Automated trading is the most superior form of trading in the modern era and automated-trading strategies can transform the entire trading process much more result-oriented.
It is a system used to execute trades through computers that are installed with a predefined set of regulations and instructions, referred to as the algorithm, and the computers execute a trade as per that algorithm.
The trades can be executed precisely at the price and volume specified with minimal execution time. It mitigates the losses caused due to the time lag across placing the order and executing the order.
Automated trading is not biased by human emotions. A human trader might continue with a loss-yielding trade with the hopes of making profits or may give let go of a profit-making trade with the fear of losing.
Top 5 Automated Trading Strategies
After going through the 5 automated trading strategies mentioned below, you would get the hang of automated trading and devise ways to get the most out of this trading method.
These strategies have been tried and tested over time, and if executed the right way, can certainly yield some decent share market gains.
1. Momentum and Trend Based Strategy
These automated trading strategies are the easiest and most extensively used. They follow the mainstream trends and the momentum in the market to execute trades.
The technical indicators such as moving averages and price level movements are reviewed to generate buy or sell orders.
These orders are automatically executed when a certain set of conditions are met as per the technical indicators mentioned.
This strategy also takes into account the historical and current price data to review if the trend could continue or not and executes decisions accordingly.
No complex predictions are needed to be made, but straight and convenient trend following. If the desired event happens, trade is executed, else not.
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2. Arbitrage Strategy
Arbitrage opportunities prevail when there exists a price difference in the securities on various stock exchanges.
Arbitrage strategy makes use of such arbitrage opportunities by enabling the computers to locate the opportunity as soon as viable and executing the trade if certain criteria are met.
If a stock is available at a lower price on a certain exchange and at a higher price on the other, the input algorithm instantaneously realizes the price differential.
The algorithm then initiates a trade to purchase on the low-priced exchange and sell on the high-priced exchange.
Situations like these demand exceptionally high speed and accuracy, which is often arduous to achieve for humans but not for automated trading.
Even though the price difference between such exchanges might not too much, the volumes of such trades must be kept high to secure decent amounts of profit.
The algorithm receives feeds from both the exchanges regarding the price of the company’s stock and using the forex rates, the price in one currency would be converted into the other.
If the algorithm identifies a large enough price differential in both listings due to the varying currency rates, a buy order would be placed automatically on the lower-priced exchange and a sell order on the higher-priced one.
After the execution of the order, the trader receives arbitrage profits.
3. Mean Reversion Strategy
Mean reversion strategy is one of the automated trading strategies based on the basic idea that the rates of security might fluctuate, but will eventually return to an average or mean value after a certain point in time.
It is sometimes referred to as the counter-trend or reversal strategy.
This strategy identifies the upper and lower price limit of a stock, then the algorithm runs to execute orders just as soon as the price surpasses the normal range.
The algorithms deduce an average price as per the historical data of the security and perform a trade anticipating that the prices would come back to the mean price point.
This implies that if the prices are exorbitant, they would come down, and if they are hit a rock bottom, they would surely go up.
This automated trading strategy is beneficial when the prices are at the extremities and the traders can gain profits from the mercurial trends of the market.
However, this strategy might even backfire when the prices might not end up reversing as fast as initially thought and by that time, the shifting average matches up with the price, causing a reduced reward to risk ratio.
4. Statistical Arbitrage Strategy
Statistical arbitrage strategy is considered as one of the short-term automated trading strategies.
It is based on the trading opportunities that surface because of the price inefficiencies or misquoting the price of the available securities.
This happens in securities that are linked to each other or are similar. It has been evident over the years that inefficiencies and misquoting are not bound to stay for a prolonged period.
They get amended in a short while and thus, automated trading becomes an efficient way to identify them and yield profits.
In this case, the algorithms are comprised of complex mathematical models that perceive the price inefficiencies swiftly and execute the trade even before the correction of the prices.
A human trader might not be able to identify such changes, regardless of his dedication and meticulousness. But the algorithm, due to its predefined instructions, can track them immediately when they happen.
According to the statistical arbitrage strategy, the algorithm immediately detects the fall in a stock price and buys it, only to be sold later when the price gets corrected, resulting in a profit.
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5. Weighted Average Price Strategy
This is regarded as one of the most efficient automated trading strategies. It is either based on a time-weighted average price or volume-weighted average price.
The orders are huge but they are not released altogether. The orders are released in small packets either via historical volume profiles of the stock or specific pore defined time slots that happen across a start and end time.
This strategy’s objective is to execute the order as close as viable to the volume-weighted average price or the time-weighted average price so that there is less impact on the market.
Thus, we conclude that there are multiple strategies to be chosen while considering automated trading.
The algorithms are designed such that they are compatible with the strategy picked by the trader and the orders get executed accordingly.
Even though it is the algorithm that places the orders, it is the trader who designs the algorithms as-well-as the strategies.