How to Get Started With Blockchain Programming

Welcome to Lumiweath!

It’s a great place to start if you want to know how blockchain works and how to become a dApp blockchain developer.

We’re going to take you step by step through the process of how to get started with blockchain programming using our easy-to-follow course.


So, you want to be a dApp developer?

Technically called dApp, perhaps you’re curious about the opportunities available in this space? 

This is the perfect place to be!

Let’s talk about what you need to do to become a blockchain developer or a dApp developer.

We’re going to cover:

  1. Understanding the blockchain landscape
  2. Building Solidity smart contracts on Remix
  3. Build websites with HTML and JavaScript
  4. Use Truffle or Hardhat to manage a dApp with Ganache
  5. Use Web3.js and MetaMask to interact with blockchain from a website
  6. Use a library like React to handle complex user interfaces

Understanding the blockchain landscape

Let’s talk about the first step: understand the blockchain landscape. This is the most general step of becoming a blockchain programmer.

You have to know about:

  • Blockchain 
  • Cryptocurrency
  • Exchanges to buy and sell cryptocurrency
  • Ethereum and other blockchains out there
  • Decentralization and why is it so revolutionary 
  • Smart contracts
  • Solidity coding language
  • NFT
  • Web3 

So all of these and more are terms and different things about the blockchain landscape that you should understand before you start jumping into building projects. 

That’s because understanding all of these terms and how the blockchain landscape is affecting the world and different industries is critical to being able to understand how it works.


What is the point of a smart contract and dApp in the first place?

For example, blockchain technology is revolutionizing finance and other industries as well, like web development, because you can build these decentralized web apps and other industries across the board.

Because with blockchain technology, you can integrate it into any software or system that is currently existing.

After you understand the blockchain landscape. you have to learn the Solidity coding language.

What is Solidity?

Solidity is the number one coding language for building dApps for the Ethereum blockchain.

The Ethereum blockchain is a blockchain specific for building a dApp. 

It was made to build decentralized applications so you can have software that uses blockchain’s decentralized technology. 

So, you have to learn the Solidity coding language because this language is used as the basis for building smart contracts and the basis of how you build any dApp or blockchain project. 

Solidity is just like any other coding language you have to learn different programming concepts:

  • Variables
  • Data types 
  • Functions 
  • Arrays
  • Mappings
  • Conditionals
  • Loops
  • Inheritance
  • Struct
  • Visibility modifiers
  • Mutability modifiers  
  • Abstraction 

All of these can also be applied to different coding languages. But Solidity is unique in some ways.

coding on the blockchain

Why do I need to learn Solidity?

So every coding language is similar, but they are not exactly the same.

You need to learn Solidity in order to build smart contracts.

Smart contracts contain self-executing code that makes up the basis of all the technology on a blockchain, whether you want to build a financial instrument like a bank or yield farming app. 

Or if you want to build out a decentralized website where you have a smart contract that handles transactions or it handles users or handles signups.

Well, all of those smart contracts that use blockchain technology are all written in Solidity.

So that’s why you have to learn Solidity.

Then you can build out some smart contracts once you have the fundamentals of programming down.

Once you know how to code in Solidity, you understand programming topics and you can build out small elements like a variable or a function. Then you can build out smart contracts.

What is a smart contract?

So a smart contract is a larger piece of code.

It’s a self-executing contract, so you can put in different fields like different pieces of data to put in the contract.

Or you can put functionality into the contract for whatever tool you’re trying to build:

  • Profit splitter
  • Limited time transactions
  • Bank 
  • Limited addresses
  • Libraries 
  • Coin
  • NFT

For example, you might have data defining who is going to be able to get the profits, and then you might have a function that will take that profit and will split it between three or more accounts.

So your smart contract defines the whole tool and it defines the software.

You would write a smart contract as a set of instructions containing all of the data of what your tool can do.

You can build out a contract with limits like limited-time transactions.

You could also build out a smart contract to represent a bank, in which case you can have data or fields to store different bank accounts and how much of a currency they hold.

programming NFTs

NFT Builds

If you want to build an NFT, you can do so with a smart contract where you can define all the data about the NFT, like its properties.

And you can also define the functions of the NFT, like can it be minted or can a new one be created?

Solidity Smart Contracts

Once you can build Solidity smart contracts, you can then go into the website portion of dApp development.

So you could stop here and just focus on Solidity, in which case you build smart contracts to deploy them onto a blockchain.

So they live on the blockchain, and then users can interact with these smart contracts directly via the blockchain as an option. 

But if you want your smart contracts to be more accessible so more people can easily access them, then you can build out a website that acts as the gateway between your smart contract and the user.

The user is any person who can go to your website, and that way they can access your smart contract via the website.

Otherwise, they’d have to access your smart contract via the blockchain, which is still possible. But it’s less accessible and fewer people are going to be able to access a blockchain directly like that because it’s much easier to just go to a website for the common person.

So if you want to build out a website portion for your smart contract to be able to reach that audience, then you have to know how to build a website. Solidity is the smart contract side and the blockchain side.


HTML & Your Website

You also have to build out the website side, in which case you have to start with HTML, a markup language similar to a coding language. 

  • Build an HTML document
  • HTML tags
  • Links
  • Images
  • Lists
  • Tables
  • Input
  • Buttons 

HTML allows you to build out the elements of a web page.

In addition to the items listed above, you can build your website with:

  • Title
  • Heading
  • Navigation Menu
  • Text
  • Images
  • Links

HTML lets you define what’s going on with your website. 

For example, if you want to build out a marketplace where users can buy and sell different tokens or different cryptocurrencies, then you have to define what is going to be on the website.

Perhaps you’re going to have your logo at the top, and then you’re going to have sign-in or sign-up buttons where users can create a new account on your website.

Then you might have the different cryptocurrencies that they can buy or exchange, and then you might have buttons to allow them to perform different actions like buy this coin.

So HTML allows you to define what are the elements on your website.

You should learn HTML coding to be able to understand how websites are put together because behind every website the core structure will be in HTML.

It’s like defining what are the elements on the page.

javascript programming book

Learning JavaScript

You should also learn JavaScript. 

JavaScript is a coding language that actually looks very similar to Solidity.

So if you know Solidity you’re actually going to find it easier to pick up JavaScript because the languages are very similar to each other. JavaScript is similar to Solidity in terms of how it looks and how you write it.

But it does have some differences.

JavaScript is for building out functionality on your website, whereas Solidity is for building out your smart contracts.

JavaScript is for building out your website functionality so you have to learn how to code in JavaScript, as well, if you want to be able to build that website portion of your software project.

You could again just build the Solidity side and keep it at that.

But many people choose to also build the website so that you can take your smart contract and you can reach people around the world much more easily.

You can reach anyone even if they don’t know much about the blockchain system!

Luckily if you know another coding language like Solidity, you’ll find it easy to pick up JavaScript.

You have to learn the fundamentals like:

  • Variables 
  • Strings
  • Numbers
  • Booleans
  • If statements
  • Arrays 
  • Loops
  • Objects
  • Functions
  • Dictionary 
  • Switch statements
  • Error handling
  • Asynchronous operations

With JavaScript, you can build out the functionality of your website.

So if you have a simple website you’ll need simple JavaScript, but if you want a more complex website you’ll need to add more complex JavaScript to be able to handle all of the tasks that you want.

If you want to have a hundred different tasks that anyone could do on the website, like buy an NFT, buy a token, or staking tokens, then all the functionality will have to write in JavaScript.

You need JavaScript so that your website can allow these things to happen.

truffle blockchain

Why do I need to learn Truffle?

You also have to learn a tool like Truffle or Hardhat in order to manage your dApp with Ganache, which is a local blockchain.

  • Build and compile smart contract
  • Deploy and migrate smart contract 

So you could just stick to the online Solidity editor known as Remix.

This is where you can just go to a website and you can start building smart contracts right away. You can actually deploy them onto the blockchain on Remix as well.

Typically, developers will choose to use more complex tools like Truffle or Hardhat because they allow you to manage a more complex dApp rather than just a simple smart contract.

So if you want to build a dApp that is more complex, then you’ll want to implement a tool like Truffle or Hardhat which are a bit more advanced.

But by the time you get to this stage, you’ll have more experience with Truffle and Hardhat to allow you to manage your project.

So it makes it easy to have your whole project in one place.


Ganache is a local blockchain that you can use to test out your projects because if you’re building out a complex project or even a simple one then you will want to test out your functionality first before you deploy it onto the Ethereum Mainnet, the main blockchain.

So you can use a free tool like Ganache to try out your functionality and your smart contract for free before you put it onto the main blockchain.

Build Smart Contracts with Truffle and Hardhat

With Truffle and Hardhat, you can build smart contracts. You can compile them then deploy and migrate them, which means you put them onto a blockchain.

After you build out a project with Truffle or Hardhat, you can then connect your smart contract side with your website side.

So you have these two sides of the project: you have the website, which is what the user is going to see when they go to your website, then you have the smart contract side which is handling all the blockchain functionality.

The user will go to the website, then the website will talk to your smart contract, the smart contract will talk back to the website, then the website will show the user the results.

So the user doesn’t have to see the smart contract, they can just see your website.


Use web3.js and MetaMask to interact with blockchain from a website.

Web3js is a tool that allows you to take a website and connect to a smart contract via the website.

It’s just an extra addition to JavaScript because it is written in JavaScript.

  • Build HTML to interact with smart contracts via the website
  • Connect to MetaMask with JavaScript web3
  • Enable the user to interact with smart contract via the website
  • Test your dApp with MetaMask and Ganache 

This is a library called web3, and it allows you to make a website in JavaScript and add more functionality to it, specifically to communicate with a smart contract.

That’s the point of web3.js and similar tools.

Using MetaMask

You also have MetaMask, which is a cryptocurrency wallet.

So with MetaMask, anyone can store cryptocurrency, either real or just for testing on a browser.

That way you can go to a website, and then you can perform transactions on that website via the browser extension.

If you want to do some kind of action, like you want to buy an NFT on that website, then you can do so via that MetaMask wallet because it’s storing your cryptocurrency.

You can take the cryptocurrency out of the wallet and send it to the website. The website will send it to the smart contract. The smart contract will give the website the NFT, and you’ll see that you now have the NFT. 

So you have to learn tools like web3.js and MetaMask because you are going to build out the HTML so that your website can interact with your smart contract.

Then you’re going to connect to MetaMask in your website so that your website can handle this functionality.

Your website has to be able to accept MetaMask transactions and then send the coins to the blockchain. Also, your website has to handle receiving results from the smart contract.

So your website has to handle if a user sends you cryptocurrency via their MetaMask wallet 

Your website has to send that cryptocurrency to the smart contract to do whatever functionality is being asked for.

The smart contract will send the results, and then the website has to send the results to the user. 

Learn to use a library like React to handle complex user interfaces.

After you have been able to build dApps, you can then build more complex steps by using a library or a framework.

One example is React, which is a web development library for building more complex websites.

Previously we talked about how you have to know HTML and JavaScript, and that’s still true.

But if you want to build more complex websites, you also have to know React or a similar library or framework.

React is actually created using JavaScript. It’s called React js, and it allows you to build more complex websites very easily. 

Think about if you have a very complex website with interactive pages with many users and many different types of functionalities.

You’re going to need a tool that allows you to organize all those pages and organize all those user interfaces. And so React is a very popular tool for web developers.

You’ll have to learn a library or a framework like React or others out there, but React is the most popular to be able to build complex websites.

If you’re just building a simple website, then you can just use HTML and JavaScript.

But if you want to build a more complex website, you have to leverage something like React.

That would be your next step.

Beginner Project Suggestions 

You can build out beginner projects using smart contracts and React.

Try to build:

  • An NFT contract and deploy on OpenSea 
  • An NFT minting dApp
  • A to-do list dApp
  • A dApp to access data storage smart contract

You can build a to-do list dApp, which is a website where users can store their to-do list items. But if they want to store an item, they have to do that over the blockchain. They have to perform a blockchain transaction and then they have to send crypto if they want to add a to-do list item.

And if they want to mark it as complete or if they want to remove the item, they have to do that all over the blockchain.

The point is to practice taking functionality and doing it over the blockchain.

You could just build a regular to-do list, which is just a website.

But if you want to implement blockchain technology, then you have to go through web3.js.

You have to go through a smart contract, and all your to-do list items are going to be stored on the blockchain.

So it’s just taking a regular website and injecting blockchain technology into it.

You can also see the benefits and the differences from building a regular website to building out a website that uses blockchain decentralized technology.

Another example is to build a decentralized app that will access a data storage smart contract so your smart contract can store some data fields. Your dApp can access them, then you can build more complicated apps once you’re comfortable building out beginner projects.

profitable apps

Profitable dApps

Try the next step to build some profitable dApps:

  • Cryptocurrency exchange 
  • Cryptocurrency trading bot
  • Bank for deposit and lending
  • Yield farming 

So some profitable advanced dApps would be a cryptocurrency exchange where users can come to your website, and they can give some cryptocurrency like bitcoin and in return get some ether. They can exchange cryptocurrencies.

Another example is a cryptocurrency trading bot where you can find some lesser-known cryptocurrencies, and you can see and watch for when they change the price or when they have different prices on different exchanges.

Therefore you can leverage the difference in price in order to get a profit as well.

You can build a bank for getting deposits and giving out lending of cryptocurrencies so users can deposit cryptocurrencies into your bank.

Then they can get rewarded for doing so, and users can also ask to take a loan of cryptocurrency from your bank. They would have to pay some fee to do so. In this way for all these projects, they’re profitable because you can leverage some of the transactional fees.

You can take a percentage, and you can reward your users for these different actions so the user has profitability but you as a developer you also have profitability.

You also have yield farming, which is another profitable dApp project very commonly used where you can put in some of your cryptocurrency, like Ether.

You’re putting your Ether coin, and as a result, you’re going to be farming a new cryptocurrency that is specific to the website.

And then users will get rewarded for putting in their bitcoin or their Ether, and they’ll be rewarded with a percentage or they’ll be rewarded with some of the new cryptocurrency as well.

So for all of these users, it’s profitable and so are you as the developer.

These are some profitable dApps to look forward to once you get to the stage where you’re comfortable building out Solidity smart contracts, you’re comfortable building out websites, you’re comfortable connecting the two together where you have a website and you can connect it and talk to smart contracts on that website.

Because then you can get to the point where you’re building out popular and profitable dApps.

Get Started With Lumiwealth!

Join Lumiwealth to learn more!

We’re going to teach you how to build tons of projects in our blockchain programming master class, where we take you from no experience whatsoever, with no coding experience required, and we’ll teach you everything you need to know about Solidity, to build out smart contracts like for a coin or an NFT, and we’ll get you started.

Then take your next steps in your blockchain developer journey!

There are tons of jobs out there.

This is a new industry because the Ethereum blockchain only began in 2015, so there’s a lot of opportunity in the space.

A lot of companies are looking to build out blockchain projects and incorporate blockchain technology because it saves them time and it’s more secure.

It saves them money, and it can also make them money.

So there are tons of companies out there hiring blockchain programmers, and there are tons of projects that you can build out yourself as a side hustle as we mentioned with some of those profitable dApps.

It’s an exciting space with tons more to be innovated!

Blockchain is a new technology, so there is a lot of exciting opportunities and growth.

So we hope to see you at LumiWealth!

Take our course on how to build the right blockchain infrastructure, learn solidity/web3 and make a profit.

Algorithmic Trading Python for finance python trading

Using Machine Learning and Python to Trade Stocks, Options and more

Traditional indicators are becoming less profitable in today’s market. That largely is the result of the adoption of algorithmic trading. Algorithmic trading is the complex but profitable process of coding an algorithm to trade for you. This algorithm can be programmed to identify changes in stock prices and will automatically buy and sell securities based on the smartest possible decisions. It’s like hiring a day trader without the potential for emotional human error.


Machine learning is a big part of algorithmic trading. However, machine learning is quite complex and might be difficult to understand, even from the perspective of a seasoned data analyst or day trader.


In this guide, we’ll demystify machine learning in the context of algorithmic trading. We’ll also break down why our machine learning with python course can give you the knowledge and skills needed to start taking full control of your portfolio.


How to Use Machine Learning to Trade Stocks and Options


People don’t realize just how much of the volume on the stock market isn’t actually humans trading. Rather, much of the trading happening now is being performed by algorithms that traders have set up for the best possible returns.


The problem with traditional trading and indicators is that all of the most common and simple trading strategies that were once used by the mostly-human traders in the stock market are completely obsolete. The few people that are still using them make up most of the human beings still operating in the market. Essentially, many traditional indicators are obsolete yet overused.


The Core of Trading: Forecasting


Algo trading isn’t just useful for automating trading practices. It’s also useful for forecasting the market. Specifically, machine learning via algo trading can do the following things:


  • Identify when to buy or sell a security. Knowing when to do so all depends on the forecasted price of the security itself.
  • Automating the process of using traditional indicators like RSI (Relative Strength Index) that many traditional traders use.


These key indicators are used to forecast the future prices of securities. When done traditionally, they can take up a lot of time and energy. Machine learning automates this process with minimal input from the trader.


If you want to learn how to use machine learning to forecast, our Machine Learning for Trading course can be quite helpful.


Problems with Traditional Trading Indicators


Traditional trading indicators are becoming obsolete, and it’s largely due to the widespread adoption of algorithmic trading with python. Specifically, the investing community Seeking Alpha has noted through research that algo trading is currently dominating 80% of the stock market. That’s a big deal– and old-school traders need to get hip to this new technology.


This also isn’t particularly new information. Profitable trading strategies that are based on simple, traditional indicators have been mostly eliminated by algo trading for the past several decades. Just as well, very simple indicator models just don’t have the capacity to capture very complex forecasting patterns that are common in the market today.


It’s already difficult for individuals to get an edge in trading. Why not implement superior technology and methodologies to get ahead in the market?


Traditional Indicators vs. Machine Learning


To better understand why machine learning in trading is better than following traditional indicators, it helps to identify their differences.


Traditional indicators are very easy to calculate and grasp. However, they offer limited ability to factor in different information and data. They aren’t really based in data science, and they are simply used too much by too many traders to make a difference anymore.


Machine learning, on the other hand, is computationally complex. Machine learning is quite a bit harder to implement and is, thus, more exclusive. By using machine learning, traders have the unlimited ability to factor in varying information and data. To put it simply, machine learning is cutting edge, exclusive, and more profitable.


Machine Learning for Finance and Trading


What is Machine Learning?


Machine learning describes the broad realm of using artificial intelligence and computer science to imitate how human beings learn through software. Machine learning is used everywhere, from the tech world to entertainment to the healthcare industry. It’s also at the core of algorithmic trading.


Machine learning is beneficial in algo trading because it makes it possible to identify patterns and behaviors in market data, and then learn from that data. Traditional algorithms are usually made by strategists and programmers. Machine learning properties eliminate the need for professionals to constantly update algorithms to keep them relevant and beneficial. Rather, machine learning updates the algorithm itself.


It’s clear that machine learning is profitable, but it is very complex. That’s why we recommend taking python for finance courses to really understand the basics of coding with python and machine learning in the context of trading. Python courses that specialize in finance/trading put more power in the hands of traders through knowledge.


Learn More About Machine Learning and Trading with Lumiwealth


At Lumiwealth, we understand the world of trading. We also understand how machine learning is becoming a core part of trading in today’s world. Being able to keep up with new tech is becoming harder, especially for experienced traders who are used to the traditional way of trading. That’s why Lumiwealth is offering machine learning trading courses to help traders take full advantage of what machine learning can offer. Our goal is to contribute to the trading community by providing top-notch machine learning and trading courses and a massive library full of videos and code to help you grasp the technical aspects of machine learning in the context of trading.


In this course, we’ll teach you how to use a variety of machine learning tools, including: Python 3, Pandas, TA- Technical Analysis Python Library, Scikit-learn, Google Colab, Google Cloud Platform, and Google Natural Language Processing. From beginning to end, we’ll cover everything you need to know about setting your tools up, training your model, generating predictions, and analyzing your results.


Lumiwealth offers a few different types of plans to suit your educational needs. Our self-directed plan provides access to our massive collection of instruction videos and sample code, so you can learn and trade at your own pace without any pressure. Our excellent and engaging live classes plan will pair you with an experienced instructor at predetermined times, so you can interact and network with other students in your group as well as your machine learning educator. Our project help/tutoring plan is a much more customized version of our live classes plan, in which you will meet an experienced instructor through video conference software. This way, you’ll be able to grasp concepts easier and begin building your custom portfolio project the correct way.


Our specialized courses will help you learn how to analyze your market investments the smart way with machine learning, make smart decisions using helpful data, and build back-testing strategies that align with your trading needs for the future. We’ll also help you understand how to code and understand machine learning in the context of trading. You might be shocked by how fast you’ll become a machine learning expert!


With all three of our course plans, you’ll be able to view hours of video, work and play with tons of code, access new future videos with lifetime access to the growing course library, and meet other learners in the Lumiwealth Discord community. If you’re ready to get started, take a look at our Machine Learning Course page to learn more about our plans and to sign up. An algorithmic trading course could significantly improve your ability to trade with data science.

Algorithmic Trading Python for finance python trading

Using Python to Automate Options Trading

Are you curious about the world of algorithmic options trading? If so, you’re not alone. The methodology is making waves in the day trading world, and for good reason. Algorithmic options trading essentially automates the trading process using python, meaning that it involves a data science-focused approach to making smart trading decisions. Analysts and traders alike are moving towards algorithmic options trading for many reasons– however, it’s vital to understand how to code in python and how to develop trading algorithms to get any success out of this trading process.


In this guide, we’ll break down the basics of how to automate options trading using python and algorithmic trading strategies. We’ll also break down our algorithmic trading course, so you can learn everything you need to know about algorithmic options trading. You might even be surprised by how quickly you’ll be able to grasp coding in python and building algorithms!


How to Use Python to Automate Options Trading

What is Algorithmic Options Trading?


Algorithmic trading, also known as algo trading, is an options trading methodology that involves using software (algorithms) for the purpose of following very specific instructions to place a trade. The trade that’s placed through algorithmic trading can generate money quickly and with a higher frequency than a human trader could dream of.


In algorithmic options trading, the instructions that an algorithm follows include a ton of different things, from timing to quantity to other mathematical models. There are so many profit opportunities for algorithmic options traders. Since algo trading removes the potential for human emotions to get in the way of smart decisions, the market becomes more systematic.


Let’s consider some different algo options trading criteria for some context. The following instructions can be programmed into the algorithm to ensure that shares are being bought and sold automatically when they reach specific values:


  • Purchase 100 shares of a stock when its 100-day moving average tops its 150-day moving average.
  • Sell those shares when the 100-day moving average dips below the 150-day moving average.


Some of the most successful hedge funds out there use algorithms. For example, Renaissance Technologies has over $110 billion in assets, Two Sigma has about $60 billion in assets, and Bridgewater has about $138 billion in assets. Clearly, algorithms can do a lot when it comes to accruing wealth, and part of how algorithms can benefit traders is through algorithmic options trading.


We want to trade options use algorithms because, to put it simply, options are complicated and complex. Why not opt for a method of trading in which all of the complicated math is done for you automatically? Algorithmic options trading makes it possible to trade when you’re away from your computer, so you’re not slouched over, slaving away like a traditional day trader.


There are even more benefits to algorithmic options trading. This process makes it possible to backtest your strategies for the most accurate results. It also takes the emotional side of trading out of the equation, which can make a huge difference in successful trades, if you think about it. It’s far too easy to panic or get excited, thus paving the way for lots of human error. Algorithm options trading automates the trade process, so there’s no option to make mistakes. Just as well, options trading using python makes it possible to implement several strategies at a time, thus diversifying your strategies for more success.


In summary, some of the benefits of algorithmic options trading are:

  1. Algo options trading is less emotional since computers are making the decisions. Emotions are known to cause problems for traders.
  2. Algorithms are very good at doing complex math (like that required with options) much faster than a human could.
  3. Algos can trade 24/7, so even if you’re in a meeting or watching a movie, the algorithm can be making winning trades for you.
  4. Option algos can allow you to trade several strategies/assets at once, which could be too much work for one person to do normally.


Algo Trading Strategy 1: Long Strangle


A strangle is a strategy commonly used in options trading. A strangle involves holding a position in a call and put option with varying strike prices, but with the exact same expiration date and asset behind them. This strategy is smart if the underlying security is likely to endure a significant price movement, but you’re not entirely sure of the direction it will take. If the asset does swing, one could make quite a bit of profit. A strangle can be very easy to program into an algorithm, as well.


Specifically, a long strangle can be very beneficial in algorithmic options trading. With a long strangle, a trader will buy a call and a put option. The profit potential is high because the call option has a limitless upside if the asset rises in price, and the put option can become profitable if the asset falls.


This is what it would look like in a payoff chart:

Long Strangle Options Strategy

Algo Trading Strategy 2: Bear Call Spread


A bear call spread (aka. A call credit spread) is an options trading strategy that is commonly used in algorithmic trading. With this strategy, one will sell a call option and collect an option premium. At the same time, the trader will purchase another call option with an identical expiration date and higher strike price.


This vertical option spread is beneficial and potentially profitable because the strike of the sold call is lower than the strike of the call that was purchased. The option premium one collects in the sold call will always be higher than the cost paid for the purchased call. This requires quite a bit of research and monitoring normally, but algorithmic options trading can automate the entire process.


This is what it would look like in a payoff chart:

Call Credit Spread Options Strategy

Learn How to Use Python to Automate Options Trading Fast with Lumiwealth


It’s no secret that the world of market trading is changing, and it’s changing fast. More and more traders are starting to invest their time and money into new technology that makes the art of trading much easier and more efficient. One way that traders are doing this is by studying data science and using python to automate their options trading strategies. With this in mind, Lumiwealth is offering algorithmic trading and options trading with python courses to help experienced and new traders alike take full advantage of data science methodologies. At Lumiwealth, our goal is to contribute to the trading community by launching coding courses and a massive, constantly updated library full of videos and code to help traders grasp the more technical aspects of algorithmic trading and options trading with python.


Our Options Trading Course Plans are split up into three options– Self directed, live classes, and project help/tutoring. Our self-directed plan is ideal for those who are busy and would prefer to learn at their own pace. Our live classes are, naturally, live and allow students to interact with/learn from other students and the instructor live over Zoom. Our project help/tutoring plans include everything from the live/self-directed plans and also give you lots of one on one time with the instructors and access to our team of developers who can write custom code for you.  This way you can get personalized help with your portfolio and current project. 

All of our courses at Lumiwealth will effectively teach you how to analyze your investments the smart way, make good decisions using proven data, and back-test your strategies. Our experienced instructors will also help you learn how to code with python, how to automate your trades, and the right way to calculate risks more efficiently. You might be surprised by how quickly you’ll start grasping these often complex subjects!

Regardless of your choice for your course plan, you’ll be able to view and access hours of videos that are continuously being updated, a huge library of code, and access to the Lumiwealth Discord community where you can network with and interact with other learners and experienced traders. Take a look at our Algorithmic Options Trading course page to learn more and sign up.

Algorithmic Trading Python for finance python trading

How to Get Started with Algorithmic Trading Using Python

Are you interested in learning about algorithmic trading using Python? For novice traders and those entering the finance industry with a career, a solid knowledge of algo trading and market technology can greatly benefit you.

In this guide, we’ll break down the basics of algorithmic trading and how to get started. We’ll also talk a bit about our algo trading courses and how Lumiwealth can help you learn how to code and engage in algorithmic trading quickly.


How to Get Started with Algorithmic Trading

What is Lumiwealth?


Lumiwealth is an algorithmic trading platform and instruction provider. We understand how important it is to be able to invest using technology, as the world of trading is constantly evolving. We offer a wide range of courses on algorithmic trading that are lead by experienced instructors.

Our goal is to help traders and investors take control of their personal financial health. If you want to become an expert in the finance field, we believe that a solid knowledge of algorithmic trading can make the biggest difference.

We’re proud to offer an algorithmic trading course, a machine learning for trading course, and many other workshops dedicated to the art of trading with algorithms and code. Stick around until the end of this guide to learn more about the courses we offer.

Now, let’s get into what exactly algorithmic trading actually is.


The Basics of Algorithmic Trading


Algorithmic trading is an investment methodology that uses data science as well as automated executions to build instructions for trading. Algo trading is different from traditional trading techniques because it takes out the need for human predictions and the risk of error that comes along with it. With algorithmic trading, one can use techniques based on data science to successfully trade, such as financial fundamentals and economic data collection.

Trading can be a very emotional process. In fact, you’ve likely heard of courses dedicated to helping day traders get a grip on their emotions to keep them from making impulsive mistakes. With algorithmic trading, there’s no need for the human side of trading– you can essentially sit back and let the code do the work for you. With algorithmic trading, we’re using software to analyze data and make trades in an automated way.


algorithmic trading flow chart


So why algorithmic trading? Why do we care? Why does it matter? 

It’s worth noting that algorithmic trading doesn’t have to be the “right” way to trade. At Lumiwealth, our goal is to help data-focused people use their skills to automate their trading practices. Algo trading is great, but it might not be the right way for you to make money. In our opinion, anyone who tries to convince you that there’s only one way to trade is simply wrong.


What we’re all about is the data. That’s the core of algorithmic trading– we’re downloading data, back-testing it, and trying new strategies. Data analysis is the backbone of excellent trading strategies because it uses proof and data science to provide insight into good trading choices. Algo trading is about building new ways to analyze and understand the right trading practices.


algo trading market volume

To really understand algorithmic trading, some coding knowledge is important. It’s a common misconception that algo trading is all about trading hundreds of times a day and that it is no different than trading in the traditional sense. In fact, you’ve probably already used algo trading without knowing it – many financial institutions already use this technology in ETFs, market making, and more. Rather than human guesswork, algo trading utilizes data to make smart decisions. Similar to trading signals, algo trading takes things a step further and fully automates the process. You can also set up your algorithm to simply notify you of changes in the market so you can take care of the buying and selling on your own.


Essentially, algo trading allows you to take control of your investments to an entirely new level, and Python is a great language to do this with.


Here’s how it works: Your algorithm will identify changing trends in the market. Let’s say that Walmart is getting more foot traffic. It’s very likely that more people are buying from Walmart, and thus Walmart will be more profitable. Your algorithm can notify you of this uptick in traffic and break down why you should invest now rather than later, or sell now. Your algorithm can handle the trading for you or simply inform you of market changes so you can make more informed decisions.


Algorithmic trading offers a ton of benefits to traders. You’ll be able to back-test your strategies or use code that has already been back-tested, so there’s no guesswork involved in your trading strategies. You’ll be able to see patterns in earlier back-tests that can help you figure out what will work and what won’t. Your strategies won’t be sullied by panicked human decisions, as it takes the emotion out of the equation. You’ll also have more time to make better investment strategies. It’s a lot easier to monitor your algorithms instead of studying the market, so you’ll have the time to branch out to other markets. The result is significantly less risk for your investments.


Algorithmic trading has been used by some of the richest people on the planet. People like Jeff Bezos, Elon Musk, and most of the people at the top of the Forbes list have used software and financial technology to accrue wealth. Clearly, there’s some merit to it. However, if you enter algorithmic trading without any knowledge of data science or coding knowledge, you could possibly lose money. That’s why it’s so vital to take algorithmic trading courses to educate yourself and start algorithmic trading quickly.

Trading AMC

Learn Algorithmic Trading the Right Way with Lumiwealth


The world of trading is constantly changing and evolving. Being able to keep up is becoming more and more difficult. That’s why Lumiwealth is offering algorithmic trading workshops to help traders take advantage of algorithmic trading methodologies. We want to contribute to the community by providing a library full of videos and code to help you grasp the technical aspects of algorithmic trading.


We offer several different types of plans to suit your unique needs. Our self-directed plan provides access to our massive collection of instruction videos and sample code, so you can learn and work at your own pace. Our live classes plan puts you in front of an experienced instructor at pre-scheduled times, so you can interact with other peers in the group as well as your instructor. Our project help or tutoring plan is a more personalized version of our live classes plan, where you will meet via video conference with an experienced instructor to ensure you are grasping concepts and building your custom portfolio project the right way.


Our courses will teach you how to analyze your investments the smart way, make good decisions using proven data, and build back-testing strategies. We’ll also help you understand how to code, automate your trades, and calculate risks more efficiently. You might be surprised by how quickly you’ll go from novice to algo trading expert!

Regardless of your choice, you’ll be able to access hours of video, tons of code, new future videos, and the Lumiwealth Discord community. Take a look at our Algorithmic Trading Using Python Course page to learn more and sign up.

Algorithmic Trading Python for finance python trading

The Spectacular Performance of Quantitative Investment Funds

At the start of the pandemic, approximately 75% of hedge funds reported losses, while others rose to new heights, outperforming even their best years. What’s the difference, you might be asking?

One of the biggest factors in the success of investment funds boils down to the kind of business strategies used to make trading decisions with python for finance. Many of the companies that survived the uncertainties of 2020 were quantitative investment funds.

This notable difference between hedge fund business strategies can help businesses continue to navigate around uncertainties that jar the investment world, leading to spectacular performances of those funds.

What Is a Quantitative Investment Fund?

A quantitative investment fund is a hedge fund that uses algorithmic strategies to make decisions regarding trading. By using a combination of automatic computer algorithms and data science to execute python trading decisions, quantitative investment funds are driven by systemic strategies and trends.

what is quantitative-investment in trading?

Compare that to hedge funds that don’t use quantitative investment strategies. These “fundamental” investment funds might use data science to influence trading decisions, but, unlike quantitative python trading algorithm strategies, fundamental trading strategies are more subjective and prone to human error

Quantitative hedge funds use intelligent, mathematical models and principles to analyze dozens or even hundreds of different economic data factors. The automated computer technology allows quantitative investment funds to research and compares both long- and short-term scenarios, cross-sectional data, and other variables to make strategic decisions as free of human judgment as possible.

Quantitative Investment Funds Soaring to New Heights

Over the last few decades, hedge funds implementing quantitative analysis practices in their python stock trading decisions have risen to the forefront of the market.

Companies like DE Shaw, Renaissance, Two Sigma, Bridgewater, and more are just a few examples of algorithmic hedge funds that have significantly outperformed those using more traditional, fundamental analyses.

Take D.E. Shaw, for example for algo trading course. The New York-based company’s largest hedge fund increased by an astonishing 19.4% in 2020 alone, despite the financial uncertainty of the pandemic and election year. The hedge fund has invested approximately $55 billion in sheer assets. Since launching in 2001, D.E. Shaw hasn’t had a single down year, with an impressive annualized net return of 11.7%.


Likewise, the Medallion Fund is considered to be one of the leading hedge funds in the entire world, with a secretive group of scientists behind the spectacular performance of this quantitative investment fund. In the past three decades, the Medallion Fund has racked up over $100 billion in trading gains. What’s most notable about Medallion is that the hedge fund made these gains in less time than its competitors, with fewer assets. Like D.E. Shaw, the company has rarely seen a loss.

Behind almost every successful hedge fund is a methodical team of data scientists and analysts who know the power of using algorithmic trading with python to secure their spot at the top of the markets.

With what we know about these quantitative investment funds, it’s clear that quantitative investment techniques go hand-in-hand with top performances in the stocks.

Lumiwealth can help you get started with stock trading with python to remove the human guesswork from your trading decisions and increase your performance. Contact us today to get started mastering quantitative investments.

Algorithmic Trading

Algorithmic Trading Benefits And Downsides


Algorithmic trading is responsible for approximately 60-73% of all U.S. equity trading, according to BusinessWire. If you’re trading stocks manually, you naturally place yourself at a disadvantage; you’re competing against robots… and who do you think will win that fight? Nine times out of ten, it’s the robots.

Who Uses This Method of Trading?

You may be wondering: who runs these algorithms and who uses this method of trading? The aforementioned stats may shock you, especially if you’ve never heard the phrase “algorithmic trading” before. Hedge funds, investment firms, and other private equity trading groups all use algorithmic trading, mainly due to its decrease in costs, unrivaled speed, and increase in trading accuracy.

If you’re a little lost and don’t know what algorithmic trading is, don’t worry. To understand how this works, we must first explain how an algorithm works. An algorithm is a set of directions, usually inputted on a computer, to solve a complex problem. In basic terms, algorithmic trading, Python utilizes different algorithms to produce meaningful data, that is then used to determine whether to buy, sell, or hold during financial trades.

what is algo trading?

More often than not, when a firm utilizes algorithmic trading, they also use some form of trading technology to make thousands of trades each second. As these trades are real-time, this allows traders to maximize profit at any given moment – there are very few methods quite as accurate. 

In more recent years, and especially since the 1980s, algo trading has increased in popularity, with many investors and investment firms choosing this new method of trading to increase their profit margins and see more accurate trading results. In fact, according to Toptal, hedge funds that utilize algorithmic trading have significantly outperformed their peers and counterparts since this time, all with reduced costs in comparison to regular trading. As of 2019, quantitative funds represented 31.5% of market capitalization, compared to 24.3% of human-managed funds.

The remainder of this article will discuss algorithmic trading in more detail, in particular, how it could make you more money.

What Are The benefits of Algorithmic Trading?

As mentioned beforehand, there are several benefits of algorithmic trading. However, the main benefits include:

  • Cut down on associated trading costs
  • Faster execution of orders
  • Trades are timed perfectly 
  • Ability to backtest
  • Quantitative strategies have dominated the market and returns

Each of these benefits will now be explained below in more detail, helping to provide you with greater insight on how exactly algorithmic trading works.

make money with algo trading

Cut down on associated trading costs 

Firstly, when you use algorithmic trading, you are able to save and cut down on associated trading costs. Transaction costs are cut due to less human interaction, freeing up liquidity towards more investments. Likewise, you will also save money on fees, depending on your investment method – so it’s well worth keeping in mind.

Human interaction previously included general fees, a fundamental analysis performed by a finance manager, and the buying and selling of trades, amongst other actions. Algorithmic trading allows you to set a buy and sell price, cuts fees, and saves you money – let the robots work with you, not against you. 

Faster execution of orders 

With a faster system in place, traders are able to exploit the smallest of profit margins to create mid to large amounts of revenue in seconds. This method is called scalping and is where a trader instantly buys a set number of shares/stocks at a lower price, then rapidly sells these on for a higher price, whether for a small profit margin (which adds up in the long run) or for a slightly larger one.

When you use algorithmic trading, you can set a buy and sell price for a stock or share. For example, if one stock dips below a certain threshold, the algorithm will purchase a set number for you. Similarly, once this same stock increases in price to your pre-determined price, these stocks/shares will be sold instantly to maximize profit. This is much more accurate than human trades, and also removes the emotion involved with investing. 

Trades are timed perfectly

Human trades require you to buy and sell manually, watching particular trades all day to purchase and/or sell at the best prices. With the predetermined buy and sell thresholds, your trades are timed perfectly. 

This allows you to exploit small dips in particular trades, compounding small profits into large gains in the long run. The decision to buy or sell the trades is still yours, but you gain greater accuracy over when to buy and sell these. 

Ability to backtest

One key advantage of algorithmic trading compared to regular trading is the ability to backtest, as mentioned by Nasdaq. Essentially, you can run algorithms based on previous data to see what parts of a trading system works, and what doesn’t. This is super beneficial and removes any potential error before purchasing stock or shares in bulk, possibly reducing a large loss.

Backtesting is not as accurate when human trading and may result in large losses – so keep this in mind if you choose the old fashioned trading method. 

Quantitative strategies have dominated the market and returns

Hedge funds such as Two Sigma, DE Shaw, Renaissance, Bridgewater, and others have been the best performing investment funds in the world for several decades. Renaissance’s average of 39% annual returns have made the founder, Jim Simmons, and everyone else at the firm extraordinarily rich. Doing the math, at 39% per year for 30 years you could have turned just $100 into $1.9 million! That’s incredible, especially considering that they’re taking on much less risk than the stock market as a whole. 

These funds are managed with greater accuracy and with a quantitative strategy, other than trading blindly, with emotion, and with an increased risk of a slip-up. 

algo trading advantages

Are there any downsides?

Now that we’ve covered the benefits of algorithmic trading, you’re likely wondering if there are any downsides, and what they may be. As with all trading, there is an element of risk; however, you wouldn’t be in the business if you didn’t know this was a factor. 

Potential downsides of algorithmic trading include:

  • Loss of internet connection could prevent your order/trade from being processed
  • Without prior testing, you may use the algorithm incorrectly and create a loss 

Once again, each of these downsides will now be discussed individually below.

Loss of internet connection could prevent your order/trade from being processed 

Firstly, as these trades require an internet connection, if your connection is to drop, even for a few seconds then your automated trades may not be placed and/or processed. This could lead to a loss, so it’s integral that you have a strong internet connection and ISP. ideally, you should have business WI-FI, as this is more reliable and more suitable to learn algorithmic trading

The same risk is present with regular human trading, but it is something to be aware of, especially if trading in larger quantities. 

Without prior testing, you may use the algorithm incorrectly and create a loss

The other downside is that without prior testing of the algorithm, you could create yourself a loss. This is easy to combat; all you need to do is play around with the system before placing any large trades. Start small until your comfortable then increase the number of trades and shares you are both purchasing and selling within a short timeframe.

You can also backtest, as mentioned previously to further increase the accuracy of your algorithm. 

How does it work and how can it make you more money?

Algorithmic trading is made possible thanks to pre-programmed computers and a set of instructions to buy and sell trades in bulk. If done correctly, this allows you to make more trades than a human ever could, exploiting slight dips in the market for quick re-sales and easy profit.

First, however, you must identify an opportunity in the market. Running this through the algorithm and calculating the potential returns, deciding whether or the trade is worth it. Some trades will generate more income than others, but algorithmic trading is a long-term game, profiting off of small trades consistently for a greater ROI.

Do you want to learn more about algorithmic trading?

To find out more about algorithmic trading, you can take our unique, top-rated course designed for finance professionals and experienced programmers, allowing you to take your python for finance expertise to the next level. Not to mention make worthwhile investments, increasing your revenue, and be better equipped to solve real-world tasks and problems.

If you would like to learn more about our algorithmic trading course, click here.

Finally, we would like to draw attention to our open-sourced GitHub project, Lumibot. A great tool to use if you trade consistently and are looking to amp up your game, perhaps with algorithmic trading now on your side. 

Algorithmic Trading Python for finance python trading

How Algorithmic Trading Could Make You Money

Did you know that only one out of every five day-traders actually makes a profit? The ever-changing world of trading can be challenging to navigate. In fact, most trading on the stock market is performed by robots, making it like playing a rigged game of chess, where your chances of winning are stacked against you. 

That’s why many day-traders have started to learn algorithmic trading to improve their odds of making money through trading.  

In this post, we discuss just exactly how algo trading using python works and how you can create an algorithmic trading robot to help increase your odds of becoming the next, big money trader. 

What Is Algorithmic Trading?

Algorithmic trading uses data science and computer-automated executions, rather than human guesswork, to create instructions for trading. Since trading activities use data science techniques like technical indicators, financial fundamentals, and economic data, this also eliminates human emotions that can interfere with the success of trading.

How Can Algorithmic Trading Benefit Traders?

Algorithm trading offers numerous benefits for traders. Once you make the switch, you’ll likely be surprised that you hadn’t been incorporating algorithmic trading strategies into your investments all along. 


Here are just a few key benefits that ultimately save you time and money.

Your Trading Strategies Are Back-tested

Algorithmic trading takes the guesswork out of your trading strategies. By reviewing past back-tests, you can more clearly see patterns, which in turn helps you figure out what’s working and what isn’t working. 

Back testing Develop Your Strategies

Your Strategies Are Less Prone to Human Error

We all know just how fallible human calculations can be, and no one wants to make grave errors when it comes to their investments. That’s where algorithmic trading can be immensely beneficial for your financial trades. 

trade losses

Since algorithmic trading strategies are executed by computer software, there’s less room for error. This means that you can steer clear of common mistakes that you would otherwise make. 

You Have More Time to Develop Your Strategies

While computers do make mistakes, it’s far easier to monitor and troubleshoot, saving you time and money on your investment strategies and other areas that are in need of your attention. 

This means you can more easily branch out to other trade markets and strategies, allowing you to have less of a risk per capita of trade investments. In other words, you’re not putting all of your eggs in one investment basket.

Where Can I Learn More?

Are you ready to step up your day trading game? Though learn algorithmic trading may sound like the ultimate secret to your trading success, knowing exactly how to navigate a new arena of the data science world is no easy feat. In fact, if you don’t know what you’re doing, you could actually lose money.

How Algorithmic Trading Could Make You Money


That’s why we have created an algorithmic trading course to help you navigate Python software and start utilizing all that algorithmic trading has to offer & help you develop trading algorithms.  

In this course, you will learn the ins and outs of Python trading, where you can:

  • Analyze your investments
  • Make better decisions about your investments using data
  • Implement back-testing strategies
  • Automate your trades 
  • Calculate the risks and potential returns on investments
  • And, most importantly, start making money from those investments

We also have an open-sourced project, called Lumibot, that you can use to access what we use in our classes. This project is free for the public and can provide you with many resources to support your algorithmic trading journey and help you with coding trading bots yourself.  

If you’re ready to get started, sign up for our free live class, where you can download the course information on how to become the next, big algorithmic trader. 


What is algorithmic trading?

Algorithmic trading is when software code (eg. Python) is used to automatically buy and sell securities (eg. AAPL stock). In other words, it is a robot that can automatically buy and sell stocks, options, futures, and more for you.

How do I get started with algorithmic trading?

To build an algorithmic trading robot you will usually have to first learn a software coding language such as Python, then use a library such as lumibot to connect to a broker and execute trades.

What is backtesting?

Backtesting is the process of creating a trading strategy, then using data from the past to see how the strategy would have performed in the past. This could be very valuable to see whether your algorithm will perform well in the future.

Where can I learn algorithmic trading?

Since there are many potential pitfalls (ways to lose money), the best way to learn algorithmic trading is by taking a course on the topic. At Lumiwealth we have several courses on algorithmic trading that have gotten great reviews.