Smart Money Concepts
How to Use Machine Learning in Trading
Photo by Jeremy Zero
Introducing Smart Money
- Access to data is no longer left to the big players. The internet has leveraged the ground for everyone.
- Smart data analysis and adaptation to new technology is a state of mind. The concepts have always been here with us — and they keep evolving, have you taken time to look at them?
- Retail traders can play against smart money by incorporating smart practices and inventions facilitated by Artificial Intelligence and Machine Learning.
- While data is a key component in smart thinking, its application without context only results in frustration.
- A successful investment career combines in-depth knowledge in strategy, tools, and effective execution of a financial plan.
It is a crazy world we live in. No pun intended. Technology has taken the world by storm.
In my opinion, it is the best thing that has ever happened since sliced bread.
From lab-grown meat to autonomous vehicles, the possibilities are just endless. How have you adapted to the disruptions so far?
Inventions have resulted in a new class of individuals netting billions of dollars annually from investments. This seems good on the outside but I can assure you it has created a heated war for survival.
In the words of Charles Darwin’s theory of evolution and natural selection, anyone who resists change becomes obsolete. There is no better way to put it. Nature favors evolution and now more than ever, surviving in the present silicone age requires fast action and adaptation techniques.
Before the dawn of technology, society used to be divided into different social classes. This denoted individuals’ different levels of wealth, status, and influence.
To effectively conduct accurate grouping of classes, 3 methods were used which can also be found here.
- Reputation Audit: This method involved gathering public opinions regarding given individuals.
- Subject Analysis: Here, an individual raised opinions concerning themselves. More or less like painting one’s portrait.
- Objective method: This involved accurate measurement and analysis of facts and figures leaving nothing to chance.
The methods later resulted in a more clear format of societal classes which can only be summarized into the haves and the have nots.
You probably know that at the top we only have approximately 1–3% who are further grouped into the lower upper class and the upper upper class.
The lower upper class represents “new/smart money” who accumulate wealth through aggressive investment into various businesses. On the flip side, the upper-upper class is the native “old money” holders from extremely rich backgrounds. They don't have to sweat or adapt much because they are rich by birth! A perfect example will be royal families.
Now, we have come to conclude a few things by this far concerning the history of wealth and classes:
Adaptation is essential by nature. To survive today in a fast-paced technology-driven world, we must be open to new inventions.
Technology has leveraged techniques used in investment and made them available for everyone.
In the following section, we cover all the prep work required to give you a competitive advantage in the markets and overall investment world.
How to Position Yourself As a Trader in Making Smart Decisions
One of the greatest mistakes we can ever commit in the market is failing to conduct due diligence. Drawing conclusions against facts/data.
In my experience, this notion not only results in poor results but has also contributed to many people dropping out of this gold mine. This is how we let the 1% continue making big advances and manipulating the markets to their advantage.
We are therefore following the money, what they do, we learn quickly and execute even faster. Adapt or die, remember?
The following are ways of getting smart as a trader
1. Invest in knowledge through constant learning
The world’s renowned investors including hedge fund managers are known to make decisions that are backed by facts and figures.
They do this by spending hefty budgets on data scientists, who are now a gem. No one wants to attempt anything without conviction. Did you know that data is the new oil in this generation?
According to markets and markets, the data science industry is expected to realize a compounded annual growth rate (CAR) of 30% which will see an estimated 37.9B in 2019 grow to 140.9B in 2024.
You should never attempt to make blind guesswork trades or rely on cheaply acquired signals when trading.
Learning never stops. Every day new inventions are made, new companies are founded and new challenges come up. All these impact decision-making and the overall economy.
An investment in knowledge pays the highest interest. Warren Buffet
Keep up with trends and research information deeply from trusted sources. In this silicone age, Artificial Intelligence and Machine Learning have saved the day by drawing clear patterns between raw data which boosts performance in the market.
Through Artificial Intelligence, Robo advisers and investment managers can conduct:
Benefits of Artificial Intelligence
- Analysis of millions of data points. These are single unique pieces of information used to create patterns. Visually, this data is turned into charts and graphs that are later interpreted.
- Execution of trades at optimal prices. The market moves according to different factors and influences. AI gives its users the best points of entry and exit known as price action.
- Market forecasts with greater accuracy. Access to information that is stored because of its value enables analysts to make informed predictions, no one gambles in the market.
- Risk mitigation for higher returns. Most hedge funds apply AI mainly to back up their highly competent analysts because they can't afford to lose money!
Here is a list of all the terms you need to be familiar with in your knowledge set:
- Deep Learning
- Natural Language Processing
- Reinforcement Learning
- Order flow Tools
As you advance your knowledge base, you would want to consider going deeper, into the real facts and figures exploited by smart money and the tools they use. This is taken care of by the following points.
2. Put Technology to work
As stated above, technology will only work for you as a trader if you use it correctly. It entails knowing your why or motive. Experienced or not, traders have a universal desire of profiting off their investments. This means keeping losses to the minimum.
Understanding Data in Trading
Data in respect to trading relates to a wide network of information. For it to be profitably used in predicting patterns, proper analysis needs to be done.
Numbers don't lie
Artificial Intelligence has enabled professionals and individuals to understand the context in data by co-relating specific data points that affect markets.
Machine Learning on the other side, makes it easy for data analysts to create algorithms that will learn proven concepts, techniques, and strategies then adapt to new statistics to offer the best outcomes in the markets.
Different Strategies Used in Trading
There is a lot of work that goes on before successful trades are placed. Among tons of strategies, the following are worth mentioning:
- News Trading Strategy. This involves keeping up with industry news and market sentiment/expectations. As a trader using this strategy, you are required to conduct further analysis because news spread fast across digital media.
- End of day Trading. Here a trader bases predictions on the previous market outcomes. To enhance greater success, a trader is required to understand price action and conduct risk management practices such as having a limit order, stop-loss order, and take profit orders.
- Day Trading. This strategy suits traders who like to profit from price movements during the day. Opening multiple positions during the day and closing them before the night helps in mitigating overnight market fluctuations.
- Swing Trading. The market moves in oscillations. Technical traders choose to base predictions on these movements by doing deeper chart analysis.
- Trend Strategy. All traders love trends. After conducting technical analysis, you can follow smart money when they set a trend in the market since they have large resources to move positions by large caps. This is a wide topic worth looking into.
- Scalping Trading. This strategy involves placing numerous short-term trades that accumulate over a specified time. Great discipline is required to minimize loss. This strategy applies mostly to day traders.
- Position Trading. Experienced traders often take and hold extended positions over months or even years. They base confident predictions on fundamentals that affect the market over years or according to historical patterns.
Machine Learning to the Rescue
As shown above, there is so much information required to make close to accurate trading decisions. Data is generally structured or unstructured. In trading, no aspect of data should be left out.
To do this effectively, algorithms that are trained in computer programs adapt these heavy data sets and executes specific functions without the influence of human emotions, fast (time is essential), and with precision.
Examples of Algorithm Trading Strategies
- Strategy Implementation Algorithms. This kind of program relies on signals from real-time market data to guide their trades.
- Arbitrage Opportunities Algorithms. They scan entire markets and spot the same stocks that are trading at different prices in the different markets. This way, they can sell higher-priced stocks and buy lower-priced ones.
- Trade Execution Algorithms. They are further divided into Volume Weighted Average Price, Time Weighted Average Price, and Percent of Value. Their instructions are usually to split orders into smaller values to help spread the impact of overall market values.
- Stealth Algorithms. The use of algorithms in trading dates back to the 1800s, this means that the market is usually under pressure of large trades executed by numerous algorithms or smart money. To stay competitive, stealth algorithms spot these opportunities in real-time.
How Machine Learning Works in Trading
Different algorithms are performing specific functions. For maximum execution of your set strategy as a trader, it is wise to incorporate different models that suit your data set.
Even the best data scientists swear to using different models to maximize the accuracy of their efforts.
The very first thing to do is framing the problem statement. Well, as a trader looking to maximize from ML, you would want to identify the most specific complex area from the market data.
Settling on options as our trading instrument, we can have a lot of data to study. Our problem can be to test the credibility of options indicators in predicting market direction.
Next, we are going to identify our most critical components/data sets which includes:
Put options: owners of put options have the freedom to short a predetermined amount of security(currencies, indexes, stocks, commodities) at a specified strike price.
Immediately, we have to consider price changes, time decay, strike price, volatility, and interest rates.
As the price of the underlying assets increases, put options gain value because of the freedom of selling on high.
On the flip side, call options guarantee the owner freedom to buy the base asset at a specified price. This could be before the expiration of the contract or on the same day of termination.
When we divide the total volume of traded put options by call options, we get the Put-Call Ratio. PCR values are further classified according to the nature of the underlying asset. This brings us to; Total PCR, Equity only PCR, and Index only PCR.
In the graph below, an increase in Equity only PCR values led to declines in the S&P 500 Index.
Traders can spot both long-term and short-term opportunities by capitalizing on the implied volatility values given by this ratio.
For our experiment, we choose a model called Support Vector Machine since we are classifying different data sets in identifying implied volatility values from the data above.
This algorithm seeks to find a classifier that maximizes the margin or shortest distance between the positive and negative data points. Applicable because we are comparing two variables that negatively correlate.
There are so many factors that need to be considered for us to get an optimal value. This is why after getting our data segmented by a hyperplane, we conduct optimization.
The algorithm is taught to follow improved procedures in reaction to new data sets thus improving the overall sentiment analysis. This resource by Analytics Vidhya offers more insight into Support Vector Machine and its massive applications.
Benefits of Using Machine Learning (ML) in Trading
- Trading the markets profitably requires fast precise action based on statistics. Machine Learning has enabled the execution of high-frequency trades beyond our mundane capabilities.
- Traders also rely on ML which is optimized to understand trend patterns and use ever-changing data points in making automated execution of large orders, especially in options trading.
- Tracing historical data is a vital concept in trading especially for those who are after long-term gains or hedging. Algorithms can fetch commands and dig into different files spontaneously while interpreting patterns fast.
- Through Natural Language Processing (NLP) algorithms can interpret what people are talking about concerning markets especially on social media. This process requires a fast response thanks to machine learning.
Having a clear strategy backed by data is a prerequisite in successful trading. After getting your way around trading jargon, the least disservice you can do to yourself is feeling overwhelmed.
So much to master with little time on the table? Different tools that will enhance your decision-making process have been developed. These are programs that maximize Artificial Intelligence with Machine Learning capabilities to only give you what you want with greater accuracy.
Before settling on any, you should conduct your due diligence to discover which service fits into your plan.
Access to data has now become a must-have for success-driven strategies.
Data in itself will be useless if it is not properly interpreted. This process has been made easier by Artificial Intelligence.
Trading profitably involves a combination of investment in knowledge and automation of processes through Machine Learning.
What do you think about this guide? Share your thoughts with me so that we may continue learning together.