Refactor DeployableTraderAgent to 1. predict a probability for a market then 2. place a bet based on its BettingStrategy

prediction_market_agent_tooling/benchmark/agents.py

@@ -87,7 +87,6 @@ def predict(self, market_question: str) -> Prediction:
         p_yes, confidence = random.random(), random.random()
         return Prediction(
             outcome_prediction=OutcomePrediction(
-                decision=p_yes > 0.5,
                 p_yes=Probability(p_yes),
                 confidence=confidence,
                 info_utility=None,
@@ -111,7 +110,6 @@ def predict(self, market_question: str) -> Prediction:
         p_yes, confidence = 1.0 if self.fixed_answer else 0.0, 1.0
         return Prediction(
             outcome_prediction=OutcomePrediction(
-                decision=self.fixed_answer,
                 p_yes=Probability(p_yes),
                 confidence=confidence,
                 info_utility=None,

prediction_market_agent_tooling/benchmark/utils.py

@@ -3,11 +3,13 @@
 
 from pydantic import BaseModel
 
-from prediction_market_agent_tooling.deploy.agent import Answer
-from prediction_market_agent_tooling.markets.data_models import Resolution
+from prediction_market_agent_tooling.markets.data_models import (
+    ProbabilisticAnswer,
+    Resolution,
+)
 
 
-class OutcomePrediction(Answer):
+class OutcomePrediction(ProbabilisticAnswer):
     info_utility: t.Optional[float]
 
     @property

prediction_market_agent_tooling/deploy/agent.py

@@ -12,6 +12,10 @@
 from typing_extensions import Annotated
 
 from prediction_market_agent_tooling.config import APIKeys
+from prediction_market_agent_tooling.deploy.betting_strategy import (
+    BettingStrategy,
+    MaxAccuracyBettingStrategy,
+)
 from prediction_market_agent_tooling.deploy.constants import (
     MARKET_TYPE_KEY,
     REPOSITORY_KEY,
@@ -25,14 +29,19 @@
     gcp_function_is_active,
     gcp_resolve_api_keys_secrets,
 )
-from prediction_market_agent_tooling.gtypes import Probability, xDai, xdai_type
+from prediction_market_agent_tooling.gtypes import xDai, xdai_type
 from prediction_market_agent_tooling.loggers import logger
 from prediction_market_agent_tooling.markets.agent_market import (
     AgentMarket,
     FilterBy,
     SortBy,
 )
-from prediction_market_agent_tooling.markets.data_models import BetAmount
+from prediction_market_agent_tooling.markets.data_models import (
+    BetAmount,
+    ProbabilisticAnswer,
+    TokenAmount,
+    TokenAmountAndDirection,
+)
 from prediction_market_agent_tooling.markets.markets import (
     MarketType,
     have_bet_on_market_since,
@@ -95,19 +104,8 @@ class OutOfFundsError(ValueError):
     pass
 
 
-class Answer(BaseModel):
-    decision: Decision  # Warning: p_yes > 0.5 doesn't necessarily mean decision is True! For example, if our p_yes is 55%, but market's p_yes is 80%, then it might be profitable to bet on False.
-    p_yes: Probability
-    confidence: float
-    reasoning: str | None = None
-
-    @property
-    def p_no(self) -> Probability:
-        return Probability(1 - self.p_yes)
-
-
 class ProcessedMarket(BaseModel):
-    answer: Answer
+    answer: ProbabilisticAnswer
     amount: BetAmount
 
 
@@ -280,6 +278,7 @@ class DeployableTraderAgent(DeployableAgent):
     bet_on_n_markets_per_run: int = 1
     min_required_balance_to_operate: xDai | None = xdai_type(1)
     min_balance_to_keep_in_native_currency: xDai | None = xdai_type(0.1)
+    strategy: BettingStrategy = MaxAccuracyBettingStrategy()
 
     def __init__(
         self,
@@ -296,6 +295,7 @@ def initialize_langfuse(self) -> None:
         self.verify_market = observe()(self.verify_market)  # type: ignore[method-assign]
         self.answer_binary_market = observe()(self.answer_binary_market)  # type: ignore[method-assign]
         self.calculate_bet_amount = observe()(self.calculate_bet_amount)  # type: ignore[method-assign]
+        self.calculate_bet_amount_and_direction = observe()(self.calculate_bet_amount_and_direction)  # type: ignore[method-assign]
         self.process_market = observe()(self.process_market)  # type: ignore[method-assign]
 
     def update_langfuse_trace_by_market(
@@ -311,6 +311,18 @@ def update_langfuse_trace_by_market(
             },
         )
 
+    def calculate_bet_amount_and_direction(
+        self, answer: ProbabilisticAnswer, market: AgentMarket
+    ) -> TokenAmountAndDirection:
+        amount_and_direction = self.strategy.calculate_bet_amount_and_direction(
+            answer, market
+        )
+        if amount_and_direction.currency != market.currency:
+            raise ValueError(
+                f"Currency mismatch. Strategy yields {amount_and_direction.currency}, market has currency {market.currency}"
+            )
+        return amount_and_direction
+
     def update_langfuse_trace_by_processed_market(
         self, market_type: MarketType, processed_market: ProcessedMarket | None
     ) -> None:
@@ -360,13 +372,15 @@ def verify_market(self, market_type: MarketType, market: AgentMarket) -> bool:
 
         return True
 
-    def answer_binary_market(self, market: AgentMarket) -> Answer | None:
+    def answer_binary_market(self, market: AgentMarket) -> ProbabilisticAnswer | None:
         """
         Answer the binary market. This method must be implemented by the subclass.
         """
         raise NotImplementedError("This method must be implemented by the subclass")
 
-    def calculate_bet_amount(self, answer: Answer, market: AgentMarket) -> BetAmount:
+    def calculate_bet_amount(
+        self, answer: ProbabilisticAnswer, market: AgentMarket
+    ) -> BetAmount:
         """
         Calculate the bet amount. By default, it returns the minimum bet amount.
         """
@@ -408,22 +422,25 @@ def process_market(
             self.update_langfuse_trace_by_processed_market(market_type, None)
             return None
 
-        amount = self.calculate_bet_amount(answer, market)
+        amount_and_direction = self.calculate_bet_amount_and_direction(answer, market)
 
         if self.place_bet:
             logger.info(
-                f"Placing bet on {market} with result {answer} and amount {amount}"
+                f"Placing bet on {market} with direction {amount_and_direction.direction} and amount {amount_and_direction.amount}"
             )
             market.place_bet(
-                amount=amount,
-                outcome=answer.decision,
+                amount=TokenAmount(
+                    amount=amount_and_direction.amount,
+                    currency=amount_and_direction.currency,
+                ),
+                outcome=amount_and_direction.direction,
             )
 
         self.after_process_market(market_type, market)
 
         processed_market = ProcessedMarket(
             answer=answer,
-            amount=amount,
+            amount=amount_and_direction,
         )
         self.update_langfuse_trace_by_processed_market(market_type, processed_market)
 

prediction_market_agent_tooling/deploy/agent_example.py

@@ -1,10 +1,10 @@
 import random
 
 from prediction_market_agent_tooling.deploy.agent import (
-    Answer,
     DeployableTraderAgent,
-    Probability,
+    ProbabilisticAnswer,
 )
+from prediction_market_agent_tooling.gtypes import Probability
 from prediction_market_agent_tooling.markets.agent_market import AgentMarket
 from prediction_market_agent_tooling.markets.markets import MarketType
 
@@ -13,16 +13,15 @@ class DeployableCoinFlipAgent(DeployableTraderAgent):
     def verify_market(self, market_type: MarketType, market: AgentMarket) -> bool:
         return True
 
-    def answer_binary_market(self, market: AgentMarket) -> Answer | None:
+    def answer_binary_market(self, market: AgentMarket) -> ProbabilisticAnswer | None:
         decision = random.choice([True, False])
-        return Answer(
-            decision=decision,
+        return ProbabilisticAnswer(
             p_yes=Probability(float(decision)),
             confidence=0.5,
             reasoning="I flipped a coin to decide.",
         )
 
 
 class DeployableAlwaysRaiseAgent(DeployableTraderAgent):
-    def answer_binary_market(self, market: AgentMarket) -> Answer | None:
+    def answer_binary_market(self, market: AgentMarket) -> ProbabilisticAnswer | None:
         raise RuntimeError("I always raise!")

prediction_market_agent_tooling/deploy/betting_strategy.py

@@ -0,0 +1,58 @@
+from abc import ABC, abstractmethod
+
+from prediction_market_agent_tooling.markets.agent_market import AgentMarket
+from prediction_market_agent_tooling.markets.data_models import (
+    ProbabilisticAnswer,
+    TokenAmountAndDirection,
+)
+from prediction_market_agent_tooling.tools.betting_strategies.kelly_criterion import (
+    get_kelly_bet,
+)
+
+
+class BettingStrategy(ABC):
+    @abstractmethod
+    def calculate_bet_amount_and_direction(
+        self, answer: ProbabilisticAnswer, market: AgentMarket
+    ) -> TokenAmountAndDirection:
+        pass
+
+
+class MaxAccuracyBettingStrategy(BettingStrategy):
+    @staticmethod
+    def calculate_direction(market_p_yes: float, estimate_p_yes: float) -> bool:
+        # If estimate_p_yes >= market.current_p_yes, then bet TRUE, else bet FALSE.
+        # This is equivalent to saying EXPECTED_VALUE = (estimate_p_yes * num_tokens_obtained_by_betting_yes) -
+        # ((1 - estimate_p_yes) * num_tokens_obtained_by_betting_no) >= 0
+        return estimate_p_yes >= market_p_yes
+
+    def calculate_bet_amount_and_direction(
+        self, answer: ProbabilisticAnswer, market: AgentMarket
+    ) -> TokenAmountAndDirection:
+        bet_amount = market.get_tiny_bet_amount().amount
+        direction = self.calculate_direction(market.current_p_yes, answer.p_yes)
+        return TokenAmountAndDirection(
+            amount=bet_amount,
+            currency=market.currency,
+            direction=direction,
+        )
+
+
+class KellyBettingStrategy(BettingStrategy):
+    @staticmethod
+    def get_max_bet_amount_for_market() -> float:
+        # No difference between markets.
+        return 10
+
+    def calculate_bet_amount_and_direction(
+        self, answer: ProbabilisticAnswer, market: AgentMarket
+    ) -> TokenAmountAndDirection:
+        max_bet_amount = self.get_max_bet_amount_for_market()
+        kelly_bet = get_kelly_bet(
+            max_bet_amount, market.current_p_yes, answer.p_yes, answer.confidence
+        )
+        return TokenAmountAndDirection(
+            amount=kelly_bet.size,
+            currency=market.currency,
+            direction=kelly_bet.direction,
+        )

prediction_market_agent_tooling/markets/data_models.py

@@ -1,10 +1,10 @@
 from datetime import datetime
 from enum import Enum
-from typing import TypeAlias
+from typing import Annotated, TypeAlias
 
-from pydantic import BaseModel, computed_field
+from pydantic import BaseModel, BeforeValidator, computed_field
 
-from prediction_market_agent_tooling.gtypes import OutcomeStr
+from prediction_market_agent_tooling.gtypes import OutcomeStr, Probability
 
 
 class Currency(str, Enum):
@@ -57,6 +57,43 @@ def __str__(self) -> str:
         return f"Resolved bet for market {self.market_id} for question {self.market_question} created at {self.created_time}: {self.amount} on {self.outcome}. Bet was resolved at {self.resolved_time} and was {'correct' if self.is_correct else 'incorrect'}. Profit was {self.profit}"
 
 
+class TokenAmountAndDirection(TokenAmount):
+    direction: bool
+
+
+def to_boolean_outcome(value: str | bool) -> bool:
+    if isinstance(value, bool):
+        return value
+
+    elif isinstance(value, str):
+        value = value.lower().strip()
+
+        if value in {"true", "yes", "y", "1"}:
+            return True
+
+        elif value in {"false", "no", "n", "0"}:
+            return False
+
+        else:
+            raise ValueError(f"Expected a boolean string, but got {value}")
+
+    else:
+        raise ValueError(f"Expected a boolean or a string, but got {value}")
+
+
+Decision = Annotated[bool, BeforeValidator(to_boolean_outcome)]
+
+
+class ProbabilisticAnswer(BaseModel):
+    p_yes: Probability
+    confidence: float
+    reasoning: str | None = None
+
+    @property
+    def p_no(self) -> Probability:
+        return Probability(1 - self.p_yes)
+
+
 class Position(BaseModel):
     market_id: str
     amounts: dict[OutcomeStr, TokenAmount]

prediction_market_agent_tooling/tools/betting_strategies/kelly_criterion.py

@@ -1,23 +1,16 @@
-from enum import Enum
-
 from pydantic import BaseModel
 
 
-class BetDirection(str, Enum):
-    YES = "Yes"
-    NO = "No"
+def check_is_valid_probability(probability: float) -> None:
+    if not 0 <= probability <= 1:
+        raise ValueError("Probability must be between 0 and 1")
 
 
 class KellyBet(BaseModel):
-    direction: BetDirection
+    direction: bool
     size: float
 
 
-def check_is_valid_probability(probability: float) -> None:
-    if not 0 <= probability <= 1:
-        raise ValueError("Probability must be between 0 and 1")
-
-
 def get_kelly_bet(
     max_bet: float,
     market_p_yes: float,
@@ -47,10 +40,10 @@ def get_kelly_bet(
     check_is_valid_probability(confidence)
 
     if estimated_p_yes > market_p_yes:
-        bet_direction = BetDirection.YES
+        bet_direction = True
         market_prob = market_p_yes
     else:
-        bet_direction = BetDirection.NO
+        bet_direction = False
         market_prob = 1 - market_p_yes
 
     # Handle the case where market_prob is 0

pyproject.toml

@@ -48,6 +48,7 @@ sqlmodel = "^0.0.21"
 psycopg2-binary = "^2.9.9"
 base58 = ">=1.0.2,<2.0"
 
+
 [tool.poetry.extras]
 openai = ["openai"]
 langchain = ["langchain", "langchain-openai"]
@@ -57,8 +58,8 @@ google = ["google-api-python-client"]
 pytest = "*"
 mypy = "^1.11.1"
 black = "^23.12.1"
-ape-foundry = "^0.8.2"
-eth-ape = "^0.8.10"
+eth-ape = "^0.8.12"
+ape-foundry = "^0.8.4"
 
 [build-system]
 requires = ["poetry-core"]

tests/markets/test_betting_strategies.py

@@ -30,7 +30,6 @@
     WrappedxDaiContract,
 )
 from prediction_market_agent_tooling.tools.betting_strategies.kelly_criterion import (
-    BetDirection,
     get_kelly_bet,
 )
 from prediction_market_agent_tooling.tools.betting_strategies.market_moving import (
@@ -204,9 +203,7 @@ def test_kelly_bet(est_p_yes: Probability, omen_market: OmenMarket) -> None:
     max_bet = 10
     confidence = 1.0
     market_p_yes = omen_market.current_p_yes
-    expected_bet_direction = (
-        BetDirection.NO if est_p_yes < market_p_yes else BetDirection.YES
-    )
+    expected_bet_direction = False if est_p_yes < market_p_yes else True
 
     # Kelly estimates the best bet for maximizing the expected value of the
     # logarithm of the wealth. We don't know the real best bet amount, but at