Forecasting Prices from Level-I Quotes in the Presence of Hidden Liquidity by Marco Avellaneda, Josh Reed, Sasha Stoikov :: SSRN

limit order book

It is worth mentioning that the trader changes her qualitative behavior depending on the liquidation and penalizing variations of the constants and her positions on inventories as the time approaches to maturity. On the optimal quotes will have just the opposite effect of when k is employed. While the other parameters are kept the same as in the Table1. Is the value function for the control problem and, moreover, the optimal controls are given by . In order to view the full content, please disable your ad blocker or whitelist our website If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact.

  • You will be asked the maximum and minimum spread you want hummingbot to use on the following two questions.
  • The genetic algorithm selects the best-performing values found for the Gen-AS parameters on the corresponding day of data.
  • The methodology might be more sound than this, but the text simply does not offer answers to these questions.
  • In view of the referees’ feedback and my own reading of your paper, I invite you to address all issues noted below.
  • This work presents RAGE, a novel strategy designed for solving combinatorial optimization problems where we intend to select a subset of elements from a very large set of candidates.

It refreshes your orders and automatically creates an order based on the spread and movement of the market. It sets a target of base asset balance in relation to a total asset allocation value . It works the same as the pure market making strategy’s inventory_skew feature in order to achieve this target. An amount in seconds, which is the duration for the placed limit orders.


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. In this paper we present a limit order placement strategy based on a well-known reinforcement learning algorithm.

For instance, even after comments about reference formatting, some references have missing publications, years, issues, or even author names . Also, there seems to be a large number of arxiv or SSRN preprints listed for references which are actually published, either as working papers by some institutions or even in peer reviewed journals . Some of these will most likely be handled by the editorial team, but the extent of the errors is too large, evidently due to the revisions made by authors being mostly superficial. In general, the legibility of the paper is hardly improved, and the revisions in this regards were mostly superficial.

It is necessary to pay more attention on the minority cases and capture the patterns of these valuable long and short signals. Then, the model trained daily or weekly can predict trading actions and the probability of each choice at every tick. The next step is to trade the securities based on the information yielded by the predictions.

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. This work presents RAGE, a novel strategy designed for solving combinatorial optimization problems where we intend to select a subset of elements from a very large set of candidates.

A Market Making Optimization Problem in a Limit Order Book

Based on that data, you can find the most popular open-source packages, as well as similar and alternative projects. Adjust the settings by opening the strategy config file with a text editor. Directly override orders placed by order_amount and order_level_parameter. Whether to enable adding transaction costs to order price calculation. 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.

That is, these agents decide the bid and ask prices of their orderbook quotes at each execution step. The main contribution we present in this paper resides in delegating the quoting to the mathematically optimal Avellaneda-Stoikov procedure. What our RL algorithm determines are, as we shall see shortly, the values of the main parameters of the AS model. It is then the latter that calculates the optimal bid and ask prices at each step.

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The results obtained in this fashion encourage us to explore refinements such as models with continuous action spaces. The logic of the Alpha-AS model might also be adapted to exploit alpha signals . The usual approach in algorithmic trading research is to use machine learning algorithms to determine the buy and sell orders directly. In contrast, we propose maintaining the Avellaneda-Stoikov procedure as the basis upon which to determine the orders to be placed. We use a reinforcement learning algorithm, a double DQN, to adjust, at each trading step, the values of the parameters that are modelled as constants in the AS procedure. The actions performed by our RL agent are the setting of the AS parameter values for the next execution cycle.

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Values that are very large can have a disproportionately strong influence on the statistical normalisation of all values prior to being inputted to the neural networks. By trimming the values to the [−1, 1] interval we limit the influence of this minority of values. The price to pay is a diminished nuance in the learning from very large values, while retaining a higher sensitivity for the majority, which are much smaller. By truncating we also limit potentially spurious effects of noise in the data, which can be particularly acute with cryptocurrency data.

For solving the combinatorial problem, RAGE generates a customizable number of random solutions, computes the objective function for each solution, and then scores each candidate element in terms of the value returned by the objective function. After that, RAGE removes a customizable number of candidate elements presenting the smallest score when considering all solutions generated. The heuristic loops performing iterations until there are left the exact number of candidates that we are looking for.

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 . The selection of features based on these three metrics reduced their number from 112 to 22 . The features retained by each importance indicator are shown in Table 1. To minimize inventory risk, prices should be skewed to favor the inventory to come back to its targeted ideal balance point.


Genetic algorithms compare the performance of a population of copies of a model, each with random variations, called mutations, in the values of the genes present in its chromosomes. This process of random mutation, crossover, and selection of the fittest is iterated over a number of generations, with the genetic pool gradually evolving. Finally, the best-performing model overall, with its corresponding parameter values contained in its chromosome, is retained for subsequent application to the problem at hand. In our case, it will be the AS model used as a baseline against which to compare the performance of our Alpha-AS model.

In particular, we propose a avellaneda & stoikov for computing tree weights based on the minimization of a convex cost function, which takes both determinacy and accuracy into account and makes it possible to adjust the level of cautiousness of the model. The proposed model is evaluated on 25 UCI datasets and is demonstrated to be more adaptive to the noise in training data and to achieve a better compromise between informativeness and cautiousness. Together, a) and b) result in a set of 2×10d contiguous buckets of width 10−d, ranging from −1 to 1, for each of the features defined in relative terms. Approximately 80% of their values lie in the interval [−0.1, 0.1], while roughly 10% lie outside the [−1, 1] interval.

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. This consideration makes rb and ra reasonable reference prices around which to construct the market maker’s spread. Avellaneda and Stoikov define rb and ra, however, for a passive agent with no orders in the limit order book. In practice, as Avellaneda and Stoikov did in their original paper, when an agent is running and placing orders both rb and ra ra are approximated by the average of the two, r .

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Picking up the ADA volatility as diffusive is highly permanent but this dynamics can allow the increments to increase only by a sequence of normally distributions. The multi-view clustering problem has attracted considerable attention over recent years for the remarkable clustering performance due to exploiting complementary information from multiple views. Most existing related research work processes data in the decimal real value space that is not the most compatible space for computers. Binary code learning, also known as hashing technology, is well-known for fast Hamming distance computation, less storage requirement and accurate calculation results. The Hamming space is most enjoyed by computers because of binary/hash codes.

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A special thank goes to Laurent Carlier for his vivid interest in academic questions around market making throughout the years. Topics in stochastic control with applications to algorithmic trading. PhD Thesis, The London School of Economics and Political Sciences.

But for now, it is essential to know that using a significant κ value, you are assuming that the order book is denser, and your spread will have to be smaller since there is more competition on the market. There is a lot of mathematical detail on the paper explaining how they arrive at this factor by assuming exponential arrival rates. There are many different models around with varying methodologies on how to calculate the value. The model was created before Satoshi Nakamoto mined the first Bitcoin block, before the creation of trading markets that are open 24/7. On Hummingbot, the value of q is calculated based on the target inventory percentage you are aiming for. LibHunt tracks mentions of software libraries on relevant social networks.

Lastly, we compare the models that we have derived in this paper with existing optimal market making models in the literature under both quadratic and exponential utility functions. 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. 2 to 5 show performance results over 30 days of test data, by indicator (2. Sharpe ratio; 3. Sortino ratio; 4. Max DD; 5. P&L-to-MAP), for the two baseline models , the Avellaneda-Stoikov model with genetically optimised parameters (AS-Gen) and the two Alpha-AS models. As stated in Section 4.1.7, these values for w and k are taken as the fixed parameter values for the Alpha-AS models. They are not recalibrated periodically for the Gen-AS so that their values do not differ from those used throughout the experiment in the Alpha-AS models. If w and k were different for Gen-AS and Alpha-AS, it would be hard to discern whether observed differences in the performance of the models are due to the action modifications learnt by the RL algorithm or simply the result of differing parameter optimisation values. Alternatively, w and k could be recalibrated periodically for the Gen-AS model and the new values introduced into the Alpha-AS models as well. However, this would require discarding the prior training of the latter every time w and k are updated, forcing the Alpha-AS models to restart their learning process every time.

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. The target for the random forest classifier is simply the sign of the difference in mid-prices at the start and the end of each 5-second timestep. That is, classification is based on whether the mid-price went up or down in each timestep.

The Q-value iteration algorithm assumes that both the transition probability matrix and the reward matrix are known. Hasselt, Guez and Silver developed an algorithm they called double DQN. 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).