Algorithmic Trading with Python - Alpaca & Polygon

1. Introdution: Hello, and welcome to the Algorithmic trading with Python using Alpaca and Polygon course. I'm your instructor, Nate Albright, a little bit about me. I've worked as a data specialist since 2018, utilizing business intelligence, data analysis, data science, and everything in between. I have experience with Python, SQL, HTML, CSS, JavaScript, and Jango. What this course is about, you're going to learn the basics of Python. You're going to create an Alpaca markets account. You're going to pull data through the Alpac API, as well as place trades through the Alpac API. Utilize polygon, dot IO to pull stock related data, and the basics of creating a trading strategy. What this course is not about, utilizing the data to create a commercial website or application, which is the broker API portion of ALPACA. You should not use the strategy examples in this course on a live trading accounts. For example, purposes only, you should create your own trading strategies, make sure they're tested and tried and true. And lastly, this is not about crypto options or Four X. We're strictly going to go through stock related data. So with that being said, let's get started. 2. Disclaimer: Course disclaimer. The strategies and information presented in this course are for educational purposes only and do not constitute financial advice. The content is designed to provide general insights into stock trading and is not intended to be a recommendation or endorsement of any particular trading strategy or investment decision. Trading in the stock market involves significant risk, and past performance is not indicative of future results. It is important to conduct your own research, consider your financial situation, consult with a qualified financial advisor before making any trading decisions. By participating in this course, you acknowledge that you are solely responsible for your own trading decisions and assume all risks associated with them. The courts creators, instructors, and associated parties make no representations or warranties regarding the accuracy, reliability, or completeness of the information provided, and they disclaim any liability for any losses or damages incurred as a result of using or relying on the strategy to discussed. Proceed with caution and trade at your own risk. 3. Download and Install Anaconda: All right. In this lecture, we're going to go through how to download and install the Anaconda environment. So First thing we're going to do. Let's go to Ancona download in Google. Click on this first link here. It's going to bring you to the Ancona download page. We're going to go ahead and skip the registration. And then you'll select depending on your operating system, either Windows Mac or Linux. I already have this installed, but for Windows users, you would just click the 64 bit, and so on for Mac and Linux. But click this. You'll get it installed. I'm just going to bring it in here. So you can see here at the top, I have the Akana download. What I'm going to do is I'm going to click to open that up. I'll move this out of the way, and it's going to take a second here to actually open that up. Once I've got that, then I can go through the prompt to install the Anaconda environment. So click next. I agree. You want to read through that. Just me next. Next. I actually have to You shouldn't have to do this, but I already have this installed. So it's going to make me do this again. Let's just say whatever Anaconda for. And we'll click Install. Once you click install, it's going to take a little bit, but it's going to go through and get all the packages installed onto your device. We're going to take a break here to let that stuff install. When it's done, you'll click Finish, in the next video we'll actually open up that environment and show you what it looks like. 4. Navigating Jupyter Notebook: All right. So now that we've actually got the antica navigator installed. We're going to go ahead and open that application up, so we'll come to our start screen, find the anticodon navigator. You're going to click on that. It's going to take a second for it to load up. Once it does, you're going to get a window that looks just like this, and you're going to scroll down until you find Jupiter Notebook. Jupiter Notebook is the environment that we're actually going to use to code. A lot of programmers use it to test out code. We're going to be able to use it for pretty much everything we want to do in this course. So I'm going to click Launch, and it's going to open a new window within your browser. So we'll give it a second to do that. It's opening up. Once we've got it open up, this is going to be your main screen. This is where you'll see all of your files. You'll be able to navigate, just like you would in a file Explore on your computer, all of the files that you've created and save. For now, we're just going to create a new file, so we'll click new here and new notebook. Now, this is going to be the screen that you'll see for a majority of the course, like I said, we're just going to select Python, and this is where you can begin to code. You can do a markdown if you just want to, for example, if we want to just words to indicate each section. So we can say, and the hash tags, the more you put, the smaller the lettering is, start here. If you click Shift Enter, it executes the cell. So as you can see, you can cut cells, you can insert cells. You can run the code if you wanted to run all cells at once, if you have a bunch of them, you can do that, functions. Pretty similar to other coding environments. It's just a little bit more friendly with the separation of the cells. So for example, if we wanted to just do five plus five. And you can click play if you want it to run. It's going to give you ten or if you wanted to just use the keyboards. There's a bunch of hot keys. You can actually look those up. I believe you click keyboard shortcuts is under help. All of these. I use some of them sometimes, so if you see something click, and I don't actually click it on the screen. This is probably what's happening on accident. But like I said, shift enter works as well two plus three, Shift Enter, and it's going to give you five. So beyond that, we probably won't use many more functions, but this is just a basic entry into first the installation and download of Anca Navigator, but more importantly, Jupiter notebooks, which we'll be using for the remainder of the course. 5. Python Basics - Data Types & Variables: Right. Now that we've got our environment set up, we're going to start going through the Python basics in the Jupiter notebook. First thing we're going to review is the data types string. So a string is any sort of characters that are wrapped within either single or double quotes. It really doesn't matter. But, for example, if I did just hello, and I check, you can use the type function. To check it. We'll get into functions later. But if I just did hello, You're going to see that it comes back as the type of a string. In array is a list of values, so it can be any group of values. It can be a combination of strings and integers or whatever it may be. For now, we'll just do numbers. So this is our array, and I'm going to copy this and then I'll print out the type for that as well. So you'll be able to see that list. List and array, it's the same thing. Integer is a whole number. So, it can be negative. It can be 1 million But it's just a whole number. So the type for that. It afloat is a decimal point. So once again, it can be negative or positive, no matter how high, if it's a decimal point, it's going to be considered a float. So 1.05, let's do that. And we'll check the type on it. Float. Now, Even though this is technically one as a whole number because we have that decimal point, it considers it a float. So if I got rid of it, changes the int, but once we have that decimal point, goes back to a float. Now, a dictionary is a way for you to store values and reference them using a key. So once you call upon that key, you are able to get the value for it. So what I mean by that is first to you, it's within curly brackets, so we'll open those up, and the key always has to be in string format. So let's just for now, we'll call it key one. And then to separate the keys and the values, you use a colon. And then you can use whatever here. We could have a nested dictionary, which just means we could start another dictionary within this dictionary. We can use an integer, we could use a float. We could use another string. It doesn't matter. Then to add another value to this diction, another key value pair to this dictionary, you separate it using a column, and we'll do key two, and we're going to make this value as five. So let me just copy this and show you the type. Dictionary. There you go. Now, if I wanted to call on. Actually, we'll show you how to call later when we go through the actual variables. For now, just know that this is the format, the syntax of a dictionary. A bool is just false. So you have to use a capital either T or F, and you'll see I'll do false for checking the type. It's a b. And then a nu value is essentially that. There's just no sort of value type, non type. Now, these different data types can be stored within a variable, meaning that we store it utilizing some sort of name, and we're able to call upon it later. We'll dive into that more as we go through some of the other sections of this Python basics. But For now what we're going to do is we'll start with our strings, and we're going to just label it as actually, what we'll do is A. So the name of this variable is A, and we'll set it equal to string. Actually, we're going to we're going to make the string just hello. So if I run this, it's not going to give me anything, but if I call the letter A shift enter, it's going to give me that variable value. So now, B, we're going to run through, let's hear, what did we do next. We did an array. So let me copy this array up here. And we're going to store B as an array. And then we ran that cell shift enter. Now, if I call on B, it provides me with that array or that list. C, we're going to do the integer. So let's here we got 1 million. Just make sure that's 1 million. 123, 123, yeah, it's 1 million. C is 1 million. We'll do D is just going to run through this quickly here, 1.05. The dictionary will be E. And F is going to be the bull, we'll call false, and then is going to be the nun type. All right, so I'm going to run this so we have all of our variables, and then we can take a further look. So obviously, if I call C shift enter, it gives me 1 million, same with D. It's just going to give me that flow value. Now, E, E, you have to use if I just call E it's going to give me the dictionary. But let's say I want to actually call upon one of the values within the dictionary. So if I wanted to get this five, I need to if I know the key, the key is q two, I want the value for K two. So what I'll do is first type in the variable, and then we open up brackets. And then type in the actual key values. K two. When I call this, it should give me five. Now if I change this to K one, it's going to give me the value four K one, which is the string string. So simple as that. That's how the dictionaries work now. If I call F, it's the bull, and then G is the. So it returns, obviously, nothing. So that is just a quick review of one, the data types, and then the variables and how to call some of those variables. Next, we're going to get into operators. 6. Python Basics - Operators: All right, we're going to continue here with operators in the Python basic section. I went ahead and created three variables to show these. So we are in the same sheet as above. Now, I know we used this A B and C as variable names already. What happens is when I shipped enter on this, it reassigns those. A is no longer a string of hello, it's an integer of ten. Same with B as being integer of five and C three. So the first operator we're going to go through is the addition. So if I take A plus B, which is ten plus five, it's going to shift enter 15 is the answer for that. Now, same thing as C. It's going to give me 13. The one thing to know here with the addition operator is that you can also use it for strings. So just real quickly, I'll show you that. So if I said hello, and I use the plus sign and said world, I put the space in there just so that it's going to come out correctly, you can see that it prints out the actual string of it. So that's just something to know, but now we'll just focus on the using numerical values. You can do this with floats as well. We're just using integers for the time being subtraction. Obviously, if you understand how the addition part works, you're going to know how to subtraction works, multiplication, we'll do eight times B, gives you 50, it's taking ten times five division, take a divided by B gives you two, it's 10/5, the remainder, what this is is it's taking and it's signified as the percent sign. It's going to take A divided by B and then present the remainder of those values. So obviously, 10/5 gives me no remainder. But if I did ten divided by C, which is three, three goes into 103 times with the remainder of one. So when we run this shift enter, it's going to give us that remaining value of one. Now, the greater than operator, so this is going to give a boll. So if I do A is greater than B, it's going to say it's true because ten is greater than five. Now, if I say A is less than B. It's going to say false because ten is greater than, like we just said, five, but if I did three is less than five or C is less than B, then it gives me true. Now, equal to. The way you check to see if two things are equal. So A e two equal signs. A is equal equal to b, presents a bull, that's false, but is A equal to A, that gives you true. The last part of this is the greater than or equal to and less than or equal to. Is a greater than or equal to b, that gives true is c greater than or equal to b. That's false, is B, greater than or equal to b. That gives true because they are equal. So it's either one, greater than or equal to. And then less than obviously works the same way. So it's a less than or equal to B that gives us false because ten is higher than five, not equal to it. C now gives us true because three is less than or equal to five. So those are the operators. Next, we're going to move into loops. 7. Python Basics - Loops: All right, we're going to continue with the Python basic section and get into loops. So we're going to go through in this same Jupiter notebook here. We're going to go through four loops first. So what this does is essentially allows you to write code to perform an action multiple times. It's looping. So the way you write syntax four a four loop is you start with four. Then you pick a name for the value each time that we're looping. So it's going to be essentially a variable name that we can utilize within the four loop function. Um, I'm just going to call it val, and then we type in and then this is where you can choose. If you have a prior variable, you can use that, if you just want to use a current one, I'll show you how to utilize two of the different ones and we'll see more throughout the course later. But the first one we'll just do is a string. So the way it loops through this string is it's going to perform a loop. For each letter within this string, so this will be five different loops. Now, the letters really don't matter. You know, I I wrote within this four loop, and you have to make sure it's indented, that's the proper syntax. Print val. What this is going to do is it's going to print the letter each time it loops through within this string here, I'm going to shift enter, and you can see it does that. But if I just wanted to do print the number one, it's going to print every time for the word hello, if I picked another five letter word, like horse. It's going to perform the same exact action, so you can see that that doesn't really matter there. The next thing I want to show is how how it loops through a list. So if we did one to 345. If I wanted to print the val, it's going to loop through five times printing the value within the list in order. You can see shift enter, one, two, three, four, five. Now, this can be utilized for adding to a certain value. So if we wanted to start with a variable x equals zero, and then we wanted to set a new value for x on each loop, but x is equal to the current value of x, which to start the loop, it's going to be zero plus the val. So the first time around, what it's going to do is it's going to take the starting value of x equals zero. We're going to create a new value for x, so x is equal to the current value of zero plus val, which in the first loop is one. So then what that will do, and we're going to print x, then the new value of x is print out one. Then the next time around, x is currently one, so we're going to create a new value for x, x is currently one plus the current loop value, which will be two. So that'll be one plus two equals three. So the next time that it loops through it'll give us three and so on. So if we shift enter, you can see exactly how that works. Next, we're going to move into while loops here. This functions a little bit differently. It essentially has to get to a point where it stops, so it's saying, it's going to loop while the current process is true. So if we say, wow, x is greater sorry, less than six, and we'll actually give it a starting value for x. Actually, just to not get confusing. We use y this time around? Y is equal to zero. That's our starting value. While y is less than six, the new value of y we're going to give is y is equal to y plus one. And then what we'll do is we're going to print y. So the first time around, what it's going to do is y is equal to zero. While y is less than six, this is true, right because it's zero, zero is less than six. We're going to run through this code here. So the new value we're going to give to y is y, which is zero plus one, so it's going to be one, so it'll print that. It's going to do this up until y is no longer less than six. So once it gets to the value of six, it's no longer going to run the code, and it will break essentially, it'll stop. So let's run this. You can see, it gets up to that value of six. Why did it get to the value of six and not stop? Here's why. Y goes through, it becomes one, two, three, four, five. Once it gets to y is equal to five, it prints that out. We have five here, so five is less than six. The new value for y is y equals five plus one is that six. So that's why it didn't stop there. Now, if you wanted it to stop, five, then you can just change that variable up. But that is a four loop versus a loop, we'll show you more on those later in the course. 8. Python Basics - Functions: All right. Moving on to functions within the Python basic section here, functions allow you to define a set of steps and parameters and call upon it later. The way that you begin to function is always the same with the letters the EF. And the next part of the syntax is to actually name the function, so we're going to call it my function. We open up parentheses. If you have parameters for when the function is called later or variables. You'll insert that here. For now, we're just going to leave it open. Insert a colon and then click Enter, drop down to the next line, make sure you are indented. Jupiter notebook will automatically do that for you when you're writing Python code, but if you're using a different IDE, then, make sure you're paying attention to that. Now, in order to return something from a function, you will use the word return and then return what you want to. So if I wanted to return the word hello, this is a very simple function. I'm going to go ahead, shift Enter to run this. And then the way that we can call on this function is by typing it out. My function, and we shift enter, and it returns that hello. So that's just the very basic form of how function works. Now, let's say we have different variables that are within our code somewhere, and they're going to change. So it's not always going to be the same thing we don't always want to return hello. What we'll do is we'll use the example of a tax payment. So we're going to start to create the function. We're going to just call this tax payment. And within these parentheses, the variables we're going to use is income. And the tax percent. So then we'll add her colon, click next. Now in order to calculate this is going to calculate the taxes that we're paying, not what's left over. So we're going to call our variable total paid. This is where we're going to create the equation to calculate this tax payment, and all that'll be is the income times the tax percent And what we'll want to do when this function is called is return the total amount paid. So we'll shift enter that creates the tax payment function. And now to actually show you how this function works. We're going to first type in the function name tax payment. And then we have to insert our two parameters, which is the income and the tax percentage. So let's say we have $100,000 in income, and we pay 25%. And taxes, we're going to enter that as a float. When we click to enter this, what it's going to do is it's going to run through this function. It's taking in our income as $100,000. It's taking in our tax percent as 25. It should return us $25,000. Shift enter 25,000. So you can see how that works. Now if we wanted to run this for somebody else who has a different income, maybe they make $80,000, and they're paying 20% tax, will shift enter, and $16,000 is 20% of 80,000. So that is how functions work. 9. Python Basics - Pandas: All right. The last part of this Python basic section we're going to cover is the Pandas library in order to install a library. If you don't have it installed already any machine, you use the word pip install. And then you type out the name of the library. In this case it's Pandas. I already have it installed. So when I shift enter, it's going to tell me that the requirements are already satisfied. You should see just a bunch of downloading and once it's finished, it'll tell you it's done. Um, then the next thing we have to actually do is to import your library. So that's as simple as import, and the Pandas library. If you want, you can just leave it as that, and when you go to call Pandas function, you type out the entire word Pandas. What's standard practice is to give it an alias of PD. So import Pandas as PD. So now we have the Pandas library import, and we can call some of the functions. Really what we want to do. Pandas is used for data import manipulation. You can pull it in from CSV files. We're going to use it to pull after we've pulled API data from AlPACA, we're going to create nice data frame tables utilizing Pandas. So that'll be a big part of it. And I'm going to give you an example of what those data frames are going to look like. So I have this just random data up here, and I'm going to go ahead and just run that data. Then what I'm going to do is show you how to create a data frame, so you can name this data frame, whatever you want. It's a variable, but standard practice is just DF equals, and then we need to call on that Pandas library. So like I said, if you didn't give it an alias, you'd have to type out Pandas, but we gave it the Alias a PD dot, and then we are creating a data frame, so it's a capital D F open parentheses, and then the data that we want to use is this variable to dictionary that we have called data. Now, This already has the way that it's formatted. It has column names in this. If it didn't for whatever reason, if it was just the arrays, you would add as the second parameter of this, the columns, and then list those out. But this does have it, so we're just going to go ahead and shift enter to create the data frame, and then we will display the data frame, and you can see it gives you a nice table. It's easy to manipulate. If I wanted to get just the first row, can type in data frame dot head is the first row and put in the number one. It's going to give me then one row. If I wanted the last row, I would type in tail. So you'll see that we utilize these two later down the line just to kind of take a peek at data. This is a tiny data frame, so we'll just pull in the entire thing. So that wraps up the Python basic section of this course. 10. Alpaca - Sign Up: All right. Next, we're going to dive into Alpaca. First thing you want to do is create an Alpaca account. So you're going to come to Alpaca dot markets, that should take you directly to this page here. When you come here on the right hand side, you'll see the sign up. As I mentioned before, going to be covering just the trading API, which is for individuals, not the broker API, which is to create commercial applications and websites. So click on Trading API, and we're not going to go through everything here. I've already got an account setup, but fill out your credentials, you might have to verify a few things through your e mail. But once you filled everything in, click Sign up. And then once you officially have your log in, get logged in there for the first time and we'll show you in the next video what that's going to look like and give you an overview of the dashboard. 11. Alpaca - Dashboard Overview: All right. Now, once you've signed up and got logged in, this is what your dashboard is going to look like. You can see we're on a paper account. Means that anything we trade with, it's just for practice, and it gives you a little message about that at the top. You're on a paper trading. No real money is being used. There's an option to open a live account where you can use actual money on the Alpaca Brokerage. But for this course, we're just going to be using paper money, that way we don't have to recognize any actual losses. So an overview of this page here, you can see what your current account balance is at. That's your portfolio right here as you make trades. It's going to show you over time your total account balance. You can change the time frame as to what it looks like, what's being displayed. This is right now just for today. We haven't made any trades. You can see there's a $10,000 balance on this paper trading account. Now, let's say you're testing a trading strategy, and down the road, you want to restart. You don't like what you've done. You want to be able to just start off with a clean number. If you come to this reset account button, this allows you to add a new value in. So, whatever that may be, if you want to do realistic, unrealistic, any number $0-100 thousand, you're allowed to type in there. So let's just put in 50,000. I'm going to click Confirm. Paper account reset. It's going to reset, reload the page, and then you'll see that my account is now at 50,000. Um, on the right hand side here, now, beyond just using the actual API, the Python code to buy and sell shares of stocks. You can do it directly here on this page. So the market is open right now it's Thursday. I'm just going to type in Apple. We'll do just a market order, which means just to execute right now, one share Goto canceled, review the order. And click Confirm. And the order has been created. You can see that my cash down here went down. The buying power, the reason why the buying power is twice as much as the cash is because it is technically a margin account. So they'll give you 100% more on top of the cash that you hold in the account. If you don't know what margin trading is, I would look into that. We're going to just focus on trading essentially with the cash that we have on hand. You can see when I scroll down further to top positions. I've only got one, that Apple share that we just bought now, and you can see it live, that it's actually trading, so our balance is moving, and you're going to see this one day change. This chart is actually going to get filled in, now that we actually own something here. And then recent orders as well, it's that share that we just bought a minute ago. Um, the last thing to go over on this right hand side here of the dashboard is the API keys. So these are the ways that you're able to enter into the code. First of all, the endpoint, and then the keys that allow you to pull data through and know that it's for your specific account. If I click Generate keys, it's going to generate me a new key, and a new secret key. Secret key. Make sure to read this note. Your secret key will disappear after refreshing or navigating away from this, and make sure you store that in a safe place. You can always regenerate the API keys later, but that's just a little something to pay attention to. We'll get into pulling these and some of the future videos, but this is just an overall summary of the Alpaca dashboard. 12. Alpaca - Alpaca Connect: One other quick thing I wanted to cover before we dive into this is this Alpaca Connect. If you click into this, this is where you have the ability to integrate apps that are built by others. You can see, I don't have any connected apps, but there are featured apps, trading view, if you're familiar with trading. You've probably been on trading before. One of the best tools for visualization. You can also develop your own apps if you want to and submit that. But if you come here, there is a ton of apps that have been created that you can integrate with your Alpaca account. So trading view, you can see. When you're on the trading view website, it's going to allow you to integrate Alpaca with it, make trades directly on trading view, utilizing your Alpaca account. So that's just a quick little tippit about the Alpaca Connect and what it entails. 13. Alpaca - Documentation Part 1 - Introduction: All right. The next thing we're going to do is go through the documentation, which will then eventually lead us into actually using the API. So we're going to click on API here. This is going to pop open a new tab for the documentation, in this video, we're going to cover the introduction section. So it's the welcome. Welcome to Al PACA. You can go ahead and read all of this. You can read about ALPACA. So if you're interested in the founders, the vision, allow 8 billion people on the planet to access financial markets. What services does Alpaca provide, you can go ahead and go through this overview, lPACA API platform. So it's a RST API. They have web sockets as well. We're obviously going to be utilizing this and not just using those dashboard tools that we had shown in the last one. Architecture, any updates and upgrades, changes, breaking changes. SDKs and tools. There's a Python library that you can utilize, and then additional resources, if you're interested, learning. They have a blog, Slack, of course, Forum, If you need any support. So for more, you can click here. System status have to date on any updates and outages. And then finally, you can read through their disclosures. 14. Alpaca - Documentation Part 2 - Broker API: All right. This next section is going to just quickly cover the broker API, as I mentioned, we're not going to be really reviewing this in this course. This is for creating commercial applications and websites. You can see that here. This is the documentation about Broker API that helps you build trading apps and brokerage services for your end users. If you are interested in this kind of thing, this is exactly where you're going to find the information to go through it. In the other sections, you're going to see how we review documentation, and utilize it, so you'll be able to use that knowledge to then refer back to this broker API and kind of go through all of the different sections. But as I had mentioned, we're not going to be reviewing this. 15. Alpaca - Documentation Part 3 - Trading API: All right. The next section of the documentation we're going to cover here is the trading API. As I had mentioned, this is what we're going to be utilizing about trading API. Trade stocks and crypto with APAC is easy to use trading API up to four times intraday, and two times overnight buying power, short selling, advanced order types, all packaged and delivered through our API. As we've already gone over, we're going to be utilizing a paper trading account. You can create a paper trading account anywhere in the world. Now, the live accounts. I believe there's restrictions based on countries, so you might want to look into that depending on where you are. But've got the account plans. You can go through, review that. Crypto trading. Understand orders fractional trading, meaning, you know, if there's a share of apple stock, Apple was trading at, I think it was around 230 when we looked at it earlier, you can buy as little as $1 worth of apple. So you don't have to buy an entire $230 worth. They have this, as mentioned for over 2000 US equities. Authentication. This is what we'll be utilizing to actually pull through the data from the API. Now this is not exactly what's going to look like. We're going to be utilizing Python as mentioned, so to look a little bit different. And we'll go through and review all of that later. We'll be utilizing that those keys, the key and the secret key with the domain, as I had mentioned. Getting started with the trading API, section outlines how to install AlPACAs, SDKs, if you're utilizing the library, the account, assets. Now, this trading API section, this is everything that's associated with your account pulling through different sets of financial data, and all that. We're going to review in the next section, which is the market data API. So we'll be utilizing trading data, the trading API and the market data API. As you can see here, assets, orders, positions, you're going to be able to pull all of this data using Python. You can see here. It's already got it set up in Python format. If for whatever reason, you care to use a different language. C sharp, whatever it may be, you can change that within the documentation review it. We'll strictly be using Python. Though, you can pull through your positions. This is going to show you what you have in your paper account. We've already gone through paper and paper versus live. Your trading account. We aren't going to get into crypto, but if that's something that interests you, this is where you can review the information on that. Option trading. We're not going to get into maybe in another course, but there's the ability to trade options as well. Part of the reason why they gave you the margin account account activities. We'll go through and pull this, the fractional trading that we had mentioned, and how to actually place those fractional trades. With the API, margin and short selling, we're going to skip, we're not going to do any short selling or trading on margin. Orders, we'll review ser protection. You can come in and review this. Not going to do any web socket streaming. And if you have any questions, this FAQ section is a good place for you to go to get any of that information figured out, have something explained to you, weighted average, whatever it may be. 16. Alpaca - Documentation Part 4 - Market Data API: All right. The last part of the documentation, we're going to review here quickly is the market data API about market data API gain seamless access to a wealth of data with ALPACA market data API offering real time and historical information for equities, options, crypto, and more. This is, as I had mentioned, where we're going to pull that market data. You can see there's a pricing structure. The basic is free. So you can get Most of the symbols that you're going to need definitely the ones that we'll need in this course. If you pay $99 a month, you can get more than that. Historical API call, you can see that you're going to get 10,000/minute, as opposed to the 200/minute that we're going to get for free. And the data is delayed slightly about 15 minutes or so. But for the purposes of this course, we're not doing Algo live trading. We won't need it if you create a strategy and you eventually do on a live account, whatever it may be. This is something you consider. But like I said, we're not going to use that. There's the options data that you can pull as well. Broker partners dates, the below subscription plans are available. So if you need even more data, you know, this is specifically for the brokers who are creating the commercial accounts. But you've got the same authentication way you can get started, we're going to show you how to do all of this, so we're not going to dive too deep into it. But just a quick overview, historically PI stock data. This is what we're going to be utilizing on this investors Exchange LLC. Pto data, we're not going to get into, but they've got historical data for that, options and news data as well. Some people, maybe you'll be utilizing a large language model, whatever it may be, maybe that's something that you want to pull in. Not going to go through the web socket stream. But then if you have any questions, as always, they have this FAQ section where you can go in and get any of your questions answered. 17. Alpaca - Authentication: All right. Now we're going to dive into the API reference, the trading API here on the Alpaca dashboard. I'm going to go ahead and click on this API here. It's going to bring me over to the documentation. I'm going to click on API reference. And the first thing we're going to do is ensure that our account is authenticated. So there's two things that we need to get. Those are the the key and the secret key. So I've showed you this before, but if you scroll down on your dashboard, click regenerate, you can generate new keys if you didn't save your secret key. Now, once this secret key, once you navigate away from the page, completely, or you click Hide, you're not going to be able to get this secret key again so make sure you store it in a safe place. I'm going to go into a blank Jupiter notebook and start creating the code. Now, this time around, we're going to show the code, essentially manually, but the videos that are to I'm going to show you a shortcut that Alpaca gives you to make it a lot easier to generate these requests that you want. So the first thing we want to do is import the requests library so that we can actually run a get request. So all that is is import requests. I want to shift enter to run that. Now we need to get a couple pieces of information here so we're going to create variables. The first three variables we're going to create are the endpoint. So we will just call it endpoint. Oops. Start typing your endpoint equals, and we're going to wrap it in ts. Then we want to create the key, and we'll grab the key. And ops. And then the secret key. We'll grab the secret key. You're going to want to put your key in your secret key somewhere safe. You're probably going to see me. Maybe just have it on the actual upener notebook, but you're going to want to store it in a safe area where you can get it, and it won't be able to be compromised by somebody else. So the next thing we need to do is create for this specific get request, the headers. And like I said, we're doing this all manually this time, but you're going to see in the videos moving forward. It's going to be a lot easier to actually get this. So just follow me here, type and accept Clon application Oops, we want this in as a string application, slash Jason. And then we want the next parameter, and the next parameter is going to be the key. So we're going to take this, and we're going to put that in quotes. And then that's going to be the key variable that we created right here. Then the next parameter is going to be the secret key. And if I can describe this, and we'll come over here, wrap it in quotes, and we call that secret right here. And then we are going to actually close off our headers dictionary here. We're going to run that, and then we're going to check to see if our connection is authenticated. So we're creating a variable called connection. We want request, running a get request here, and the URL parameter is equal to our endpoint variable and the headers simply is just equal to our headers variable. Now, we're going to go ahead and run this. Now, if we authenticated, we're going to get a 200 response. If we didn't, we're going to get a 400 something response. I'm going to shift enter to run that. You can see we did not properly authenticate. That's because we need to add something to our end point here, you can see when we're authenticating, we need this accounts at the end of the endpoint. So we're just going to go ahead and grab that. We're going to add that here. We're going to shift enter to run all these again, and you can see that we got that 200 response, meaning that our account is properly authenticated. 18. Alpaca - Accounts: All right, moving on in the API reference. Under the trading API, we're going to move to the accounts. What you'll notice right away is this section over here. This is what I was talking about how it's going to get easier from here on out. You know, it's not there in the authentication, but in the accounts it is. So the language might default for you to Shell. You're going to want to go ahead and click on Python. And what this is going to do is auto generate the COB need in order to pull from the API. So for the accounts, what we need to do is go and grab our key and our secret key, and then it's going to populate those into the header for us. We'll be able to just copy that and run it. So I've got a Jupiter notebook with my keys in it. I told you to store those somewhere wherever you might have them drop in the key, and you can see it auto generates down here in the request. Grab the secret key, and then do the same thing right here. Points already been added. All you need to do then is just go ahead and copy this request. Come to your Jupiter notebook, paste it. It imports requests for you. It does all of it. It's got the Endpoints the URL. Your headers are created. We're going to shift enter, and it's going to shoot back out your account information. You can see it's got my buying power and my cash and whatever it may be for the account. 19. Alpaca - Assets: All right. Next, we're going to move on to the assets. So this is going to be getting information about assets. We're going to go ahead and we've already got our key and our secret key pulled in here. We're going to copy this get assets. Request. We're going to come over to a blank Jupiter notebook. Run this. It's going to give us a limit issue. So what we need to do is cut this down. So, as I mentioned before, there's going to be more parameters beyond just these headers of the key in the secret key. So, what we'll do is for the exchange. We'll do NASDAQ. Ops. So that should, hopefully, and you can see it's not get added in the headers. It's getting added up here in the actual URL. So let's copy this again. We'll come back, and we will replace that. We're going to run it, and it's going to take a second for it to load. And what we actually might do is, I'm going to import JSN, and see if I can make this look a little bit cleaner. So We will do JSI dot loads, response text, and hopefully this gives us a cleaner version. Right. So it just makes it a little bit nicer to look through, you can actually see the exchange, all of the associated information with each of these assets. So there is, if you know, specifically, you can see here, if you know the ID, the symbol, you can do it specifically like that for the request. So if I did Apple, You can see it got added here. Apple. We're going to copy that. I'm going to come back over here, and we will run that request. Now, let's do this one more time here. Let's grab this json dot loads and put that here. And it makes it look nicer. Now, you can see four specifically, Apple, all of the associated asset information you can get from this request. 20. Alpaca - Corporate Actions: All right, moving on with the trading API API reference, we're going to come down to corporate actions, and we're actually going to come not to specific announcement because you need to know the idea of the announcement, but retrieve announcements Returns, announcements, data, given specified search criteria. So we already have our key in here in our secret key if you don't go ahead and populate those, so it makes your get request easier. The other thing, we want to narrow this down using some of these required parameters over here. So the first thing that we need to do is answer what kind of announcement? So we can put in a few of them. If you want, it says, comma delimited. I'm just going to do dividend announcements. Dividend. So you just typed that in exactly as it's shown here. You can even copy it. And then you're going to do a date range. So it says since and until. Now, the thing to note here is the date range is limited to 90 days, so you can't do this for an entire year. But what I'll do is I'll just do in this specific format right here, so I'm going to do the first of this year. Until we'll do 1 March. How about that? It should be auto populating the dates in here. Yeah, you can see that. We've got the dividend, we've got the sins and the until. So we'll go ahead and copy that, come over to a Jupiter notebook. We're going to paste that, and it's going to re all of these dividend announcements during that time period. Now, If you wanted it for a specific symbol, like we could do it for Apple. Let's try that. I'm going to copy it. I'll come down and I'll paste it. Actually, what I'm going to do is I'm going to do what we've done before and do the JSI dot Loads. And let's just make sure that JSI is imported and we'll shift enter, and you can see it populates that nice. There was a dividend announcement. Declaration date was February 8 for that effective the 12th. So you can see how you can narrow the parameters down if you want to or you can open it up. We'll actually make this one look nice too here with all of the different. I'm just going to copy this to make it easier on myself. All the different announcements for all of the companies. It's going to take a second load and boom. We've got all sorts of dividend announcements, so that's how the corporate actions works. 21. Alpaca - Orders: All right. Moving on, we're going to get into the order section of the trading API. We're going to go through creating an order getting the orders, we're going to get requests to get the orders and then deleting all of the current orders. Once you pull your orders, then you can do things more specifically. L get an order, if you know the ID of it, replace an order by the ID or delete one specific order by its ID. We're just going to run through these top three here. So we'll start out with creating an order. So places a new order for the given amount. And request may be rejected if the account is not authorized for trading or if the tradeable balance is insufficient to fill the order. So we've already got our key and our secret key filled in. If you haven't made any changes or switch devices, you should be good to go here within your account. It started the request down here with the correct end point, got the orders on the end there. There is a few requirements here. So for this first part to create the actual order, wants to know a symbol. We'll do Microsoft. And the quantity, let's say we want to buy ten shares of Microsoft. The side, we either want to buy or sell, we're going to buy. Type is either market limit, stop, stop limit, trailing stop. This is for stocks, which is what this course is focused on not on options or crypto. We're going to leave it as market, which means whatever the current market price is. That's where we're going to buy it at. You should usually do this with some sort of limit order just to make sure for whatever reason, a random Black Swan event happens, you didn't get screwed over. But for just example purpose is beleaving it as a market order, T and force is We could do Good till clothes. So you can run through here the different options that you have Day Good until cancels, my bad on the terminology there. A few different kinds. We'll leave it as day for now. And that should be. Now you can specify some of the other information here for now. We're just going to do a basic one. You can see down here if you get some sort of response, either, 43 or 422. This is what they mean, just in case they're shoot back at you. But we're going to go ahead and copy the request that's been created here. I'm going to come to a new Jupiter notebook under Create. I'm going to go ahead and paste that. You can see all of our information was inserted there. I actually want this to be a JSN dot lows so that it looks nicer, and we're going to go ahead and run that. And Oh, I misspelled Microsoft, so we can actually fix that right here. Misspelled the ticker. So let's try that one more time. Runs. You can see that it was created, submitted, all the information that is applicable to this specific order. If we come over to our account, and we scroll down. You can see that that Microsoft order was just created in our recent orders. It was filled just a few seconds ago here. So that's the very basics of creating a buy order. We'll move on here, go down to get orders. So this is going to pull any open orders that we might have. We don't have any. So what we'll do is we'll just on the dashboard, create one, so you can see it. So I'm going to create one and I'll do it for I guess we'll just use Apple again. That's just an easy one to remember here. Maybe we'll do Facebook. All right. We'll do MTA. Now, we're going to create a market order, like I said, actually, no, sorry, we're going to do a limit order that way that the order is open, and we'll set the price below its current value so that it remains open while we go to pull all of our current orders. So let's review this. We're going to confirm it. Confirmed, you can see that this order is now opened. It's a limit by 500, so if the price drops to $500 or below, then it's going to execute it. Its current price is What was it around 50510, 25? So what we're going to do is we're going to do this Git order. It's going to pull all of your orders. I mean, if you want to get specific, you can add some parameters. If not, just go ahead and copy this, and we'll scroll down here to the Git section our Jupiter notebook. We'll paste that to the JSON dot loads again. Like I said, We do that, so it comes out nicer. And we'll run that, and we can see that we do have a single order. If we had a bunch of them, there would be a list of them, but we just have this one meta order that's open, right? So it's Ba open. The limit price is 500. You just saw us do all of that. Now the last thing we want to do is we can delete all of our current open orders. So if I come back here, all orders, this is a delete request, attempts to cancel all open orders, a response will be provided for each order that is attempted to be canceled. If an order is no longer cancelable, the server will respond with status 500 and reject the request, which you can see right here, failed to cancel. There's no parameters. We're going to go ahead and just copy this. And you can see that that order is still open, right? And once we run this, so we'll Jin dot loads. We're going to go ahead and run. Status 200. We come back here. We can see that the 200 to your multi status body and array of objects that include. Okay. So we got a 200 instead of a two oh seven. Let's see if that made a difference. It didn't. So it's canceled. So that's how you can create an order to execute right away. You could do the same thing. You could create a limit order from Python if you wanted to as well. Just did it on the dashboard to make it easy, how you can pull your current open orders, and then how you can delete your orders. As I had mentioned at the beginning of the video, you can do it by specific ID. So if you knew the ID, for example, here's the asset ID, if we wanted to just do this, we could take this, and if it was still open, if we wanted to delete by this, we could just type in the order ID here, and that would work for us. But we had just done it we deleted all of our orders. So that is the basic foundation for creating, getting and deleting orders on Alpaca. 22. Alpaca - Positions: All right, next, we're going to cover positions, the trading API here. So the two that we're going to go through here is first all positions open. A all open positions. What this does is it just goes and fetches any of your current open positions. Close all positions. The other one we're going to do, which we'll do just that, close all of your current open positions. If you specifically want to get an open position, and you know the symbol or the ID. You can go ahead and do that specifically. Same thing with closing it specifically, For this example, all we're going to do is just all open, get the information, close all of them. We're not doing anything with options here, so we can just ignore that for now. But let's start off here with the all open positions. It's easy enough. You've already got your key and your secret key placed in there. Go ahead and copy this request. Come to a new Jupiter notebook. Paste it. We're going to json dot loads, Shift Enter, run that. What this does is it fetches all of our current open positions. So for Apple, we currently have two shares, and we can verify that in our dashboard here. Yeah, we have two shares of Apple, and it should say for Microsoft, we have ten shares. So let's come back here. Microsoft, Quantity available is ten. So that did exactly as we were looking for. Like I had said, if you specifically wanted to close one of these, what you can do is take the asset ID. So if we just wanted to only close our apple positions, we can go ahead and copy that and bring it over to the documentation, and we're going to just close Apple here. Because we could do that, and it'd be all set to go for us. We could copy this and then paste it and run it. But we're going to actually close all of our positions, so there's nothing for us to do, cancel orders. We don't have any orders, so I don't even know if we need to it doesn't look like it's required field. Cancel all open orders before liquidating all positions. You could do that if you did. And maybe it's safe if that's something you want to do to just go ahead and put true. So there's no other parameters. We'll go ahead and copy this. We can see we currently have these two open positions. We're going to go ahead and it's a delete request. And as always, we're going to make it look nicer with the JS on dot loads, when it returns the value, run it. Now, it should show us that these positions are closed. So we got a 200 status for Apple. We got a 200 status for Microsoft, meaning that they are closed, and you can see our Microsoft just finished closing there. So we have no more open positions, and that's how the positions API portion works. 23. Alpaca - Portfolio History: Alright, moving right along. We're going to get into portfolio history here. So this is a get request. Returns T series data about equity and profit loss of the account in requested time span. Now, you can specify the time frame here. Looks like none of these are required fields. So we're going to go ahead and just grab this. We've already got our key in our secret key in here if you don't drop those in there so that they populate in the headers. We're going to go ahead and copy this data, come over to a new Jupiter notebook. We're going to change this print to JSN dot loads, and we're going to run this. We got to import JSan. So make sure you import JSN, now we rerun that. Now this is going to give us time series data on the account. This is showing these are actually times right here, time stamps. What we're going to do is convert this into an actual usable list here. We'll take this data and we will create a data frame out of it as we had done earlier. So actually, what we'll have to do is import Pandas. If you don't know how to do this, or you're having trouble with Pandas, make sure you revert back to the previous lecture about it. But pd data frame, and we have to a PD, importing Pandas as PD, and we're going to take this data, and we're going to shift to run it. And then it should have created a nice data frame for us that shows the timestamp now, what we would like to do is actually convert this. This is second since a period in time, It's standard practice for Python. We can convert this time stamp to a date time. We're not going to do it in this lecture. But this is day by day the value of the account. You can see this is the base value that we started out with. But as time went on, you know, our equity went for 50,000 down to 40 9998 and so on. Up until today in one day increments, you can see, that's defined by the one day there. So the profit or loss is actually decreasing. We had Apple over a short period of time here and it decreased in value, so that's what you're seeing there. So this is the portfolio history. 24. Alpaca - Watchlists: All right. Moving right along here to watch lists. There's a bunch of different options for watch lists here. You can get really deep in the weeds with specifics. All we're going to do here is actually create a watch list and then get the items from it. You can create a watch list on your dashboard if you wanted to. We're going to actually just go ahead and do it utilizing the API. So I'm going to come back to the documentation. There is a required parameter of the name. This is the name of the watch list, so you can identify it if you have multiple. I'm just going to call it Watch list one. Then the other thing is to you can just create the watch list without adding any specific stocks to it, but that's not going to do much for you, so we're going to create a separated list of symbols. So we're going to do Apple, Microsoft, Facebook, and you can see these populating here. We've got the name and the symbols populated in this Palo dictionary that gets hold in for this response to go ahead and post, which is to create. We're going to copy this then. As long as you have your key and your secret key in there, just check those two things. Come to a Jupiter notebook, and we'll paste it. As always, we want to import JSan so that we can San dot loads. The response here, and looks like we were not Microsoft asset, not found Apple, Microsoft Meta. All right. Maybe we have to let's. Okay, so it looks like it's not a common separated list like this. We need to do it one piece at a time, Microsoft. And let's add one more. Let's see if this makes a difference. Let's hear it. How is our code set up? Yeah. Okay, so it's separating it. Instead of it all being wrapped in the same exact quotes, it's going to separate it. So we can actually even do it right here if we wanted to, but for example purposes, I'm going to go ahead and do it the proper way. And it looks like that's populated correctly. Now, let's go ahead and copy this and replace this current code. We did already import JSN, when we ran the last one, but we'll do it again, and we will JSI dot loads, and we run this, and we should be good to go. Now, I created this watch list, Watchlist Watch List one. It's got Apple in there. It's got Microsoft in there, Microsoft Corporation, and it's got Meta, which is the parent company of Facebook. So now if we come over to our dashboard, maybe we have to give it a refresh here to see the watch list If it's not showing up, we'll just have to go ahead and pull it. Yeah, I might just be a little bit delayed here on why that's not showing up. So we will go ahead and just pull the watch list. Using the G, so get all watch list here. There's no parameters to put in as long as you're key in secret in there. We're going to copy that. We're going to come over here, and this is going to be our get watch list, and we will paste the code. Jonas. We'll run that, and it's going to return our watch list, which is Watch list one, and it contains Oh, this was a previous one we had created here. So sorry. Wh list one is the one that we had just created. It gave us the account ID. So if I take Sorry, not the account ID. The actual watch list ID if I take this ID, and I wanted to get watch list by ID. Then this is where I can specifically pull the watch list that we just created, and then that should give us the information on that. So let's try this one more time. We're going to get watch list one, paste this JCs Shift Enter. And there you have it so that one we had just created, you can see Um, we are pulling specific information on that watch list as Apple Microsoft and Meta. 25. Alpaca - Account Configurations: All right. Moving on to the account configurations here. This is going to be very basic. So just as it says, get the current account configurations values in order to get that, as long as you have your key and your secret key already in here, if you don't, go ahead and grab those and put them in. Come down here, copy the request code that's automatically generated for you. I've got a Jupiter notebook open. We're just going to go ahead and paste that in there. We'll shift enter to run it. It's going to provide you with the values. I'm actually going to chase that load. So it looks nicer, and it's going to go ahead and provide you with your account configurations. So, for example, fractional trading is true. So what fractional trading is is Alpaca allows you to buy a share of something. Where you buy a piece of it. So a specific dollar value, instead of having to buy the entire thing, so say there's Apple is trading at $200 a share. And you only want to buy $50 worth of apple. You are allowed to buy a fractional portion of an apple share. It'll increase in value the same with the percentage, but you'll obviously just own a smaller portion of that. Let's go back to the reference here. Check out this account configurations. This is where you can actually update or configurations on your account. So if you wanted to change any of these, you can go ahead and change that. So if I wanted to change my fractional trading to false on my account. I can go ahead and update this field here and come back here, and we will paste that, and it's going to return the updated value to you. So if we run this, now it says, fractional trading is false, and I'm just going to go ahead and change that back to true because we want to be able to use fractional trading and boom it's true. So that is the count configurations, pulling through your current configurations and then updating any of the configurations if you need to. 26. Alpaca - Account Activities: All right, moving on to account activities. You can see your retrieve account activities. It returns a list of activities on your account. So what we'll do is we'll just show you exactly what we mean by that. You can type in parameters, specifically if you want to, if you wanted to find your activities, between a certain date range, you can specify that here in any of these query params. But for now, we're just going to pull everything, so make sure you got your key, in your secret key in there. It's automatically going to update this endpoint, and all you need to do is copy this get request here. Come over to a Jupiter notebook. We do want to import JSN so that we can make it look nicer, and then we'll paste it. You can see that everything's already in there. We'll exchange print with Jason dot load, so it looks nicer when it returns, shift enter to go ahead and get that to return. All of our account activity, and you can see this is the stuff that we've shown you previously filling certain transactions, so we had that cell of the Microsoft. More cell Microsoft, More cell Microsoft, we sold it bunch of them. I think maybe we had like five or something like that, sell the Apple. We were getting rid of everything on the account. We were going through one of the prior lectures. If you come down, there is a few other activity types in here, besides just the fill that we have here, like this activity type, JLC. So let's say that we only wanted to get our fills. We can go ahead and come over to this option right here. Retrieve account activities of specific type. So, this activity type here, you can see those labeled, within each of these items in the dictionary that was returned here. So we just want to look at the fills. So we'll go ahead and type that in here. It's going to automatically update. You can see that updated here in the endpoint, and we will go ahead and copy this and JSN dot loads. Return that, and then it's going to give you only the fills, you can see that that other activity type the J&LC is no longer being displayed anymore. So this is how you can retrieve your a account activities. 27. Alpaca - Calendar: All right, moving on to the calendar info. So we're going to do a request to get calendar info, what this is, the calendar API serves the full list of market days from 1970 all the way out to 2029. It can also be queried by specifying a start or an end date right here if you want only a specific period of time. In addition to the dates, the response also contains the specific open and closed times for the market days. Taking into account early closure. So we're going to go through that, take a peek at what this is going to give you, in case for whatever reason, you need to know if there are certain days where the market closes early. So make sure you've got your key and your secret key already in here. It's going to go ahead and create this get request for you as always. We're going to copy that, come over to a Jupiter notebook. We're going to import JSN, just so that we can ensure the response we get is nicely formatted. We're going to go ahead and paste and then exchange that replace that print with Jason Dt Loads. Go to print it. It's going to take a second because there is a lot of dates here, and you can see it does date back all the way from 1970. This is January 2 of 1970, and it goes all the way forward into 1973 is what it's displaying here. If you want to get more recent data because this is such a large return, you're going to have to specify the dates that Jupiter notebook can only handle so much here. What we will do is, since you see over and over again, the market's closing, it's opening at 9:30 A.M. And it's closing at 4:00 P.M. We're going to go ahead and look to see if there's any dates where it doesn't close at that time. If it doesn't Yeah, if it doesn't close at that time, then what's the point of even using this? So what we'll do is within our same cell we have here, we're going to create a four loop. So we'll do four item in and we're going to use that same JSN dot loads. So we're going to loop through the JSN data that was returned. And we want to see if the close is not equal to four. So why why would they return this? I every single day that the markets open, it closes at four. So we'll do if the item. So each time it loops through the item, and the specific piece of the item that we want to look at is the close, and we want to see if it does not equal that 1,600. It has it in as a string. So that's how we're going to create it as well, and we will then open that up. And if it does not equal that 4:00 or that 1,600, we want to print the entire item. So we can see all of the applicable information. So let's go ahead and run this. It's going to go ahead and run it. And as you can see, there is a ton of dates returned for each year going all the way until 2029 as to where the close date does not equal to 1,600. What does it equal to? It equals to 1:00, 1:00 P.M. That's Eastern time. And if you take a look at the dates for this, it's all because of holidays. So this is going to be, I assume Black Friday, so Black Friday every year. There's an early close. Right around 4 July, there's an early close, and then right around Christmas time, there's an early close as well. So it varies from year to year. I think they've changed it. I could be wrong on that, but just based off this, that's what it kind of looks like. We can see in this year 2024.'ve got three of them. So the day before 4 July, it closed at one. I believe this is the day after Thanksgiving, so Black Friday, closes early, and then Christmas Eve, it closes early as well. So that's how you can utilize the calendar feature. 28. Alpaca - Clock: All right. The next item to get into is the clock G Market clock. Info, this is very basic. The clock API serves the current market time stamp, whether or not the market is currently open, as well as the times of the next market open and close. And you'll see when we go ahead and do that get request, it's going to give us that exact information. So make sure your key, your secret key, and there is always. It's automatically going to generate the endpoint, this URL. Go ahead and copy that, head over to a Jupiter notebook, want to import. Jason so that we can make this look nicer when it returns to response. And as always, Jason dot loads, shift enter. We're going to run that, and it's going to return the current market times stamp. So today is September 12, 2024. Current time is 11:54. I'm an Eastern time zone. This should return the Eastern time zone, no matter what. You'll have to verify that just to be sure. But I believe that's the case. The market is currently open. It's Thursday at, like I said, 11 54, so it's open. The next open is tomorrow, the 13th, which is a Friday, at 9:30. Closes at four, next close, is going to be today's close, which is at 4:00 P.M. So that is the clock get request. 29. Alpaca - Crypto Funding: All right. The last part of this trading API reference is the crypto funding. As I had mentioned, prior to this, we are not going to be getting into crypto of this, so we're just going to briefly go over it. If you're interested in this, I would suggest doing further research to figure out how exactly to utilize this. You can use the skills that we've given to you with these other requests. This is a bit more complex. There's a ton of different Gits posts and deletes that you can go through for funding your actual wallets. If I'm going to come over to the dashboard here, you can see that there are cryptos available. So the symbols will be not just BTC for Bitcoin, but USD at the end. That's the entire symbol, and you can see it provides that label on it, and it'll give you the price of it. I think ethereum is ETH, USD. Yeah, so it's going to provide you that as well. So you can see that it is an option to purchase crypto and to fund your wallet and all that good stuff. But we're not going to get into that. So that will wrap up the trading API reference portion of this course. 30. Alpaca - Stocks - Historical Auctions: All right. Now we're going to move on into the Alpaca market data API section of the API reference here. Starting with the stock data. This is going to be what we pay the most attention to. There's a ton of different git requests. We're just going to start at the top here with the historical auctions. So the historical Auctions endpoint provides auction prices for the stock symbol between the specified dates. So you can see, ensure you have your key and your secret key in here. If you don't, you can always regenerate those. If you come to this front page here, click regenerate on the dashboard. Grab those, put those in here so that they auto fill within the request. Just makes less work for you. While you stay signed in, it's going to keep those auto filled every time you click onto a new section of the market or the API reference. If you want to continue, if you have codes stored somewhere and you want to continue to use the same key in secret key, make sure you keep those in the safe place, so you can just grab them and not have to worry about updating your code. Let's start out here with the top parameter. This is the only required one, which is the symbol or symbols. It's a comma separated list. You can see they give you an example of that with Apple and Tesla. We're going to we'll do Apple and we'll do Microsoft. And we'll do Meta as well, which is Facebook's Pad Company. So that's the only required one. If we wanted to specify the date, we could. The default is the beginning of the current day. You can see that. The default for the end time is the current time, if the fee does not sip, or if the user has a subscription 15 minutes before the current time otherwise. Okay? There's a limit, so you can limit the number of records returned based on us just doing the current day, we're not going to get a ton of records, so we don't have to worry about that. You can probably ignore these other ones. I mean, if you really want to identify the currency, go ahead. Otherwise, we're just going to copy the request that AlPACAs API reference generated for us. Come over to a notebook, and you can see how it's messing with it already. We're going to go ahead and first import JSN, and then we will copy or we will paste that get request that we just took from AlPACa, shift enter, to run it. Let me actually change this to JSN dot load, so it looks nicer. And then what it's going to do is it's going to show us the auctions for each of these symbols that we specified Apple. You can see the apple price $2,222.87 MTA, showing the price 52575 on the 12th here, which is today. And then Microsoft, as well, you can see that it's $427.09. So that is the historical auctions API poll. 31. Alpaca - Stocks - Historical Bars: Alright. The next lecture we're gonna get into here is on historical bars. The historical bars get requests. This is more than likely the data that you'll be using. It's what I utilize. It has those key pieces that you want when you're analyzing data, and I'll show you what I mean by that. When I actually pull it, but the first thing we'll do is just set up that pull. So we want to type in symbols. This is a required parameter. So we'll do Apple and Meta. Which is once again, Facebook's parent company. The time frame is a required field. So we'll do and it gives you these options here. We're just going to do one day. So we wanted to pull whoops. We wanted to pull for one day. And then we'll come back and we'll fill out more of these parameters in a sec to give you an idea. But for now, we'll just run this in a blank Jupiter notebook. We want to import JS on as always. And then we're going to paste that request in there. And we want to replace this with json dot loads. And we click Run. And now what it's going to do is it's going to run. The default is just for a day, and it might even specify that. Yeah. The default for this is the beginning of the current day. So that's why we're only going to get one day's worth of bar information for each of these for Meta, and for Apple. But these key pieces here, this is the closing price of today. This is the high price of today, the low price, the open price. This is the time that they actually logged it at. So you can see that that's the close. This is the volume, and then this is the weighted volume. So what I want to do is actually go ahead and pull a time frames worth of this specific time frame. We'll do the beginning of this year. Till the end of not the end, but today. So we'll put in tomorrow's date, which is 13 September, the other thing that it might give you an issue with is, if this is on SIP, you're going to want to make sure that you put in the investors Exchange IEX. And then once you have that, go ahead and copy this, and we'll come back over here and we're going to replace this. And since you've already imported J on the Jupiter Notebook, You technically don't have to re import it again. It's already ran unless you restart the kernel, but you don't have to go ahead and type that out again. So we're just going to run this. It's going to provide that. We need to changes to Jon dot loads. I got to get request. And you can see it starts with the first trading day of this year for Apple, and that is 2 January here, and it provides you all that data. Now, If you want to take this data and put it into a nice data frame, which I would highly suggest it makes it easier to look at. What we can do is we've shown this in previous lectures, but we want to import Pandas as PD, and then we want to create a data frame. So I'm going to create two separate data frames, one for Apple, and the other for Microsoft, which are Meta, me, which will be starts right here. Yeah, it's in the middle of there. So we want to create Apple as a data frame. How we do that is we give it the data frame variable name. I just have Apple symbol, and then the underscore the App, you'll see me do that a lot throughout this course. And then we have to type in pd dot data frame, open it up. And then we need to put in our JSN data. So Json dot loads. The same thing that we were showing above response dot text. And then the other piece of it is it starts this dictionary with this bars and then goes into this, so we need to go into the dictionary type out bars, and I'll actually just show you what it looks like when we don't do that. So, I'm missing a parentheses there. So if I just ran it like this, you can see that it doesn't split it up the way that we wanted to. It's taking bars as the top section of the hierarchy, and then just creating two records out of it. So if we 2 bars that bars key. And then we run this. This will look like this. Now it's going to grab all the records for both of these, but not split up the data. We want these to be two separate data frames where each column is separated for the clo, low, and so on. So then the last part of it we have to do is specify in this diary, the symbol. So if we run that, now you can see this is the Apple data, and we're going to go ahead and just copy this. And we'll do the same thing for Meta. So all you have to do then is just replace the symbol in the dictionary, and then the variable name. So we'll run that. And then let's run MEDA here and you're going to see this price change right now it's 185, at the beginning of the year for Apple. For Meta, it was 346, now it's 525. So that is a basic overview of pulling data for historical bars. The one thing you might notice, depending on how big your date ranges is at the bottom here. For us, it says next page token none. That's because everything was able to fit in one request. But if you have a large enough request, you're going to have to iterate through multiple pages and pull that data. We'll show you that in a different lecture. But for now, we just wanted to run through and show you what it looks like to grab that historical bar data and start to organize it. 32. Alpaca - Stocks - Next Page Token: All right. In this lecture, we're going to cover something that I had previously mentioned the next page token feature. So when you'll need to use that is when you send a request, and you only get part of the information that you're looking for. You'll need to loop through utilizing a next page token to get the next page of data until there is no more data. So, I've already got this coded out. We're going to run through all of it. So if you want to code along, I'll try to go slow enough so that you can follow what I'm doing. But there's just a lot here so I want to ensure that I didn't miss anything. So the first thing that we're going to do is we're going to go ahead and grab and the API reference here, we're going to go ahead and grab the request. So we'll start off with the symbol. I'm just using Apple. I'm utilizing one symbol for the first go around for simplicity's sake, in the future, as you get more complex data polls. You can expand upon this, but for now, we're just going to use Apple. The time frame, we're going to do one day. Of course, make sure that you have your key and your secret key values in here. I expanded the time frame from what we were utilizing the prior lecture. I did the beginning of 2020, all the way until today's date. And then the last thing that you'll need to do is if this feed is showing SIP, you want to make sure unless you have a premium subscription, you want to make sure you change DEX, or you're going to get an error when you go to run the request. So So you have all that information. Go ahead and copy this response here. And we'll come over to a Jupiter notebook. I am just in the same Jupiter notebook I used for the prior lecture, but you can just open a blank one if you want to. Like I said, we're going to show you all the code here. So I'm going to replace this, and what we're going to do is import JSan as always and JSan dot loads, and we're going to shift enter to run that. And it's going to fire back with us the data. You can see it starts in 2020 here. And if we scroll down, you're going to see that there is a next page token. So what that means is we didn't get all the data up to today. You can see it stops at July 7, sorry, July 17 of this year. But it provides the next page token, meaning we need to utilize this token, run another request to get the rest of the data. So just to break that down a little bit further so you can see exactly how the dictionary is separated. I'm just running a quick. You can choose to do this if you want to, but I just figured for visualization purposes, I want to show you. I'm going to run a fore loop in this response to show you the highest items within the dictionary, the dictionary of hierarchy. That first one, of course, is going to be the bars, but then the second one, which we've pointed to before, but didn't get into is that next page token. So shift enter to run this. You can see we've got the bars, which is here in the dictionary. And then on the same hierarchy, we have, at the end here this next page token. So that's just what we were showing you there. Now, I want to show you how the next page request is done. So we're utilizing essentially the same exact, request that we have up here, but we're doing it with one extra parameter, which is that next page token. And we can see that next page token is here. We're going to go ahead and copy that. And then we can actually come over to the API reference, and there is a field where we can insert this page token and it's going to update our Git request. So I'm going to paste that in here, and you can see that this endpoint was updated with the page token, and it has that page token value that we had just pulled. So I'm going to copy this and then come back to our Jupiter notebook, and you'll be able to see when I paste this and we Jason dot loads this. This is going to show it should show that second page of data, which is beyond, I think it was July 17 of this year, all the way until today's date. So we're going to shift enter to run that. I ran and you could see it starts, just like I said at the 18th, and then it's going to give us all the way until the market hasn't opened yet today, it's going to give us all the way until yesterday. So the close price yesterday of Apple was $222.85. The high was $223.50 3.5 cents and so on. So that's how that works. Now, the way to do it, whereas if you have a bunch of pages is you essentially want to loop through, Um, so at the bottom here, like I said, just try to follow along with what I've got. It might be a lot. You might have to pause the video. Um But I would suggest trying to code this out yourself as I'm doing it. So we're going to start with from the beginning here, the initial request that we did at the top import request. We are going to utilize Pandas. So import Pandas PD. That's just the alias that we utilize when we call the Pandas functions. And then if you haven't already import SN. This request is what you should be getting originally from Alpaca. I'm going to get rid of this. It'll just be this original request that doesn't have the next page token on it, and you can copy that, and you can start right here, paste that in. And then it's going to return to you that response. We're actually going to set this response equal to a variable so we can hold it and utilize it within our wile loop. So I just chose the variable name of Json Bars. You can choose whatever you want, whatever makes the most sense to you. But we are setting the original request, which is this in the response equal to variable. We'll utilize that in a sec. We want to create an empty data frame. So I just created the variable name of DF. It's pretty standard. It's equal to Pd data frame. It's a blank data frame. The reason we want to do this is because we are going to be adding to the data frame while we loop. So we need somewhere to add this data that we're pulling through to, and that's an empty data frame. The last thing is the loop dependency. So we're going to go ahead and actually, this loop dependency we don't need. I just realize, so you can go ahead and just remove that. So what we're going to do is create a wile loop where While true, it's going to continue to loop. So until it's broken essentially is what that means. We're going to continue to loop through. So you can end a while loop using the phrase break. We'll get down to that in a second here. But while true, we are going to first get the current page. So we had ran this request. This is the original request. We need to get that data and create a temporary data frame out of it. So I labeled this data frame as Ad DF. That's just For now, you can give it any variable name that you want to, but you have to label it something. It's going to get recreated over and over again as you create new pages. So the first page, we're taking this temporary data frame of Ad DF, we're taking that we should utilize this JSN dot bars. And we're going to go into the bars section of the dictionary, and within that, we're going to go into the Apple symbol. Like I said, we're only using Apple for this example, but we need to specify where we're going in, especially if there's more than one symbol. So this is our temporary data frame that's getting created. It's going to look just like this data frame that we have created above, but it's only showing up until 17 July of this year, just like this request is showing here. So that's our temporary data frame, and what we want to do with that data frame is we want to add it to our empty data frame. So our current data frame that we have, which is going to be our final product is there's nothing in it. We want to re establish it. So we're taking the new value of DF is equal to and what you want to do is PD, which is the Pandas function dot CAT. What KCAT does, the KCAT function allows you to essentially do that catenate two data frames together. So we want to take first our empty data frame, and then add to it the Ad DF. So we want to add all of the values that we got For the first page of this request. So that's been done. Now the new data frame has the first page worth of data, then we want to create the next page. So the first thing we want to do is check if the next page token is equal to none. If it's not, then that means that there is data on the next page. If there is or if the next page token is equal to none, then we're going to break it. That's what I was talking about. So For now, the next page token is equal to JS bar or the JSI bars within the dictionary, the next page token. You can see that here. The next page token, it's going to be a value. Whatever that value is, we need to take that, and we need to insert that into the end point. Now, it's going to change each time. So with that happening, what we need to do is we need to create a variable, which has already been done of next page token and add that as part of the string of the request. So this request here is just like I had shown you prior. When you add this page 123, within the URL here, you're going to see that page token is added, and then the value is being displayed. That value that we want to insert changes with each loop because the next page token value changes on each page. So we will need to within this, create a variable add it to the string next page, it's going to run the proper page then. We're going to re establish the JSON bars variable with the new request for the current page, and then it'll start to loop over again. Like I said, if once this is done, the as it's going through this if statement, I first, this next page token is none, it's going to break and that's going to be the end of it. This has already been done. If not, then what will happen is we're going to establish the JSON bars value for the next loop. So it's going to go ahead and loop around. So I know that was a lot, like I said, if you need to pause it or slow it down, whatever it may be, by all means do so. But at the end of it, we should get this data frame that has combined each of the pages. In this instance, it looks like there's just one additional page to the current one. But if we go ahead and run this, our code should work to where it gives us, of course, our start in 2020 here, and this may be a limitation on the period of time that you can grab for, and that's why I didn't start at the first. I'll have to look into that. But the biggest thing here is that you can see now we have grabbed all the way up until yesterday's date, all the values for it, getting our closes, highs open, lows, whatever it may be, the pages have been combined together. So in a nutshell, that's how the next page token works, and how you can ensure that you're grabbing all of the data and not just getting a part of it when you're going to do your analysis. It may be that you think you're grabbing all of it without realizing that there is additional pages of data that need to be grabbed. 33. Alpaca - Stocks - Latest Bars: All right. Next, we're going to get into the latest bars request. So this is the latest multi bars endpoint returns the latest minute aggregated historical bar data for the ticker symbols provided. So it's going to just do exactly as it says, provides the latest bars here. So we're going to use Apple, and we need to change the SIP IEX, based on our account. We've already got our key and our secret key in there. The request is created. This is a pretty simple one based off of everything else we've done before. I'm going to go ahead and paste this, import JSN, change this to JSN, dotads. And then it should give us the latest bar. Now the market's not open, so it might give us something from yesterday, but shift enter. We're going to run it. Right. Yeah, so it gives us from yesterday. That's okay. If the market was open, which it will be in 7 minutes, it would just give us the latest bar, including, you know, all the close high open and the volume metrics. But for now, we just get yesterday's end of day numbers. So that is the latest bars. 34. Alpaca - Stocks - Historical Quotes & Condition Codes: All right, for this lecture, we're going to combine two of these get requests here. So that's going to be looking at the historical quotes and the condition codes. So the historical quotes, it's the historical quotes, API provides que data for a list of stock symbols between the specified dates, and it goes on the return results are sorted by symbol first, then by time stamp. So it's in chronological order. This means that you're likely to see only one symbol in your first response, if there are enough quotes for that symbol to hit the limit you requested on that request. Situations, you would utilize that next page token. If you haven't reviewed this lecture, I would go ahead and do that so you understand how that works to ensure you're getting all the data. We're going to keep this very basic for now. So we're going to go ahead and get historical quotes for Apple. We are going to specify a date so we get more than just today because the default is just the beginning of the current day. So we're going to do just the first of this month, and then we'll do it through yesterday. In my time zones here. So we'll need to change the SIP to I X as always, and then we'll see if that is good enough from now. So as long as you have your key in your secret key, go ahead and copy the request. We'll head over to a blank Jupiter notebook here. I just have the markdown code split up here so I can separate the two, but we will go ahead and paste this, and we want to import JSO so that we can make this look nice and easy to understand, and we will shift enter to run that. Then it should give us for Apple, the historical quotes here. So you can see it provides you with information on the historical quotes when it was. But more importantly for this lecture, this is the condition code. So this is an R, and you'll see what that means in a second here. So I'm going to go ahead and now run the condition codes request, so we'll come back up here. And you can see that you have to select between two of these parameters. We're just going to do it for quotes, since we're looking at historical quotes here. And then you can leave this the one character name of the tape. It's going to auto generate the request. I'll come back here and grab that. We'll paste that here. We already have in this notebook. The JSN library imported, so you don't have to add that in again. But we're going to do the Json dot load Shift Enter. And you can see it gives you all the condition codes. So this R here that we were showing you up above for each of these quotes that you're seeing, that means the regular market maker open. So that is how you can understand the information within these historical quotes, what those condition codes mean. 35. Alpaca - Stocks - Exchange Codes: All right, so real quick, similar to the prior lecture, there's also exchange code, so this returns the mapping between the stock exchange codes and the corresponding exchange names. So this is just going to be there's no parameters. If you've got your key and your secret key, go ahead and grab that, we're just going to utilize the same your notebook, we were in prior. We can go ahead and paste that and changes to the JSN dot loads, and it's going to return all of the exchanges and their key, their code. So we were using the parameter for the X. So that's V. So if we come up here and look at those quotes again, you can see that V is displayed there. So real basic, but those are the exchange codes and what each of them mean. 36. Alpaca - Stocks - Latest Quotes: All right. Moving right along to the latest quotes. So similar to the latest bars that we showed you, the latest Multi quotes endpoint provides the latest bid and ask prices for each given ticker symbol. Now, this is going to show the actual latest quote. Now that the market is open, when we had showed you the bars, it wasn't, but we're going to do it for Apple, ensure that the fee is the IEX here. The investors exchange, and then you can go ahead and copy this and come over to a blank Jupiter notebook. Paste that in, import JSon so that we can use the Json dot loads. And then it's 945 right now, when we run this, it should give us exactly. So this is giving us a quote for 9:45 A.M. This is displaying, I believe, in UTC. So that's just the standard for a server, but it is 945. If I run it again, I should see the seconds change there exactly. So it's at 40 seconds, and it's giving you the ask price, $2,222.49 and the bid price, $222.43. 37. Alpaca - Stocks - Snapshots: All right, next, we're going to get into snapshots. So the snapshot endpoint for multiple tickers provides, and that's comms separated. The latest trade latest quote minute bar daily bar, and previous daily bar data for each given ticker. So it's essentially a combination of a bunch of things that we've already gone through here. So we're just going to run it for Apple, the feed. We're going to make sure it's IE X. The investors Exchange, and we as long as the are key and secret here in there, we will copy this request that's auto generated, come over to an empty Jupiter notebook. Paste that. Going to import JSN. And then we'll walk through this and see if it gives us exactly what it says it does, so we'll shift enter to run. There's the daily bar, so I believe it said, it gives you the daily bar. We've got the latest quote. So it says, the latest quote. Should have the latest trade. Yep, that's the first part of it. Latest trade data. It says that right here. The minute bar and the previous daily bar. So minute bar daily bar. All of that is what's displayed here with the applicable information. So if you're looking to get all of that data in one spot, this is where you can use the snapshot tool. 38. Alpaca - Stocks - Historical Quotes & Latest Trades: All right, to conclude this section of the course. We're going to go through the historical trades and the latest trades. The rest of these requests are all single symbol. So for a lot of these, we really did just use one symbol. So it's essentially the same process. Just utilizing the procedures that we've shown you throughout this entire course so far for a single symbol. I'm not going to go through each one of those, but if you click into m, I'm sure you can follow parameters to run those requests. Now, the historical trades will run first, so the historical stock trades API provides trade data for a list of stock symbols between the specified dates. Let's do multiple symbols for this one, might as well. Obviously, if you need to use the next page token, go ahead and do that. The default for this is just the current day, so we're going to run it for this month, so far, and we will do 24 today's the 13th, and we will get this historical trades data. We want to make sure that we change the SIP to IEX. And we can go ahead and pull this request, as long as you have your key in secret key. We'll come over to a blank Jupiter notebook. Paste that. We want to import Jan and then change this to JS, shift enter to Run, and it's going to give us those historical trades. You can see there. There's the next page token at the top. Uh, if you need to go ahead and utilize that, but for Apple, the Apple trades, it's going to give you a bunch of information, the timestamp for these trades. You can see. These are all trades that occurred. Right as the market open, this is in UTC time. Make sure you're paying attention to that. Depending on how much was given to us. Yeah, we're not going to be able to see the rest of it. We have to paginate through in order to get we're only at 9:49 on this for Apple trades, and that's on the third of this month, which must have been the first trading day. So, um For time's sake, I'm not going to go through and paginate it, but if you need to, you can either refer back to the next page token lecture, or if you know how to do it, just go ahead and do that to make sure you can pull all the data for these historical trades. Now, we'll get into the latest trades. So let's come back over to the API reference to Latest trades. The latest multi trades endpoint provides the latest historical trade. Data for multiple given ticker symbols. We'll use Apple and Meta again. And then the feed, we need to change dX. And then we'll go ahead and copy that request. We will paste it. We don't need to import Jason again, since we've already done that within this Jupiter notebook, Jason dot load shift enter to run that, and it's going to give us the latest trades for each of these symbols. So Met's most recent trade. You can see it's 1030 right now. This is the most recent trade. What the price was at for that trade, $522.04 and for Apple. Um, it's about the same time, just slightly off there. But the latest price for it was $222 and 2.5 cents. So that is the historical trades and latest trades. As I had mentioned, this is going to be a wrap on the stocks section of the Alpaca market data API. 39. Alpaca - Screener: All right. Moving on to the next section of the Alpaca market data API, we're skipping over the crypto four X and logos just because of this course's main focus and the way our account is set up. Currently don't have access to that logo section or the four X. But Um, we'll come down to the screener. The screener shows you most active stocks, so as it says, returns the most active stocks by volume or trade count. By default, returns the top ten symbols by volume. So this will probably change each time. You run this just because like it says, it's based off of that top ten by volume or trade count, and that seems to change throughout the day week and month. So we're going to come over to empty Jupiter notebook. We're going to go ahead and paste this request that we had just copied, if you don't have your key and secret key already in here. Go ahead and do that. If you wanted to, right now, the default like it says is ten for this, let's say, let's just change it. Let's do 50. This is going to give us the top 50 most active stock. So we'll come down and copy this request. You can change to buy trades instead of volume if you want to. We're going to leave it as volume. I'm just going to get rid of this and paste that in. Import JSN as always so that we can JS loads to make shift enter to run it, and it's going to give you the top trades by volume years, as you can see. You know, I lists out all of the trade counts for that specific symbol. So the top one here in Nvidia, and it's going to give you the volume for it as well. And just as we specified in order, there is 50 of them. So it does give it to you in looks like it's descending order here of highest volume all the way down to the lowest volume for that top 50. So that is how you use the screener. 40. Alpaca - News: All right, next, we're going to move into news. The news articles here returns the latest news articles across stocks and crypto by default, returns latest ten news articles. So you can obviously expand that limit. Just going through the parameters here. You can pick the date range that you want that for, how you want it to be sorted, and that's by the date. So either oldest to newest or newest to oldest symbols, if you want to specify exactly which stocks you're looking for, otherwise, it's just going to default to any. Like I just mentioned the limit. And then there's a few other options as well. What we're going to do is, we're just going to run it without adding any parameters in, and then we can add a few parameters in and take a peek. So what you would use this for, maybe if there's certain information that you want to pull from these news articles, maybe if you are creating a large language model, and you want to use the information within this for some sort of machine learning algorithm, that could be it as well. But either way, we're going to come to a new Jupiter notebook. We're going to paste in that request. We just copied. Import JS and as always, JSon doads. Shift enter to run. And then you can see it's going to give us the ten latest news articles. So top one here, Goldman Sachs maintains buy on Maderna lowers price target to 139. Tells you the source gives you a couple of different pieces of information as well as the URL that links it directly to that specific article. So like I said, we'll run through one more of these where we add in some parameters. It's pretty straightforward, though, the information that you would need from a specific article, and then you get linked to it if you want to pull more of the information beyond just the headline there. So let's do it for let's see what we had on the first of this month. And Yeah, the fault is the current times. What we'll do is we'll just do till the next day. So, the second. And we're going to do it for let's see if we get anything for Apple. I don't know, maybe it's limited. You know, if you only did it for one day, if there's nothing written about that, now it's likely that there's something written about Apple every day. But I guess we'll see here in a second. We'll just leave that a fault as ten. And then for now, we'll use that. I'm going to go ahead and paste this in there. We've already got Jason imported into this Jupiter notebook. So we'll just change the print to Jason Dt loads, shift enter to run, and we got it. So it looks like there's one article here. Two articles. Yeah, we got two headlines. We got this one, iPhone 16 anticipation layoffs, Apple's Intelligence Impact and more, and this was from Benzinga and this one as well, Appi Open AI state, VIDAs Blackwell Delay, US AI warfare preparedness and More this week in AI. So it's pretty straightforward. You know, if you just use the parameters that are given to you in the API reference, but this is how you can get the latest news articles for whatever the purpose you would be using it for whether creating that large language model or if it's just reviewing information, finding it all in one place for a specific symbol. 41. Alpaca - Corporate Actions: All right. Next, we're running get into the corporate actions. This is slightly different under the market data than it is under the trading API data, which we looked at earlier. This endpoint provides data about the corporate actions for each given symbol over a specified period of time. So let's just get right into it here. Make sure you've got your key and your secret key loaded up here so that it automatically gets placed into this request that's being generated. We're going to type in symbol. So we'll do exactly what they did, Apple and Tesla. Types. You can see there's a ton of different types. If there's a forward split, reverse split, unit split, cash dividends, stock dividend, a lot more options than the other corporate action. Pull. The start date and the current date are the end date. Sorry, default to the current day. There's a limit on how much you can pull. What I'm going to do is I'm going to do let's just do the cash dividend. Now, let's do a forward split, see if we have any forward split for this year. 2409. Now we're going to start with the first of this year, and then the end date we'll do is today. We'll see if we get anything back on this, if not, we might have to come back here, choose a different type. But for now, we're going to copy this. Head over to an empty Jupiter notebook, paste it. We already had a file name corporate action, so I just put V two in there. But the naming convention is totally up to you as to how you want to do that. We're going to import JS, JS, dot loads, shift enter to run it. And it looks like there's nothing for this. Now, let's see if there was let me see if I broaden the time range. Let me do 2020, see if that helps. So let's copy this again. And actually, if we really wanted to, we didn't want to recopy in, everything, we can just come into the endpoint and change that from 24, 2024 to 2020. So let's shift, run that again. Okay. We did get something here. We've got Apple had a forward split looks like September 1. It's kind of funny this month. But of 2020. That's the redemption date executed on the 31st of the date prior, and it was a four to one split. Tesla had one in 2020, same date, actually, it looks like. A five to one split, and then one in 2022, as well, three to one split. So you could come in and whatever you may be looking for. There's a bunch of different options here for you to go ahead and pull it, just put in whatever parameters you want. As many symbols as you want to for what you're looking for, could be one, could be, I'm not sure what the limit is. Maybe it's 100. Maybe there is no limit, either way. You might just get bogged down. With this limit over here, you'll probably have to use that next page if you're pulling a large amount. But that is the corporate actions under the market data API. 42. Alpaca - Options: All right, to moving on to the final lecture of this part two of the Alpaca Market Data API. The final section is the options. We're not going to get too far in the weeds here, but we'll at least just go through one request on this. This is going to function the same as some of the other stuff we've done for, you know, pulling historical bars, exchange codes, and so on. But obviously it will contain options data. So the one thing we will do here is I'll just show you how to pull an option chain if you're familiar with options. This will make sense. If not, then you'll have to do some additional research if you are curious about it. I'm going to use Apple as always. That's the one required parameter here. You will have to change this to indicative, everything else if you care to put parameters in. Feel free by all means. I'm just going to go ahead and run this. Like I said, I'm just doing it. For example, purposes, anyways, so I'm going to come to a blank Jupiter notebook. Paste that. Import JSN. Son dot loads shift enter to run this, and it's going to provide the option change based off of that the default parameters. You could see it's a long chain here of a ton of different options. You know, it's going to give you that ask price, the bid price. It just depends on exactly what part of the chain you're looking at here as to what data you're going to get. So I would suggest organizing more probably will look better if you put this into a data frame, easier to run through the chain, it'll look similar to the UI that you would see on a website for a broker. But like I said, just a quick run through of what a request for that looks like, all of the other requests here, you know how to run those based off of our prior lectures. So that will wrap up the Alpaca Market data API section. 43. Polygon - Sign Up: All right, now we're going to get into the polygon dot IO section of the course here. This just expands upon the data that you can pull makes a little bit easier. Some of the items that we would have to create manually when pulling data directly from Alpaca are just offered directly by Polygon. Um, so it's up to you whether you want to consider using polygon or not. I APAC is enough for you, then by all means, but I still think it's worth considering looking at the data that you can get from Polygon. So first things first, come to Polygons website polygon dot O. Make sure you're at the right one. There's a few companies named polygon. So like I said, just make sure you're at polygon.io. This will be the page that you come to. First things first, just go ahead and sign up. It's free to sign up, so you'll get access to some free data. And we'll get into that in the next lecture. But for now, just add your e mail address, password, any verifications that you need to do through e mail, and once you get signed up, then you can proceed to the next lecture. 44. Polygon - Pricing: All right, once you've signed up, go ahead and get logged in, and this will be the page that you're taken to the Min dashboard here. What we're going to review is the pricing that is offered by Polygon here, as I had mentioned, there is free options. But if you want more data, then you can go ahead and look at this to see what else you might need. So, as you saw, come to the pricing page here, scroll down a little bit. And you can see the four pricing options listed. Note that this is specifically for stocks. So if you want to buy additional data for options, indices, currencies, these are all separate. So say you wanted to buy the starter for stocks, so that you get all this, that'd be 29 bucks a month, and if you wanted to get currencies data as well, and you wanted the starter, that's an additional 49 bucks a month. So the starter plan doesn't go across all four. You have to buy them depending on whether it's stocks, options, indices or currencies individually. So that's just a heads up Now, four stocks, which is what this course is focused on. There is the basic plan. Like I said, five API calls a minute, end to day data, so you're not going to get it throughout the day, two years of historical data. So maybe that's enough for you. Maybe it's not. You get the 100% market coverage, which is across all plans, reference data, fundamentals data, corporate actions, which we reviewed what corporate actions entails in the Alpaca data, aggregates, and then you do get technical indicators. So maybe while you're taking this course, that's going to be enough for you. Maybe it's up to you. If it's not, go ahead and look through to see what extras you get in the individual plans. I actually have the starter plan for this, so you might see me pulling larger amounts of data that maybe you get an error for because it's too much. I'm going to try to avoid that, but if you do get those errors, then just know that that's exactly why. You know, you get more data, so it's a higher time frame, five years instead of two years. The data is only delayed 15 minutes. So if you are do if you are trading in real time, that real time data is only going to come with this $200 a month plan. If you know you're going to utilize this for more than just a month, if you know it's going to be at least a year, looking at the annual plan you can save per month, but know you'll be paying upfront. So you're saving 60 bucks throughout the entire year by purchasing the annual plan. So Just wanted to show everybody what the pricing options are. I make you aware of how each of these options indices and currencies are separate purchases from the stock plans, when you buy, whether it's a starter developer or advance, whatever it may be. 45. Polygon - Documentation: All right, in the last part of this Polygon introsection, we're just going to quickly review the documentation here and what we're going to be going over in the future lectures. So under Docs, come to documentation here, the Polygon IO Stocks API provides rest endpoints that let you query the latest market data from all US stock exchanges. Also find data on company financials, stock market holidays, corporate actions, and more. You can see here on the left hand side, similar setup to how APAC is. We're going to go through all of these market endpoints, and then all of the reference data endpoints as well. On the right hand side, you can see your current plan, as I had mentioned prior. I'm on that starter plan, the $29 a month. The different libraries. We're obviously using Python. There is a client library that you can use. And then there's the different types of securities, options, indices, four X, crypto that has documentation on it. But as mentioned prior, we will be going through stocks because that is the main focus of this course. 46. Polygon - Aggregates: All right. The first part of this polygon Do documentation is the market data endpoints. We're going to go ahead and review that. This is a little bit more complicated than doing it in Alpaca. Alpaca does a majority of the work for you, but if you were able to follow along with the Alpaca portion, this part should allow you to do the same. So we're going to start, like I said, with the aggregates here pulling aggregate bars, get aggregate bars for a stock over a given date range and a custom time window. So there are the parameters, just like Alpaca Head, you can go ahead and change those if you need to, you can change the time frame, whatever it may be, set the limits, the sort order, and all that, now, all this is going to give you is the API end points. You can see that's created here. I'm going to go ahead and copy that, and then we are going to head over to our Jupiter notebook, and we're going to go ahead and start creating this. Now, if your API key is not already in there. Then you can go ahead and go find that by going to API keys, and then the API key is going to show up, copy it, make sure that it's in there. If you're signed in, it should automatically show up in the documentation, though. So let's just head back over to where we were. Give it a second to load here, aggregate bars. We're going to leave it just how it is. If you want to change the ticker you can, I'm just going to leave it. I'm going to leave this date frame. You can widen it, you can shorten it whatever may be. But I'm going to copy this endpoint. And in a blank Jupiter notebook, we're going to first, we have to import three libraries. We want to import requests, which was auto generated the last time, import JSI, which we would always do. And then we're going to use Pandas to create a data frame out of this aggregate data. So we want to import Pandas as PD. So we're going to shift enter to run that. Then the next step is we want to actually create the API request. So we're doing it for Apple. I'm just going to call it using Apple's Ticker, API or Apple URL. We're going to open up some quotes and paste in that endpoint that we were given. We want to then create the response variable, which is using that request library. In a get request, and we're going to put in that this variable we just created, which has the endpoint, and then I'm going to go ahead and make the response look nice. Usually, like you'd seen when you just print it. It doesn't give you a nice format, so we're going to create one more variable, the Apple data, and it's going to be nicely formatted using the Json dots, apple response text. All right. And then we can go ahead and just write it one more time so it shows up. And if we shift enter, it should provide us with Oh, it looks like I made a mistake here. This should be Apple UL. Shift Enter, and then we get our apple data in that specified time frame. It shows us the volume, the weighted volume, open, close, high, low. The time, which is in since Epoch. So we're going to show you how to convert that. In a second, it's a standard and a lot of programming languages where it'll return the value since 1970, and you just have to convert it, and then the number of transactions in that period. So we want to make this data look nice. So we're going to go ahead and use a little logic to create a data frame out of this data. The first thing we want to do is actually create the data frame. So we're going to call it just Apple DF, and we are going to set that equal to data frame and we want to use the data that we had created, so it's that Apple data. But then if you take a look, we need to go a step down in the hierarchy of the dictionary. We don't want this stuff. We want just what's within the results here. So we're going to put results, and we'll see how this runs. Let's do this. Actually, I'll put it within the same cell. So we'll shift enter. And you can see we get the data frame. So what you'll probably notice is obviously, you know, we can relabel these, and we'll probably do that later on, maybe not in this lecture, but there's no column that specifies the ticker. So if we had multiple symbols within this that we're showing data for, we'd want to be able to see what data corresponds to which symbol. The other thing is this time, like I said, it's showing it since EPOC. We're going to convert both of them. We're going to add the symbol column, and we're going to convert the time column. So that this data frame is a little bit more useful for us. So the next thing we're going to do is very simple to add the symbol column. We're just going to utilize the same data frame to create a column, you just create a variable that uses the data frame, and then the name of the new column. So we're going to just call this ticker. Set that equal to the value that you want. So we want it to be Apple. Now, if I ran this again, shift Enter, you can see that we just created this ticker column. Now, the next thing that we want to do, and this one's going to be a little bit more involved is to convert this here, these seconds, SPAC. The way we're going to do that is first, we need to convert that column to a string. So the way we're going to do that is we're going to take that column. And we're not creating a new column with the same name, but we're re creating the value within it. So we can use that same thing, the same column header, which is that just t, and we're going to set that equal to the same data frame column, but we're going to now take the type, use the as type built in function as a string. So now we've converted this column to a string. The reason we have to do that is because this has three, too many zeros, which you would find out as you went through this later on, you wouldn't know that ahead of time, but you would find out eventually that you have too much data within this column. We need to remove those. First, we're going to convert it to a string. There's a multitude of ways to do this. This is just the way I chose to do it. Now we want to remove those last three characters within the values of that column, and we're going to do that by creating a function called remove character, and the input is going to be for the row. And all we want to do is return row, the value t, and we want to return everything except for the last three values in that string. So we can go ahead and move on down. And then the last thing we have to do is we have to four that column, we have to apply the function that we just created. So we're going to do Apple DF, and you just apply it to the data frame as a whole because we specified which column in the row and which column we want it to be applied to what our function is, which is, and the axis we want to happen on, which is the column. So axis equals one, and then the last part of it would be to Then convert that to the actual date time format. Once again, with that column, we keep working on it, the T, set it equal to PD, and we're using the Pandas two data frame, and we want it four E, Apple data frame, T column, and the unit is equal to seconds here. All right. So that should be good. You probably have to pause a couple of times if you're following along. But if we go ahead and then shift run this, it should apply converting that column to a string, removing the last three characters, applying that function that removes the last three characters, and then applying the date time conversion of the seconds count so that it's actually a readable value. So we'll shift enter to run this, Oh, this should be two date time, not two data frame. That's my fault. So two date time should enter one more time. And you can see that it did convert that. Correctly, that was within our time frame there. If we go back and look, you could see from the ninth to 10 February. So you do get this air. When you do that. The reason that it's providing that air is because it wants you to pass it to cast it as an int or a float. Instead of a string, we left it as a string. Um, if you want to remove that, especially if you're using future versions. What we'll do is before we convert this is we're going to just as we had done up here, I might actually go ahead and just copy this. We converted to a string up there. We want to just convert it back to an int. If we shift enter to run it, you can see that that goes away. So that is the proper way that you should do it moving forward. But as you can see, we pulled that aggregate data, added our ticker column, and then converted the column values of the date time so that it's actually a readable. 47. Polygon - Grouped Daily: All right, the next item we're going to cover in the market data end points is the grouped daily bars. So get the daily open high, low end close, sometimes referred to as the OHLC for the entire stock slash equities market. So the only required parameter here is just the date. So we'll do yesterday's date. And you can update the other parameters if you want to. I'm just going to leave it. I left our previous Jupiter notebook open. I'll just make it easier for stealing the logic like you saw in the last lecture. It doesn't give us as nice a request setup like Alpaca does. We have to do a little bit of the work on our own. But it's not too terrible. So first, I'll just copy the libraries I need to import into this empty Jupiter notebook. Ship to enter to run that. And then I'm going to actually go and get this data as well, and we're going to make some adjustments to it. So for one, we're going to have to update this URL. And what we're going to do is, we're going to go to get rid of the apple. Because this is not specific to apple. And then we're going to go ahead and get the endpoint that we're given here, we're going to copy that, and we'll come and paste that. And then we're going to go ahead and run this, see what it looks like. And you can see it's going to give you a ton of data for each specific ticker. The next thing we want to do is we want to actually convert this into a data frame. So we're just going to call DF data frame, and we're going to use that data and to enter, actually we want to run that with DF so that we can see exactly what it looks like here. Oh, made a mistake. I need to it needs to be data, and I believe it's results. Yeah. All right. So we'll update that. There you go. It gives you the actual ticker this time. The only thing we would need to do is to update this time. So let's go ahead and grab that logic. We use last time again where We are converting all of this. So copy and paste that into here. Like I said, we're going to remove the apple out of all of these. So get rid of this apple, get rid of this apple. Make sure you've got the lower case t there because the ticker is defined by an uppercase T. We will remove the apple here here. And we'll just make sure we get rid of all of these. Looks like we did. So then what we're doing again here is we're converting that column T to a string so that we can then get rid of the last three characters with this function. We're going to apply the function to that column in the data frame. We're going to convert the type back. And then we're going to convert the column to date time. So let's go ahead and run this shift enter, and you can see that we've got that updated date time. So this is the group daily bars. You can see it's specific for this date. Date doesn't change for all of these different ticker symbols. If you wanted to, you could look at just a specific one. If if you wanted to define, let's see if we can do MTA, if MTA is included in here, which it should be. We're going to make the Meta DF equals DF, and we want it where the Ticker column equal to meta. Shift. We're going to run this, with that in there, shift enter, and, looks like we have some misleading logic here. So let me redo this. What we want to do actually is within this data frame, we want the Ticker column here, two equal meta, and then we're going to close that bracket. So now, this should be the update of logic. This should be correct. That was a mistake. So let's do this one more time, Shift Enter, and now it gives you just that Snippet where it's the meta Column. So that is the groups daily bars. 48. Polygon - Daily Open & Close: Alright, next, we're going to cover the Daily pen enclosed. This is similar to the group daily, but it's just for one specific symbol. You can use any symbol you want to. I just chose MDN here. It's for a specific date. So we're going to just click on this. It'll give you this nice interactive calendar, or you can type it in yourself. But to ensure the Foma is correct, we're going to use this, choose yesterday's date. And then Go back to that grouped daily Jupiter notebook we just worked on so that we can copy some of this code. So we want to input their libraries, shift enter, and then we're going to grab the request as well. So the only thing we'll need to change here on this is the actual endpoint. So delete that. Come back to polygon here. We're going to copy this endpoint, and we will paste into the URL shift enter to run, and it's going to give you the information, the open high low closed volume after our hours in pre market data. For Meda on whatever specified date you choose. 49. Polygon - Previous Close: Alright, next, we're going to do the previous clothe. This auto defaults to the previous days Open High low close for the specified stock ticker. So we'll do META again. This is going to give us same data as we did in the last lecture, just because we picked yesterday's date. But for this one, it auto defaults for you. Leave that previous Jupiter notebook open, if not. Go ahead and open it up so we can go ahead and copy. Some of the code here, import the libraries. I guess you don't really need to import pandas for these if you're not creating a data frame, but it doesn't hurt to have it in there. And then we're going to go and get this data as well for the request, and we're just going to update this URL end point. So let's go and grab that from polygon. We're going to copy it, paste it in here, and then we're going to shift enter to run this. And there you go, gives you the open high load close volume transaction and the time for it as well. You could convert the time if you want. We know that it's yesterday's date. 50. Polygon - Trades Last Trade Quotes and Last Quote: All right. So the next items in this market data endpoints list is the trades. Last trade quotes and last quote. Our current account doesn't allow us to have this. So if you want to upgrade, if this is information that you want, I'll show you in a second here, how you can see if it's something you might want. You can go ahead and upgrade, but you need at least a developer account, which I believe is it's either 80 or $100 a month. So just something that you can consider. But just so you can see what kind of data you'll be getting on the right hand side here at the response. This is for trades, what you'll be getting. This gets trades for a ticker symbol in a given time range. The last trade gets the most recent trade for a given stock and all of the applicable information. The quotes is quotes for a ticker symbol in a given time range, and then this is the last quote. So this information is something you think you might need, you can consider upgrading. We're not going to utilize it, so we're just going to keep our current account subscription. 51. Polygon - Snapshots: All right. Next up, we're going to review the snapshot data. So Alpaca had something similar here. There's a few different items we're going to go through. If you have one of your previous files handy, I would go ahead and grab that so that you can first get the libraries and go ahead and put those on a new Jupiter notebook. I put these markdown cells in there. You don't have to do that. It's just my way of separating it for you to be able to see it. So we can shift enter to run the libraries, and then we're going to go ahead and grab this as well so that we can form the request. We're going to remove the endpoint, and then what I'm going to do is I'm just going to place this in each of these, and we'll go get the respective endpoint so that we can run these. So first is the A Tickers. So in Polygon, under Snapshots, you got all Tickers, get the most up to date market data for all traded stock symbols. So if you want to specify the tickers, you can. We're just going to go ahead and copy this and we're going to come back to our Jupiter notebook, put the endpoint in, shift enter to run it. It's a lot of data, so it's going to take a second. But it goes ahead and runs for all Tickers snapshot of data, a bunch of different fields. So if this is data you're looking for, then go ahead and do the A Ticker snapshot. The gainers are losers next, which is defined as the most up to date market data for the current top 20 gainers or losers of the day in the stocks and equities market. So Um, you can choose either gainers or losers. Let's say we'll just do losers, just so that we change one of the parameters. We can go ahead and copy that end point here and paste it into our Jupiter notebook, shift enter to run it, and it's going to give us the top ten. It's going to show you those changes. This one is down 56%. The next one looks like it's down 43%. So these are huge losers. Just because let's change this to gainers. See what that looks like. See what kind of increase we have? Yeah. This is up over 154% today. So that's the gainers and losers. Next, we'll go to a Ticker Snapshot, which is the most up to date market data for a single traded stock ticker. We'll do it for Meta, and we will copy this endpoint. Head back to our Jupiter notebook, paste that in here to the request, ship Enter. And that's going to give us the snapshot for just this single ticker here, all the information. That you're looking for in one place if you don't want to return all of the tickers like above. And then finally, we're going to do universal snapshot, which is snapshot for assets of all types. So it doesn't look like there's any required parameters here. Let's see if we get any pushback on that. So we'll copy the end point, come over here, paste it, ship enter to run it, and it's going to give us and I think this does have specifications in it. Let's see here. Oh, yeah, we've got common separated. So this is just a default values, but What we can do is just take a peek at these specific ones that it gives us if you want to be more specific, then you can do that within the parameters. But that is the snapshots on polygon. 52. Polygon - Technical Indicators: All right. The final lecture on this Market Data end points is on the technical indicators. This is one of the nice things about polygon here is that it automatically provides this data to you instead of you having to manually create it. If you do have to manually create it, it's not the end of the world, but this just makes it a lot easier. So go ahead and get that polygon aggregates Jupiter notebook pulled up. Going to be easy for us to go ahead and grab some of that data for what we're going to do. Some of that code, excuse me, for what we're going to do within this specific lecture. So get the libraries first in a new Jupiter notebook, go ahead and put those in and shift enter to run. I've got this split up by the different indicators here. You don't have to do that, but it just helps with organization. So the first is the SMA, which is the simple moving average. If you're not familiar with the simple moving average, the MA, Mac Dean, RSI, go ahead and do some research on those to see exactly what they are. For this lecture, we're just going to go through how to pull the data for it. So For the SMA, you're going to have to put in a symbol, so we're going to do Apple. And then there's a few other parameters you can go ahead and fill in if you want to. We're just going to do we're just going to leave it as the default here and see how many days worth of data it gives us. The window is calculating, you know, how any days of trading, we're going to average the price over. This has it defaulted to 50. We'll just leave it as that. And then the number of results returned is ten for now. So we'll copy this endpoint. Actually, before we copy that, let's go ahead and grab this request data here. We'll paste that in, and we will remove the endpoint. We had created previously, and now we will grab the new endpoint. And we'll go ahead and paste that in here. It's a long one, and then we will shift enter to run that. You can see that it gives us ten different values. What we're going to do is actually, we will add a cell underneath this one so that we can and we will add a cell. So that we can convert this into a data frame. So if we just create a data frame DF equals, we've already got that Pandas library imported. DF equals p data frame, and then we're going to take this apple data and get the specific values. Nope, sorry. Yeah, no, that should be values. Okay. So we'll grab that. And we will shift enter to run that. No, it looks like we have to do results the n values. So two steps in the dictionary hierarchy. There you go. So then it gives you those ten different values. It should be the last ten days, but what we'll do is to ensure that we know which dates, these are four. We're going to utilize that function to convert the timestamp into readable format. So we'll come back to our aggregates and we'll grab this code here that will allow us to convert it, and we'll just have to change it up a little bit. So paste that in there. The first thing is four, and we just have it named DF, so we will actually, we'll make it easy on ourselves. We'll re label this as Apple DF. And then the column name is actually timestamp. So instead of t, we want this to be timestamp, converting that to a string, and then we will create the function. To remove the last three, we know now that we need to remove those in order for this to function properly. And then we will apply this function to our data frame. We will then convert that column type back so that we don't get that warning on the deprecation. And then finally, we will perform the conversion. And this should provide us with the properly formatted date shift enter to run that. And you can see, yes, it is for the last ten trading days including today, we're in the middle of the day here. So this value will probably change. Let's hear it's 220-26-5604, if I rerun it, It's not changing. So maybe it's doing it based off of yesterday. So that we'll have to check out. Yeah, I might be taking yesterday's date into effect. So we'd have to look further into that either way, it's going to give you less ten values if you want to be more specific, make sure you fill that out within the parameters. I'm not going to do all this for these other indicators, but I'll just run the requests anyways. So let me copy this. Into here, and we're going to remove this endpoint, and now we're going to get the exponential moving average, and we'll come down here and we'll go ahead and grab the endpoint for this, and we're going to paste that in there and run it. Then this is the exponential moving average. And then the Mac D, I'm going to go ahead and grab this data again. And if you want to ensure that you are not re establishing the variable, make sure you give these different variable names rather than continuing to use the same one because once I created this response right here, this SMA one essentially went away. No longer is the values for Apple data. It's now the EMA one. Now, for the MACD, we're going to get the endpoint for it. And we'll come back to polygon and see if we need anything inserted here. You can change the windows if you want to. Otherwise, just go ahead and grab that endpoint, paste in there. We're going to shift enter to run it, and then it should give us our values for those ten different time frames. And then finally, the last thing we're going to do is the relative strength index indicator, and we will get rid of all this. And go grab the endpoint for it. And we've already got Apple in there, and we're going to just come down. You can change the window if you want. We're going to leave it, paste that endpoint, shift enter to run it, and then you can see. It's going to give you those values. So that is the technical indicators, and that is going to wrap up the market data endpoints section in polygon for this course. 53. Polygon - Tickers & Exchanges: All right, the next part of the course, we're going to dive into the reference data endpoints, starting with the tickers and the exchanges, the two kind of go hand in hand for putting together some of the parameters here. I'm going to show you what I mean by that in a second. The first thing we want to do is if you have the data still, the code for one of the prior lectures, I would go ahead and open that up so that you can easily just copy and paste some of the code. So first, the library's request Jason and Pandas, we want to import that, shift enter to run it. Then we'll actually start with the exchanges because we're going to use the exchange parameter within the Tickers request. So the first thing you want to do is if this is blank, we're just focusing on stock, so you can change that. You can change this locale if you want to. We'll do US before I copy that, I'm going to grab this request here and we'll just clean it up a little. So I'm going to paste that. I'm going to get rid of all of the apple. So that it's just generic, and then we will remove the endpoint because we want to put our new one in. So there's a request all cleaned up. The template is waiting for us to just grab this endpoint, paste it in there. We're going to shift enter, and it should provide us with the list of exchanges here and the applicable information. The next thing we're going to do is we're actually going to go to the Tickers now, and actually, let's copy this and we'll paste it down here and remove this endpoint, and this will just be our template so that we can grab the Tickers data. We're going to we'll just use this first exchange. We need to grab this code here as a parameter. And because we're going to get so much data back from this, we need to be able to narrow this down a little bit. So the market, if this isn't already make that stocks, the exchange, we're going to use that code that we just got. And then the rest of it, I think I'm going to leave. If this isn't maxed out, you might want to do that just so you make sure you don't miss anything. So now let's go ahead and grab the end point for this. And we're going to paste that here for Tickers, and we will shift enter, and it's going to provide us all of the stock tickers that are on that exchange and some applicable information. To make this easier to read, I'm just going to make a data frame out of it. So data frame DF equals pd data frame, and we'll add the data data in there. And then we'll run that shift Enter. And there you go. Now you can see, Oh As always, we need to find in the dictionary because it's not doing that. We need to do it for results, and that'll give us a cleaner table. So one more time, and there you go. All the information that's displayed within this request is now in a nice clean data frame. So that is the tickers and exchanges. 54. Polygon - Ticker Details: All right, next is the Ticker details here. They have it designated as the V three. So you can see that in the end point there. We're going to go ahead and set up just as we usually do based on the code from prior lectures. We're going to take that in an empty data frame and just kind of get the bones of this Jupiter notebook. Sure you're in the cadence by now doing that, but we want to get this request, template set up, remove the end point, and then we just need to go and enter the parameters for this. We'll do MTA. And then if we want to specify a date, it says specify point in time to get information about the ticker available on that date. We're just going to leave it as the default here. That means the most recent data available. So we're going to copy this endpoint. We'll paste it in here, and then we will give it a shift enter to run, and it is going to provide ticket details about MEDA. There's a ton of different information here. You can see the description of M Meta is the world's largest online social network with nearly 4 billion family of apps, monthly active user, so on ya ya yada. More information than you'll probably end up needing here, but if for whatever reason you need some of it, something such as the shares outstanding, whatever it may be. This is exactly where you can find that you can see the market cap here. Looks like we're into the trillions is that? Yeah, 1.35 trillion. So that is ticker details. 55. Polygon - Ticker Events: All right, moving right along, we're to get into the Ticker events endpoint. So let's go ahead and set up our new blank Jupiter notebook. Shift enter to run the libraries that we want to pull in. And then we'll go grab the template for request. And we're going to go ahead and get rid of that endpoint, and then we want to come back to Polygon, and we want to get the end point here. The parameters that required is the ID, the type you can put in there if you want to. For the ID, it can currently be a ticker a CUSIP or composite FIGI. So when a given ticker, we return event for the entity currently represented by that ticker. So we have meta typed in here. That's just what we're going to use for this, makes it easy. We're going to come back here, put in this end point, shift enter to run it, and then we get returned any recent events. So it looks like the most recent event we're looking at here is the Ticker changed from FB, when it changed from Facebook, apparently, to Meta. So Facebook's parent company is Meta, and that's the event. We're looking at. This was back in 2022, when that happened, it looks like the original this might have been W. Facebook actually went public with this Ticker. So that is Ticker Events. 56. Polygon - Ticker News: All right. Next up is Ticker News. I get the most recent news articles relating to a Stock Ticker symbol, including a summary of the article and a link to the original source. So this has a few parameters you can fill in. I'm going to go ahead and actually just use Boeing. So the Ticker symbol for Boeing is BA. I'm going to set up our new Jupiter notebook, just as we always do for pulling this polygon data. So I'm going to import to the libraries, and then I'm going to get this template set up for the request. Remove the previous endpoint and go ahead and get the new endpoint. So we'll copy this. We didn't fill in any other parameters. We're going to get just ten news articles back here. We should. Let me put in the new endpoint, Shift Enter, run, and there we go. So the top one here is from the Motley Fool. It's got the home page of the Motley Fool. It's got the Logo URL, the favicon URL. But more importantly, it's got the Article URL. The title of the article is Boeing stock going to 240. One Wall Street analysts thinks so. It gives us the image URL in a short description. Boeing is facing an extended strike by the International Association of Machinists and Aerospace workers, which could significantly impact its cash flow and production ramp up. The company needs to resolve the dispute quickly to avoid further financial strain. And as you go further and further down, of course, there's related Tickers. So this I recognize is Southwest, but then it moves into eventually, you'll get the next article. So that is how the Ticker news works. You can specify which company you're looking at and get the latest news. Maybe, as I've mentioned, something similar for AlPACA. You can utilize this for creating a large language model, pulling in a bunch of news related data. So that is Ticker news. 57. Polygon - Ticker Types: All right. Next is the ticker types. So list all ticker types that polygon dot IO has. So there's a bunch of different options for the asset class here. We're just going to leave that blank. We're going to set up our blank Jupiter notebook, as we always do with the libraries first shift enter to run, and then we are going to grab the request to use as a template here. We're going to get rid of the current endpoint in there and go and grab the ticker types endpoint. We'll copy that, paste it in here, shift enter run, and you can see it's going to list out all of these. We can, if we want to. We can turn this into a data frame. It's easier to see data frame. And we will use data. We got to use the results portion of the dictionary, close the parenthese. We will shift enter and run that and you can see all of the different assets that we have here, the description, stocks, indices. You can see common stock preferred stock, warrant rights, corporate bond, and so on. That is the ticker types. 58. Polygon - Market Holidays & Status: All right. Next, we're going to go through the market holidays and market status get requests. So starting with the holidays, going to do the same setup as always in a blank Jupiter notebook. We'll import those libraries, shifted enter to run those, and then we will grab the request template here, get rid of the current endpoint. Now, this one is going to be the Market Holidays. And we'll go to Polygons documentation here. There is no parameters to even put in, so we're just going to copy this endpoint here. We'll paste it, and we'll run it, and then you'll get a list there of all of these. Now, if we wanted to, we could turn this into a data frame. So why not? Let's just do it within the same cell here. So what we'll do is the data frame. We'll call it the Holiday data frame. How about that? Holiday DF equals p data frame data. And then we will run this shift enter, and there you go. You are going to get all of the dates for it. And this is in ascending order, and you'll get exactly what it is. And what the market status is. Early close, looks like the next one is Thanksgiving. It's close on Thanksgiving. Both the NASDAQ and New York Stock Exchange. And there's an early close. This looks like it's Black Friday. It says Thanksgiving here, but it's the day after Thanksgiving, and it tells you when. So that is the market holidays. Now, let's move on to market status. So market status. What we'll do is we'll come up here and just grab this and we'll go ahead and paste This in there, we'll remove this endpoint, and we'll go and get the market status endpoint from Polygons documentation. Once again. There's no parameter here, so we're just going to go ahead and grab the endpoint. We'll paste it in here, shift enter to run, and you're going to get the current status of the market. As you can see the documentation, it says, get the current trading status of exchanges in overall financial markets. And you can see just that. It's not after hours right now. It's not early hours. It's currently open. So you can see NASDAQ, the Dow, the market itself. They're all open. What the server time is, in general, says it's 3:07 P.M. And that's Eastern time. It's going to end up closing in about 53 minutes or so. So that is the market holidays and market status. 59. Polygon - Stock Splits: All right. Next up, we've got stock splits. Get a list of historical stock splits, including the Ticker symbol, the execution date, and the factors of the split ratio. So there is something within ALPACA. I know we've done this before, but this is polygon's way of doing it. So if we want to specify the symbol, Yeah, we'll go with the Apple because I know Apple's done splits. I'm not sure if Boeing has So we're going to go ahead and get in a blank Jupiter notebook. We're going to get the template setup as always. So we'll import our libraries, shift enter to run, and then we'll get our request template here. Paste that and remove the endpoint that we currently have in there to grab the new one stock splits. Come and copy that. And then we'll come back here. We'll paste that in there and Shift Enter, we will run to see the stock splits, just so that we can make this look a little bit nicer. We're going to turn it into a data frame. Data, and this is in the result section of the dictionary here. And we will close that and we will run the data frame. So you can see here, there's been since 2005, there's been three of them. We've got in 05, there was a two to one split in 2014, there was a seven to one split, and then most recently back in 2020, there was a four to one split for Apple stock. So that is stock splits. 60. Polygon - Dividends: All right. Next up is dividends. Get a list of historical cash dividends, including the Ticker symbol, declaration date, dividend date, record date, pay date, frequency, and amount. So we're going to do this. We'll do dividends for Boeing. Let's first set up our blank Jupiter notebook, utilizing. Code, we've used previously, shift enter to run to import those libraries, and then we're going to get just this request template here and remove the previous endpoint. So then we need to come over to Polygon. We've already put in Boeing as the ticker. If we wanted to put any additional parameters in we could. Maybe we want to look at 25 different dividends. So we'll change that, as well, and we'll do it in order of pay date. Why not? So we're going to copy this. We'll come back here past that endpoint, shift enter to run this, and then we will convert this into a data frame so that it's easier to see. As always, it's the results section of the dictionary, and we will go ahead and run that and take a peek. And you can see all of these different dividends for Boeing looks like Boeing got rid of their dividend in 2020, right after the pandemic really hit everything. So must have made sense there. They had to make some decisions. I know it was a super volatile stock. So they haven't paid out a dividend since then. But this is the list of the last 25 dividends paid out by Boeing. You can see the amounts for each of these declaration date and any other applicable information. 61. Polygon - Stock Financials: All right. Next is the stock financials. So get historical financial data for a stock ticker. The financials data is extracted from XBRL from company SEC filings using the methodology outlined, and you can click here to check that out. It says, this API is experimental. It seems to provide a lot of very valuable data, especially if you're trading off something that has to do with any sort of financials, such as the income statement or the balance sheet. Let's go ahead and just test this out here. First, we need to set up our files. So as always, we're going to import our libraries and we'll shift enter to run that and then get our template for the request. We're going to remove that end point here because we need to put our new one in. I just put Apple here in the parameters. You can put whatever you want to. There's any other specifications you'd like to add by all means, do so. But I'm just going to leave it as is. Limits going to be ten, meaning it's going to get us the ten most recent filings. So I'm going to copy this, and we will come back here and insert this Shift Enter, and you can see the most recent filing is, through June 30 of 2023 to June 29 of 2024. Within it, it's got for Apple, in the financials. It has the cash flow statement, income statement, and a few others, just to make sure that we know exactly what's in each of these. We're just going to run some quick code to see what we've got. Four item in data, which is what we call this is data, we need to get into the results section, and we'll just run it for the most recent item. So there's ten in here. We'll do it for the most recent one and see which financial, statements we've got within this. So we're going to type in financials, and we'll just loop through the items, so we want to print out what the item is, and this is going to tell us which financial statements we have to look at from the most recent filing, shift enter. We've got the cash flow statement, income statement, balance sheet, and the comprehensive income. Now, if it helps you you can go through and loop and set up a data frame that pulls through this data and makes it look nice and organized. We're not going to do that now, but just know that you have all of this data available to you. So that is the stock financials. 62. Polygon - Conditions: All right, quick one here. Conditions. List all conditions that polygon dot IO uses. Let's go ahead and set up our plan Jupiter notebook as always, shift run the libraries. Then we'll go and grab the template for the request. Add that in here. We will remove the current endpoint, and we will look to grab this one. We're not going to add any parameters or change any come back here, drop in the endpoint, shift enter, to run it, and we've got a list of conditions here. And let's actually put this in a data frame. So DF equals p data frame data results. And we'll close that and we will shift enter to run and there you go. You have your conditions list here. If you wanted to, you could dive further into the update rules, but it's going to list each condition out and what it is and the mapping, each of the applicable fields. 63. Polygon - Related Companies: All right. Last item in the reference data endpoints on polygon here is the related companies, get a list of tickers related to the queried ticker based on news and returns data. So we're just going to do Apple, make it easy here. We've got a blank Jupiter notebook. As always, we'll get our libraries in there. Shift enter to run that, and then we will get the template for the request and remove the current endpoint. So that we can go ahead and grab the new one. There's only one parameter. It's just the ticker for which companies you would like to see related to or which company you would like to see other companies related to. So we will go ahead and enter this end point in there, shift enter to run this, and we are going to get this list here. We can make it look nice in a data frame, even though this is definitely eligible. We'll just for good measure and practice, we'll put it in a data frame. And Microsoft, Google, Amazon, Google. This must be Alphabet, Tesla, Nvidia, Meta, Netflix, Disney, and Berkshire Hathaway. This is the B class. So those are the top ten. Related companies for Apple, that's how I use the related companies endpoint. 64. Basic Trading Strategy - Get Data: In this section of the course, we're going to dive into how to set up a basic trading strategy. So there is a bunch of different ways that you could do this. I just wanted to provide an example, so you had an idea, and you can kind of build off of that. We're going to go through my five steps of getting there. Like I said, you know, whatever works for you. There's not necessarily a correct way to do this. But this is one of the ways that you can get it set up. So we're going to start with just getting the data. So that's where I usually begin is grabbing the data that I need. So we're going to add a few different cells here. And the first cell we want to import as always the libraries that we use. So requests JSN, and Pandas as PD. I'm going to use polygon to grab data. It just has a cleaner feel to it, and it has a little bit more data, so that's what I'm comfortable with. So I'm going to go ahead and start setting up the template for the request from Polygon. So we're going to call this the URL, and we'll grab that URL in a second to grab the aggregate data. So we're just going to start with the OHLC data. And then we'll expand upon that in later sections when we want to grab additional fields indicators, all that. We want to get the response. So that response is going to be the request dot G, and that'll be this URL up here. And then we want to define that data and that request as the JSI dot loads of the Apple response. Text. That text. There you go. So let's go ahead and grab the endpoint for the aggregate. So we want to do it for Apple. We want to keep it as the one day. I do want to do it from the beginning of 2023 up until today's market is still open. You can see it's 226 here on a Wednesday. So I'm going to do it up until yesterday because I want to get some recent data. I want to make sure I'm getting enough data, so we'll do 917. And we'll go ahead and copy this endpoint, and we'll drop that in here. And then I'm going to run this first cell and the second cell. We didn't print out what that looks like. Just to keep it clean for now, you've seen it in prior lectures. What I can do is I can just show you if that makes it easier. There's what it looks like in the JSON format. So I'm going to get rid of that. And then I'm going to go ahead and start to create the actual data frame. So we're going to call it this is the aggregate data. The Apple ag DF equals pd dot data frame. You've seen this before when creating a data frame. It's the Apple data, and as always, if you need to go ahead and look at this, then be sure to just run this up here you can see the JSon structure in a dictionary. We just know off the top of our heads here because we've done it quite a few times that we need to get the result section of the dictionary from that JSon response. So that'll be our data frame. If I just ran that, You can see what that dictionary that data frame looks like. I want to add in a column. We're going to add additional fields in the future here, but just so I don't forget, I'm going to add a column, and we're going to just call it Ticker. And this is for Apple. That's what we had created the end point out of was Apple. So we just want to make sure we dropped that in there. And now, if I ran this, You'll be able to see that. We'd added the ticker column in there for Apple. We're going to go ahead and organize that stuff later, but for now, the other thing I want to do is to just run through that code we had done in prior lectures to convert this into a real date time. So the first thing we're going to do is for this data frame. We want to for the date time column, which is labeled as T. We want to convert that to this but as type of string. That's going to make this right now, it's an integer. We want to make it a string. Then we want to create the function to remove the last three characters because that always messes up our when we our conversion. So we will return everything in this column ops besides the last three characters. So everything up until the last three, is what that is saying right there. So then we need to apply this to the data frame. We'll keep adding in that data frame there, and we want it specifically for this column. And then what we want to do is take that data frame. Ply the name of our function on axis one. Don't forget that part there. Then, of course, because we get that new deprecated feature of not converting it back to an integer, it'll cause problems in future versions. We want to then just essentially do this. I'm going to make this easy on myself. But instead of converting it to a string, we want the type to be in. And then we will finally here do the actual conversion. So that's done by PD that data frame. P is a pan dysfunction, two date time. I've messed that up for two date time, and then we want to grab this data frame. And that column, and we want to convert that utilize the unit equals S. Then finally, we'll just go ahead and print the final version of this, and hopefully this T column will be converted to the new format shift enter to run that O, looks like I messed up. Where does it right here. So this is going to be not DF DF, but Ag, because it's aggregates, and now this column has been converted to the new date type. So that is it for the first part of this, which is just the G data section. 65. Basic Trading Strategy - Add Additional Fields: All right. The next thing we want to do is add additional fields to this data frame that we've pulled. So we've got the aggregates. We've got the open high low closed volume all of that data. But we said we want to drop in some other stuff. We want to create a strategy based off of a technical indicator. We would need to go and grab that data. So we're going to use the 25 period simple moving average, so we need to go and grab that. So here's what we'll do. We're going to start setting up the request for the SMA 25. And how we're going to do that is estimate 25 URL. As always, we're going to leave that blank just for now. Actually, here's what we'll do. We'll go and get it from Polygon, make sure you're under technical indicators, simple moving average, Apple, make sure the window is 25, and then make sure this limit is a higher number so that you're capturing more than enough data when we go to combine that with the last two years, almost two years of data that we had pulled before. So get that endpoint copied and go ahead and paste that. And we won't have to worry about that later. Now, we want to get the SMA 25 response variable equal to, as always, the requests. We've already got the request library imported into this. We did earlier when we get the aggregates into this Jupiter notebook. I don't worry about adding the import requests again. SMA 25, we want the data, which is equal to. We use that nice JS dot loads formatting SMA 25 response text. Then finally, we want to create a data frame out of this, and we're going to call it the Apple MA 25 data frame equals p data frame, and this is where we will utilize this data, and we have to go twice if you remember into the dictionary for this into the results, and then into the values section. So let's go ahead and see what this looks like. B that up. Shift Enter. We never closed this. Close that shift enter. All right. So here's what that looks like for now. We want to convert this time stamp. So what we'll do is start to create the code for that, so convert date time. And I'm going to go ahead and copy the code from up above. There's just going to be two things that we're going to need to do in order to change this. The first one is to change this actually to time stamp, right? So It's currently labeled as time stamp, as you can see. We're also going to change it to be the T so that it aligns with the way that it's labeled up here. So, and then we want to relabel all these. So now what's going to happen is we're going to have two columns. One is going to be this time stamp in this format, and the other one is this new T column we're creating that's based off of this time stamp one, but this T column is going to have the proper conversion. So let's make sure we change all of these data frame labels out with the proper variable name, and that should be good enough for that. Let's see if we run that, what that looks like, shift enter to run. So like I said, we were creating it with the new new column with the label of T so that it aligns when we go to merge, but this time stamp one is still here. So we're just going to do one last thing and I'm going to squeeze that in here, which is to and we'll show you how to do this again, but I want to for now so we don't forget. This column is labeled as value. Say we were pulling multiple simple moving averages. They would all be labeled as value. We don't want that, so we want to relabel the column name. So we're going to do that by let me just make it easy on myself. We're going to pull this data frame name, and we're going to set it equal to a renamed data frame, where the columns, and this is a dictionary, but we only need to do it for one value is now going to be relabeled to SA 25. We might change that later, but for now, that's where we're going to leave it, so we'll shift enter to run that. And you can see that that value column is now relabeled as SEMA 25. I'm going to do the merge now for these two data frames. So the SMA 25 data and all of our aggregate data. Reason we want to do that so that it's all in one place. The way we're going to merge it is on this time column. So since we already know that it's only for Apple, that's fine, if not, you would have to use some logic so that you would be looking at the Ticker name and the time. But we don't have to do that. So the merge is going to be pretty simple. We're going to utilize Pandas for it, and the way we'll do that is, I'm going to create the Apple data frame by using PDs so the Pandas, merge function. And the way we'll do this is We're going to take first what we're merging the other data into. So this is our main apple ag data frame. We want to merge this with the data frame that we just created, which was the Apple SMA 25 data frame. We want to merge. What do we want to merge on? We want to merge on that column. So that is T here is the same data type as T here. And then we want to do it as an inner so that it only returns where they both have data. That's the reason we set up the first one with a specific time frame the other one, we just grabbed as much as we could to ensure we captured all of it. So this should merge the two together and we'll just take a peek at it when it's done, so shift enter to run that. Forgot a comma here, more time shift enter. Now you can see our aggregate data is all in there. And then we have the columns from the simple moving average data frame as well, dropped in there for the correct date. This time stamp is in there as well just because it was in that data frame. We'll show you how to drop that in the future when we're cleaning everything up. But for now, we're just going to do one more thing, which is when we're taking a peek at the logic for this, we want to look at the prior days close and SMA. So we want the previous close, and we want the previous SMA 25. So within each row, we don't only want to look at the SMA, but what was the prior days SMA. So how we do that is we will take our data frame, and we're going to create a new column in the data frame. Called previous close. And then we'll take our data frame the column that we want to shift on, which is C. It's the close and shift function built in. And then how many do we want to shift? We want to shift one, which means we want to look one back. So for this date right here, we want to be looking at the close that was one prior. So it's going to show up at the end of here, previous close as $222.66, and you'll see what I mean by that in a second. And then we want to do this for the SMA as well. So we're going to do pre SMA 25, and we want to do it on the SMA 25. That's this and we to shift it one time. And then what we'll do is we'll just show you what that looks like. We'll shift enter to run that. And now you can see. So like I said, this previous close for this date is 12507, and you can see that the close for the prior day is 12507. So I think that's enough data for now in the next lecture. We're just going to go through how we can get this cleaned up because this data frame is a little bit of a mess right now. 66. Basic Trading Strategy - DataFrame Cleanup: All right. The next part of this is the data frame cleanup. So that's taking this data that we've pulled together to a little out of sorts and cleaning it up. The first thing we're going to do is actually just drop some data, so anything that we don't need. It looks like this time stamp column we're not going to need. We already converted that to this T column in the proper readable date time. And we're also not going to utilize this n, which has to do with the number of transactions. We're just going to utilize volume. And actually, I don't even know if we'll utilize it, but we'll keep it in there just for reference purposes for now. So how we can drop columns is just by utilizing the drop function. So we're going to make the new data frame, which uses the same name equal to drop, and then we need to outline the columns that we want to drop. And we do that in an array, so time stamp and n. Those are the two that we want to drop, and then we will just run this to see what that looks like to ensure those are no longer there. And you can see that time stamp and the n are no longer here. So now the next thing we're going to do is we are going to rename these columns. They've got just letters, they could look a little bit nicer, so we'll show you how to rename. So that Apple data frame, equal to that, and we want to rename it. The reason we're setting it equal to the same name is because we want to keep that data frame, but just clean it up instead of creating a new data frame and then a new data frame. Sometimes it's worth doing that so that you don't screw up. But for now, we kind of know what we're doing, so we're just going to relabel it with the same name. So the columns that we want to rename. This is going to be a dictionary. So we will start this off with the volume, which is the B. We want to relabel this. And the way we do that is in dictionary format. We're going to give it a nice label of volume. And then, we'll move on to the next one, and I'm just going to kind of fly through these. So the weighted volume. We will re label weighted volume, then it is the open, and I'm going to take these and make it easier myself. Just copy a bunch of them. This will be open H is the high the low and then the close. Then I'm going to relabel the ticker column to just give it an upper case. It's going to be ticker. And then we want to do the SMA 25 is upper case as well. And then I think we've got a couple more here. Then we need to do the previous close and the previous SMA. We'll do pre close. We're going to make this labeled like that. So it looks a little nicer. Reve SA 25. I believe that's how it's labeled, yep. And we'll do the same kind of format here. Reve SMA 75 or 25. And then the last thing is this T. That's actually our date time, so that's exactly how we're going to label it. Is the date time. So now that shod rename all of these columns, and we're just going to run that to ensure that it has. And Shift Enter. Looks like we never closed this. Glad that they're now Shift Enter. And boom, you've got them all relabeled, nice new labels. I would just say the last thing is they look a little out of order. Like, it'd be nice probably to just have this ticker as the very first column. So what we're going to do is we'll show you how to reorder it. For this one, I actually am going to create a new data frame name so that we can know that this is the final one. We'll just call it final Apple data frame. This is the data we are going to use when we're back testing and executing strategy. So the way you do this is you take that data frame and within two arrays, you just type out your order. So what we can do is copy all of these. And we'll just grab all of these. If you hold down Control and click, it allows you to add multiple cursors. So we're going to copy all this. Oh, actually, that's not going to work because there's multiple spaces in between these. So here's what we'll do. That's fine. We're just going to type it out. So first thing I want is the ticker and then we'll just run through these. So this is just what makes the most sense for me looking at it. It doesn't really matter at the end of the day. If you didn't want to rename these, it's totally fine. I just like to look at my data frame when it's set up in somewhat of an organized fashion. So not a required step, but it might help you. We're showing you how to do that here at the volume weighted volume SMA 25. The previous close. The previous SMA 25. And I think that's it. Okay. So we can go ahead and run that and looks like we made a mistake here. EMA 25. Oh, it looks like we never properly relabeled this. That's why. So let's try this one more time. We're going to run that. Now we've got that. So then now for the reorder. This Estimate 25, that's the way it's labeled in their shift enter. And now everything's been reordered, so that tickers up at the front date time, and then it just makes a little bit more sensors more of a flow. So that is going to be our final data frame. And next, we can then move into the back testing strategy. 67. Basic Trading Strategy - Backtest Strategy: Alright, now that we've got our data all set up and cleaned, we need to move into the back testing portion of this. So B testing is just checking to see how a strategy would perform based off historical data. So it's a little backwards because you don't create your strategy until you've back tested it. So you sort of create that strategy within the B test, but in terms of execution, that's going to have to be a whole new set of codes. So that's why that comes last. For now, though, we'll start to create this back testing strategy. What we're going to do is we're going to test to see if the previous close is below the previous the previous EMA 25, and the current open, so the day's current open is above the current SEMA 25, then we're going to place a buy order. So if that's the kind of strategy, we're going to be testing. It's going to be a lot of code here, so just try to follow along positive video if you need to. The first thing we need to do is because we had these shifts, we've got these two rows that have not a number in for the previous close in the previous SMA, we just want to remove those. So We want to remove first row. We could have done this in the cleanup stage, but we're doing it here. For that data frame we created, all we need to do is just reset it equal to the same data frame, but for everything beyond the first row. So this ILOC in the first row. That's the index locator. So now that we have that, we just need a few variables. This is up to you, how exactly you want to do these. You don't necessarily have to hard code. Some of these things, some of it you do, but either way, we're just going to make it easier. So, if we're going to start with the balance of $10,000. Remember, this is just back testing, it's not real money, so it's up to you. I mean, e something that's sort of realistic if you do plan on trying to create a strategy, If you're just messing around, it can be whatever you want. We're just going to use 10,000 as a starting point, the number of shares we're going to trade each time we enter a trade. We're going to leave this as ten. The reason we're creating a variable out of it is because it's going to make it easy for us to change that if we need to. And the same for these next two, which is our profit amount. So we if the trade gains 10%, we want to take our profit and if if the trade loses, 5%, we want to cut our losses. So those are our starting variables. Next, we're going to move into the actual trade strategy BC test here. And The way we'll do this is we're going to iterate through each row of data to see if we need to enter a trade, and we'll do that by using a four loop. So in order to iterate through a data frame, you set it up where there's essentially two dimensions of it. There's the index, which is this and the row. So that's to grab the column within each row. So for row in, and we're going to utilize this data frame, the last thing you have to do is use this function of I a rows. So it's going to iterate through each row on the loop. So we're going to create the logic to enter the trade. So as we had set up here, if and we're going to open this up. There's going to be two pieces to it because we have if this and this. So we'll just kind of get the bones in there, and then we'll come back here and actually put in the data. So I and this is where that row comes in. So for the current row that we're on, the column we're looking for is referenced as the previous close is less than the previous SMA 25. And the current row, the open, assuming that this trade would execute right at market open is greater than the current. SMA 25. Then we will enter the trade. We want to take a few variables from this. So they're going to come in handy in a minute here, but we want to set our take profit value, so not just that we want to take 10%, but what does that look like based off of the current price? So we're going to do the take profit is equal to, and we're going to do that open price times one plus the profit amount. So that's our take profit, and then our stop loss So when the trade is going to stop at a loss, if we don't hit our profit, is the open times one minus the loss amount. So this is going to give us a value that's 10% above the open. This is going to give us a value that's 5% below the open. And then the last thing we want to do, we're going to have a loop within a loop because what's going to happen is say we're going through, we're iterating, no, no, no. Yes. This one wants to open a trade. If that's the case, we then have to analyze the data based off of everything in the future to figure out which day we then close the trade at. So we're going to create a data frame that is only everything in the future based off of when we actually open the trade. So it's based off the current loop. What we're going to do is take create this data frame based on the final Apple data frame where it is everything from the current index forward. So that's where we can utilize this end right here. And then we need to create our second loop. So this is see how the current trade performs. So the other reason you need to do this is because this is loop through row by row, say you have two consecutive rows that open stuff up. You don't want to have this overlap you want this one to go through and look first through every row that comes after, and then the next one to do the same thing. So that's how this is going to function. This is going to get to a row. It's going to do the loop. It's going to go through everything after that specific index, and then it's going to head back to the next row. So that's why we have to do this nested loop here. So this loop, we want to so that we don't get our variables confused with the four loop designations, we're going to just call this x n and x row in, and that's just this current dF here, and that we have to use that same it rows to iterate and the logic for this is going to be to close the trade if we have to take profit or to close the trade if we have to stop loss, otherwise, just keep going. I row the close, as we're iterating through each of these, it's greater than the take profit, actually will do greater than or equal to because if it's equal to, we want to close it as well once it hits it. Then we're going to create this gain loss variable. So that's going to be how much we would gain on the trade. We're going to call it gain loss because we're going to use the same thing for when we're doing the stop loss. So this is the calculated by taking the close of the current four loop and subtracting the close of the original four loop row. So then we need to update our balance if this take profit does hit, and the balance is equal to the balance, the current balance plus the gain loss. Now, if this hits the take profit, that's it. We want to break it. We don't want this to keep looping through. That'll be the stopping point. It doesn't need to go all the way to the end for whatever reason. We don't want to go ahead and iterate through again so that it picks up some other form of the code if it fell back down to the stop loss even after it hit the take profit or whatever it may be. So we have to break it. Next is ECIF, The row close, and this is going to be for hitting the stop loss is less than the stop loss that we had created up top, which is 5% below the current day's open price. The gain loss then is going to equal the reverse of what we just did. So it's the row close, the original one. Minus x row close. The balance is then going to be the same equation as above. So we're adding that gain loss to it. If this is a negative number, that means it's technically subtracting. Then we want to break that. Otherwise, we're just going to pass and continue to search the rows to see until we hit one of those. To kind of visualize this. I'm going to add a print statement for each of the original loops. So how we'll do that is and I'll take this down a row. We want to print, and I'm going to use an F string literal, which allows me to add variables within a string, utilizing the curly brackets. So I'm going to say the trade. I'm going to label when the trade happens. So this is pulling the clothes for each row. I'm going to pull the date so that I can see when a trade happened, so row date time, and we can oops, need to add that at the end of here, curly bracket with let's say gain or loss of, and then what we want to do is add in here. O gain loss variable so that we can see how much we actually gained your loss. And the one other thing I actually want to do we can't forget to close that is I want to round this so that we only show. It's going to give us a ton of points after the decimal, a ton of values after the decimal, I should say. I want this to only show 22 values after the decimal, so Run the nearest sent there. Then the last thing we'll do is close off this if statement with an else pass. So if we're not going to open up a trade, you can just go ahead and pass. And then the final thing I want to do is print the final balance, and so we'll use string literal again. We'll say final balance. And we'll pull through that balance variable. Balance. Actually, just so that we can get a little spacing. This is just to give you a line break. You can type in print and then just add in an empty string. But now we're going to go ahead and run this, see if we have any issues here. This should hopefully print out each of our trades in the final balance. So shift, run that. Looks like we do have an error. We're going to have to go and fix this. So that loss shod say loss amount. All right. Let's check one more time. Shift E, another err. Uh, we're missing an L here. So current DF. Here's that shift enter. This is what happens with your hard code, and now that's good to go. So we fix those up. We can see all of our trades here. When they happened, what the gain was, looks like there was no losers. So none of them hit the stop loss. And we ended with the final balance of $10,123.46. So that is the back testing of it. Now we will show you how you can actually execute the strategy. 68. Basic Trading Strategy - Execute Strategy: All right, in this final lecture of this section on the creating a basic trading strategy, we're going to create the code to actually execute the strategy. So for purposes of this course, we're just doing it within the same Jupiter notebook that we've been using for all of the other lectures. But something you'll probably want to get as a scheduled task using some sort of website, whether you have it hosted on like Python anywhere or you're using data breaks or any other website. Maybe digital ocean allows you to run scheduled scripts, where it will run every day at 9:30 A.M. And you don't have to actually manually execute it yourself. Or 930 is just the open time for the market or maybe you wanted to execute it at a different time or always be looking to open a trade, whatever it may be, Like I said, you know, you're probably not going to want it all to be in the same place as all of your other code that you've done for back testing, but we're going to keep it in here for now just so we can outline the code for you, and then you can choose what you want to do with it. So, like I said, this is for a buy order where you would run this, whether you're doing it manually, which is not suggested or if you have a scheduled task on some sort of server, it would run at market open. We want to first create the variables for it. So we want this just trade ten shares per trade when it opens. The reason we outlined that is because then you can use this variable within your code and easily change it instead of having to go and change it within the code. It's simple for you to find. Same with the profit amount, which we're going to do at 10%, just like we did in the back test and the loss amount at 5%, just like we did before. What you want to do is ensure that you are, especially if we're using the same strategy as this back test. We want to grab the data before the market opens. So before market open, get data so that it's ready to go, and we want the previous days close because that's going to help us make a decision. Technically, you could run this at market open. This just might make it a little bit faster. T is everything in terms of trading. But we want the previous close only looking at the final value for that. Close boom. There's that and the previous SMA is equal to this final and we'll do the same thing here. All right. Change that to the SMA. There are those values that you would want to. The next thing we want to do is actually, I'm going to open up a new cell here and split this apart. So now, when the market opens, we want to get data. So this is previous data here. We want to get the current days data. So we're going to do that through setting up a request from Polygon. Let me get the template created here, and we'll get the endpoint for current days data. So in Polygon, we're going to be looking for daily open close. Will be for the current day if it runs once the market opens. So we want it for Apple. We want it. You can hard code this. We're going to do it for today. But I'd suggest having a today function in there, you can pull the today's date, and then you won't have to worry about updating this endpoint, but we're just going to add in there. The manual form of it. The current response equals requests dot G as always, and we're going to take this current URL. And the current data is equal to JCN dot loads, and we want to grab this text, the current open will be from this data. We want it to be open. And we can actually test to ensure we're pulling that correctly real quick here. So let's do this. Yeah, that's the open. That's what we want to pull here. Let's get rid of that. Let's get rid of that. And we can go ahead and move on then to the current days. SMA 25. We can copy this code, but we can change this to SMA, so we're updating that and then we want that for here for here and for here. What we want to do is then get the endpoint from Polygon, so we'll come to simple moving average. We want to do it for today. Like I said, we're hard coding this, but I would suggest not doing that. 25 window. We're going to copy this, and we will paste that in here. And then what we'll do is we're actually going to check to make sure we're pulling that data correctly. Shift enter run that. Okay. So we're just getting it for today, so we can get rid of that, and the other thing we need to do then is to just make sure we're grabbing the correct value there. So if you saw from what I just did, and maybe I'll just walk through it. You need to first go in the dictionary to get results. Then we need to grab the values. There's only one value. We want to grab the first one. This is in an array, this right here, so we have to get we have to utilize the actual index for it. And then the last thing we want to get is the value we don't care about the time stamp, we want the value of it, oops. And that should be good for that. And so we can go ahead and run that. And then the last thing on this execution is actually running the strategy. So we can go ahead and start plotting that out. And run strategy, and then we create our if statement. All right. And actually, before we continue with this, I just realized, this is grabbing the first row we want, the value for the most previous row. So you get that using a minus one, it goes reverse. So that's going to get us our correct values for the most previous from the data frame from what we had already pulled before. You'd want to do this before the current day actually opened up so that you're not pulling the current days as the most previous values for these. If you did, for whatever reason, then you would just make sure that those are twos, but for this, it just needs to be a one. So Back to the strategy here. So we need to create the if statement, which is going to tell us if we can enter into a trade. So we'll get the bones in here and just like our back test, but we're going to use the variables that we've created. So the previous SMA is less than the previous close. Sorry. Previous close is less than the previous SMA, and the current open is greater than the current SMA, and it looks like we actually labeled this as current SMA open. We just wanted to label this as we'll call current SMA 25. Current EMA 25. Sorry for moving around so much here. It's a lot of code. So if you need to pause, then by all means, do so you can understand what we're doing here. Once we've done this, if this is true, then we're going to open up that trade. We need to define our take profit, which is the current open times one plus the profit amount that we had defined up here. And our stop loss equal to the current open. So assuming this is ran right at the open of the market. Times one minus the loss amount. So that's going to give us stop loss of 5% below the current days open and a take profit of 10% above the current days open. So now let's execute this by order. First thing we need to do is come to Alpaca, and this is where we can actually create the code for this. It's going to make it nice and easy, as you had seen earlier. So let's go ahead and come to Alpacas API reference under orders, create order. We're going to do it for Apple. The quantity. We might hard code that, but for now, put in ten just like we did within our code. The type market, and we'll leave these all the same. The one thing that I do want to add here, is Z Bta open is a take profit and a stop loss. So the way you have to set these up is by adding them. So we need to add the field. You need to define for the take profit, a limit price, and you have to type it in just like that. We're going to update this value within our code. And then for the stop loss, you need to add in a stop price. So once we have that, then we should be good to go to copy this. So we'll copy that request. We'll come back over, and we'll paste it, make sure everything is indented as it should. I'm going to remove this request an import because it's already in here. We don't need it. Now, the one thing we need to update is the take profit value. So what we'll do is because this is a string, we want to make sure we keep the same data type. So we're going to create a string. Within that, we're going to put our take profit value that we had created up here. And then we're going to do the same thing for the stop loss. And this will be the stop loss that we had created up here. And then that should be good to go. The last thing is, we will just end this if statement with the pass, and Then it will pass if these are not true and will not enter a trade. So I would execute this. It's past market close. This should work here as just a basic strategy. I all values are true here. This is how you would execute a basic trading strategy. As I've said in the course, don't take these as something that you should go ahead and just perform. This probably won't be a winning strategy. I just wanted to give you an idea of how you can set it up. Create your own strategies to hopefully become profitable, but always make sure you're testing. It's best when you're doing this stuff to utilize a paper account so that you're not losing your own money. This is it's difficult, can be done, but it's difficult. So make sure you take all the proper precautions. But that is going to wrap up this section on creating a basic trading strategy. I recommend that you go back for any parts that you don't understand and pause and review it and test, run the code yourself to see how it's functioning. And that should give you a better idea as to how to do this. But come up with other ideas, pull in different indicators, whatever it may be, have some fun with it. So that will be the end of the basic trading strategy lecture. 69. BONUS: Stop Loss & Take Profit: Right, I'm going to show you how to utilize a take profit and a stop loss when placing an order on the Alpaca Markets API. We're going to do this on Navida currently trading at 1:36 48 in order to build out the request for this. We're going to head over to the API here. And first, we're just going to take a look at the documentation. Now, they call this a bracket order. So if we go look at the bracket order, it's going to show us the actual structure that our payload needs to be, because what this is doing is it's placing your original order, and then it's placing one cancels other order. So whether the take profit hits or the stop loss hits, it's going to cancel the other one out. So let's build the request based off of this right here. I'm actually going to just copy this to ensure that we have the right structure, and I'm going to just paste it in here for now just as a reference because I'm going to be switching this documentation over to the API reference for um, in order to build a request, like I said. So there's a few things we have to pay attention to. The first thing, we have to type in the ticker, and ALPACa makes it nice. It just starts to build out your request here. Before we forget, let's get our key and our API key directly on the dashboard. If you don't have them, you can regenerate them. So go ahead and click your key, copy that over and then go grab the secret key and then copy that over, and that's going to populate down here in the request. Now, let's do this for a quantity of ten. Going to do a buy. It'll be a market buy of Navida ten shares right away with the stop loss and take profit. We're going to do a good till canceled order. So leave this open. This market order until it is canceled. And then this is where we can begin to build out some of the other items. So we do want to make sure that we have bracket in here. Um, you need that order class or the stop loss and take profit are not going to be placed correctly. So do not forget to this. I know it's kind of confusing because it has it kind of great out in the beginning. You need to click on Bracket. So then the next piece of it is we can build the T profit. So, like I said, what are we trading at right now? 13662, let's say we want to take profit at 1:40. So very specifically, you have to type this in exactly how I do. So in the new key, it'll be a limit order. So you type in limit price. And like I said, 140 is the value on that for the take profit, and then the stop loss is going to be a stop limit. So there's actually two different values we have to place in here. The first is the stop price. So like I said, it's trading at 1:36 or whatever that is, whatever I said, we'll have a stop price of 13050. And then the limit price after that stop hit, this is a stop limit. After that stop hits is going to be 130. Like I said, you need to type in this limit price, stop price and limit price right here exactly how I have it. Make sure you have it placed in here. That's the way it's required to do. Why they don't give you presets for it? I'm not sure, but just make sure you're paying attention to that. So that should be good. Now if we go ahead and copy this request over, we can Paste that in there, make sure that we have all the pieces. So we have the side by the symbol, the type, so side, the symbols down here in the type quantity, time and force. So we've got a quantity. We have a time and force. We have the order class, important one. That's the last item, and then we have our take profit and our stop loss. So I'm going to get rid of this, and we should be actually, I'm going to do one more thing. I'm going to import JSON because when you print the return response, it looks just in paragraph format. I like JSON. It shows you in JSON format. So import that JCO dot loads, Shift Enter Run. It's going to give you this long return message because it's technically placing three different orders. And if we come to our dashboard and scroll down, we can see that the Navdia market By was placed, and we have this stop limit at 1:30, and we have this limit to sell at 1:40. That's our take profit there. So, you know, that's kind of the way you have to look at it to see that your stop loss and take profit are actually placed. But if you have the three trades placed right there, one of them being the actual original order, and then these other two orders that are contingent on the price moving up or down, then you're good to go. So that is the take profit and stop loss in Alpaca Markets API. 70. Course Conclusion: All right, you have made it to the conclusion of this course. I want to say, thank you. We appreciate you taking the course. If you do have any questions, feel free to reach out. We'll be sure to address those. Any suggestions as well, things that might need to be added, changed, expanded upon, whatever it may be. We do plan on updating the course as need be, whether it's something that we realize or something that you realize. And finally, congratulations, you did it. You completed the course.