avellaneda_market_making
¶
π Strategy Info¶
 Folder: /hummingbot/strategy/avellaneda_market_making
 Configs: avellaneda_market_making_config_map_pydantic.py
 Maintainer: None
π Strategy Tier¶
Community strategies have passed the Minimum Voting Power Threshold in the latest Poll and are included in each monthly release. They are not maintained by Hummingbot Foundation but may be maintained by a community member.
π Summary¶
This strategy implements a market making strategy described in the classic paper Highfrequency Trading in a Limit Order Book written by Marco Avellaneda and Sasha Stoikov. It allows users to directly adjust the risk_factor
(gamma
) parameter described in the paper. It also features an order book liquidity estimator calculating the trading intensity parameters (alpha
and kappa
) automatically. Additionally, the strategy implements an order size adjustment algorithm and its order_amount_shape_factor
(eta
) parameter as described in Optimal HighFrequency Market Making. The strategy is implemented to be used either in fixed timeframes or to be ran indefinitely.
π¦ Exchanges supported¶
 SPOT CLOB CEX
π οΈ Strategy configs¶
Parameter  Type  Default  Prompt 

exchange 
string  Enter your maker spot connector  
market 
string  Enter the token trading pair you would like to trade on exchange 

execution_timeframe 
string  Choose execution timeframe ( infinite / from_date_to_date / daily_between_times ) 

start_time 
string  Please enter the start date and time (YYYYMMDD HH:MM:SS) OR Please enter the start time (HH:MM:SS)  
end_time 
string  Please enter the end date and time (YYYYMMDD HH:MM:SS) OR Please enter the end time (HH:MM:SS)  
order_amount 
decimal  What is the amount of base_asset per order? 

order_optimization_enabled 
bool  True  Do you want to enable best bid ask jumping? 
risk_factor 
decimal  Computed  Enter risk factor (πΎ) 
order_amount_shape_factor 
decimal  Computed  Enter order amount shape factor (Ξ·) 
min_spread 
0  Enter minimum spread limit (as % of mid price)  
order_refresh_time 
decimal  How often do you want to cancel and replace bids and asks (in seconds)?  
max_order_age 
decimal  1800  How long do you want to cancel and replace bids and asks with the same price (in seconds)? 
order_refresh_tolerance_pct 
decimal  0  Enter the percent change in price needed to refresh orders at each cycle 
filled_order_delay 
decimal  60  How long do you want to wait before placing the next order if your order gets filled (in seconds)? 
inventory_target_base_pct 
decimal  50  What is the inventory target for the base asset? 
add_transaction_costs 
decimal  False  Do you want to add transaction costs automatically to order prices? (Yes/No) 
volatility_buffer_size 
decimal  200  Enter amount of ticks that will be stored to calculate volatility 
trading_intensity_buffer_size 
decimal  200  Enter amount of ticks that will be stored to estimate order book liquidity? 
order_level_mode 
int  1  How many orders do you want to place on both sides? 
level_distances 
decimal  0  How far apart in % of optimal spread should orders on one side be? 
order_override 
json  
hanging_orders_mode 
bool  False  How do you want to handle hanging orders? (track_hanging_orders/ignore_hanging_orders) 
should_wait_order_cancel_confirmation 
bool  True  Should the strategy wait to receive a confirmation for orders cancellation before creating a new set of orders? (Not waiting requires enough available balance) (Yes/No) 
π Description¶
Approximation only
The description below is a general approximation of this strategy. Please inspect the strategy code in Trading Logic above to understand exactly how it works.
Overview¶
The strategy continuously calculates optimal positioning of a market maker's buy and sell limit orders within an order book, based on the following information:
 Current order book liquidity
 Asset price volatility
 Desired portfolio allocation (target inventory)
 Trading session timeframe
 Risk factor (user choice)
There is two main values that are calculated by the model, based on the factors mentioned above:
 Reservation price: A price different from the market mid price, that will be used as reference to create orders.
 Optimal spread: The best possible spread from the reservation price where the orders will be created.
Compared to the previous version these parameters were removed:
parameters_based_on_spread
max_spread
vol_to_spread_multiplier
volatility_sensibility
inventory_risk_aversion
order_book_depth_factor
closing_time
Parameter min_spread
has a different meaning, parameter risk_factor
is being used differently in the calculations and therefore attains a different range of values.
Reservation Price¶
The farther the current inventory is from the desired asset allocation (as defined by the inventory_target_base_pct
parameter), 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
.
For example, If the strategy needs an asset to be sold to reach the inventory_target_base_pct
value, sell orders will be placed closer to the mid price than buy orders.
Optimal Spreads¶
The Optimal spread values (which defines at what price each order will be created) is a function of the order book liquidity, asset price volatility and trading session timeframe. Each factor have an influence on the value calculated:
 Low order book liquidity = Smaller optimal spread value
 Low price volatility = Smaller optimal spread value
 Time closer to the end of the trading session = Smaller optimal spread value
Risk Factor¶
The final piece of information that influence both Reservation price and Optimal Spread values is the risk_factor
(gamma
).
This value is defined by the user, and it represents how much inventory risk he is willing to take.
The closer the risk_factor
is to zero, the more symmetrical will be orders will be created, and the Reservation price will be pretty much equal to the market mid price.
In that case, the user is taking more inventory risk, because there will be no skew on the orders positions aiming to reach the inventory_target_base_pct
.
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.
It's a unitless parameter, that can be set to any nonzero value as necessary, depending on the inventory risk the user is willing to take.
NOTE: The
risk_factor
is defined relative to the instant volatility of the asset given in absolute price values. For all assets the valuesrisk_factor
can attain should be roughly within the same range, however there can be a few exceptions where the parameter would require a significantly different value to start having an effect on the Reservation price and on the Optimal Spread As an example, for asset A, arisk_factor = 1
can already have a noticeable effect, while for asset B, therisk_factor
must be at least around 10 to have any noticeable effect. The only way to find a value for therisk_factor
is to experiment with different values and see it's effects on the Reservation price and the Optimal spread. Based on our experience common values of this parameter are between 1 and 20, however it is unrestricted on the upper side, therefore if necessary its value can be even 100 or 1000, although it's not very common.
Given the right market conditions and the right risk_factor
, it's possible that the optimal spread will be wider than the absolute price of the asset, or that the reservation price will by far away from the mid price, in both cases resulting in the optimal bid price to be lower than or equal to 0. If this happens neiher buy or sell will be placed. To prevent it from happening, users can set the risk_factor
to a lower value.
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. The further away the reservation price is from the mid price, the more aggressive the strategy is in pursuing its target portfolio allocation, because orders on one side will be far more likely to be filled than on the other.
ETA (Order size adjustment)¶
If users choose to set the eta
parameter, order sizes will be adjusted to further optimize the strategy behavior in regards to the current and desired portfolio allocation.
With a value of eta = 1
, buy and sell orders will have the same size. A different value will create assymetrical order sizes, with the goal to reach the inventory_target_pct
faster.
Order levels¶
Users have an option to layer orders on both sides. If more than 1 order_levels
are chosen, multiple buy and sell limit orders will be created on both sides, with predefined price distances from each other, with the levels closest to the reservation price being set to the optimal bid and ask prices. This price distance between levels is defined as a percentage of the optimal spread calculated by the strategy. The percentage is given as the level_distances
parameter. Given that optimal spreads tend to be tight, the level_distances
values should be in general in tens or hundreds of percents.
Trading logic flow¶
Step  Meaning 

Are buffers filled?  Are instant volatility indicator and trading intensity indicator buffers full? 
Are characteristics estimated?  Are order book liquidity / trading intensity parameter estimations available? 
Is infinite timeframe?  Is the trading session set to be infinite or constrained to from_date_to_date or daily_between_times ? 
Are multiple levels defined?  Is value of order_levels higher than 1? 
Is minimum spread defined?  Is value of min_spread higher than 0? 
Timeframes¶
The original AvellanedaStoikov model was designed to be used for market making on stock markets, which have defined trading hours. The assumption was that the market maker wants to end the trading day with the same inventory he started.
Since cryptocurrency markets are open 24/7, there is no "closing time", and the strategy should also be able run indefinitely, based on an infinite timeframe.
NOTE: AvellanedaStoikov also considered the possibility of running the model on an infinite timeframe
The strategy allows three possible timeframes to be used:
infinite
 No closing time for the trading session is consideredfrom_date_to_date
 The strategy will begin trading on thestart_time
(YYYYMMDD HH:MM:SS) and stop at theend_time
(YYYYMMDD HH:MM:SS), as one single trading session.daily_between_times
 The strategy will run as multiple trading sessions, and every day will begin to trade atstart_time
(HH:MM:SS) and stop atend_time
(HH:MM:SS)
For the infinite
timeframe the equations used to calculate the reservation price and the optimal spread are slightly different, because the strategy doesn't have to take into account the time left until the end of a trading session.
Both the start_time
and the end_time
parameters are defined to be in the local time of the computer on which the client is running. For the infinite
timeframe these two parameters have no effect.
Asset Characteristics Estimation¶
The strategy calculates the reservation price and the optimal spread based on measurements of the current asset volatility and the order book liquidity. The asset volatility estimator is implemented as the instant_volatility
indicator, the order book liquidity estimator is implemented as the trading_intensity
indicator.
Before any estimates can be given, both estimators need to have their buffers filled. By default the lengths of these buffers are set to be 200 ticks. In case of the trading_intensity
estimator only order book snapshots different from preceding snapshots count as valid ticks. Therefore the strategy may take longer than 200 seconds (in case of the default length of the buffer) to start placing orders.
The trading_intensity
estimator is designed to be consistent with ideas outlined in the AvellanedaStoikov paper. The instant_volatility
estimator defines volatility as a deviation of prices from one tick to another in regards to a zerochange price action.
Minimum Spread¶
The minimum_spread
parameter is optional, it has no effect on the calculated reservation price and the optimal spread. It serves as a hard limit below which orders won't be placed, if users choose to ensure that buy and sell orders won't be placed too close to each other, which may be detrimental to the market maker's earned fees. The minimum spread is given by the minimum_spread
parameter as a percentage of the mid price. By default its value is 0, therefore the strategy places orders at optimal bid and ask prices.
References¶
 Highfrequency Trading in a Limit Order Book  Avellaneda, Stoikov
 Optimal HighFrequency Market Making  Fushimi, Rojas, Herman
βΉοΈ More Resources¶
Highfrequency trading in a limit order book: The seminal 2008 paper on market making, published in Quantitative Finance, by Marco Avellaneda and Sasha Stoikov.
A comprehensive guide to Avellaneda & Stoikovβs marketmaking strategy: A comprehensive walkthrough of the classic avellaneda market making strategy that is based on a famous classic academic paper.
Avellaneda strategy: A technical deep dive: We explain how we modified the original AvellanedaStoikov model for the cryptocurrency industry, as well as how we simplified the calculation of key parameters (Greeks).
New Avellaneda Market Making Strategy Demo + AMA  Hummingbot Live: Demo of the latest iteration of Avellaneda Market Making strategy
Check out Hummingbot Academy for more resources related to this strategy and others!