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fedecaccia avellaneda-stoikov: Avellaneda-Stoikov HFT market making algorithm implementation


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. Therefore, Likert-type variables cannot be used as segmentation variables of a traditional cluster analysis unless pre-transformed.

  • 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 order to recall the models easier, we call the model studied in in Case 1 in Sect.
  • However, it does not work well to realize the consistency of the teams’ performance.
  • Optimal dealer pricing under transactions and return uncertainty.

Therefore, the corresponding HJB equation can be obtained by applying the stochastic control approach. Of exists and is unique that should be guaranteed by the verification theorem so that this classical solution is the value function of the HJB equation and the spreads, defined by , are indeed the optimal ones. 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 ETH asymmetry between the bid and ask order amount. 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.

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Due to the non-linearity and volatility of stock prices and the unique nature of financial transactions, it is essential for the prediction method to ensure high prediction performance and interpretability. However, existing methods fail to achieve both the two goals simultaneously. To fill this gap, this paper presents an interpretable intuitionistic fuzzy inference model, dubbed as IIFI. While retaining the prediction accuracy, the interpretable module in IIFI can automatically calculate the feature contribution based on the intuitionistic fuzzy set, which provides high interpretability of the model. Also, most of the existing training algorithms, such as LightGBM, XGBoost, DNN, Stacking, etc, can be embedded in the inference module of our proposed model and achieve better prediction results. The back-test experiment on China’s A-share market shows that IIFI achieves superior performance — the stock profitability can be increased by more than 20% over the baseline methods.

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. We have designed a market making agent that relies on the Avellaneda-Stoikov procedure to minimize inventory risk. The agent can also skew the bid and ask prices output by the Avellaneda-Stoikov procedure, tweaking them and, by so doing, potentially counteract the limitations of a static Avellaneda-Stoikov model by reacting to local market conditions. The agent learns to adapt its risk aversion and skew its bid and ask prices under varying market behaviour through reinforcement learning using two variants (Alpha-AS-1 and Alpha-AS-2) of a double DQN architecture.

Related work on machine learning in trading

Is the sum of the corresponding quantity over all of the orderbook levels . S′ is the state the MDP has transitioned to when taking action a from state s, to which it arrived at the previous iteration. R is the latest reward obtained from state s by taking action a. Discover a faster, simpler path to publishing in a high-quality journal. PLOS ONE promises fair, rigorous peer review, broad scope, and wide readership – a perfect fit for your research every time. The description below is a general approximation of this strategy.

3 that the strategy is profitable even when there are adverse selection effects in the model due to the expectations of the jumps. We design a market-making model \`a la Avellaneda-Stoikov in which the market-takers act strategically, in the sense that they design their trading strategy based on an exogenous trading signal. The market-maker chooses her quotes based on the average market-takers’ behaviour, modelled through a mean-field interaction. We derive, up to the resolution of a coupled HJB–Fokker–Planck system, the optimal controls of the market-maker and the representative market-taker. This approach is flexible enough to incorporate different behaviours for the market-takers and takes into account the impact of their strategies on the price process.

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 . Where tj is the current time upon arrival of the jth market tick, pm is the current market mid-price, I is the current size of the inventory held, γ is a constant that models the agent’s risk aversion, and σ2 is the variance of the market midprice, a measure of volatility. The farther the current inventory is from the desired asset allocation , the greater the distance between reservation price and the market mid price. The strategy skews the probability of either buy or sell orders being filled, depending on the difference between the current inventory and the inventory_target_base_pct.

  • 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 optimal spread will have to be smaller since there is more competition on the market.
  • The actions performed by our RL agent are the setting of the AS parameter values for the next execution cycle.
  • In such context, fuzzy numbers have been suggested as a way to recode Likert-type variables.
  • However, existing methods fail to achieve both the two goals simultaneously.
  • 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.

Moreover, the spread can also be considered to be normally distributed due to its skewness and kurtosis values. For a fixed inventory level q and a representation of the asset volatility which are obtained from one simulation. These are additional parameters that you can reconfigure and use to customize the behavior of your strategy further. To change its settings, run the command config followed by the parameter name, e.g. config max_order_age. On the whole, the Alpha-AS models are doing the better job at accruing gains while keeping inventory levels under control.

In order to see the time evolution of the process for larger inventory bounds. 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.

What is Avellaneda & Stoikov market making strategy?

The basic strategy for market making is to create symmetrical bid and ask orders around the market mid-price. 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.

The will be based on two different choices of utility functions, quadratic and exponential, in the sequel. The max_order_age parameter allows you to set a specific duration when resetting your order’s age. It refreshes your orders and automatically creates an order based on the spread and movement of the market. An amount in seconds, which is the duration for the placed limit orders.

To 5 show avellaneda stoikov market making 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. Inventory Risk Aversion is a quantity between 0 and 1 to measure the compromise between mitigation of inventory risk and profitability. When parameters are closer to 0, spreads will be almost symmetrical. 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. 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.

What is Avellaneda-Stoikov formula?

The control strategy places symmetrical trade around the ‘mid-price’ rather than the reservation price. The Avellaneda-Stoikov (AS) strategy, will be placing trades at ra = r + δa and rb = r − δb.

On Hummingbot, the value of q is calculated based on the target inventory percentage you are aiming for. Pulling all of that together was mathematically complicated due to the fact that client flows are discrete while trading on liquidity pools is continuous. Sorry, a shareable link is not currently available for this article.

The performance of the proposed methodology is investigated with recent state-of-the-art works and International Cricket Council rankings using the Spearman Rank Correlation Coefficient for all the 3 formats of cricket, i.e., Test, One Day International , and Twenty20 . The results indicate that the proposed ranking methods yield quite more encouraging insights than the recent state-of-the-art works and can be acquired for ranking cricket teams. A closed-form solution for options with stochastic volatility with applications to bond and currency options.

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The higher the value, the more aggressive the strategy will be to reach the inventory_target_base_pct, increasing the distance between the Reservation price and the market mid price. To minimize inventory risk, prices should be skewed to favor the inventory to come back to its targeted ideal balance point. You might have noticed that I haven’t added volatility(σ) on the main factor list, even though it is part of the formula.

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