Announcing the release of v0.4-alpha

We are excited to announce the release of v0.4-alpha! 🏄‍♂️

Highlights include support for authoring reward functions in a code file, the ability to specify the time of recommendation, and ingestion support for transaction/correlation ids. Authoring reward functions in a code file is a significant improvement to the developer experience than specifying functions inline in the YAML manifest, and we are looking forward to your feedback on it!

If you are new to, check out the getting started guide and star spiceai/spiceai on GitHub.

Highlights in v0.4-alpha

Upgrade using spice upgrade

The spice upgrade command was added in the v0.3.1-alpha release, so you can now upgrade from v0.3.1 to v0.4 by simply running spice upgrade in your terminal. Special thanks to community member @Adm28 for contributing this feature!

Reward Function Files

In addition to defining reward code inline, it is now possible to author reward code in functions in a separate Python file.

The reward function file path is defined by the reward_funcs property.

A function defined in the code file is mapped to an action by authoring its name in the with property of the relevant reward.


    - reward: buy
      with: buy_reward
    - reward: sell
      with: sell_reward
    - reward: hold
      with: hold_reward

Learn more in the documentation:

Time Categories can now learn from cyclical patterns, such as daily, weekly, or monthly cycles.

To enable automatic cyclical field generation from the observation time, specify one or more time categories in the pod manifest, such as a month or weekday in the time section.

For example, by specifying month the engine automatically creates a field in the AI engine data stream called time_month_{month} with the value calculated from the month of which that timestamp relates.


    - month
    - dayofweek

Supported category values are: month dayofmonth dayofweek hour

Learn more in the documentation:

Get recommendation for a specific time

It is now possible to specify the time of recommendations fetched from the /recommendation API.

Valid times are from pod epoch_time to epoch_time + period.

Previously the API only supported recommendations based on the time of the last ingested observation.

Requests are made in the following format: GET http://localhost:8000/api/v0.1/pods/{pod}/recommendation?time={unix_timestamp}

An example for quickstarts/trader

GET http://localhost:8000/api/v0.1/pods/trader/recommendation?time=1605729600

Specifying {unix_timestamp} as 0 will return a recommendation based on the latest data. An invalid {unix_timestamp} will return a result that has the valid time range in the error message:

  "response": {
    "result": "invalid_recommendation_time",
    "message": "The time specified (1610060201) is outside of the allowed range: (1610057600, 1610060200)",
    "error": true

New in this release

  • Adds time categories configuration to the pod manifest to enable learning from cyclical patterns in data - e.g. hour, day of week, day of month, and month
  • Adds support for defining reward functions in a rewards functions code file.
  • Adds the ability to specify recommendation time making it possible to now see which action recommends at any time during the pod period.
  • Adds support for ingestion of transaction/correlation identifiers (e.g. order_id, trace_id) in the pod manifest.
  • Adds validation for invalid dataspace names in the pod manifest.
  • Adds the ability to resize columns to the dashboard observation data grid.
  • Updates to TensorFlow 2.7 and Keras 2.7
  • Fixes a bug where data processors were using data connector params
  • Fixes a dashboard issue in the pod observations data grid where a column might not be shown.
  • Fixes a crash on pod load if the training section is not included in the manifest.
  • Fixes an issue where data manager stats errors were incorrectly being printed to console.
  • Fixes an issue where selectors may not match due to surrounding whitespace.


Community started with the vision to make AI easy for developers. We are building in the open and with the community. Reach out on Discord or by email to get involved. We will also be starting a community call series soon!