Market makers buy and sell products on both sides of the book and earn profits from the difference between the bid and ask prices. This difference, known as the spread, makes them a good bet for day traders. Traders that copy the market makers’ strategies are also considered market makers, since their trades fill up around the market price. But a market maker’s position is risky and they must carefully hedge their position to minimize the risk.
Some market makers have adopted programmatic trading to reduce transaction costs. These strategies integrate with exchange APIs and increase uptime and liquidity. With the help of automation, order books have become thicker and the execution price for big orders has been close to the fair price. Automated trading reduces impact costs and volatility, and is more effective than human traders. As a result, the number of market makers has increased. However, these strategies are not suitable for everyone.
When market making trading strategies, market makers will try to earn a tiny markup. However, the main objective is to sell and buy as often as possible. The market makers take the risk of losing their investment when they purchase shares, so they will try to shift it to another place. A simple example of risk offloading would be if two cryptocurrency exchanges had similar liquidity levels, and the market maker would place a limit order on the one with low liquidity, and immediately send a market order on the other exchange with higher liquidity.
Another strategy to avoid lopsided order books is to use price-based events. This type of trading strategy eliminates the dependence on assumptions by using events that happen in real time. They also have lower latency requirements. This type of market-making strategy allows deep reinforcement algorithms to learn non-linear market dynamics. But it is a risky strategy to use for speculative trading, so be sure to check it out before using it.
Investing in stocks is risky and should only be undertaken with the right knowledge. A market maker has the skills to determine which stocks are worth buying and selling at the best price. Market makers are required to maintain liquidity in the market. This means that they can help to facilitate the trading process for other traders. But the risks are high. A market maker must be able to balance the risks of losing money. That’s why a market maker should be able to trade a wide range of markets.
Reinforcement learning has been successfully applied to automated trading. The success of this method is highly dependent on the reward function, also known as the feedback signal. In previous research, researchers proposed a framework for deep reinforcement learning and demonstrated its ability to generalize across currency pairs. In this current paper, we study the impact of seven reward functions on the performance of DRLMM in cryptocurrency market making. Using price-based events as inputs, the new framework we present here can help traders with automated market making.