What is Algorithmic Trading? How it Benefits and Adopted by traders!
What is algorithmic trading?
Algorithmic trading aims to eliminate human error, but follows a standard strategy based on statistics, that is, computers can run 24/7 with minimal supervision.
Compared with computers, computers can provide many advantages. They can also accurately analyze data and react to changes within a few milliseconds. Most importantly, they never consider emotions when making decisions. As a result, many investors have discovered that as long as machines use the right strategy, they can become excellent traders.
This is how the field of algorithmic trading develops. Although it started with computerized trading in traditional markets, the rise of digital assets and 24-hour trading has pushed this approach to new heights. Business and cryptocurrency were born for each other. Users still need to develop their own strategies, but if applied properly, these techniques can help: students take their hands off the steering wheel and let the math work.
What are the Primary Strategy?
The basic idea behind most algorithmic trading revolves around using software to identify profit opportunities and use them faster than humans. The most common practices are momentum trading, mean reversion, arbitrage and various machine learning strategies.
Most algorithmic trading strategies focus on identifying market opportunities based on statistical data. Impulse trading aims to track current trends; reversion to the mean means looking for statistical differences in the market; arbitrage looks for differences in spot prices on different exchanges; machine learning strategies try to automate more complex philosophies or integrate several at a time. These are not simple profit guarantees. Traders need to know when and where to implement the correct algorithm or "robot".
Robots are usually tested based on historical market data called backtests. This allows users to test their strategy in the real market they want to introduce, but with the established steps of the past. The risks associated with this may include "overfitting", that is, the robot is constructed based on historical data that does not truly reflect the current situation, resulting in a strategy that is not actually designed. A very simple example: you developed and tested a robot with bull market data, but started to run it on a bear market. Obviously, you will not see the expected profit.
NOTE: There are Various Strategy in the Market and you can make of your Own But Below are some examples that are most Profitable Strategies and adopted by Professionals.
Momentum trading Strategy
Momentum buying and selling is primarily based totally across the common sense that if a foremost fashion is already seen in the market, then that fashion is plausibly going to keep at the least till indicators start to are available in that it has ended.
The idea behind impulsive trading is that, for example, if a particular asset mainly moves in one direction for a few months, then it can be assumed that the trend will continue at least until the beginning of the data. Buy any situation and make a profit on each pump, and vice versa, if you go short. Of course, traders need to know when the market shows signs of a trend reversal, otherwise this strategy may change quickly.
It should also be noted that traders should not try to buy and sell at actual lows and highs, or the so-called "catch the knife" strategy, but should take profit-taking at a reasonable level of insurance. Algorithmic trading is very suitable for this situation, because users can simply set their favorite percentage, and the rest is done by code. However, if the market goes sideways or fluctuates so much that there is no clear trend, using this method alone may be ineffective. Moving averages are excellent indicators for tracking trends.
As the name suggests, a moving average is a line on a price chart that shows the average price of an asset in x days (or hours, weeks, months, etc.). 50, 100, or 200 are used, but different strategies will focus on different time frames for trading forecasts.
Generally speaking, a trend is considered to be strong when it is far above or below the moving average, and when it is close to or above the moving average is considered to be a weak trend. In addition, moving averages based on longer time periods tend to have more weight than moving averages based on the last 100 hours or similar time periods.
Mean Reversion Strategy
Average return means that, according to statistics, the price of an asset should tend to the historical average price, and extreme deviations from this price mean overbought or oversold conditions and the possibility of return.
Even if Bitcoin (BTC) is actually only in a bear market, there may be significant highs or lows that deviate from historical price development. By looking at long-term averages, the algorithm can safely bet that large deviations from these prices are unlikely to last for a long time and will place orders accordingly.
For example, one of this form is called standard deviation reversal, which is measured by an indicator called Bollinger Bands. Essentially, these bands act as upper and lower limits for deviation from the central moving average. Judging from these extreme conditions, the possibility of a center reversal soon will be very high.
Another form of mean reversal may occur on multiple assets, using this method is called pair trading. Assume that two assets are traditionally related, that is, if one asset rises or falls statistically, the same thing will happen to the other asset. It is designed to move one of these assets and then trade based on the possibility that another product will soon follow suit. The time frame of these deviations can sometimes be short, which makes this automation strategy more valuable.
Arbitrage Strategy
Arbitrage is a strategy that takes advantage of the price difference of the same asset in multiple markets.
Sometimes, the same product, such as a commodity or currency, may temporarily have different prices on different exchanges. This can provide excellent access to those who trade fast enough between these markets to compensate. Opportunity. It can be designed to check different assets in different markets and trade immediately after discovering differences.
This method is not too difficult, but compared to slow-reacting traders, quick-reacting traders have another advantage. This is a strategy in which high-frequency trading absolutely has obvious advantages, because it is the traders who take advantage of these advantages. Market conditions that led to the collapse of the price gap.
Machine Learning Strategy
Machine learning and artificial intelligence can take algorithmic trading to a new level. Not only can more advanced strategies be applied and adjusted in real time, but the use of new technologies such as natural language processing news articles also provides more opportunities to gain specific insights into market trends.
Algorithms can already make complex decisions and make decisions based on predefined strategies and data, but with the help of machine learning, these strategies can be updated according to actual work conditions. Machine learning algorithms can evaluate multiple strategies and optimize subsequent transactions based on maximum returns, rather than "if/then" logic. Although the setup still needs to work, it means that even if market conditions exceed the original parameters, traders can trust their robots.
A popular machine learning strategy is called Naive Bayes. In this process, the learning algorithm operates based on previous statistics and probabilities. For example, historical market data shows that Bitcoin has fallen by 70% for three consecutive days. The Bayesian algorithm detects that it has not been used in the last three days and automatically places an order based on the increased possibility today. These systems are highly customizable, and each trader will set his own parameters for things such as risk. And rewards, but once you are satisfied with the balance, you can make it work with minimal disturbance.
Another benefit of machine learning is the ability of machines to read and interpret messages. By searching for keywords and developing the right strategy, these types of bots can take action in a matter of seconds when positive or negative news appears. They will be as precise as possible because the logic they contain is precise and therefore difficult to implement, but when properly configured, they still take precedence over other operators.
NOTE: This is a hint of a new direction for automated trading, so a robot that works in this way may be more difficult to find, more expensive to access, or less predictable compared to some best practices.
Order Chasing Strategy
Order chasing is the practice of looking at some very large orders and then trying to move them quickly, assuming that this will lead to further price changes.
Predicting large orders from major players usually requires internal information, and trading this information is usually illegal. However, some high-frequency traders have found a legal way to extract data from over-the-counter trading forums called "dark pools." These types of trading forums do not need to send your order data as an exchange in real time, so their trends tend to have a delayed impact on the market. By collecting and applying this data faster than ordinary traders, users of this technology can have a huge advantage over those who don't.
For example, if you see that the dark group fills a large order, it means that it will be very fast. If this data is shared with other markets, there may be a large number of small sellers responding to their orders. Before moving, you can stay ahead of the trend and become one of the first to sell, which means you can easily cash in when the temperature drops in the fall. This method is not illegal, as long as the data is collected through channels. Yes, many algorithmic traders have chosen this strategy.
Volume-Weighted Average Price (VWAP) Strategy
The volume-weighted average strategy divides large orders and uses the historical volume profile of a particular stock to dynamically place a defined smaller block on the market. The purpose is to execute orders close to the volume weighted average price (VWAP).
Time Weighted Average Price (TWAP)
The weighted average price strategy divides large orders and releases certain smaller blocks to the market dynamics in a time interval evenly distributed between the start and end times. So as to minimize the impact on the market.
Percentage of Volume (POV)
Until a transaction order is completely filled, the algorithm will continue to send some orders based on a certain market participation rate and transaction volume. The related "turn-based strategy" places orders at a user-defined percentage and increases or decreases the participation rate when the stock price reaches the user-defined level.
Benefits of Algorithmic Trading for Traders
- The transaction is carried out at the best price.
- The order is immediately and accurately placed (the possibility of execution at the required level is high).
- Transactions are synchronized correctly and instantly to avoid major price changes. lower the transaction cost.
- Automatically review multiple market conditions at the same time. Reduce the risk of manual errors during operation.
- You can use the available real-time and historical data to test algorithmic trading to ensure that it is a viable trading strategy.
- Reduce the possibility of human traders making mistakes due to emotional and psychological factors.
Technical Requirements for Algorithmic Trading
The use of computer programs to implement algorithms is the last component of algorithmic trading, followed by backtesting (testing the algorithm in historical periods of past trading performance to see if it is profitable to use it). The challenge is to convert the determined strategy into an integrated computer that can access the orders below the trading account. The following are the requirements for algorithmic trading are as follow:
- Understand computer programming to write the required trading strategies,
- Use programmers or ready-made trading software;
- Network connection and access to the trading platform for placement;before the system is introduced into the actual market, after the system is created The ability and infrastructure to conduct tests.
- The historical data available for backtesting is based on the complexity of the rules implemented in the algorithm.
Where can i Start Algo-Trading for Cryptocurrency Or in Indian Markets?
There are many websites that provide various trading algorithms, which can then be connected to the digital asset exchange of your choice.
There are some services that can help you quickly set up algorithmic trading. Websites for Cryptocurrency Trading such as TradeSanta, WazirX and Binance & Websites For Indian Demat Account for NSE/BSE Trading such as Upstox, Angel broking, Zerodha offer different types of accounts, which vary from free to quite expensive depending on the tools available. A free account usually provides many options to help you get started, but if you want to get started, a paid account can be very helpful.
These sites often provide tutorials and other materials to help you understand how to find a robot and what strategies are needed. Although not all services are compatible with all exchanges, you will find that almost all the largest and most popular exchanges support most of these products. Some even have special promotions for using robots on specific platforms, so users should have multiple choices.
Conclusion
Of course, you can learn more technologies and services, but if you need to get into algorithmic trading, this guide should provide you with the basics. Take the time to learn as much as possible, and soon you will decide whether an automation strategy is right for you.
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