optimus_template.yaml

logging:
  # The desired logging level.
  # Allowed values are "debug", "info", "warn", "error", "panic" and "fatal"
  level: debug

# Resource restrictions settings.
restrictions:
  # CGroup name for Optimus.
  cgroup_name: /optimus
  # Maximum CPU cores allowed for consumption.
  cpu_count: 0.5

# Global account settings.
# Used by all components unless overridden.
ethereum: &ethereum
  # Path to the keystore.
  key_store: "{{KEYSTORE}}"
  # Passphrase for the keystore.
  pass_phrase: {{PASSWORD}}
# Node settings.
node:
  # Account settings.
  ethereum: *ethereum
  # Node trusted endpoint.
  endpoint: {{ADMIN_ADDRESS}}@127.0.0.1:15030

benchmarks:
  # URL to download benchmark list, use "file://" schema to load file from a filesystem.
  url: "{{BENCHMARKS_LIST}}"

# Marketplace polling settings.
marketplace:
  # Account settings.
  ethereum: *ethereum
  # DWH endpoint.
  endpoint: "{{DWH_ENDPOINT}}"
  # Interval of current orders caching, that are fetched from the marketplace.
  interval: 30s
  # Minimum price threshold for fetching orders from the marketplace.
  # Used to prevent regression poisoning.
  # Optional.
  min_price: 0.003 USD/h

# Optimization engine settings.
optimization: &optimization
  # Since v0.4.6 Optimus contains all models with predefined well-tuned
  # settings.
  # However it is possible to override them. All of them have default
  # values specified in this config example.
  # The main thing here is the "model". It must have "type" field and
  # model-specific settings.
  # Currently we support the following models: batch, greedy, genetic
  # and branch_bound.
  model:
    # Batch model combines several other models to be able to run them
    # simultaneously and select the most profitable optimization result.
    # By default the following logic happens: if the matched orders count
    # is small enough the branch_bound algorithm is activated, which gives
    # the optimal solution.
    type: batch
    # Brute force model activation settings.
    brute:
      # Number of matched orders to activate the following model.
      # Must be small enough, because brute force models may consume all CPUs
      # on the machine.
      match: 8
      # Model settings.
      model:
        # Branch and bound model.
        type: branch_bound
        # Maximum tree height. After reaching the specified value the model
        # returns the best it found.
        height_limit: 6
    models:
        # Greedy algorithm is a fast and simple optimization heuristic, that
        # can find solution, but possibly not the optimum.
        # We use linear regression to reduce the number of dimensions, which is
        # the number of benchmarks, to a single one to be able to apply greedy
        # technique.
      - type: greedy
        # Orders with weight lesser than the specified will be ignored.
        weight_limit: 1e-3
        # Stop trying to pack sorted-by-weight orders after reaching the
        # specified value.
        exhaustion_limit: 128
        # Regression type.
        # Currently supported regressions: linear least squares (lls) and
        # non-negative least squares (nnls).
        regression:
          # Linear least squares regression.
          type: lls
          # Specific gradient descent options.
          # Alpha is the learning rate.
          # If the learning rate alpha is too small we will have slow
          # convergence.
          # If alpha is too large J of theta may not decrease on every
          # iteration and may not converge.
          alpha: 1e-6
          # Regularization term.
          regularization: 6.0
          # Maximum number of iterations.
          max_iterations: 1000
      - type: greedy
        # Orders with weight lesser than the specified will be ignored.
        weight_limit: 1e-3
        # Stop trying to pack sorted-by-weight orders after reaching the
        # specified value.
        exhaustion_limit: 128
        # Regression type.
        # Currently supported regressions: linear least squares (lls) and
        # non-negative least squares (nnls).
        regression:
          # Linear non-negative least squares regression.
          type: nnls
        # Genetic algorithm is a meta-heuristic that tries to optimize the
        # worker's knapsack using analogy with biological processes.
      - type: genetic
        # Packed genome starts with populations with randomly picked orders.
        genome: packed
        # Size of the population. Large populations increases the probability
        # of more optimized solution, but consumes more CPU.
        population_size: 256
        # Maximum number of population generations, after reaching those
        # evolution process is stopped.
        max_generations: 128
        # Maximum algorithm work time.
        # If reached, the best attempt is selected.
        max_age: 5m
      - type: genetic
        # Decision genome starts with populations with no orders selected.
        genome: decision
        # Size of the population. Large populations increases the probability
        # of more optimized solution, but consumes more CPU.
        population_size: 512
        # Maximum number of population generations, after reaching those
        # evolution process is stopped.
        max_generations: 64
        # Maximum algorithm work time.
        # If reached, the best attempt is selected.
        max_age: 5m

# Map of workers this bot manages defined by its trusted endpoints with their
# settings.
workers:
  # Use worker ETH address here, but the account used must have the proper
  # access.
  {{WORKER_ADDRESS}}@127.0.0.1:15010:
    # Account settings.
    ethereum: *ethereum
    # Epoch is an interval of scanning worker's free resources and selling them
    # by cutting ask plans.
    # Also this value is used when it's time to replace non-spot deals to
    # preliminary calculate whether current ask plans are still profitable.
    # Optional.
    # Default value is 60s.
    epoch: 60s
    # How the bot should filter orders. Possible values are: "spot_only".
    order_duration_threshold: 72h
    # Orders that haven't been sold for this interval will be canceled.
    # Optional.
    # Default value is 5m.
    stale_threshold: 5m
    # Each epoch for both spot and nearly-finished deals Optimus tries to
    # optimize the next most-profitable ask plan configuration.
    # If the new set will gain more profit/hour than the specified value
    # some of ask plans will be replaced.
    # Zero value is forbidden, because it leads to spot deals being replaced
    # each epoch.
    # Required.
    # Possible dimensions are USD/s, USD/h. May be specified either as absolute
    # or relative. For example "0.02 USD/h" would mean that Optimus will
    # reassembly all bids if a new combination gives more than "0.02 USD/h"
    # profit that the existing one.
    # If you want to activate relative threshold in percents, specify "%" after
    # the value, for example "2.5%".
    price_threshold: 5%
    # When activated Optimus will not create ask plans, but continues doing
    # the rest calculations instead. Useful for debugging.
    # Optional.
    # Default value is false.
    # dry_run: true
    # Identity level specifies which kind of customers this bot allows to work
    # with.
    # Required.
    # Possible values are: anonymous, registered, identified and professional.
    identity: anonymous
    # Prelude timeout for fetching input properties for the optimization step.
    # Used to detect hanging workers and/or DWH.
    # Optional.
    # Default value is 30s.
    prelude_timeout: 30s
    # Optimization engine settings.
    optimization: *optimization
    # Simulation settings.
    # When specified Optimus is switched into simulation mode for this worker.
    # No orders from the marketplace except specified will be fetched.
    # This mode is required mostly for debugging.
    #simulation:
    #  orders: [42, 43]
    # Specifies how Optimus will create ask-plans for this worker.
    # Possible values: precise, entire_machine.
    # The "precise" value means the most precise way to cut and sell the
    # machine using various optimization algorithms.
    # The "entire_machine" value means that either the most profitable forward
    # order will consume the entire machine or the set of spot orders depending
    # on the total profitability.
    plan_policy: entire_machine