Avellaneda Market Making Hummingbot Docs Hummingbot Client Documentation


We call trading cycles the interval of time where spreads start the widest possible and end up the smallest. Once the cycle is reset, spreads will start again, being the widest possible. This parameter, denoted by the letter gamma, is related to the aggressiveness when setting the spreads to achieve the inventory target. It is directly proportional to the asymmetry between the bid and ask spread. The limit bid and ask orders are canceled, and new orders are placed according to the current mid-price and spread at this interval.

  • Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication.
  • The strategy is implemented to be used either in fixed timeframes or to be ran indefinitely.
  • Thus, the DQN approximates a Q-learning function by outputting for each input state, s, a vector of Q-values, which is equivalent to checking the row for s in a Qs,a matrix to obtain the Q-value for each action from that state.
  • The selection of features based on these three metrics reduced their number from 112 to 22 .
  • An Avellaneda strategy feature that recalculates your hanging orders with aggregation of volume weighted, volume time weighted, and volume distance weighted.

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. Under real trading conditions, therefore, there is room for improvement upon the orders generated by the closed-form AS model and its variants.

Sortino ratio

Besides, we find that the number of signals generated from the system can be used to rank stocks for the preference of LOB trading. We test the system with simulation experiments and real data from the Chinese A-share market. The simulation demonstrates the characteristics of the trading system in different market sentiments, while the empirical study with real data confirms significant profits after factoring in transaction costs and risk requirements. Automated market making ,,,, is accomplished with algorithms that place simultaneously buy and sell orders for a given asset, seeking profits from their price difference. This kind of high-frequency trading algorithms constantly readjust the orders to buy at lower prices and sell at higher prices so as to maintain and earn their difference . The task is to set GALA such prices that get enough executions for achieving maximum profits with minimal risk.

What are the algorithms used in forex trading?

  • Trend-following Strategies.
  • Arbitrage Opportunities.
  • Index Fund Rebalancing.
  • Mathematical Model-based Strategies.
  • Trading Range (Mean Reversion)
  • Volume-weighted Average Price (VWAP)
  • Percentage of Volume (POV)
  • Implementation Shortfall.

This 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. Today, Bayesian inference – the application of Bayes’ theorem – is widely and successfully applied in science, engineering, medicine, and machine learning next to philosophy. Indeed, two scholars noted in the preface of their book on Bayesian epistemology, “Bayes is all the rage in philosophy” (Bovens et al., 2003). However, since its introduction in 1763, the theorem was mainly viewed as an obscurity at the fringes of mathematics. It perhaps would never have come to prominence were it not for a mathematician friend of Bayes and later the polymath Pierre-Simon Laplace, who both developed and extended the original ideas.


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. To overcome this problem, a deep Q-network approximates the Qs,a matrix using a deep neural network. The DQN computes an approximation of the Q-values as a function, Q(s, a, θ), of a parameter vector, θ, of tractable size.

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An avellaneda market makingic trading strategy, also known as a “bot,” is an automated process that creates/cancels orders, executes trades, and manages positions on crypto asset exchanges. A strategy, like a computer program, enables traders to adapt to market conditions in an automatic and continuous manner. The Sharpe ratio is a measure of mean returns that penalises their volatility.

Automated Market Makers: Mean-Variance Analysis of LPs Payoffs and Design of Pricing Functions

DRL has been used generally to determine the actions of placing bid and ask quotes directly [23–26], that is, to decide when to place a buy or sell order and at what price, without relying on the AS model. Spooner proposed a RL system in which the agent could choose from a set of 10 spread sizes on the buy and the sell side, with the asymmetric dampened P&L as the reward function (instead of the plain P&L). Combining a deep Q-network (see Section 4.1.7) with a convolutional neural network , Juchli achieved improved performance over previous benchmarks. 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 .


By default, when you run create, we ask you to enter the basic parameters needed for a market-making bot. Total rewards are distributed GMT evenly per minute, which is then allocated to our miners according to how tight their spreads are and their order amount. Users run Humminbot Client or their algo-trading solution of choice, and provide liquidity to one or more of the sponsored token pairs. Users sign-up to our platform and connect their read-only exchange API keys, so trading activity can be monitored and rewards allocated.


The architecture of the https://www.beaxy.com/ DQN is identical to that of the prediction DQN, the parameters of the former being copied from the latter every 8 hours. 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 ).


Here the single best-performing model was Alpha-AS-2, winning for 16 days and coming second on 10 (on 9 of which losing to Alpha-AS-1). Alpha-AS-1 had 11 victories and placed second 16 times (losing to Alpha-AS-2 on 14 of these). AS-Gen had the best P&L-to-MAP ratio only for 2 of the test days, coming second on another 4. The mean and the median P&L-to-MAP ratio were very significantly better for both Alpha-AS models than the Gen-AS model. Post-hoc Mann-Whitney tests were conducted to analyse selected pairwise differences between the models regarding these performance indicators.

Quant Finance

In particular, there are important deficiencies in the methodological section that seriously hinder the understanding of the work as well as the results obtained. Their robustness is also unclear, so I have doubts as to whether the conclusions are supported by the results presented. We tested two variants of our Alpha-AS model, differing in the architecture of their hidden layers.

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. The kNN is a nonparametric estimation method especially appropriate for handling financial time series having stochastic characteristics . The kNN is elaborated to construct directly a model of the mid-price distribution, more precisely the empirical probability density function of the prices by weighted linear composition of the targets. We designed an adaptive Bayesian Nearest Neighbor Tool for extrapolation of mid-prices using a normalized kernel as weighting function. The kernel bandwidth is kept fixed to maintain a stable balance between the bias and variance.

  • Furthermore, the threshold of signals can be adjusted according to investors’ risk aversion.
  • Overall, however, days of substantially better performance relative to the non-Alpha-AS models far outweigh those with poorer results, and at the end of the day the Alpha-AS models clearly achieved the best and least exposed P&L profiles.
  • A second contribution is the setting of the initial parameters of the Avellaneda-Stoikov procedure by means of a genetic algorithm working with real backtest data.
  • The RL agents (Alpha-AS) developed to use the Avellaneda-Stoikov equations to determine their actions are described in Section 4.1.
  • However, adding secure points to a WANET can be costly in terms of price and time, so minimizing the number of secure points is of utmost importance.
  • Today, Bayesian inference – the application of Bayes’ theorem – is widely and successfully applied in science, engineering, medicine, and machine learning next to philosophy.

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