Algo Trading (also known as automated trading, algorithmic trading, or black-box trading) employs a computerized program that is based on a group of instructions to perform a trade. this type of trading carries the ability to garner profits at a speed and frequency that is hard to be achieved by a human.
Algorithmic trading is purely the automated buying and selling of financial elements such as stocks, bonds and futures. It rests on a networked linking to an electronic exchange, or broker, and a way of programmatically buying, selling and carrying out other tasks associated with trading, such as watching price action and market revelation.
Algo Trading (also known as automated trading, algorithmic trading, or black-box trading) employs a computerized program that is based on a group of instructions to perform a trade.
This type of trading carries the ability to garner profits at a speed and frequency that is hard to be achieved by a human.
Algorithmic trading is purely the automated buying and selling of financial elements such as stocks, bonds and futures.
It rests on a networked linking to an electronic exchange, or broker, and a way of programmatically buying, selling and carrying out other tasks associated with trading, such as watching price action and market revelation.
An Algo trading strategies is generally based on the deeper analysis of historical data to check whether a particular stock will give good returns in the future. The strategy can be executed either manually or automatically. It is much like a human trader performing the trade but, in this case, all things happen in an automated manner.
What are the major benefits of Algorithmic Trading?
There could be numerous benefits of algorithmic trading including:
- Option to perform trading at the most attractive prices
- Order placement remains quick and precise
- Trade timing remains correct which averts major price variations
- Low transaction costs
- Less margin of error as compared to trading done by human traders
- Simultaneous automated monitoring on various market scenarios
- It can be back tested using obtainable past and real-time analytics to check the viability of the trading strategy
It is important to note that algorithmic trading is considered high-frequency trading (HFT), which strives to leverage the large number of orders made at high speeds across different markets and various parameters based on preset instructions.
In the present context, algorithmic trading can be performed in several ways like:
- Short-term traders, market traders, speculators, and sell-side members – They seek to benefit from algorithmic trade execution. Moreover, they can maintain enough liquidity for sellers in the marketplace.
- Mid- to long-term investors, insurance firms, mutual fund and pension fund companies – They resort to algorithmic trading to buy stocks in large volumes without affecting stock prices.
- Systematic traders, hedge funds, trend chasers – They find this strategy quite effective as they can program their trading rules and perform trading automatically.
Algorithmic trading is found to be more systematic than trading done on the efficiency of the trader’s own thinking and intellect.
Algorithmic Trading Strategies
Though this type of trading is largely automated, there are various Algo Trading strategies or approaches that can be chosen to get the best outcomes in terms of better returns and reduced costs. These strategies are primarily considered opportunities. Here are the most popular algorithmic trading strategies being used:
1. Arbitrage Opportunities
Purchasing a dual-listed stock at a reduced price in a single market and concurrently selling it at a higher price in a different market provides the price difference as risk-free profit or arbitrage. The same procedure can be simulated for stocks vs. futures instruments as price differentials vary in different scenarios. Realizing an algorithm to determine such price differentials and placing the orders in an effective manner results in lucrative opportunities.
2. Mathematical Model-based Strategies
Various prevalent mathematical concepts, like the delta-neutral trading strategy, enable traders to execute trading on a mix of options and the fundamental security. Delta neutral is essentially a portfolio strategy that is comprised of various positions with counterweighing positive and negative channels—a ratio likening the variation in the price of an asset, typically a marketable refuge, to the conforming change in the price of its derivative. Hence, the overall delta of the assets leads to zero.
3. Trend-following Strategies
It is one of the most widely applied algorithmic trading strategies today. The trends followed are typically based on moving averages, price level changes, channel breakouts, and associated technical indicators. The reason behind its popularity is that it doesn’t involve any prediction or assumption. Trades are executed on the basis of the incidence of wanted trends, which are quite simple and direct to perform using various algorithms without encountering the difficulty of predictive analysis. Employing 50- and 200-day moving averages is a prevalent trend-following strategy used by traders these days.
4. Index Fund Rebalancing
Index funds are found to have fixed periods of rebalancing to take their holdings to par with their corresponding benchmark guides. This promulgates beneficial opportunities for algorithmic traders, who leverage expected trades that offer 20 to 80 basis points profits given the number of stocks in the index fund before the index fund rebalancing. Such trades are executed via algorithmic trading systems for timely performance and unmatched prices.
5. Volume-weighted Average Price (VWAP)
Volume-weighted average price strategy diversifies a huge order and delivers dynamically priced smaller masses of the order to the market using stock-specific historical volume profiles. The aim is to process the order near to the volume-weighted average price (VWAP).
6. Mean Reversion
Mean reversion strategy is based on the notion that the extreme and low prices of a stock are a provisional spectacle that returns to their mean value (average value) periodically. Classifying and stating a price series and executing an algorithm based on it enables automatic trade placement when the value of an asset breaks in and goes out of the set band.
7. Percentage of Volume (POV)
Till the trade order is completely done, this algorithm keeps forwarding fractional orders basis the set participation ratio and as per the volume traded in the markets. The associated “steps strategy” makes orders at a user-set percentage of market volumes and augments or decreases this participation rate when the stock price attains user-defined levels.
8. Time Weighted Average Price (TWAP)
Time-weighted average price strategy divides a big-size trade order and presents dynamically set smaller masses of the order employing proportionally divided time brackets between the beginning and the end. This algorithmic trading strategy aims at executing the order close to the general price between the start and end periods thus reducing market influence.
Technical specifications for Algorithmic Trading
- Computer-programming knowhow to program the desired trading strategy, dedicated programmers, or ready-to-use trading software.
- Excellent network connectivity and seamless access to trading platforms for order placement
- Accessibility to market data inputs that will be evaluated by the algorithm for opportunities to place orders.
- The aptitude and structure to back test the system after it is built before going live
- Verified historical data for back testing on the basis of the complexity of rules given in the algorithm.
Conclusion – Algo Trading Strategies
There is one thing that you must know about algo trading is that it is not that easy and executable as it seems. There has been tough competition in this trading segment too. And, as prices change in mili and even microseconds, the chosen strategy should be full-proof for any such circumstances.
Apart from this, other common bottlenecks that can hamper the performance are network issues, time delays between order placement and execution, system malfunction, and flawed algorithms. Always remember that the more intricate an algorithm, the stringent backtesting it needs to deliver desired outcomes.