Avellaneda Market Making

How it Works

The Avellaneda Market Making Strategy is designed to scale inventory and keep it at a specific target that a user defines it with. To achieve this, the strategy will optimize both bid and ask spreads and their order amount to maximize profitability.

In its beginner mode, the user will be asked to enter min and max spread limits, and it's aversion to inventory risk scaled from 0 to 1 (Being 0 more driven to profit but less to inventory in target, and 1 being driven to keep tight control of inventory at the expense of less profit). Additionally, sensitivity to volatility changes will be included with a particular parameter vol_to_spread_multiplier, to modify spreads in big volatility scenarios.

In expert mode, the user will need to directly define the algorithm's basic parameters described in the foundation paper, and no recalculation of parameters will happen.

Prerequisites

Inventory

You will need to hold a sufficient inventory of quote and or base currencies on the exchange to place orders of the exchange's minimum order size.

Minimum Order Size

When placing orders, if the order's size determined by the order price and quantity is below the exchange's minimum order size, then the orders will not be created.

Basic Parameters

We aim to teach new users the basics of market-making while enabling experienced users to exercise more control over how their bots behave. By default, when you run create, we ask you to enter the basic parameters needed for a market-making bot.

exchange

The exchange where the bot will place the bid and ask orders.

Prompt:

market

Token trading pair symbol you would like to trade on the exchange.

Prompt:

order_amount

The order amount for the limit bid and ask orders. Ensure you have enough quote and base tokens to place the bid and ask for orders. The strategy will not place any orders if you do not have sufficient balance on either side of the order.

Prompt:

parameters_based_on_spread

The parameter acts as a toggle between beginner (parameters_based_on_spread=True) and expert mode (False). When equal to true, the strategy will require min spread, max spread, volatility multiplier, and inventory risk aversion, while if set to False, it will only ask for risk_factor, order_book_depth_factor, and order_amount_shape_factor.

Prompt:

min_spread

The minimum spread related to the mid-price allowed by the user for bid/ask orders.

Prompt:

max_spread

The maximum spread related to the mid price allowed by user for bid/ask orders

Prompt:

vol_to_spread_multiplier

vol_to_spread_multiplier will act as a threshold value to override max_spread when volatility is a higher value. The value should be higher than 1.

Prompt:

volatility_sensibility

The Volatility Sensibility will recalculate gamma, kappa, and eta after the value of volatility sensibility threshold in percentage is achieved. For example, when the parameter is set to 0, it will recalculate gamma, kappa, and eta each time an order is created. The default value for the parameter is 20.

You can visit this introduction and detailed information on this parameter from this link to know more.

Prompt:

inventory_risk_aversion

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. This will tend to generate more profitability. 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.

Prompt:

order_refresh_time

An amount in seconds, which is the duration for the placed limit orders. The limit bid and ask orders are canceled, and new orders are placed according to the current mid-price and spread at this interval.

Prompt:

inventory_target_base_pct

It sets a target of base asset balance in relation to a total asset allocation value (in percentage value). It works the same as the pure market making strategy's inventory_skew feature in order to achieve this target.

Prompt:

Advanced Parameters

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.

All 3 parameters ‘order_book_depth_factor’, ‘risk_factor’ and ‘order_amount_shape_factor’ are customizable only in expert mode (toggled by ‘parameters_based_on_spread’=False).

order_book_depth_factor

This parameter denoted by the letter kappa is directly proportional to the order book's liquidity, hence the probability of an order being filled. For more details, see the foundation paper.

Prompt:

risk_factor

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. For more details, see the foundation paper.

Prompt:

order_amount_shape_factor

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 asymmetry between the bid and ask order amount. For more details, see the foundation paper.

Prompt:

closing_time

This parameter will be the limit time (measure in days) for this “trading cycle”. 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.

Prompt:

order_optimization_enabled

Allows your bid and ask order prices to be adjusted based on the current top bid and ask prices in the market. By default, this parameter is set to True.

Prompt:

max_order_age

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.

Prompt:

order_refresh_tolerance_pct

The spread (from mid-price) to defer the order refresh process to the next cycle.

To know more about this parameter you can visit this link

Prompt:

filled_order_delay

How long to wait before placing the next set of orders in the case at least one of your orders gets filled.

For example, with a filled_order_delay = 300 when an order created by the bot is filled, the next pair of orders will only be created 300 seconds later.

Prompt:

add_transaction_costs

Whether to enable adding transaction costs to order price calculation.

Prompt:

volatility_buffer_size

The number of ticks used as a sample size for volatility calculation.

Prompt:

order_levels

Quantity of orders to be placed on each side of the order book.

For example, if order_levels = 2 , it will place 2 Buy Orders & 2 Sell Orders.

Prompt:

order_override

Directly override orders placed by order_amount and order_level_parameter.

To start this override feature, users must input the parameters manually in the strategy config file they intend to use. Learn how to access config files with this documentation.

Below is a sample input in dictionary format.

The key is a user-defined order name and the value is a list which includes buy/sell, order spread, and order amount:

order_override:
  order_1: [buy, 0.5, 100]
  order_2: [buy, 0.75, 200]
  order_3: [sell, 0.1, 500]

  # Please make sure there is a space between : and [
  # order_1 label can be renamed to custom labels

Hanging Orders

An Avellaneda strategy feature that recalculates your hanging orders with aggregation of volume weighted, volume time weighted, and volume distance weighted.

config hanging_orders_enabled

config hanging_orders_aggregation_type

config hanging_orders_cancel_pct

Adjust the settings by opening the strategy config file with a text editor.

# Whether to stop cancellations of orders on the other side (of the order book),
# when one side is filled (hanging orders feature) (true/false).
hanging_orders_enabled: true

# Select way of aggregating hanging orders. Whether if leaving them as they are or calculating a resulting hanging order
# default = no_aggregation, volume_weighted, volume_time_weighted, volume_distance_weighted

hanging_orders_aggregation_type: volume_weighted

# Spread (from mid price, in percentage) hanging orders will be canceled (Enter 1 to indicate 1%)
hanging_orders_cancel_pct: .5