Algorithmic trading strategies also referred to as black-box or automated trading, is a developing trend that has seen a huge increase in recent years. A study illustrates that it presently accounts for up to 80 percent of all forex trading.
As the name means, algorithmic crypto trading associates the execution of orders employing a drawn set of instructions or algorithms on a computer program. The focus is to maximize speed and also data processing beyond human abilities, by replacing the slower traditional human trading.
Algorithmic Trading Strategies
Chances at faster profitability drive the choice of strategies employed. This article shall comprehensively discuss various profitable algorithmic trading strategies that traders can use while trading.
1. Trend-following Strategies
They are the most usual and most straightforward of all strategies to enact, simply as it lacks the requirement to forecast or predict crypto prices. They include the following trends to anticipate movements, channel breakouts, in price levels, averages, and also other technical indicators as applied.
Trades are kick-started by the happenings of a desired or favorable trend. Such a trend includes purchasing an asset as its value trend increases and selling it as the trend goes down. Complicated predictive analysis is, hence, not needed.
2. Trading before Index Fund Rebalancing
The nature of index funds needs an adjustment or periodic rebalancing of their portfolio within a set time span. Rebalancing helps match the holdings or latest prices of such funds with their benchmark indices. Hence, a profitable chance is formulated for active investors, many of whom include algorithmic traders, capitalizing on the index rebalancing impact.
The stake may go up to 80 basis points profits varying on the stock number. Algorithmic trading systems in crypto start the trade by timely implementing it at the best values. This is considered among the best algorithmic trading strategies.
In a forex trade, there prevail stocks that are dual-listed in varied markets at varied prices. Taking benefit of the price difference by purchasing such stock at a lower value from one market and selling it to the other industry at a higher value is termed as arbitrage. It enables for a risk-free profit with no negative outflows.
The term can also imply to present stocks vs. futures since values vary from time to time. Algorithmic trading strategies play an important role in evaluating the price variations and making the orders in time.
4. Delta-neutral Strategy
It is a form of mathematical model-based approach. Such approaches use proven mathematical models to enable trading on a set of choices and are related to security.
The delta-neutral strategies formulate a reference position unlikely to be impacted by small alterations in the stock values. The overall “delta” value is created as close to zero as possible to assure this.
5. Mean Reversion
Also implied to be range trading, it depends on the notion that both the highs and lows of the stock have a temporary dimension and should periodically move back to their mean values (average price). A market price lower in comparison to the mean price implies stocks are lucrative to purchase as they are speculated to increase. A market price beyond the mean is also expected to decline. An algorithm automatically trades the assets when a deviation from the defined average price is seen.
6. Percentage Of Volume (POV)
An algorithm is designed following a defined ratio of the traded market volume to prevail trading until the trade order is met. There is an automatic adjustment of the rate of participation, restricting it to a specified percentage of stocks in the complete traded volume.
7. Implementation Shortfall
Among the algorithm trading strategies, implementation shortfall implies the variation between the decision price and the average trade prices of the traders, which include taxes and commissions. The reference value quoted by the trader is used as a benchmark.
The strategy tries to benefit from chance cost delays by trading off the real-time market and saving on the cost of the order. The algorithmic speed of implementing the order capitalizes on this, rising as stock prices become desirable and slowing when values become untenable.
Requirements To Implement Algorithmic Trading Strategies
The algorithmic trading strategies are only complete when the algorithm is enacted using a computer program. The needs are:
- Knowledge of computer programming to craft the strategy.
- Access to stock trading channels and a stable network connectivity
- Access to data feeds from the industry that will be analyzed by the algorithm.
- Ability and infrastructure to carry a backtest of the system before real-life market use
- Access to historical information for backtesting according to the rules on complications implemented by the algorithm
Algorithmic trading strategies enable for correctly timed trades, with a decreased risk of manual errors when used. An article by Nasdaq indicates that the main advantage of algorithm trading is discarding human emotions, which creates irrational decisions during trading. The other benefits involve the ability to backtest and decrease costs.
The loss of human control and the requirement for constant monitoring of power loss and connectivity are otherwise primary drawbacks to algorithmic trading. The requirement to know the programming language binds the traders to learn the skill of developing the algorithms. These trading strategies can be done using various algorithmic trading software that is available.