A Simple Market Making Strategy

Depending on avellaneda market making sizes, P&L targets, expected price moves, to name a few variables, a market maker can asymmetrically skew the bid and ask prices of their quotes. This fine-tuning introduces feedback mechanisms leading to non-linear behaviour in the market. In essence, successful market making not only depends on formal constraints but is the result of a delicate mix containing additional creative considerations.

Such control provides the seminal framework of Avellaneda and Stoikov which solves analytically the problem of maximizing a utility formula including effects from the inventory risk. The solution is an equation for the so called reservation price, which is an adjusted discounted mid-price in proportion to a risk aversion parameter, the position, the volatility and the terminal time. The trades are placed symmetrically around the reservation price using the spread , not exactly around the current mid-price.

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The minimum_spread parameter is optional, it has no effect on the calculated reservation price and the optimal spread. It serves as a hard limit below which orders won’t be placed, if users choose to ensure that buy and sell orders won’t be placed too close to each other, which may be detrimental to the market maker’s earned fees. The minimum spread is given by the minimum_spread parameter as a percentage of the mid price.

In Section 2, we introduce some basic concepts and describe the input LOB datasets. What started in April 2019 as a simple open-source market-making bot, has now grown into an extensible framework that lets you design custom trading strategies on any crypto asset exchange. Overall, both Alpha-AS models obtain higher and more stable returns, as well as a better P&L-to-inventory profile than AS-Gen and the non-AS baseline models. That is, they achieve a better P&L profile with less exposure to market movements. Conversely, test days for which the Alpha-ASs did worse than Gen-AS on P&L-to-MAP in spite of performing better on Max DD are highlighted in red (Alpha-AS “worse”). On the P&L-to-MAP ratio, Alpha-AS-1 was the best-performing model for 11 test days, with Alpha-AS-2 coming second on 9 of them, whereas Alpha-AS-2 was the best-performing model on P&L-to-MAP for 16 of the test days, with Alpha-AS-1 coming second on 14 of these.

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This intelligent algorithm performs local density approximation in the vicinity of the closest temporal patterns chosen with an appropriate distance measure. The rationale is that similar deterministic trends may persist for short periods during the evolution of the price. The k-Nearest Neighbour , method is applied to find close patterns of the recent trend ending at the last point, and such nearest segments are exploited to make probabilistic predictions for the mid-price. The models underlying the AS procedure, as well as its implementations in practice, rely on certain assumptions. Statistical assumptions are made in deriving the formulas that solve the P&L maximization problem. For instance, Avellaneda and Stoikov (ibid.) illustrate their method using a power law to model market order size distribution and a logarithmic law to model the market impact of orders.

• In practice, researchers usually define one membership function for each Likert-type scale, not considering the peculiar characteristics of neither questions nor respondents.
• The optimal bid and ask quotes are obtained from a set of formulas built around these parameters.
• Historically, highly technical quantitative hedge funds and trading organizations with the technology and ability to run sophisticated algorithms at scale have dominated the market-making sector.
• You might have noticed that I haven’t added volatility(σ) on the main factor list, even though it is part of the formula.
• In this article, we will have an in-depth discussion on the subject of how to proactively defend ourselves against informed traders and market manipulators.

Are they scaled by some scaling parameter beforehand – and what data is this parameter estimated from ? If not, how much data is lost by only using the price differences with absolute values smaller than 1? Also, if the market candle features are “divided by the open mid-price for the candle”, does this mean that all of those higher than the mid-price would be would be truncated to 1? The methodology might be more sound than this, but the text simply does not offer answers to these questions.

Double DQN is a deep RL approach, more specifically deep Q-learning, that relies on two neural networks, as we shall see shortly (in Section 4.1.7). In this paper we present a double DQN applied to the market-making decision process. Typically, in the beginning the agent does not know the transition and reward functions. It must explore actions in different states and record how the environment responds in each case.

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Avellaneda Market Making

Through repeated exploration the agent gradually learns the relationships between states, actions and rewards. It can then start exploiting this knowledge to apply an action selection policy that takes it closer to achieving its reward maximization goal. Inventory Risk Aversion is a quantity between 0 and 1 to measure the compromise between mitigation of inventory risk and profitability. When parameters is closer to 1, will increase chances of one side of bid/ask to be executed with respect to the other, in that way forcing inventory to converge to target while decreasing the final profit. Likert-type scales are commonly used in both academia and industry to capture human feelings since they are user-friendly, easy-to-develop and easy-to administer.

The Asymmetric dampened P&L penalizes speculative positions, as speculative profits are not added while losses are discounted. Where the 0 subscript denotes the best orderbook price level on the ask and on the bid side, i.e., the price levels of the lowest ask and of the highest bid, respectively. The procedure, therefore, has two steps, which are applied at each time increment as follows. PLOS ONE promises fair, rigorous peer review, broad scope, and wide readership – a perfect fit for your research every time. For asymptotic expansions when T is large you should read the paper by Guéant, Lehalle, and Fernandez-Tapia here or the book of Guéant The financial mathematics of market-liquidity. The model was created before Satoshi Nakamoto mined the first Bitcoin block, before the creation of trading markets that are open 24/7.

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When markets make large trending movements, simple market makers are very susceptible to something called “adverse selection” and can quickly become unfortunate victims due to attacks by sophisticated traders (who are called “informed traders”). Avellaneda & Stoikov only provide very limited protections under these circumstances . In this article, we will have an in-depth discussion on the subject of how to proactively defend ourselves against informed traders and market manipulators.

In this way, the individual uncertainty against each question is considered equal and constant. To overcome this limitation and to reduce the expert’s subjectivity, in this study an adaptive membership function based on CUB model is suggested to pre-transform Likert-type variables into fuzzy numbers before the adoption of a clustering algorithm. After a theoretical presentation of the method, an application using real data will be presented to demonstrate how the method works. An important part of a trader’s success, especially those who trade frequently, is the ability to recognize patterns in trading data.

In expert mode, the user will need to directly define the algorithm’s basic parameters described in the foundation paper, and no recalculation of parameters will happen. A large proportion of market making models derive from the seminal model of Avellaneda and Stoikov. Dealers make money by providing liquidity to clients but face flow uncertainty and thus price risk.

Table 6 compares the results of the Alpha-AS https://www.beaxy.com/s, combined, against the two baseline models and Gen-AS. The figures represent the percentage of wins of one among the models in each group against all the models in the other group, for the corresponding performance indicator. For every day of data the XRP number of ticks occurring in each 5-second interval had positively skewed, long-tailed distributions. The means of these thirty-two distributions ranged from 33 to 110 ticks per 5-second interval, the standard deviations from 21 to 67, the minimums ran from 0 to 20, the maximums from 233 to 1338, and the skew ranged from 1.0 to 4.4. Where Ψ(τi) is the open P&L for the 5-second action time step, I(τi) is the inventory held by the agent and Δm(τi) is the speculative P&L (the difference between the open P&L and the close P&L), at time τi, which is the end of the ith 5-second agent action cycle. Mean decrease accuracy , a feature-specific estimate of average decrease in classification accuracy, across the tree ensemble, when the values of the feature are permuted between the samples of a test input set .

Ensure you have enough quote and base tokens to place the bid GALA and ask for orders. The strategy will not place any orders if you do not have sufficient balance on either side of the order. We aim to teach new users the basics of market-making while enabling experienced users to exercise more control over how their bots behave.