Prediction Market Agent Tooling

Tooling for benchmarking, deploying and monitoring agents for prediction market applications.

Setup

Install the project dependencies with poetry, using Python >=3.10:

python3.10 -m pip install poetry
python3.10 -m poetry install
python3.10 -m poetry shell

Create a .env file in the root of the repo with the following variables:

Deploying and monitoring agents using GCP requires that you set up the gcloud CLI (see here for installation instructions, and use gcloud auth login to authorize.)

MANIFOLD_API_KEY=...
BET_FROM_PRIVATE_KEY=...
OPENAI_API_KEY=...

Benchmarking

Create a benchmarkable agent by subclassing the AbstractBenchmarkedAgent base class, and plug in your agent's research and prediction functions into the predict method.

Use the Benchmarker class to compare your agent's predictions vs. the 'wisdom of the crowd' on a set of markets from your chosen prediction market platform.

For example:

import prediction_market_agent_tooling.benchmark.benchmark as bm
from prediction_market_agent_tooling.benchmark.agents import RandomAgent
from prediction_market_agent_tooling.markets.market_type import MarketType
from prediction_market_agent_tooling.markets.markets import get_binary_markets

benchmarker = bm.Benchmarker(
    markets=get_binary_markets(limit=10, market_type=MarketType.MANIFOLD),
    agents=[RandomAgent(agent_name="a_random_agent")],
)
benchmarker.run_agents()
md = benchmarker.generate_markdown_report()

This produces a markdown report that you can use for comparing agents side-by-side, like:

Benchmark results

Deploying

Deprecated: We suggest using your own infrastructure to deploy, but you may still find this useful.

Create a deployable agent by subclassing the DeployableTraderAgent base class, and implementing the answer_binary_market method.

For example, deploy an agent that randomly picks an outcome:

import random
from prediction_market_agent_tooling.deploy.agent import DeployableTraderAgent
from prediction_market_agent_tooling.markets.agent_market import AgentMarket

class DeployableCoinFlipAgent(DeployableTraderAgent):
    def answer_binary_market(self, market: AgentMarket) -> bool | None:
        return random.choice([True, False])

DeployableCoinFlipAgent().deploy_gcp(...)

Safe

Agents can control funds via a wallet primary key only, or optionally via a Safe as well. For deploying a Safe manually for a given agent, run the script below:

poetry run python scripts/create_safe_for_agent.py  --from-private-key <YOUR_AGENT_PRIVATE_KEY> --salt-nonce 42

This will output the newly created Safe in the terminal, and it can then be copied over to the deployment part (e.g. Terraform). Note that salt_nonce can be passed so that the created safe is deterministically created for each agent, so that, if the same salt_nonce is used, the script will not create a new Safe for the agent, instead it will output the previously existent Safe.

You can then specify this agent's Safe address with the SAFE_ADDRESS environment variable.

Monitoring

Monitor the performance of the agents deployed to GCP, as well as meta-metrics of the prediction market platforms they are deployed to.

This runs as a streamlit app on a localhost server, executed with:

PYTHONPATH=. streamlit run examples/monitor/monitor.py

Which launches in the browser:

Monitoring

The Market Platforms

The following prediction market platforms are supported:

Platform	Benchmarking	Deployment	Monitoring
Manifold	✅	✅	✅
AIOmen	✅	✅	✅
Polymarket	✅	❌	❌

Prediction Markets Python API

We have built clean abstractions for taking actions on the different prediction market platforms (retrieving markets, buying and selling tokens, etc.). This is currently undocumented, but for now, inspecting the AgentMarket class and its methods is your best bet.

For example:

from prediction_market_agent_tooling.config import APIKeys
from prediction_market_agent_tooling.markets.agent_market import SortBy
from prediction_market_agent_tooling.markets.omen.omen import OmenAgentMarket

# Place a bet on the market closing soonest
market = OmenAgentMarket.get_markets(limit=1, sort_by=SortBy.CLOSING_SOONEST)[0]
market.place_bet(outcome=True, amount=market.get_bet_amount(0.1))

# View your positions
my_positions = OmenAgentMarket.get_positions(user_id=APIKeys().bet_from_address)
print(my_positions)

# Sell position (accounting for fees)
market.sell_tokens(outcome=True, amount=market.get_bet_amount(0.095))

This API can be built on top of to create your application. See here for an example.

Contributing

See the Issues for ideas of things that need fixing or implementing. The team is also receptive to new issues and PRs.

We use mypy for static type checking, and isort, black and autoflake for linting, and pre-commit to minimise unwanted pushes to the public repositories. These all run as steps in CI, but pre-commit also needs to be installed locally using the provided install_hooks.sh script.

Metadata

Release files for prediction-market-agent-tooling 0.69.40

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