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.

 

 

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.

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:

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:

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.