualizations may be the best option. After all, who knows what data is right for you to be looking at as much as you! So now, let’s go over how to plot stock charts and technical indicators against each other using the Alpha Vantage API.

First, make sure to have pandas and matplotlib installed by typing into the terminal pip install pandas + ENTER and then repeating the step with pip install matplotlib. Now, when the code below is run, the terminal produces the corresponding chart. By using this code as an example, we can add indicators to be superimposed on the same chart.

Stock trading using python can be exciting for retail traders and professionals alike.

Today’s stock market is more accessible than ever and the data used by professional traders is now available to anyone.

Day trading doesn’t only happen on Wall Street, it happens in every city on the planet and many of the men and women executing these trades are novices. But, do day traders even make any money?

I’ve always heard it said that 90% of aspiring traders fail. In reality, that number is much higher.

If the definition of failure is not becoming a professional day trader, then almost everyone fails.

One of the best-documented reasons for failure in new traders is the concept of random reinforcement. Random reinforcement in this context is when a trader attributes a random event to intuition or skill.

The market is dynamic, it rewards bad behavior and punishes good behavior all the time. For an uninformed trader, this can be deadly.

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A lucky streak mistaken for a divine gift has been the downfall of many would-be profitable traders. A loss of those profits and more usually follows. This is why quantitative data analysis like the kind discussed here is so important. This especially goes for traders starting out on their path to profitability.

Looking through historical price action, time series, and technical indicators can give traders a window into the world of pattern recognition where there is a real discernible edge in trading.

In a market where more volume trades through algorithms than through human beings, arming oneself with only the tools of human intuition has become foolish. With the proper tools and developed skill sets, day traders do make plenty of money. But without technology, this is almost impossible.

The logic that data analysis like the python API discussed has become vital to the success of any trader is unquestioned. But, what’s the limit on how much this can help? Can a trader use python to predict stock prices? Well, not exactly. The market is incredibly complex, and no trader has a crystal ball allowing them to see into the future. Still, billions of dollars flow into quantitative hedge funds every year in search of even the smallest edge over the competition. This alone should tell you that quantitative analysis is not a fruitless exercise. Traders should not get their hopes up that learning how to use a python API will grant them predictive powers. If it was that easy everyone would do it! There is an edge in data analysis and crunching the numbers has made billions on Wall Street. All I’m saying is don’t go shopping for that new Ferrari because you downloaded on API.

Stock trading can be very rewarding. It is an intellectual challenge that draws some of the brightest minds in the world to test their ability in beating the market. Because of this, traders need every advantage they can get to take profits out of the market day after day. And quantitative data analysis through tools like the python APIs mentioned here are among the most popular ways to gain a leg up. The beauty of using APIs like these is that they are so easy to learn and simple to use, even if your programming experience is limited. They are a well-worth-it tool in the belt of the modern trader. Good luck and happy trading!

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