high frequency Avellaneda Stoikov market making model Quantitative Finance Stack Exchange Leave a comment

An agent that simply applies the Avellaneda-Stoikov procedure with fixed parameters (Gen-AS), and the genetic algorithm to obtain said parameters, are presented in Section 4.2. 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.

This Avellaneda-Stoikov baseline model (Gen-AS) constitutes another original contribution, to our knowledge, in that its parameters are optimised using a genetic algorithm working on a day’s worth of data prior to the test data. The genetic algorithm selects the best-performing values found for the Gen-AS parameters on the corresponding day of data. This procedure helps establish AS parameter values that fit initial market conditions.

Sortino ratio

The data for the first use of the genetic algorithm was the full day of trading on 8th December 2020. Our algorithm works through 10 generations of instances of the AS model, which we will refer to as individuals, each with a different chromosomal makeup . In the first generation, 45 individuals were created by assigning to each of the four genes random values within the defined ranges. These individuals run through the orderbook data, and are then ranked according to the Sharpe ratio they have attained. For each subsequent generation 45 new individuals run through the data and then added to the cumulative population, retaining all the individuals from previous generations.

• This helps the algorithm learn and improves its performance by reducing latency and memory requirements.
• So, if T is high enough, each step in which q is not zero, the reservation price could be too high , and so the election of bid and ask quotes (both above or below the mid-price).
• The greater inventory risk taken by the Alpha-AS models during such intervals can be punished with greater losses.
• 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.

As we shall see in Section 4.2, the parameters for the direct Avellaneda-Stoikov model to which we compare the Alpha-AS model are fixed at a parameter tuning step once every 5 days of trading data. What is common to all the above approaches is their reliance on learning agents to place buy and sell orders directly. 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.

Minimum Order Size

The market microstructure, which can be stated as the research on the strong trading mechanisms managed for the financial securities, has been equipped with the contributions by the books Hasbrouck and O’Hara . We relied on random forests to filter state-defining features based on their importance according to three indicators. Various techniques are worth exploring in future work for this purpose, such as PCA, Autoencoders, Shapley values or Cluster Feature Importance . Other modifications to the neural network architectures presented here may prove advantageous. We mention neuroevolution to train the neural network using genetic algorithms and adversarial networks NEAR to improve the robustness of the market making algorithm.

PhD Thesis, The London School of Economics and Political Sciences. Using the exponential utility function and the results are provided for the following models. And for the stock price dynamics which are provided in each model definition. Moreover, the spread can also be considered to be normally distributed due to its skewness and kurtosis values.

While we do not change the rest of the parameters in Table1 and we observe our expectations in solutions which can be tracked by Table8, in coherence with . While keeping the other parameters same as in the Table1, our above expectation matches with the solutions obtained and be seen Table7. Increases as the trader expects the price to move up, she sends the orders at higher prices to get profit from the price increase which meets with our expectation. On the other hand, the results show that our strategy has a lower standard deviation. It can be also seen that the inventory of the trader reverts to zero more quickly than the symmetric strategy and the standard deviation of the inventory is produced less in the strategy.

The number of ticks used as a sample size for volatility calculation. This parameter denoted by the letter kappa is directly proportional to the order book’s liquidity, hence the probability of an order being filled. Vol_to_spread_multiplier will act as a threshold value to override max_spread when volatility is a higher value. The minimum spread related to the mid-price allowed by the user for bid/ask orders. Ensure you have enough quote and base tokens to place the bid and ask for orders.

Buy low, sell high: A high frequency trading perspective

Through repeated explon 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. Our community is full of market makers and arbitrageurs who are willing to help each other make the best use of Hummingbot. You can join our Discord channel to talk about the hummingbot, strategies, liquidity mining, and anything else related to the cryptocurrency world and receive direct support from our team. This is the default mode when you create a new strategy, but if you have your model to determine these values, you can deactivate the “easy” mode by setting config parameters_based_on_spread to False. You might have noticed that I haven’t added volatility(σ) on the main factor list, even though it is part of the formula.

The AS model generates bid and ask quotes that aim to maximize the market maker’s P&L profile for a given level of inventory risk the agent is willing to take, relying on certain assumptions regarding the microstructure and stochastic dynamics of the market. Extensions to the AS model have been proposed, most notably the Guéant-Lehalle-Fernandez-Tapia approximation , and in a recent variation of it by Bergault et al. , which are currently used by major market making agents. Nevertheless, in practice, deviations from the model scenarios are to be expected.

The latter are a result of extreme outliers for the https://www.beaxy.com/-AS models from days in which these obtained a very poor (i.e., high) value for Max DD. The medians, however, are very similar to the median for the Gen-AS model. Mann-Whitney tests comparing the four daily performance indicator values (Sharpe, Sortino, Max DD and P&L-to-MAP) obtained for the Gen-AS model with the corresponding values obtained for the other models, over the 30 test days. Number of days either Alpha-AS-1 or Alpha-AS-2 scored best out of all tested models, for each of the four performance indicators. The btc-usd data for 7th December 2020 was used to obtain the feature importance values with the MDI, MDA and SFI metrics, to select the most important features to use as input to the Alpha-AS neural network model.

To start, we set up a high-frequency trading model in order to gain from the expected profit by building trading strategies on limit buy and sell orders. The model we will explore is based on a stock price that is generated by Poisson processes with various intensities representing the different jump amounts to employ the adverse selection effects. Consequently, the Alpha-AS agent adapts its bid and ask order prices dynamically, reacting closely (at 5-second steps) to the changing market. This 5-second interval allows the Alpha-AS algorithm to acquire experience trading with a certain bid and ask price repeatedly under quasi-current market conditions.

The Avellaneda-Stoikov procedure underpinning the market-making actions in the models under discussion is explained in Section 2. Section 3 provides an overview of reinforcement learning and its uses in algorithmic trading. The deep reinforcement learning models (Alpha-AS-1 and Alpha-AS-2) developed to work with the Avellaneda-Stoikov algorithm are presented in detail in Section 4, together with an Avellaneda-Stoikov model (Gen-AS) without RL with parameters obtained with a genetic algorithm. Section 5 describes the experimental setup for backtests that were performed on our RL models, the Gen-AS model and two simple baselines.

As usual, you can create a new strategy on Hummingbot using the create command. Since this is a market-making strategy, some configurations will be similar to the pure market-making strategy, so we will cover what is different in this article. Reading the paper, you won’t find any direct indication of calculating these two parameters’ values.

Kumar , who uses Spooner’s RL algorithm as a benchmark, proposes using deep recurrent Q-networks as an improved alternative to DQNs for a time-series data environment such as trading. Gašperov and Konstanjčar tackle the problem be means of an ensemble of supervised learning models that provide predictive buy/sell signals as inputs to a DRL network trained with a genetic algorithm. The same authors have recently explored the use of a soft actor-critic RL algorithm in market making, to obtain a continuous action space of spread values . Comprehensive examinations of the use of RL in market making can be found in Gašperov et al. and Patel . In electronic markets, any trader can become a market maker who provides the liquidity to the markets in Limit Order Books ; and market makers are allowed to submit the orders on both buy and sell sides of the market by the trading mechanisms. Deciding for the best bid and ask prices that a market maker sets up is a hard and complex problem in many aspects due to the fact that the problem should be tackled as a combined problem of the modeling the asset price dynamics and the optimal spreads.

With these avellaneda and stoikov, the AS model will determine the next reservation price and spread to use for the following orders. In other words, we do not entrust the entire order placement decision process to the RL algorithm, learning through blind trial and error. Rather, taking inspiration from Teleña , we mediate the order placement decisions through the AS model (our “avatar”, taking the term from ), leveraging its ability to provide quotes that maximize profit in the ideal case.