diff --git a/poetry.lock b/poetry.lock
index ddad64f6..d3fe2bbb 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -740,13 +740,13 @@ redis = ["redis (>=2.10.5)"]
 
 [[package]]
 name = "cached-property"
-version = "1.5.2"
+version = "2.0.1"
 description = "A decorator for caching properties in classes."
 optional = false
-python-versions = "*"
+python-versions = ">=3.8"
 files = [
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+    {file = "cached_property-2.0.1-py3-none-any.whl", hash = "sha256:f617d70ab1100b7bcf6e42228f9ddcb78c676ffa167278d9f730d1c2fba69ccb"},
+    {file = "cached_property-2.0.1.tar.gz", hash = "sha256:484d617105e3ee0e4f1f58725e72a8ef9e93deee462222dbd51cd91230897641"},
 ]
 
 [[package]]
@@ -1066,96 +1066,105 @@ uvicorn = {version = ">=0.18.3", extras = ["standard"]}
 
 [[package]]
 name = "ckzg"
-version = "1.0.2"
+version = "2.0.1"
 description = "Python bindings for C-KZG-4844"
 optional = false
 python-versions = "*"
 files = [
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 [[package]]
@@ -1237,13 +1246,13 @@ files = [
 
 [[package]]
 name = "cohere"
-version = "5.11.1"
+version = "5.11.2"
 description = ""
 optional = false
 python-versions = "<4.0,>=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -1892,18 +1901,18 @@ files = [
 
 [[package]]
 name = "duckduckgo-search"
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 description = "Search for words, documents, images, news, maps and text translation using the DuckDuckGo.com search engine."
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 python-versions = ">=3.8"
 files = [
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 click = ">=8.1.7"
-primp = ">=0.6.4"
+primp = ">=0.6.5"
 
 [package.extras]
 dev = ["mypy (>=1.11.1)", "pytest (>=8.3.1)", "pytest-asyncio (>=0.23.8)", "ruff (>=0.6.1)"]
@@ -2319,13 +2328,13 @@ test = ["pytest (>=6)"]
 
 [[package]]
 name = "faker"
-version = "30.8.0"
+version = "30.8.1"
 description = "Faker is a Python package that generates fake data for you."
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -2354,13 +2363,13 @@ requests = ">=2.28.1,<3.0.0"
 
 [[package]]
 name = "fastapi"
-version = "0.115.3"
+version = "0.115.4"
 description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
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 python-versions = ">=3.8"
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 [package.dependencies]
@@ -2797,13 +2806,13 @@ test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit",
 
 [[package]]
 name = "google-api-core"
-version = "2.21.0"
+version = "2.22.0"
 description = "Google API client core library"
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 python-versions = ">=3.7"
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 [package.dependencies]
@@ -2975,13 +2984,13 @@ grpc = ["grpcio (>=1.38.0,<2.0dev)", "grpcio-status (>=1.38.0,<2.0.dev0)"]
 
 [[package]]
 name = "google-cloud-functions"
-version = "1.17.0"
+version = "1.18.0"
 description = "Google Cloud Functions API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
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 [package.dependencies]
@@ -2993,13 +3002,13 @@ protobuf = ">=3.20.2,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4
 
 [[package]]
 name = "google-cloud-resource-manager"
-version = "1.12.5"
+version = "1.13.0"
 description = "Google Cloud Resource Manager API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
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 [[package]]
 name = "google-cloud-secret-manager"
-version = "2.20.2"
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 python-versions = ">=3.7"
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 [package.dependencies]
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 [package.extras]
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 ]
 
 [package.dependencies]
@@ -5960,13 +5969,13 @@ test-tools = ["click (>=8.1.0,<9)", "coverage (>=6.4.4,<8.0.0)", "jsonschema (>=
 
 [[package]]
 name = "open-aea-cli-ipfs"
-version = "1.58.0"
+version = "1.59.0"
 description = "CLI extension for open AEA framework wrapping IPFS functionality."
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.dependencies]
@@ -5976,13 +5985,13 @@ pytest = ">=7.0.0,<7.3.0"
 
 [[package]]
 name = "open-aea-ledger-cosmos"
-version = "1.58.0"
+version = "1.59.0"
 description = "Python package wrapping the public and private key cryptography and ledger api of Cosmos."
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.dependencies]
@@ -5994,13 +6003,13 @@ pycryptodome = ">=3.10.1,<4.0.0"
 
 [[package]]
 name = "open-aea-ledger-ethereum"
-version = "1.58.0"
+version = "1.59.0"
 description = "Python package wrapping the public and private key cryptography and ledger api of Ethereum."
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.dependencies]
@@ -6861,13 +6870,13 @@ test = ["coverage", "django", "flake8", "freezegun (==0.3.15)", "mock (>=2.0.0)"
 
 [[package]]
 name = "prediction-market-agent-tooling"
-version = "0.55.0"
+version = "0.55.1"
 description = "Tools to benchmark, deploy and monitor prediction market agents."
 optional = false
 python-versions = "<3.12,>=3.10"
 files = [
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 ]
 
 [package.dependencies]
@@ -6898,7 +6907,7 @@ pydantic-settings = ">=2.4.0,<3.0.0"
 pymongo = ">=4.8.0,<5.0.0"
 python-dateutil = ">=2.9.0.post0,<3.0.0"
 safe-cli = ">=1.0.0,<2.0.0"
-safe-eth-py = ">=6.0.0b14,<7.0.0"
+safe-eth-py = ">=6.0.0b41,<7.0.0"
 scikit-learn = ">=1.3.1,<2.0.0"
 sqlmodel = ">=0.0.22,<0.0.23"
 streamlit = ">=1.31.0,<2.0.0"
@@ -6960,7 +6969,7 @@ watchdog = "^3.0.0"
 [package.source]
 type = "git"
 url = "https://github.com/agentcoinorg/predictionprophet.git"
-reference = "5a7e9e4300d53acf32e09bd4b995c1722c3a7131"
+reference = "main"
 resolved_reference = "5a7e9e4300d53acf32e09bd4b995c1722c3a7131"
 
 [[package]]
@@ -7011,19 +7020,19 @@ murmurhash = ">=0.28.0,<1.1.0"
 
 [[package]]
 name = "primp"
-version = "0.6.4"
+version = "0.6.5"
 description = "HTTP client that can impersonate web browsers, mimicking their headers and `TLS/JA3/JA4/HTTP2` fingerprints"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.extras]
@@ -7390,18 +7399,18 @@ test = ["pytest (>=7.0.0)", "pytest-xdist (>=2.4.0)"]
 
 [[package]]
 name = "py-evm"
-version = "0.10.1b1"
+version = "0.10.1b2"
 description = "Python implementation of the Ethereum Virtual Machine"
 optional = false
 python-versions = "<4,>=3.8"
 files = [
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 [package.dependencies]
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 eth-bloom = ">=1.0.3"
 eth-keys = ">=0.4.0"
 eth-typing = ">=3.3.0"
@@ -7413,9 +7422,9 @@ trie = ">=2.0.0"
 
 [package.extras]
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@@ -7453,51 +7462,54 @@ varint = ">=1.0.2,<2.0.0"
 
 [[package]]
 name = "pyarrow"
-version = "17.0.0"
+version = "18.0.0"
 description = "Python library for Apache Arrow"
 optional = false
-python-versions = ">=3.8"
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 test = ["cffi", "hypothesis", "pandas", "pytest", "pytz"]
@@ -7509,7 +7521,6 @@ description = "Pure-Python implementation of ASN.1 types and DER/BER/CER codecs
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 [package.extras]
@@ -8522,13 +8532,13 @@ files = [
 
 [[package]]
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-version = "1.0.2"
+version = "1.0.3"
 description = "Python client for Replicate"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -8646,44 +8656,46 @@ pyasn1 = ">=0.1.3"
 
 [[package]]
 name = "safe-cli"
-version = "1.2.1"
+version = "1.4.0"
 description = "Command Line Interface for Safe"
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 python-versions = ">=3.8"
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 [package.dependencies]
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 colorama = ">=0.4"
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 prompt-toolkit = ">=3"
 pygments = ">=2"
 requests = ">=2"
-safe-eth-py = "6.0.0b17"
+safe-eth-py = ">=6.0.0b41"
 tabulate = ">=0.8"
+typer = "0.12.5"
 
 [package.extras]
 ledger = ["ledgereth (==0.9.1)"]
-trezor = ["trezor (==0.13.8)"]
+trezor = ["trezor (==0.13.9)"]
 
 [[package]]
 name = "safe-eth-py"
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+version = "6.0.0b41"
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 optional = false
-python-versions = ">=3.8"
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 [package.dependencies]
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 py-evm = ">=0.7.0a1"
 requests = ">=2"
-safe-pysha3 = {version = ">=1.0.0", markers = "python_version >= \"3.9\""}
+safe-pysha3 = ">=1.0.0"
 web3 = ">=6.2.0"
 
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@@ -8988,23 +8995,23 @@ test = ["pytest"]
 
 [[package]]
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+version = "75.3.0"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.extras]
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-type = ["importlib-metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.11.*)", "pytest-mypy"]
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+type = ["importlib-metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.12.*)", "pytest-mypy"]
 
 [[package]]
 name = "shapely"
@@ -9450,13 +9457,13 @@ streamlit = ">=1.33"
 
 [[package]]
 name = "starlette"
-version = "0.41.0"
+version = "0.41.2"
 description = "The little ASGI library that shines."
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -10063,13 +10070,13 @@ files = [
 
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+version = "4.66.6"
 description = "Fast, Extensible Progress Meter"
 optional = false
 python-versions = ">=3.7"
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 [package.dependencies]
@@ -10083,13 +10090,13 @@ telegram = ["requests"]
 
 [[package]]
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+version = "4.46.1"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
 optional = false
 python-versions = ">=3.8.0"
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 [package.dependencies]
@@ -10209,25 +10216,21 @@ test = ["vcrpy (>=1.10.3)"]
 
 [[package]]
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+version = "0.12.5"
 description = "Typer, build great CLIs. Easy to code. Based on Python type hints."
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-python-versions = ">=3.6"
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+click = ">=8.0.0"
+rich = ">=10.11.0"
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 typing-extensions = ">=3.7.4.3"
 
-[package.extras]
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-
 [[package]]
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 version = "2.9.0.20241003"
@@ -10432,13 +10435,13 @@ files = [
 
 [[package]]
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 description = "Virtual Python Environment builder"
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 python-versions = ">=3.8"
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 [package.dependencies]
@@ -10633,18 +10636,17 @@ wasabi = ">=0.9.1,<1.2.0"
 
 [[package]]
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 eth-hash = {version = ">=0.5.1", extras = ["pycryptodome"]}
@@ -10777,13 +10779,13 @@ files = [
 
 [[package]]
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 [package.dependencies]
@@ -10960,93 +10962,93 @@ test = ["pytest"]
 
 [[package]]
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 description = "Yet another URL library"
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 python-versions = ">=3.9"
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+    {file = "yarl-1.17.0.tar.gz", hash = "sha256:d3f13583f378930377e02002b4085a3d025b00402d5a80911726d43a67911cd9"},
 ]
 
 [package.dependencies]
@@ -11076,4 +11078,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.0"
 python-versions = "~3.10.0"
-content-hash = "be881837b401244d41efac87cfe56921db248ef994e096cbeea58ffb30d2ca45"
+content-hash = "10e4fecf6ff24730a6465f1c4e928173a2f1f854f3605bdb8a794b87a0b065b3"
diff --git a/prediction_market_agent/agents/arbitrage_agent/data_models.py b/prediction_market_agent/agents/arbitrage_agent/data_models.py
new file mode 100644
index 00000000..06f143f7
--- /dev/null
+++ b/prediction_market_agent/agents/arbitrage_agent/data_models.py
@@ -0,0 +1,93 @@
+import typing as t
+
+from prediction_market_agent_tooling.markets.agent_market import AgentMarket
+from prediction_market_agent_tooling.tools.betting_strategies.utils import SimpleBet
+from prediction_market_agent_tooling.tools.utils import check_not_none
+from pydantic import BaseModel
+
+
+class Correlation(BaseModel):
+    near_perfect_correlation: bool | None
+    reasoning: str
+
+
+class ArbitrageBet(BaseModel):
+    main_market_bet: SimpleBet
+    related_market_bet: SimpleBet
+
+
+class CorrelatedMarketPair(BaseModel):
+    main_market: AgentMarket
+    related_market: AgentMarket
+    correlation: Correlation
+
+    def __str__(self) -> str:
+        return f"main_market {self.main_market.question} related_market_question {self.related_market.question} potential_profit_per_unit {self.potential_profit_per_bet_unit()}"
+
+    def potential_profit_per_bet_unit(self) -> float:
+        """
+        Calculate potential profit per bet unit based on high positive market correlation.
+        For positively correlated markets: Bet YES/NO or NO/YES.
+        """
+
+        if self.correlation.near_perfect_correlation is None:
+            return 0
+
+        bet_direction_main, bet_direction_related = self.bet_directions()
+        p_main = (
+            self.main_market.current_p_yes
+            if bet_direction_main
+            else self.main_market.current_p_no
+        )
+        p_related = (
+            self.related_market.current_p_yes
+            if bet_direction_related
+            else self.related_market.current_p_no
+        )
+        denominator = p_main + p_related
+        return (1 / denominator) - 1
+
+    def bet_directions(self) -> t.Tuple[bool, bool]:
+        correlation = check_not_none(self.correlation.near_perfect_correlation)
+        if correlation:
+            # We compare denominators for cases YES/NO and NO/YES bets and take the most profitable (i.e. where denominator is the lowest).
+            # For other cases we employ similar logic.
+            yes_no = self.main_market.current_p_yes + self.related_market.current_p_no
+            no_yes = self.main_market.current_p_no + self.related_market.current_p_yes
+            return (True, False) if yes_no <= no_yes else (False, True)
+
+        else:
+            yes_yes = self.main_market.current_p_yes + self.related_market.current_p_yes
+            no_no = self.main_market.current_p_no + self.related_market.current_p_no
+            return (True, True) if yes_yes <= no_no else (False, False)
+
+    def split_bet_amount_between_yes_and_no(
+        self, total_bet_amount: float
+    ) -> ArbitrageBet:
+        """Splits total bet amount following equations below:
+        A1/p1 = A2/p2 (same profit regardless of outcome resolution)
+        A1 + A2 = total bet amount
+        """
+
+        bet_direction_main, bet_direction_related = self.bet_directions()
+
+        p_main = (
+            self.main_market.current_p_yes
+            if bet_direction_main
+            else self.main_market.current_p_no
+        )
+        p_related = (
+            self.related_market.current_p_yes
+            if bet_direction_related
+            else self.related_market.current_p_no
+        )
+        total_probability = p_main + p_related
+        bet_main = total_bet_amount * p_main / total_probability
+        bet_related = total_bet_amount * p_related / total_probability
+        main_market_bet = SimpleBet(direction=bet_direction_main, size=bet_main)
+        related_market_bet = SimpleBet(
+            direction=bet_direction_related, size=bet_related
+        )
+        return ArbitrageBet(
+            main_market_bet=main_market_bet, related_market_bet=related_market_bet
+        )
diff --git a/prediction_market_agent/agents/arbitrage_agent/deploy.py b/prediction_market_agent/agents/arbitrage_agent/deploy.py
new file mode 100644
index 00000000..349b1b51
--- /dev/null
+++ b/prediction_market_agent/agents/arbitrage_agent/deploy.py
@@ -0,0 +1,190 @@
+import typing as t
+
+from langchain_core.output_parsers import PydanticOutputParser
+from langchain_core.prompts import PromptTemplate
+from langchain_core.runnables import RunnableSerializable
+from langchain_openai import ChatOpenAI
+from prediction_market_agent_tooling.deploy.agent import DeployableTraderAgent
+from prediction_market_agent_tooling.gtypes import Probability
+from prediction_market_agent_tooling.loggers import logger
+from prediction_market_agent_tooling.markets.agent_market import AgentMarket
+from prediction_market_agent_tooling.markets.data_models import (
+    BetAmount,
+    Position,
+    ProbabilisticAnswer,
+    TokenAmount,
+    Trade,
+    TradeType,
+)
+from prediction_market_agent_tooling.markets.markets import MarketType
+from prediction_market_agent_tooling.markets.omen.omen import OmenAgentMarket
+from prediction_market_agent_tooling.markets.omen.omen_subgraph_handler import (
+    OmenSubgraphHandler,
+)
+from prediction_market_agent_tooling.tools.langfuse_ import (
+    get_langfuse_langchain_config,
+    observe,
+)
+from prediction_market_agent_tooling.tools.utils import utcnow
+
+from prediction_market_agent.agents.arbitrage_agent.data_models import (
+    CorrelatedMarketPair,
+    Correlation,
+)
+from prediction_market_agent.agents.arbitrage_agent.prompt import PROMPT_TEMPLATE
+from prediction_market_agent.db.pinecone_handler import PineconeHandler
+from prediction_market_agent.utils import APIKeys
+
+
+class DeployableArbitrageAgent(DeployableTraderAgent):
+    """Agent that places mirror bets on Omen for (quasi) risk-neutral profit."""
+
+    model = "gpt-4o"
+    # trade amount will be divided between correlated markets.
+    total_trade_amount = BetAmount(amount=0.1, currency=OmenAgentMarket.currency)
+    bet_on_n_markets_per_run = 5
+    max_related_markets_per_market = 10
+    n_markets_to_fetch = 50
+
+    def run(self, market_type: MarketType) -> None:
+        if market_type != MarketType.OMEN:
+            raise RuntimeError(
+                "Can arbitrage only on Omen since related markets embeddings available only for Omen markets."
+            )
+        self.subgraph_handler = OmenSubgraphHandler()
+        self.pinecone_handler = PineconeHandler()
+        self.pinecone_handler.insert_all_omen_markets_if_not_exists()
+        self.chain = self._build_chain()
+        super().run(market_type=market_type)
+
+    def answer_binary_market(self, market: AgentMarket) -> ProbabilisticAnswer | None:
+        return ProbabilisticAnswer(p_yes=Probability(0.5), confidence=1.0)
+
+    def _build_chain(self) -> RunnableSerializable[t.Any, t.Any]:
+        llm = ChatOpenAI(
+            temperature=0,
+            model=self.model,
+            api_key=APIKeys().openai_api_key_secretstr_v1,
+        )
+
+        parser = PydanticOutputParser(pydantic_object=Correlation)
+        prompt = PromptTemplate(
+            template=PROMPT_TEMPLATE,
+            input_variables=["main_market_question", "related_market_question"],
+            partial_variables={"format_instructions": parser.get_format_instructions()},
+        )
+
+        chain = prompt | llm | parser
+        return chain
+
+    @observe()
+    def calculate_correlation_between_markets(
+        self, market: AgentMarket, related_market: AgentMarket
+    ) -> Correlation:
+        correlation: Correlation = self.chain.invoke(
+            {
+                "main_market_question": market.question,
+                "related_market_question": related_market.question,
+            }
+        )
+        return correlation
+
+    @observe()
+    def get_correlated_markets(self, market: AgentMarket) -> list[CorrelatedMarketPair]:
+        # We try to find similar, open markets which point to the same outcome.
+        correlated_markets = []
+        # We only wanted to find related markets that are open.
+        # We intentionally query more markets in the hope it yields open markets.
+        # We could store market_status (open, closed) in Pinecone, but we refrain from it
+        # to keep the chain data (or graph) as the source-of-truth, instead of managing the
+        # update process of the vectorDB.
+
+        related = self.pinecone_handler.find_nearest_questions_with_threshold(
+            limit=self.max_related_markets_per_market,
+            text=market.question,
+            filter_on_metadata={
+                "close_time_timestamp": {"gte": utcnow().timestamp() + 3600}
+            },  # closing 1h from now
+        )
+
+        omen_markets = self.subgraph_handler.get_omen_binary_markets(
+            limit=len(related),
+            id_in=[i.market_address for i in related if i.market_address != market.id],
+            resolved=False,
+        )
+
+        # Order omen_markets in the same order as related
+        related_market_addresses = [i.market_address for i in related]
+        omen_markets = sorted(
+            omen_markets, key=lambda m: related_market_addresses.index(m.id)
+        )
+
+        print(f"Fetched {len(omen_markets)} related markets for market {market.id}")
+
+        for related_market in omen_markets:
+            result: Correlation = self.chain.invoke(
+                {
+                    "main_market_question": market,
+                    "related_market_question": related_market,
+                },
+                config=get_langfuse_langchain_config(),
+            )
+            if result.near_perfect_correlation is not None:
+                related_agent_market = OmenAgentMarket.from_data_model(related_market)
+                correlated_markets.append(
+                    CorrelatedMarketPair(
+                        main_market=market,
+                        related_market=related_agent_market,
+                        correlation=result,
+                    )
+                )
+        return correlated_markets
+
+    @observe()
+    def build_trades_for_correlated_markets(
+        self, pair: CorrelatedMarketPair
+    ) -> list[Trade]:
+        # Split between main_market and related_market
+        arbitrage_bet = pair.split_bet_amount_between_yes_and_no(
+            self.total_trade_amount.amount
+        )
+
+        main_trade = Trade(
+            trade_type=TradeType.BUY,
+            outcome=arbitrage_bet.main_market_bet.direction,
+            amount=TokenAmount(
+                amount=arbitrage_bet.main_market_bet.size,
+                currency=pair.main_market.currency,
+            ),
+        )
+
+        # related trade
+        related_trade = Trade(
+            trade_type=TradeType.BUY,
+            outcome=arbitrage_bet.related_market_bet.direction,
+            amount=TokenAmount(
+                amount=arbitrage_bet.related_market_bet.size,
+                currency=pair.related_market.currency,
+            ),
+        )
+
+        trades = [main_trade, related_trade]
+        logger.info(f"Placing arbitrage trades {trades}")
+        return trades
+
+    @observe()
+    def build_trades(
+        self,
+        market: AgentMarket,
+        answer: ProbabilisticAnswer,
+        existing_position: Position | None,
+    ) -> list[Trade]:
+        trades = []
+        correlated_markets = self.get_correlated_markets(market=market)
+        for pair in correlated_markets:
+            # We want to profit at least 0.5% per market (value chosen as initial baseline).
+            if pair.potential_profit_per_bet_unit() > 0.005:
+                trades_for_pair = self.build_trades_for_correlated_markets(pair)
+                trades.extend(trades_for_pair)
+
+        return trades
diff --git a/prediction_market_agent/agents/arbitrage_agent/prompt.py b/prediction_market_agent/agents/arbitrage_agent/prompt.py
new file mode 100644
index 00000000..d907e779
--- /dev/null
+++ b/prediction_market_agent/agents/arbitrage_agent/prompt.py
@@ -0,0 +1,12 @@
+PROMPT_TEMPLATE = """Given two markets, MARKET 1 and MARKET 2, provide a boolean value that represents the correlation between these two markets' outcomes. Return True if the outcomes are perfectly or nearly perfectly correlated, meaning there is a high probability that both markets resolve to the same outcome. Return False if the outcomes are perfectly or nearly perfectly inversely correlated, and finally return None if the correlation is weak or non-existent.
+Correlation can also be understood as the conditional probability that market 2 resolves to YES, given that market 1 resolved to YES.
+In addition to the correlation value, explain the reasoning behind your decision.
+
+[MARKET 1]
+{main_market_question}
+
+[MARKET 2]
+{related_market_question}
+
+Follow the formatting instructions below for producing an output in the correct format.
+{format_instructions}"""
diff --git a/prediction_market_agent/agents/invalid_agent/deploy.py b/prediction_market_agent/agents/invalid_agent/deploy.py
index 46c3c4b4..64b6348b 100644
--- a/prediction_market_agent/agents/invalid_agent/deploy.py
+++ b/prediction_market_agent/agents/invalid_agent/deploy.py
@@ -22,7 +22,9 @@ class InvalidAgent(DeployableTraderAgent):
     supported_markets = [MarketType.OMEN]
 
     def verify_market(self, market_type: MarketType, market: AgentMarket) -> bool:
-        if self.have_bet_on_market_since(market, since=self.same_market_bet_interval):
+        if self.have_bet_on_market_since(
+            market, since=self.same_market_trade_interval.get(market=market)
+        ):
             return False
 
         # If the market is new, don't bet on it as the potential profit from market invalidity is low.
diff --git a/prediction_market_agent/agents/specialized_agent/deploy.py b/prediction_market_agent/agents/specialized_agent/deploy.py
index b4ab72ea..68987249 100644
--- a/prediction_market_agent/agents/specialized_agent/deploy.py
+++ b/prediction_market_agent/agents/specialized_agent/deploy.py
@@ -28,6 +28,7 @@
 class GetMarketCreatorsStalkerMarkets:
     # Do as many bets as we can for the special markets.
     bet_on_n_markets_per_run = MAX_AVAILABLE_MARKETS
+    n_markets_to_fetch: int = MAX_AVAILABLE_MARKETS
     # These tends to be long-running markets, it's not interesting to bet on them too much.
     same_market_bet_interval = timedelta(days=7)
     supported_markets: t.Sequence[MarketType] = [MarketType.OMEN]
@@ -35,14 +36,13 @@ class GetMarketCreatorsStalkerMarkets:
     def get_markets(
         self,
         market_type: MarketType,
-        limit: int = MAX_AVAILABLE_MARKETS,
         sort_by: SortBy = SortBy.CLOSING_SOONEST,
         filter_by: FilterBy = FilterBy.OPEN,
     ) -> t.Sequence[OmenAgentMarket]:
         available_markets = [
             OmenAgentMarket.from_data_model(m)
             for m in OmenSubgraphHandler().get_omen_binary_markets_simple(
-                limit=limit,
+                limit=self.n_markets_to_fetch,
                 sort_by=sort_by,
                 filter_by=filter_by,
                 creator_in=SPECIALIZED_FOR_MARKET_CREATORS,
diff --git a/prediction_market_agent/agents/think_thoroughly_agent/benchmark.py b/prediction_market_agent/agents/think_thoroughly_agent/benchmark.py
index 56ea48ce..0fd849d5 100644
--- a/prediction_market_agent/agents/think_thoroughly_agent/benchmark.py
+++ b/prediction_market_agent/agents/think_thoroughly_agent/benchmark.py
@@ -20,6 +20,7 @@
     MarketFees,
     SortBy,
 )
+from prediction_market_agent_tooling.markets.market_fees import MarketFees
 from prediction_market_agent_tooling.markets.markets import (
     MarketType,
     get_binary_markets,
diff --git a/prediction_market_agent/agents/think_thoroughly_agent/deploy.py b/prediction_market_agent/agents/think_thoroughly_agent/deploy.py
index 74ae51a2..7851301a 100644
--- a/prediction_market_agent/agents/think_thoroughly_agent/deploy.py
+++ b/prediction_market_agent/agents/think_thoroughly_agent/deploy.py
@@ -32,7 +32,7 @@ def answer_binary_market(self, market: AgentMarket) -> ProbabilisticAnswer | Non
         )
 
     def before_process_markets(self, market_type: MarketType) -> None:
-        self.agent.update_markets()
+        self.agent.pinecone_handler.insert_all_omen_markets_if_not_exists()
         super().before_process_markets(market_type=market_type)
 
 
diff --git a/prediction_market_agent/agents/think_thoroughly_agent/models.py b/prediction_market_agent/agents/think_thoroughly_agent/models.py
index 12cc44c1..49e2243b 100644
--- a/prediction_market_agent/agents/think_thoroughly_agent/models.py
+++ b/prediction_market_agent/agents/think_thoroughly_agent/models.py
@@ -7,11 +7,14 @@
 class PineconeMetadata(BaseModel):
     question_title: str
     market_address: HexAddress
+    close_time_timestamp: int
 
     @staticmethod
     def from_omen_market(market: OmenMarket) -> "PineconeMetadata":
         return PineconeMetadata(
-            question_title=market.question_title, market_address=market.id
+            question_title=market.question_title,
+            market_address=market.id,
+            close_time_timestamp=int(market.close_time.timestamp()),
         )
 
 
diff --git a/prediction_market_agent/agents/think_thoroughly_agent/think_thoroughly_agent.py b/prediction_market_agent/agents/think_thoroughly_agent/think_thoroughly_agent.py
index 72ec637e..d0577b1e 100644
--- a/prediction_market_agent/agents/think_thoroughly_agent/think_thoroughly_agent.py
+++ b/prediction_market_agent/agents/think_thoroughly_agent/think_thoroughly_agent.py
@@ -1,4 +1,3 @@
-import datetime
 import typing as t
 from abc import ABC
 from uuid import UUID, uuid4
@@ -179,14 +178,6 @@ def _build_llm(model: str) -> BaseChatModel:
         )
         return llm
 
-    def update_markets(self) -> None:
-        """We use the agent's run to add embeddings of new markets that don't exist yet in the
-        vector DB."""
-        created_after = utcnow() - datetime.timedelta(days=7)
-        self.pinecone_handler.insert_all_omen_markets_if_not_exists(
-            created_after=created_after
-        )
-
     @observe()
     def get_required_conditions(self, question: str) -> Scenarios:
         researcher = self._get_researcher(self.model)
diff --git a/prediction_market_agent/db/pinecone_handler.py b/prediction_market_agent/db/pinecone_handler.py
index f7c4cec2..c2cef2f6 100644
--- a/prediction_market_agent/db/pinecone_handler.py
+++ b/prediction_market_agent/db/pinecone_handler.py
@@ -13,7 +13,7 @@
 from prediction_market_agent_tooling.markets.omen.omen_subgraph_handler import (
     OmenSubgraphHandler,
 )
-from prediction_market_agent_tooling.tools.utils import DatetimeUTC
+from prediction_market_agent_tooling.tools.datetime_utc import DatetimeUTC
 from tqdm import tqdm
 
 from prediction_market_agent.agents.think_thoroughly_agent.models import (
@@ -70,14 +70,17 @@ def filter_markets_already_in_index(
 
         """
         ids_market_map = {self.encode_text(m.question_title): m for m in markets}
-
         all_ids = list(ids_market_map.keys())
-        # index.list() returns [[id1,id2,...],[id4,id5,...]], hence the flattening.
-        ids_in_vec_db = [y for x in self.index.list() for y in x]
+        ids_in_vec_db = self.get_existing_ids_in_index()
         missing_ids = set(all_ids).difference(ids_in_vec_db)
         filtered_markets = [ids_market_map[id] for id in missing_ids]
         return filtered_markets
 
+    def get_existing_ids_in_index(self) -> list[str]:
+        # index.list() returns [[id1,id2,...],[id4,id5,...]], hence the flattening.
+        ids_in_vec_db = [y for x in self.index.list() for y in x]
+        return ids_in_vec_db
+
     def insert_texts(
         self,
         ids: list[str],
@@ -112,6 +115,8 @@ def deduplicate_markets(markets: list[OmenMarket]) -> list[OmenMarket]:
     def insert_all_omen_markets_if_not_exists(
         self, created_after: DatetimeUTC | None = None
     ) -> None:
+        """We use the agent's run to add embeddings of new markets that don't exist yet in the
+        vector DB."""
         subgraph_handler = OmenSubgraphHandler()
         markets = subgraph_handler.get_omen_binary_markets_simple(
             limit=sys.maxsize,
@@ -145,12 +150,17 @@ def insert_all_omen_markets_if_not_exists(
                 )
 
     def find_nearest_questions_with_threshold(
-        self, limit: int, text: str, threshold: float = 0.25
+        self,
+        limit: int,
+        text: str,
+        threshold: float = 0.25,
+        filter_on_metadata: dict[str, dict[str, t.Any]]
+        | None = None,  # see https://docs.pinecone.io/guides/data/filter-with-metadata#additional-filter-examples for details on the fitler parameter
     ) -> list[PineconeMetadata]:
         # Note that pagination is not implemented in the Pinecone client.
         # Hence we set a large limit and hope we get enough results that satisfy the threshold.
         documents_and_scores = self.vectorstore.similarity_search_with_relevance_scores(
-            query=text, k=limit, score_threshold=threshold
+            query=text, k=limit, score_threshold=threshold, filter=filter_on_metadata
         )
 
         logger.debug(f"Found {len(documents_and_scores)} relevant documents.")
diff --git a/prediction_market_agent/run_agent.py b/prediction_market_agent/run_agent.py
index 953275ed..104a9a69 100644
--- a/prediction_market_agent/run_agent.py
+++ b/prediction_market_agent/run_agent.py
@@ -12,6 +12,9 @@
 from prediction_market_agent_tooling.deploy.agent import DeployableAgent
 from prediction_market_agent_tooling.markets.markets import MarketType
 
+from prediction_market_agent.agents.arbitrage_agent.deploy import (
+    DeployableArbitrageAgent,
+)
 from prediction_market_agent.agents.coinflip_agent.deploy import DeployableCoinFlipAgent
 from prediction_market_agent.agents.invalid_agent.deploy import InvalidAgent
 from prediction_market_agent.agents.known_outcome_agent.deploy import (
@@ -78,6 +81,7 @@ class RunnableAgent(str, Enum):
     social_media = "social_media"
     omen_cleaner = "omen_cleaner"
     ofv_challenger = "ofv_challenger"
+    arbitrage = "arbitrage"
     market_creators_stalker1 = "market_creators_stalker1"
     market_creators_stalker2 = "market_creators_stalker2"
     invalid = "invalid"
@@ -105,6 +109,7 @@ class RunnableAgent(str, Enum):
     RunnableAgent.ofv_challenger: OFVChallengerAgent,
     RunnableAgent.prophet_o1preview: DeployablePredictionProphetGPTo1PreviewAgent,
     RunnableAgent.prophet_o1mini: DeployablePredictionProphetGPTo1MiniAgent,
+    RunnableAgent.arbitrage: DeployableArbitrageAgent,
     RunnableAgent.market_creators_stalker1: MarketCreatorsStalkerAgent1,
     RunnableAgent.market_creators_stalker2: MarketCreatorsStalkerAgent2,
     RunnableAgent.invalid: InvalidAgent,
diff --git a/pyproject.toml b/pyproject.toml
index 51934d73..8fe614c7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -24,7 +24,7 @@ llama-index = "~0.9.0"
 duckduckgo-search = "*"
 crewai = "^0.36.1"
 replicate = "*"
-typer = "^0.9.0"
+typer = ">=0.9.0,<1.0.0"
 web3 = "^6.14.0"
 mypy = "^1.8.0"
 types-requests = "^2.31.0.0"
@@ -35,11 +35,12 @@ poetry = "^1.7.1"
 poetry-plugin-export = "^1.6.0"
 functions-framework = "^3.5.0"
 cron-validator = "^1.0.8"
-prediction-market-agent-tooling = { version = "^0.55.0", extras = ["langchain", "google"] }
+prediction-market-agent-tooling = { version = "^0.55.1", extras = ["langchain", "google"] }
 pydantic-settings = "^2.1.0"
 autoflake = "^2.2.1"
 isort = "^5.13.2"
 markdownify = "^0.11.6"
+tavily-python = "^0.5.0"
 microchain-python = { git = "https://github.com/galatolofederico/microchain.git", rev = "de75f1d4a073b7c54f824b409733f4f70d40a61b" }
 pysqlite3-binary = {version="^0.5.2.post3", markers = "sys_platform == 'linux'"}
 psycopg2-binary = "^2.9.9"
@@ -51,7 +52,7 @@ tweepy = "^4.14.0"
 langchain-pinecone = "^0.1.1"
 pinatapy-vourhey = "^0.2.0"
 plotly = "^5.22.0"
-prediction-prophet = { git = "https://github.com/agentcoinorg/predictionprophet.git", rev = "5a7e9e4300d53acf32e09bd4b995c1722c3a7131" }
+prediction-prophet = { git = "https://github.com/agentcoinorg/predictionprophet.git", branch = "main" }
 transformers = "^4.43.3"
 openfactverification-kongzii = "^0.2.0"
 
diff --git a/tests/agents/arbitrage_agent/__init__.py b/tests/agents/arbitrage_agent/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/agents/arbitrage_agent/test_arbitrage_agent.py b/tests/agents/arbitrage_agent/test_arbitrage_agent.py
new file mode 100644
index 00000000..b5696787
--- /dev/null
+++ b/tests/agents/arbitrage_agent/test_arbitrage_agent.py
@@ -0,0 +1,61 @@
+import typing as t
+from unittest.mock import Mock, patch
+
+import pytest
+from prediction_market_agent_tooling.markets.agent_market import AgentMarket
+from prediction_market_agent_tooling.markets.omen.omen import OmenAgentMarket
+
+from prediction_market_agent.agents.arbitrage_agent.deploy import (
+    DeployableArbitrageAgent,
+)
+from tests.utils import RUN_PAID_TESTS
+
+
+@pytest.fixture(scope="module")
+def arbitrage_agent() -> t.Generator[DeployableArbitrageAgent, None, None]:
+    with patch(
+        "prediction_market_agent_tooling.tools.langfuse_.get_langfuse_langchain_config"
+    ):
+        agent = DeployableArbitrageAgent()
+        agent.chain = agent._build_chain()
+        yield agent
+
+
+@pytest.fixture(scope="module")
+def main_market() -> t.Generator[AgentMarket, None, None]:
+    m1 = Mock(OmenAgentMarket, wraps=OmenAgentMarket)
+    m1.question = "Will Kamala Harris win the US presidential election in 2024?"
+    yield m1
+
+
+@pytest.fixture(scope="module")
+def related_market() -> t.Generator[AgentMarket, None, None]:
+    m1 = Mock(OmenAgentMarket, wraps=OmenAgentMarket)
+    m1.question = "Will Kamala Harris become the US president in 2025?"
+    yield m1
+
+
+@pytest.fixture(scope="module")
+def unrelated_market() -> t.Generator[AgentMarket, None, None]:
+    m1 = Mock(OmenAgentMarket, wraps=OmenAgentMarket)
+    m1.question = "Will Donald Duck ever retire from his adventures in Duckburg?"
+    yield m1
+
+
+@pytest.mark.skipif(not RUN_PAID_TESTS, reason="This test costs money to run.")
+@pytest.mark.parametrize(
+    "related_market_fixture_name, is_correlated",
+    [("related_market", True), ("unrelated_market", False)],
+)
+def test_correlation_for_similar_markets(
+    arbitrage_agent: DeployableArbitrageAgent,
+    main_market: AgentMarket,
+    related_market_fixture_name: str,
+    is_correlated: bool,
+    request: pytest.FixtureRequest,
+) -> None:
+    other_market = request.getfixturevalue(related_market_fixture_name)
+    correlation = arbitrage_agent.calculate_correlation_between_markets(
+        market=main_market, related_market=other_market
+    )
+    assert correlation.near_perfect_correlation == is_correlated
diff --git a/tests/agents/arbitrage_agent/test_correlated_market_pair.py b/tests/agents/arbitrage_agent/test_correlated_market_pair.py
new file mode 100644
index 00000000..b53daad0
--- /dev/null
+++ b/tests/agents/arbitrage_agent/test_correlated_market_pair.py
@@ -0,0 +1,82 @@
+from unittest.mock import Mock
+
+import numpy as np
+import pytest
+from prediction_market_agent_tooling.markets.agent_market import AgentMarket
+from prediction_market_agent_tooling.markets.omen.omen import OmenAgentMarket
+
+from prediction_market_agent.agents.arbitrage_agent.data_models import (
+    CorrelatedMarketPair,
+    Correlation,
+)
+
+PERFECT_POSITIVE_CORRELATION = Correlation(near_perfect_correlation=True, reasoning="")
+PERFECT_NEGATIVE_CORRELATION = Correlation(near_perfect_correlation=False, reasoning="")
+
+
+def build_market(p_yes: float) -> AgentMarket:
+    m1 = Mock(OmenAgentMarket, wraps=OmenAgentMarket)
+    m1.current_p_yes = p_yes
+    m1.current_p_no = 1 - m1.current_p_yes
+    return m1
+
+
+def assert_profit(p1: float, p2: float, correlated_pair: CorrelatedMarketPair) -> None:
+    expected_profit = (1 / (p1 + p2)) - 1
+    assert np.isclose(
+        expected_profit,
+        correlated_pair.potential_profit_per_bet_unit(),
+        rtol=expected_profit * 0.01,
+    )
+
+
+def assert_bet_amounts_ok(
+    correlated_pair: CorrelatedMarketPair,
+    main_p: float,
+    related_p: float,
+) -> None:
+    total_bet_amount = 10
+    actual_bet = correlated_pair.split_bet_amount_between_yes_and_no(
+        total_bet_amount=total_bet_amount
+    )
+    expected_bet_main = total_bet_amount * main_p / (main_p + related_p)
+    expected_bet_related = total_bet_amount * related_p / (main_p + related_p)
+    assert np.isclose(
+        actual_bet.main_market_bet.size,
+        expected_bet_main,
+        rtol=expected_bet_main * 0.01,
+    )
+    assert np.isclose(
+        actual_bet.related_market_bet.size,
+        expected_bet_related,
+        rtol=expected_bet_related * 0.01,
+    )
+
+
+@pytest.mark.parametrize(
+    "p1, p2, corr, m1_bet_yes, m2_bet_yes",
+    [
+        (0.5, 0.8, PERFECT_POSITIVE_CORRELATION, True, False),  # YES/NO
+        (0.8, 0.5, PERFECT_POSITIVE_CORRELATION, False, True),  # NO/YES
+        (0.2, 0.5, PERFECT_NEGATIVE_CORRELATION, True, True),  # YES/YES
+        (0.8, 0.5, PERFECT_NEGATIVE_CORRELATION, False, False),  # NO/NO
+    ],
+)
+def test_profit(
+    p1: float, p2: float, corr: Correlation, m1_bet_yes: bool, m2_bet_yes: bool
+) -> None:
+    m1 = build_market(p1)
+    m2 = build_market(p2)
+    correlated_pair = CorrelatedMarketPair(
+        main_market=m1, related_market=m2, correlation=corr
+    )
+    p_market1 = m1.current_p_yes if m1_bet_yes else m1.current_p_no
+    p_market2 = m2.current_p_yes if m2_bet_yes else m2.current_p_no
+
+    assert_profit(p_market1, p_market2, correlated_pair)
+    assert_bet_amounts_ok(correlated_pair, p_market1, p_market2)
+
+    bet_direction_main, bet_direction_related = correlated_pair.bet_directions()
+
+    assert bet_direction_main == m1_bet_yes
+    assert bet_direction_related == m2_bet_yes
diff --git a/tests/db/test_pinecone_handler.py b/tests/db/test_pinecone_handler.py
index be8158bd..dc737be3 100644
--- a/tests/db/test_pinecone_handler.py
+++ b/tests/db/test_pinecone_handler.py
@@ -37,7 +37,9 @@ def test_pinecone_handler() -> Generator[PineconeHandler, None, None]:
     texts = TRUMP_MARKETS + BIDEN_MARKETS + UNRELATED_MARKETS
     metadatas = [
         PineconeMetadata(
-            question_title=text, market_address=HexAddress(HexStr(""))
+            question_title=text,
+            market_address=HexAddress(HexStr("")),
+            close_time_timestamp=0,
         ).model_dump()
         for text in texts
     ]