Forex quantitative trading trends and strategies

With the development of computer technology, the "contest" between humans and computers has become increasingly obvious. At present, most domestic foreign exchange traders still use subjective and manual trading methods, but it cannot be ignored that the international mainstream trend is increasingly inclined to use computers instead of manual execution of transactions, and quantitative trading will become one of the important directions of future development.

The essence of finance is to look for patterns in historical data and try to find more likely outcomes. When it comes to summarizing patterns, computers have a clear advantage, provided by a large amount of historical data and efficient processing technology. Quantitative trading is a trading method that uses computers to sift through a large amount of data to select a variety of "high probability events" that can bring excess returns, and then formulate strategies and make decisions. Quantitative trading eliminates human emotional factors. It is like a sword of absolute rationality, which can focus on the maximum effect.

[Classic Strategies for Quantitative Trading]

Strategy is the core part of quantitative trading, which is directly related to whether the trading is profitable. At present, the common quantitative trading strategies in the market include global macro strategy, α coefficient strategy, arbitrage strategy, CAT strategy and so on.

Global macro strategy: This strategy can be divided into directional trading and relative value trading strategies. Directional trading is based on direction judgment, such as buying the US dollar or the US dollar index if you are optimistic about the US economy; relative value trading strategies are based on price spread decisions, such as the US dollar is strong and the euro is weak, and the European and American currency pairs are allocated. Macro models usually consider economic and fundamental data, combined with market data.

Macro strategies typically operate only on financial products with better liquidity, with longer holding periods and infrequent transactions.

Alpha coefficient strategy: Alpha coefficient strategy is also known as market neutral strategy. According to CAPM theory, ROI is divided into beta return on the overall risk of the market and alpha return on the financial product itself. Market neutral strategy seeks absolute return by avoiding market risk beta and maximizing alpha. The specific operation is to hedge by shorting foreign exchange, stocks, and futures derivatives while long the corresponding products. As long as the purchased products outperform the market ups and downs, profits can be made.

Arbitrage strategies: Arbitrage strategies refer to strategies that profit by buying undervalued products and selling overvalued products when the prices of two or more related varieties deviate from a reasonable range. This is a directional operation that seeks arbitrage opportunities based on price fluctuations. Arbitrage strategies are classified into complex categories, including statistical arbitrage, futures arbitrage, ETF arbitrage, event arbitrage, etc. The benefits mainly depend on the diversity and refinement of arbitrage strategies.

CTA strategy: CAT strategy is Commodity Trading Advisor Strategy, which is literally translated as Commodity Trading Advisor Strategy. CAT strategy is mainly divided into trend trading strategy and mean recovery strategy, of which trend trading strategy is more widely used. Trend trading strategy aims to remove market noise, find current market trends and establish positions, so as to profit from the trend. The strategy focuses on volatility, which will bring large gains when the market has large directional fluctuations. The mean recovery strategy mainly conducts inter-period and cross-symbol pairing transactions, and conducts reversal arbitrage according to the spread ratio trend.

The above are common strategies in the market. The formulation of actual quantitative trading strategies is more complex and changeable, and sometimes multiple strategies are combined to achieve the best results.

The life cycle of a quantitative strategy

In the process of formulating a specific quantitative trading strategy, it usually goes through several stages such as formulating the strategy, verifying and correcting, tracking the real deal, applying the real deal, and failing the strategy.

Developing a strategy: Investors first need to develop a basic strategy, which may be derived from their own investment experience or the success examples of others. Investors can choose their own approved technical indicators to build logic and supplement relevant factors to form a complete quantitative strategy, and then conduct historical backtesting to obtain a simple strategy model.

Verification and correction: Investors need to screen and optimize the parameters based on this strategy, observe the impact of different parameters on the strategy, such as Sharp ratio, maximum drawdown rate, etc., and modify and update the strategy. Through repeated modification and testing, a feasible new strategy is finally obtained.

Real tracking: After the strategy is determined, investors don't have to rush to apply it. They should first let the strategy be tested in the future market to confirm its authenticity and effectiveness. This link is the key to screening high-quality strategies and weeding out poor-quality strategies.

Real Application: After confirming the effectiveness of the strategy for a period of time, investors can apply the strategy to trade. During the trading process, the effectiveness of the strategy needs to be continuously monitored.

Strategy failure: The market is changing, and a strategy cannot always be effective. If the model has failed, investors need to react quickly, adjust the model or terminate the strategy.

[Foreign exchange quantitative trading based on rationality]

Forex quantitative trading is considered by some to be a rational way of trading. Quantitative trading is more objective and rational in the decision-making process than active investing. For example,decodeForex-related philosophy also emphasizes the importance of data-driven decision-making.

foreign exchange marketThere are many changes, and humans are easily influenced by emotions. Greed, fear, hesitation, recklessness, etc. are all common weaknesses that can lead to decision-making mistakes. After the model is determined and put into use, quantitative investment is completely executed by the computer. Generally, human intervention is avoided, which can reduce the emotional impact to a low level and achieve rational and objective investment.

Investors often use traditional Chinese medicine and Western medicine as metaphors for traditional investing and quantitative trading. Traditional investing is like traditional Chinese medicine, relying on experience and understanding. The process cannot be replicated and is difficult to communicate in words, but it can solve difficult problems. Quantitative investing is like Western medicine. It has a fixed method and rigorous logic. Even if the person who created the method is not there, others can continue to develop and improve over time.

The use of quantitative foreign exchange trading, like having a reliable tool, can bring good returns when the conditions are right. But this does not mean that quantitative investing is a panacea. It may also have problems such as incomplete historical data leading to inaccurate models, homogeneous models producing competitive transactions, and network outages. Over-reliance on computers can also bring risks. How to weigh it is ultimately up to investors to judge for themselves.