Dr David Moche explains the types of algorithmic trading strategies and how quantum computing will help
LONDON, UK, May 3, 2021 /EINPresswire.com/ – There are basically five distinct types of trading strategies when it comes to automated or algorithmic trading. They are:
2. Average reversion
3. Market making
4. Statistical arbitrage
5. Quantitative methods based on sentiment or technical indicators
Perhaps the simplest strategy is to simply follow market trends with a buy or sell order generated based on a set of conditions met by technical indicators.
Essentially, you have to build your algorithmic trading strategy based on the market trends that you determine using statistics. This strategy can also examine historical and current data to anticipate weather trends that are likely to continue or reverse. Another fundamental type of algorithmic trading strategy is average reversion systems, which work on the assumption that the markets move. 80% of the time, people who use this strategy typically calculate an average asset price using historical data and trade with the expectation that the current price will revert to the average price.
A market maker or liquidity provider is an organization or person who quotes both a buy and sell price of a financial instrument or a commodity held in stock, in the hope of achieving a profit on the supply gap. Market making provides liquidity to securities that are not often traded on a stock exchange. The market maker can increase the supply-demand equation for securities.
Statistical arbitrage, or stat darb, is related to the statistical mispricing of at least one asset based on the expected value of those assets. Stat Darb is also a subset of medium reversion strategies as a trading strategy. Statistical arbitrage is a vigorously quantitative and computerized way of dealing with stock trading.
One of the most widely recognized statistical AAB strategies is Beyer trading, where a pair of cointegrated assets is seen. The failing asset is expected to rise and is bought while the performing asset should lose value and is sold.
Statistical arbitrage has become an important power for both hedge funds and investment banks. Many proprietary banking transactions now revolve to varying degrees around statistical arbitrage.
Sentiment Based, have you ever tried sentiment based trading? All in all, this strategy can do it for you. A news-based algorithmic trading system is usually linked to newswires, automatically producing trading signals. Depending on how the actual data is depleting relative to market consensus or past data, as you have probably guessed, it takes a solid foundation in financial market analysis and PC programming to have the ability to create complex trading algorithms. Quantitative or quant analysts are regularly trained in Python C or Java programming before they can develop algorithmic trading systems.
So how will quantum computing revolutionize trading?
Quantum Trading presents a compelling new way to look at technical analysis and will help you use the proven principles of modern physics to forecast financial markets.
The use of the theory of relativity and quantum physics is necessary to make sense of the behavior of prices and to predict highs and lows in the medium to long term.
Classic algorithms take a long time to deal with complex problems like those used in the world of trading. Whereas quantum algorithms can handle the same problem in a fraction of the time.
With quantum machine learning, you can take both the number of vectors and their dimensions and get the result at an exponential increase in speed compared to classical algorithms. This means that traders can make decisions faster and more accurately.
So how does Qfinity use quantum algorithms to help FVP business customers?
Qfinity Labs uses a quantum algorithm to estimate credit risk more efficiently than Monte Carlo simulations can do on conventional computers.
We estimate the required economic capital, i.e. the difference between the Value at Risk and the expected value of a given loss distribution. The economic capital requirement is an important risk measure because it summarizes the amount of capital required to remain solvent at a given confidence level.
We implement this problem for a realistic loss distribution and analyze its scaling to a realistic problem size. In particular, we provide estimates of the total number of qubits required, the expected circuit depth, and how this translates into an expected runtime under reasonable assumptions about future quantum fault-tolerant hardware.
Using this modeling, we developed 3 trading algorithms based on the risk / reward ratio:
With F3 being the highest risk-reward strategy for more experienced investors and F2 and F1 reducing risk-reward ratios based on the risk appetite of the client.
Algorithm | Returns | Capital protection
F1 | 2-4% | 100%
F2 | 4-6% | 95%
F3 | 6-10% | 90%
We have developed these algorithms to ensure that FVP Trade clients of all experience levels can take advantage of quantum technology in their trading portfolio.
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