trader-quickstart

A quickstart for the trader agent for AI prediction markets on Gnosis at https://github.com/valory-xyz/trader

System Requirements

Ensure your machine satisfies the requirements:

• Python == 3.10
• Poetry >=1.4.0
• Docker Engine
• Docker Compose

Resource Requirements

• You need xDAI on Gnosis Chain in one of your wallets.
• You need an RPC for your agent instance. We recommend https://getblock.io/.

Run the script

Clone this repository locally and execute:

chmod +x run_service.sh
./run_service.sh

Once the command has completed, i.e. the service is running, you can see the live logs with:

docker logs trader_abci_0 --follow

To stop your agent, use:

cd trader; poetry run autonomy deploy stop --build-dir trader_service/abci_build; cd .. 

Observe your agents

1. Check out this handy app: https://predictions.oaksprout.repl.co/

2. Use the trades command to display information about placed trades by a given address:

   cd trader; poetry run python ../trades.py YOUR_SAFE_ADDRESS; cd ..

   Or restrict the search to specific dates by defining the "from" and "to" dates:

   cd trader; poetry run python ../trades.py YOUR_SAFE_ADDRESS --from-date 2023-08-15:03:50:00 --to-date 2023-08-20:13:45:00; cd ..

3. Use this command to investigate your agent's logs:

   cd trader; poetry run autonomy analyse logs --from-dir trader_service/abci_build/persistent_data/logs/ --agent aea_0 --reset-db; cd ..

   For example, inspect the state transitions using this command:

   cd trader; poetry run autonomy analyse logs --from-dir trader_service/abci_build/persistent_data/logs/ --agent aea_0 --fsm --reset-db; cd ..

   This will output the different state transitions of your agent per period, for example:

   

   For more options on the above command run:

   cd trader; poetry run autonomy analyse logs --help; cd ..

   or take a look at the command documentation.

Update between versions

Simply pull the latest script:

git pull origin

Remove the existing trader folder:

rm -rf trader

Then continue above with "Run the script".

Advice for Mac users

In Docker Desktop make sure that in Settings -> Advanced the following boxes are ticked

Advice for Windows users

We provide some hints to have your Windows system ready to run the agent. The instructions below have been tested in Windows 11.

Execute the following steps in a PowerShell terminal:

1. Install Git and Git Bash:

   winget install --id Git.Git -e --source winget

2. Install Python 3.10:

   winget install Python.Python.3.10

3. Close and re-open the PowerShell terminal.

4. Install Poetry:

   curl.exe -sSL https://install.python-poetry.org | python -

5. Add Poetry to your user's path:

   $existingUserPath = (Get-Item -Path HKCU:\Environment).GetValue("PATH", $null, "DoNotExpandEnvironmentNames")
   
   $newUserPath = "$existingUserPath;$Env:APPDATA\Python\Scripts"
   
   [System.Environment]::SetEnvironmentVariable("Path", $newUserPath, "User")

6. Install Docker Desktop:

   winget install -e --id Docker.DockerDesktop

7. Log out of your Windows session and then log back in.

8. Open Docker Desktop and leave it opened in the background.

Now, open a Git Bash terminal and follow the instructions in the "Run the script" section as well as the subsequent sections. You might need to install Microsoft Visual C++ 14.0 or greater.

Advanced usage

This chapter is for advanced users who want to further customize the trader agent's behaviour without changing the underlying trading logic.

Policy weights

This script automatically sets some default weights to the agent's policy as a warm start to help convergence and improve tool selection. These data were obtained after many days of running the service and are set here. As a result, the current weights are always deleted and replaced by this strategy which is considered to boost the initial performance of the service.

However, you may have found better performing policy weights and would like to remove this logic. It can easily be done, by removing this method call, here, in order to set your own custom warm start. Setting your own custom weights can be done by editing the corresponding files in .trader_runner. Moreover, you may store your current policy as a backup before editing those files, using the following set of commands:

cp ".trader_runner/available_tools_store.json" ".trader_runner/available_tools_store_$(date +"%d-%m-%Y")".json
cp ".trader_runner/policy_store.json" ".trader_runner/policy_store_$(date +"%d-%m-%Y")".json
cp ".trader_runner/utilized_tools.json" ".trader_runner/utilized_tools_$(date +"%d-%m-%Y")".json

Tool selection

Sometimes, a mech tool might temporarily return invalid results. As a result, the service would end up performing mech calls without being able to use the response. Assuming that this tool has a large reward rate in the policy weights, the service might end up spending a considerable amount of xDAI before adjusting the tool's reward rate, without making any progress. If a tool is temporarily misbehaving, you could use an environment variable in order to exclude it. This environment variable is defined here and can be overriden by setting it anywhere in the run_service.sh script with a new value, e.g.:

IRRELEVANT_TOOLS=["some-misbehaving-tool", "openai-text-davinci-002", "openai-text-davinci-003", "openai-gpt-3.5-turbo", "openai-gpt-4", "stabilityai-stable-diffusion-v1-5", "stabilityai-stable-diffusion-xl-beta-v2-2-2", "stabilityai-stable-diffusion-512-v2-1", "stabilityai-stable-diffusion-768-v2-1"]