A price action algorithmic trading strategy will look in to the previous session high and low prices or open and close, and it’ll trigger a buy or sell order if similar levels are achieved in the future.

For example you can create an algorithm to enter long or short orders if the price moves above point A, or if the price falls below point B. This is a popular algorithm among scalpers who want to make sequences of quick but small profits throughout the day on highly volatile markets, this process is called as high-frequency trading (HFT).

You need to assess whether and when you want to go long or short and also need to consider measures to help you manage your risk, such as stops and limits for creating a price action algorithmic trading strategy.

You can configure a price action trading algorithm depending on the market, the time frame, the size of the trade and the time of the day when the algorithm should work thus it will help you capture volatility as the markets open or close.

Example :

• Place a buy trade of 10 lots of EUR/USD if the EUR/USD falls and reaches the price 1.1290. Close 1 lot of it if the price falls by 10 pip. For every 10 pip fall in EUR/USD, increase the short position by 1 lot.
• Sell 1000 shares of Netflix (NFLX) if the price rises above 700. For every 0.1% decrease in price below 700, sell 10 shares whereas for every 0.1% increase in price beyond 700, buy 10 shares.

A technical analysis algorithmic trading strategy depend on technical indicators including Moving Average, Bollinger bands, stochastic oscillators, the relative strength index and many more.

To create a technical analysis strategy, you’ll need to research and be comfortable using different technical indicators. For example, you can create algorithms based on Moving Average by knowing the trend of the market. Whether you open or close depends on your attitude to risk, and whether you have a long or short position in a rising or falling market.

Here in technical strategy you become less interested on price and more involved in using indicators or a combination of indicators to trigger your buy and sell orders.

Example :

Let’s understand it with a simple example – Algorithmic trading based on Moving Average indication

• Moving Average Algorithmic trading is most popular and very simple to implement. This algorithm buys a tradable asset (e.g., currency pair) if its current market price is below its average market price over some period and vice versa sells a tradable asset if its market price is more than its average market price over some period. Here, we consider a 50-day moving average trading algorithm.
• The algorithm buys EUR/USD if the current market price is trading below the 50-day moving average and sells EUR/USD if the current market price is trading above the 50-day moving average.
• The green arrow indicates a point in time when the algorithm would’ve bought the currency, and the red arrow indicates a point in time when this algorithm would’ve sold currency.

As the name suggest, a combination algorithmic trading strategy make use of both price action and technical analysis to understand the market and confirm the potential movement of the price. Algorithms can then go with long or short orders based on this information.

Combination trading strategy needs you to study the historical price action of the desired tradable asset. This means understanding of different technical indicators properly and collect the details of asset’s previous price movements.

In a combination strategy, you will have to decide on the entry type – buy or sell, and when you want the algorithm to trade during the day. You can configure a combination strategy depending on the market, the time frame, the size of the trade and the different indicators that the algorithm is designed to use.

Algorithmic trading gives the below advantages:

• High chance of order to be executed at desired level.
• Order is executed instantly and at best possible price
• Reduces manual error in placing orders.
• Reducing transaction costs.
• Simultaneous automated checks on various market conditions
• It can be back tested with available historical and real-time data to see if it is a viable trading strategy.
• Eradicates emotional and psychological factors thus reduces possibilities of human mistakes.

Benefits of Algorithmic trading to different investors

Different types of investors use Algorithmic trading for many forms of trading and investment activities including:

• Short-term traders –  Market makers, arbitrageurs, and speculators benefit from fast and automated trade execution.
• Sellers – Algorithmic trading also helps in creating sufficient liquidity for sellers in the market.
• Medium and long-term investors or buy-side firms – This segment includes mutual funds, pension funds, insurance companies that use algorithmic trading to purchase stocks in large quantities when they do not want to influence stock prices with discrete, large-volume investments.
• Systematic traders – Systematic traders include trend followers, hedge funds, or pair’s traders and Algorithmic trading benefit them as they program their trading rules and let the program trade automatically.

• Dependence on Technology: The biggest disadvantage of algorithmic trading is its complete dependence on technology. The trade orders, in most of the cases, reside on the computer, and not on the server. This means that if the internet connection is lost, the order will not be sent for execution.
• Loss of Human Control: Algorithmic trading is completely automated. The humans are not left with the room for making any discretionary choices. Suppose if a trader realizes before the execution of the order that the strategy will not work in the particular scenario, he cannot abandon the program and stop the trade in that scenario.
• Not all strategies can be automated: There are few most worthy strategies that are almost sure-shot. However, all strategies cannot be automated and converted into an algorithm. So, the use of such strategies is not possible in algorithmic trading. 
• Short life span of the algorithms: Almost 98% of the algorithms have a very short lifespan. They work till they are suitable, and then suddenly stop working in the rapidly changing market. So they require to be fixed or recreated further.