The procedure, therefore, has two steps, which are applied at each time increment as follows. For asymptotic expansions when T is large you should read the paper by Guéant, Lehalle, and Fernandez-Tapia here or the book of Guéant The financial mathematics of market-liquidity. The final piece of information that influence both Reservation price and Optimal Spread values is the risk_factor . 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.

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. 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 Alpha-AS agent receives an update of the orderbook every time a market tick occurs.


We mention neuroevolution to train the avellaneda stoikov market making network using genetic algorithms and adversarial networks to improve the robustness of the market making algorithm. 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. This is an efficient way of arriving at quasi-optimal values for these parameters given the market environment in which the agent begins to operate. From this point, the RL agent can gradually diverge as it learns by operating in the changing market.


And then we show how to incorporate those tiers into the model,” says Barzykin. In the paper, clients are divided into two tiers based on their sensitivity to price changes. Some clients need to take certain positions, and their activity is less likely to be influenced by changes in price, while others are more likely to trade when they see an attractive price. In Section 2, we introduce some basic concepts and describe the input LOB datasets. Trading strategy with stochastic volatility in a limit order book market. It is observed that the thickness of the market prices is correlated with the trading intensity inversely.

2 Results with the exponential utility function

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.

mid price

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.

Equilibrium Price and Optimal Insider Trading Strategy Under Stochastic Liquidity with Long Memory

The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. Participant privacy or use of data from a third party—those must be specified. Adjust the settings by opening the strategy config file with a text editor. Directly override orders placed by order_amount and order_level_parameter.

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A continuous avellaneda stoikov market making space, as the one used to choose spread values in , may possibly perform better, but the algorithm would be more complex and the training time greater. In setting the risk_factor it’s important to observe the reservation price in regards to the mid price. If the user wishes the spread between these two prices to be wider, the risk factor should be set to a higher value.

This parameter is a value that must be defined by the market maker, considering how much inventory risk he is willing to be exposed. Currencies, though this can vary depending on market volatility and client flows. “Under standard assumptions of risk tolerance and daily turnover, the model indeed confirms that this level of internalisation is optimal on average,” says Barzykin. The finding correlates with current industry practices, while the optimal risk neutralisation time derived from the model was also in line with market norms.

Thus, the Alpha-AS models came 1stand 2nd on 20 out of the 30 test days (67%). The mean and the median of the Sharpe ratio over all test days was better for both Alpha-AS models than for the Gen-AS model , and in turn the Gen-AS model performed significantly better on Sharpe than the two non-AS baselines. Allows your bid and ask order prices to be adjusted based on the current top bid and ask prices in the market. This parameter will be the limit time for this “trading cycle”.


Other https://www.beaxy.com/, such as the Sortino ratio, can also be used in the reward function itself. Another approach is to explore risk management policies that include discretionary rules. Alternatively, experimenting with further layers to learn such policies autonomously may ultimately yield greater benefits, as indeed may simply altering the number of layers and neurons, or the loss functions, in the current architecture. Maximum drawdown registers the largest loss of portfolio value registered between any two points of a full day of trading. 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. Again, on 9 of the 12 days for which Alpha-AS-1 had the best Sharpe ratio, Alpha-AS-2 had the second best; and for 10 of the 13 test days for which after Alpha-AS-2 obtained the best Sortino ratio, Alpha-AS-1 performed second best.

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. In contrast, exchanges in the Chinese A-share market publish the level II data, essentially 10-level LOB, every three seconds on average, with 4500–5000 daily ticks. This snapshot data provides us with the opportunity to leverage the longer tick-time interval and make profits using machine learning algorithms. In order to analyze the experimental results, we work on the models that we have derived using different metrics. It is salient to mention that the market maker modifies her qualitative behavior in various situations, i.e., changing inventory levels, utility functions.

Both Alpha-AS models performed better than the rest on 19 days. Meanwhile, AS-Gen, again the best of the rest, won on Sortino on only 3 test days. The mean and the median of the Sortino ratio were better for both Alpha-AS models than for the Gen-AS model , and for the latter it was significantly better than for the two non-AS baselines.

What is crypto market making?

Market making in crypto is an activity whereby a trader simultaneously provides liquidity to both buyers and sellers in a financial market. Liquidity is the degree to which an asset can be quickly bought or sold without notably affecting the stability of its price.

We model the market-agent interplay as a Markov Decision Process with initially unknown state transition probabilities and rewards. Α is the learning rate (α∈), which reduces to a fraction the amount of change that is applied to Qi from the observation of the latest reward and the expectation of optimal future rewards. This limits the influence of a single observation on the Q-value to which it contributes. @RRG Right, this makes sense that the market-maker can place quotes improving on the current midprice. So I guess the fact that the plot in the original paper does not show crossing between the quotes of the market-maker and the midprice is just a matter of coincidence.


The DQN computes an approximation of the Q-values as a function, Q(s, a, θ), of a parameter vector, θ, of tractable size. There are various methods to achieve this, a particularly common one being gradient descent. The models underlying the AS procedure, as well as its implementations in practice, rely on certain assumptions.

Market-making by a foreign exchange dealer – Risk.net

Market-making by a foreign exchange dealer.

Posted: Wed, 10 Aug 2022 07:00:00 GMT [source]

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