It is a well-known fact that once you have the right knowledge that is needed for trading with algorithms, you can save your time, effort and money. Here is how manual trading differs from algorithmic trading:
Mostly accurate and quick
Check on emotions
Quick reaction to volatility
Requires break or time to take rest
Considering the benefits of learning algorithmic trading, this article includes:
Common troubles and solutions to them
Well, basically algorithmic trading is beneficial almost in all aspects of trading activity right from entering the market to exiting the market. Here, I will share some such scenarios to give you an overview of the troubles a trader faces and the solutions to them.
In this section, we will be covering the following troubles and solutions henceforth:
Trouble 1: A bearish market scenario
A bearish market scenario can be an expected one. For instance, a fall in investor confidence because of the announcement of an undesirable person taking up the position in authority.
Or, it can be an unexpected one. For instance, a tragic incident like Fukushima nuclear disaster in 2011 in which suddenly the Nikkei 225 index went down. According to the sources, the effect had also spread across the Asia-Pacific region and Europe.
Solution 1: Creating specified parameters
In a bearish market scenario, algorithmic trading works by adjusting your trades in the financial markets with regard to the change in trend. For example, in a sudden bear situation such as the Fukushima disaster or Covid-19, the algorithms can be instructed to work quickly to exit the market when the value of your financial securities falls abruptly. Also, by putting a stop-limit order or a stop-loss order, the algorithms can be instructed to minimize the effect of the downward trend and save you from incurring a huge loss. As soon as covid-19 became a pandemic, the financial markets started to witness a downward trend. In this case, the algorithmic traders got an upper hand since their algorithms worked in a fraction of seconds to exit the market when required. Algorithmic trading in commodities, bonds, derivatives etc. helps diversify the portfolio.
Trouble 2: Fluctuations in prices during day trade
As much as is day trading convenient because of several trading opportunities during the day, the trouble is to determine the best trading opportunity. In day trading, there are innumerable fluctuations in prices. And on certain days the fluctuations are more than usual.
Solution 2: Finding the most significant opportunity to enter the market and exiting when the opportunity is not good
If you are taking the advantage of small fluctuations in the prices of financial securities between the opening and closing time, algorithmic trading can work to enter and exit the market at the most significant time. In day trading, the main aim is to capture the most profitable time periods in the financial markets throughout the day. Since algorithms work fast and can execute maximum trades within a fraction of seconds, algorithmic trading is the best option.
Trouble 3: Where to invest?
Trading in the financial markets needs to be done wisely and after foreseeing the consequences. Investing your funds in only the stock market or only in bonds may not serve your purpose of finding the best trades. The question remains “where to invest?”
Solution 3: Hedging
Hedging is a risk management practice in financial markets which helps to offset losses by taking an opposite position in a related asset. Hedging includes investing in derivatives, options and futures contracts. Algorithms act quickly and find out the safest or the best financial security to park your funds in. This way your hedging practice becomes quick and more accurate than if it was done manually.
Trouble 4: Which trading strategy to select?
The trading strategy makes up for the final decision making since the right strategy is extremely essential for carrying out the best trades. If the trading strategy will not be created according to the market situation, the trader will not achieve the expected results.
Solution 4: Implementation of the right trading strategy
The trading strategies such as news-based, swing, scalping etc. offer maximum accuracy during the trade execution and lessen the risk. When properly researched and executed, a trading strategy helps the trader.
Trouble 5: How to ascertain the effectiveness of the trading strategy?
A trading strategy needs a base or proof of its effectiveness before implementing it in the live market. Without knowing the possible consequences of the strategy, much of the capital can be lost.
Solution 5: Backtesting
With algorithmic trading, backtesting the strategy on the basis of historical data takes place with ease. Backtesting the strategy is one of the important parts of strategy formulation since backtesting gives you a fair idea about the capability of trading strategy. In case the backtesting results are not up to the mark, we can abandon the strategy and save out capital that could have been lost if the strategy was directly applied without backtesting.
Trouble 6: Jumping into the live market directly
Directly starting trading in the live market may lead to some undesirable outcomes such as losing the capital invested. Trouble arises when there is no “practice trading” which is known as paper trading as a beginner in trading domain.
Solution 6: Paper trading
Although algorithmic trading is completely dependent on algorithms and the requirement of human traits is extremely less, creating trading strategies for execution requires human intervention. And, human intervention means there are chances of errors occurring which may result in losing a part of or entire capital. With the help of paper trading, you do not need to trade with the actual money in the live market. Instead, you will be trading with virtual money. Paper trading can be a lot beneficial if you are a beginner and do not want to risk the actual funds in the financial markets.
Frequently Asked Questions
Does algorithmic trading overcome emotions completely?
All an algorithm does is follow the instructions set by a human and make logical decisions when the market fluctuates. Hence, you do not end up making decisions on the basis of emotions such as fear, greed, disappointment etc. But the trader must execute the trades only after backtesting the trading strategy thoroughly and ensuring the efficiency of the strategy. This way, the human emotions are significantly reduced. However, during a drawdown as well as a peak, an algorithmic trader must keep a check on the emotions and only should rely on the thorough backtesting (logic) and not on fear (during drawdown) and excitement (during a peak).
How much human intervention does algorithmic trading require?
Algorithms are created by human beings for checking all the financial markets simultaneously in order to find the best trading opportunities. Then, the algorithms enter and exit the market at the best time. An algorithmic trading setup, henceforth, must not require much of human intervention except for creating algorithms and keeping a check on any glitch which rarely ever happens. Also, a trader must have all the power to manually exit the trades in case of an unanticipated situation such as Covid-19.
How does machine learning help with algorithmic trading?
Machine learning is a field that is concerned with programming thea system which improves itself with experience. In machine learning, human intervention is very little and once the computer/algorithm is programmed with set instructions, it learns the pattern and imitates the same every time.
For example, machine learning regression algorithms are used to model the relationship between variables such as the stock price and market volatility.
These machine learning algorithms are used by trading firms for:
- Analyzing historical market trends using large data sets
- Determining the accuracy of a strategy
- Determining the optimal set of strategy parameters
- Making trade predictions etc.
- Trading Using Machine Learning In Python
- Introduction to XGBoost in Python
- Artificial Intelligence & Machine Learning in Trading
Where do I need to invest time and effort in learning algorithmic trading?
In order to learn algorithmic trading, you must be knowledgeable in the core concepts of the same. The core concepts such as quantitative analysis, financial markets knowledge and programming skills are imperative in this domain.
You can invest your time and efforts in a programme/course like EPAT which is a 6 months comprehensive educational programme for learning everything you need to know about algorithmic trading.
Suggested read: Essential Books on Algorithmic Trading
How do algorithms trade?
Algorithms are nothing but a programmed set of instructions that convey the system to work in a particular manner. For instance, if you wish to invest in the stock market and you choose 100 shares from index fund SPDR S&P 500 Trust ETF, you must follow these steps that a quantitative analyst goes about while implementing algorithmic trade.
Considering that algorithmic trading is quick and more accurate than manual trading, we discussed some beneficial facts. The core is that the algorithms (set of instructions) help you trade with maximum accuracy. Also, algorithmic trading saves much time, effort and funds in the financial markets. Hence, with the right amount of knowledge, experience and perseverance, algorithmic trading successfully keeps one out of trouble.
Learn algorithmic trading from foundational level to intermediate level with our Learning track titled Algorithmic Trading for Everyone.
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