Short-Term Market Changes and Market Making with Inventory by Jin Gi Kim, Sam Beatson, Bong-Gyu Jang, Ho-Seok Lee, Seyoung Park :: SSRN

machine learning

So, as the trading session is getting closer to the end, order spreads will be smaller, and the reservation price position will be more “aggressive” on rebalancing the inventory. And as you can see, the ask offers will be created closer to the market mid-price since the optimal spread is calculated with the reservation price as reference. But this kind of approach, depending on the market situation, might lead to market maker inventory skewing in one direction, putting the trader in a wrong position as the asset value moves against him. This parameter denoted in the letter eta is related to the aggressiveness when setting the order amount to achieve the inventory target. It is inversely proportional to the asymmetry between the bid and ask order amount.

  • Inventory management is therefore central to market making strategies , and particularly important in high-frequency algorithmic trading.
  • Hence, market makers try to minimize risk by keeping their inventory as close to zero as possible.
  • It works the same as the pure market making strategy’s inventory_skew feature in order to achieve this target.
  • Similarly, on the Sortino ratio, one or the other of the two Alpha-AS models performed better, that is, obtained better negative risk-adjusted returns, than all the baseline models on 25 (12+13) of the 30 days.

The Avellaneda & Stoikov model was created to be used on traditional financial markets, where trading sessions have a start and an end. The inventory position is flipped, and now the bid offers are being created closer to the market mid-price. It’s easy to see how the calculated reservation price is different from the market mid-price .


Section3 is dedicated to the study of the control and Hamilton-Jacobi-Bellman equations for the model proposed in Sect. 3.2.1, we consider the case of the jumps in volatility of the price. The paper is also equipped with an Appendix on how to use the method of finite differences for the numerical solution of the corresponding nonlinear differential equation. 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. This kind of scales generate ordinal variables made up of a set of rank ordered items. Since the distance between two consecutive items cannot be either defined or presumed equal, this kind of variable cannot be analysed by either statistical methods defined on a metric space or parametric tests.

  • It is necessary to pay more attention on the minority cases and capture the patterns of these valuable long and short signals.
  • In particular, a new definition of fair price, which we call the Volume Adjusted Mid Price consistently outperforms the mid price, from the perspective of a market maker.
  • On the optimal quotes will have just the opposite effect of when k is employed.
  • Cryptocurrency markets are 24/7, so there is no market closing time.

These models, therefore, must learn everything about the problem at hand, and the learning curve is steeper and slower to surmount than if relevant available knowledge were to be leveraged to guide them. Figure3 depicts one simulation of the profit and loss function of the market maker at any time t during the trading session in the left panel. The profit and loss performance of the trading is displayed by the cash level histogram in the left panel. 3 that the strategy is profitable even when there are adverse selection effects in the model due to the expectations of the jumps. As we shall see shortly, the reward function is the Asymmetric dampened P&L obtained in the current 5-second time step. In contrast, the total P&L accrued so far in the day is what has been added to the agent’s state space, since it is reasonable for this value to affect the agent’s assessment of risk, and hence also how it manipulates its risk aversion as part of its ongoing actions.

Basic concepts of LOB and market order

The same set of parameters obtained for the Gen-AS model are used to specify the initial Alpha-AS models. The goal with this approach is to offer a fair comparison of the former with the latter. By training with full-day backtests on real data respecting the real-time activity latencies, the models obtained are readily adaptable for use in a real market trading environment. Data normalization for features and labeling for signals are required for classification. Instead of simply labeling the mid-price movement as in Kercheval and Zhang and Tsantekidis et al. , we consider the direct trading actions, including long, short, and none. This approach is inspired by the previous application of deep learning to trade signals in the context of VIX futures (Avellaneda et al., 2021).

jumps in volatility

A vertex cover is a set of vertices where every edge is incident to at least one vertex. The minimum weighted connected VC problem can be defined as finding the VC of connected nodes having the minimum total weight. MWCVC is a very suitable infrastructure for energy-efficient link monitoring and virtual backbone formation. In this paper, we propose a novel metaheuristic algorithm for MWCVC construction in WANETs. Our algorithm is a population-based iterated greedy approach that is very XRP effective against graph theoretical problems.

1 illustrates the bid and ask prices and their 5-level queues for a stock at two consecutive time points . In this study, we implement a LOB trading strategy to enter and exit the market by processing LOB data. For mature markets, such as the U.S. and Europe, the real-time LOB is event-based and updates at high speed of at least milliseconds and up to nanoseconds. The dataset from the Nasdaq Nordic stock market in Ntakaris et al. contains 100,000 events per stock per day, and the dataset from the London Stock Exchange in Zhang et al. contains 150,000.

We explain the idea of the algorithm and illustrate its operation through sample examples. We implement the proposed algorithm with its competitors on a widely used dataset. From extensive measurements, we obtain that the algorithm produces WCVC with less weight at the same time its monitor count and time performances are reasonable. We study optimal trading strategy of a market maker with stock inventory in the presence of short-term market changes, especially changes in trading intensity of market participants and stock volatility.

A single parent individual is selected randomly from the population , with a selection probability proportional to the Sharpe score it has achieved (thus, higher-scoring individuals have a greater probability of passing on their genes). The chromosome of the selected individual is then extracted and a truncated Gaussian noise is applied to its genes (truncated, so that the resulting values don’t fall outside the defined intervals). The new genetic values form the chromosome of the offspring model. At the start of every 5-second time step, the latest state (as defined in Section 4.1.4) is fed as input to the prediction DQN. The sought-after Q values–those corresponding to past experiences of taking actions from this state– are then computed for each of the 20 available actions, using both the prediction DQN and the target DQN (Eq ). The data on which the metrics for our market features were calculated correspond to one full day of trading .

stochastic volatility

AlphaGo learned by playing against itself many times, registering the moves that were more likely to lead to victory in any given situation, thus gradually improving its overall strategies. The same concept has been applied to train a machine to play Atari video games competently, feeding a convolutional neural network with the pixel values of successive screen stills from the games . One way to improve the performance of an AS model is by tweaking the values of its constants to fit more closely the trading environment in which it is operating. In section 4.2, we describe our approach of using genetic algorithms to optimize the values of the AS model constants using trading data from the market we will operate in. Alternatively, we can resort to machine learning algorithms to adjust the AS model constants and/or its output ask and bid prices dynamically, as patterns found in market-related data evolve. To this approach, more specifically one based on deep reinforcement learning, we turn to next.

What is the reservation price?

This is avellaneda and stoikoverally achieved by applying various root-finding algorithms that can handle the complexity and high-dimensionality of the equation. The training of the neural network has room for improvement through systematic optimisation of the network’s parameters. Characterisation of different market conditions and specific training under them, with appropriate data , can also broaden and improve the agent’s strategic repertoire. The agent’s action space itself can potentially also be enriched profitably, by adding more values for the agent to choose from and making more parameters settable by the agent, beyond the two used in the present study (i.e., risk aversion and skew). In the present study we have simply chosen the finite value sets for these two parameters that we deem reasonable for modelling trading strategies of differing levels of risk. This helps to keep the models simple and shorten the training time of the neural network in order to test the idea of combining the Avellaneda-Stoikov procedure with reinforcement learning.

This part intends to show the numerical experiments and the behaviour of the market maker under the results given in Sect. For the case of exponential utility function, now we explore the results of optimal controls obtained by solving the HJB Eq. We also plan to compare the performance of the Alpha-AS models with that of leading RL models in the literature that do not work with the Avellaneda-Stoikov procedure. Comparison of values for Max DD and P&L-to-MAP between the Gen-AS model and the Alpha-AS models (αAS1 and αAS2). Table 8 provides further insight combining the results for Max DD and P&L-to-MAP.

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In order to see the time evolution of the process for larger inventory bounds. Is the value function for the control problem and, moreover, the optimal controls are given by . No significant differences were found between the two Alpha-AS models. Single feature importance , an out-of-sample estimator of the individual importance of each feature, that avoids the substitution effect found with MDI and MDA .


Adjust the settings by opening the strategy config file with a text editor. Whether to enable adding transaction costs to order price calculation. The spread (from mid-price) to defer the order refresh process to the next cycle. When placing orders, if the order’s size determined by the order price and quantity is below the exchange’s minimum order size, then the orders will not be created. You will need to hold a sufficient inventory of quote and or base currencies on the exchange to place orders of the exchange’s minimum order size. In order to view the full content, please disable your ad blocker or whitelist our website

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The market-maker can post competitive bid and ask prices that improves on the current market price in order to manage the inventory. However, I do not see any specification of bounds for this reservation price and therefore I think there is no guarantee that ask prices computed by the market-maker will be higher or bid prices will be lower than the current price of the process. But this kind of approach, depending on the market situation, might lead to the market maker inventory skewing in one direction, putting the trader in a wrong position as the asset value moves against him.

For example, if you are trading BTC-USD but want to focus on keeping your inventory 100% on BTC, you set this value to 100. After choosing the exchange and the pair you will trade, the next question is if you want to let the bot calculate the risk factor and order book depth parameter. If you set this to false, you will be asked to enter both parameters values. On hummingbot, you choose what the asset inventory target is, and the bot calculates the value of q. But WAVES as its value increases, the distance between the mid-price and the reservation price will increase when the trader inventory is different from his target.

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