The best advice for every professional trader would be to build and use custom algorithmic trading software. Why? Because, like many other things, software does algorithmic trading better than humans. The following strategies presented here can be implemented into the computer program to automatize trading processes. It is up to you what strategy to integrate in the program. But, once this is done, you can take a rest and leave everything to your custom built algo trading software.
- What is Algorithmic Trading?
- Basics of Algorithmic Trading
- Benefits of Algorithmic Trading
- Algorithmic Trading Strategies
- Technical Requirements for Algorithmic Trading
What is Algorithmic Trading?
Algorithmic trading or algo trading is a type of trading that follows certain instructions to close the most profitable deals. Algorithmic trading program can place bets at higher speed and with more chance to win than humans because it is programmed to follow a certain algorithm strategy. Humans, on the other hand, are driven by emotions and they are physically unable to monitor high number of deals or place bets driven only by cold calculations.
Algorithmic trading program places bets at higher speed and with more chance to win
Basics of Algorithmic Trading
Trading algorithms follow the instructions that consider quantity, time, and price of the possible deals. The instructions can be based on any mathematical model. This systematic approach to trading brings more revenue seeing as how it minimizes risk. It also optimizes the whole process and makes it automatic, saving a lot of time.
Benefits of Algorithmic Trading Applications
Here are some of the most useful benefits of algorithmic trading:
- Multiple market check that happens simultaneously
- Bets are not placed manually which reduces the risk of error
- Less expensive transaction fees
- Correct timing helps to avoid significant change of price
Algorithmic Trading Strategies
There are many different strategies and all of them can be used to program trading software to buy or sell automatically, depending on the initial instructions.
This strategy is profitable thanks to price differentials. When a dual-listed stock is bought at a lower price in one market, it can be sold at a higher price in another market at the same time. This is known as risk-free price differential or arbitrage. With the algorithm that identifies price differentials and places orders accordingly one can make significant profits with no risk.
To follow trends in moving averages is the most widespread strategy in algorithmic trading. Trading that uses this strategy also monitors channels breakouts and price levels. This strategy is the simplest because it does not include predictions or price forecasts. Frequency of the desirable trend dictates this kind of trading. Therefore, this strategy can be easily implemented into the algorithm. You will have a programmed algorithm based on 50- and 200-day moving averages.
Index Fund Rebalancing
When it comes to index funds there are certain time intervals when re-balancing occurs. It happens to bring index fund holdings to par with their benchmark indices. Traders capitalize on expected trades offering 20 to 80 basis points profits which depends on the quantity of stocks in certain index funds right before re-balancing happens.
Mathematical model-based strategies
There are proven mathematical models like, for example, delta-neutral strategy. Delta-neutral option consists of the variety of positions with either positive or negative deltas. This ratio is a comparison of the asset’s price change and change in the price of its derivative.
Mean Reversion or Trading Range
This stock algorithm is based on the assumption that high or low prices are temporary and that assets revert to their average value periodically. Certain algorithm can adjust buying and selling to when prices go above or below their average price.
Volume-weighted Average Price (VWAP)
Using VWAP strategy consists of breaking up a large order into smaller ones to release it to the market, using stock specific historical volume profiles. It is done, so the order is executed close to the volume-weighted average price (VWAP).
Time Weighted Average Price (TWAP)
TWAP strategy consists of breaking up a large order into smaller ones to release it to the market, using evenly divided time intervals between start and end time. It is done to minimize market impact by placing the order closer to the average price between the start and end times on the market.
Percentage of Volume (POV)
This algorithm sends partial orders that are adjusted to the defined participation ratio and considering the volume traded on the markets. The so-called “steps strategy” makes orders at a percentage of the market volumes defined by user. The algorithm either increases or decreases the participation rate depending on whether the stock price reaches levels defined by a user.
Implementation shortfall is the sum of execution cost and the opportunity cost incurred in case of adverse market movement between the time of the trading decision and order execution. Using this strategy, the aim is to keep the implementation shortfall as low as possible. This strategy increases targeted participation rate if stock prices move to a trader’s advantage and decreases it if the stock price moves to a trader’s disadvantage. In other words, it decreases the possibility of a trader to lose if price changes in between the time decision is made and time it is executed.
Other Non-Usual Trading Algorithms
There are high-tech front-running algorithms at play as well. These algorithms detect other algorithms on the side used by a sell-side market maker. Thus, traders are encouraged to use algorithm strategies not to lose to those who already uses algorithm strategies to identify large order opportunities.
Technical Requirements for Algorithmic Trading
After you’ve chosen the algorithm strategy it is time to implement it, using a computer program. After that the program is backtested, which consists of using the algorithm on the historical behavior of the stock-market to see if using the algorithms would have been profitable. What begs for solution is to transform the existing algorithm strategy into a computer program that has access to a trading account to make orders. Here are some of the requirements of this process:
- Historical dataset for backtesting;
- Infrastructure and ecosystem to backtest the algorithm before it is used on real markets;
- Computer programming knowledge, trading software or hired software developers to program algorithm trading strategy;
- Access to market data feeds to allow its thorough monitoring by the algorithm;
- Access to trading platforms and network connectivity to allow placing orders.
Suppose DELL is listed on the New York Stock Exchange (NYSE) and London Stock Exchange (LSE). You build an algorithm that detects arbitrage opportunities, considering the following facts: 1) NYSE trades in dollars while LSE trades in British pound sterling; 2) NYSE and LSE have 7-hours difference, which means that the time both stock markets are open equals 1.5 hour.
The algorithm needs to read the incoming price feeds of DELL from both exchanges, monitor USD/GBP currency exchange rate, track time, and have the ability to place orders through brokers, considering the fee.
When the algorithm notices price discrepancy that might happen to be profitable, the program should place a buy order for a lesser price to sell it for a bigger price. If the orders are executed correctly, the arbitrage profit (derived from the price difference) will be present.
New technologies and ultra innovative software allow many possibilities for the implementation of algorithmic trading strategies. It is impossible for a human to oversee thousands of little changes that happen in seconds. But, considering these changes and foreseeing market fluctuations can result in huge revenues. Trading algorithms eliminate human factor mistakes but they are complex systems that should be developed with precision in order to fulfill the expectations of traders.
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