Add helper script for unfinding a market (#503)

.gitignore

@@ -162,3 +162,5 @@ cython_debug/
 
 tests_files/*
 !tests_files/.gitkeep
+
+bet_strategy_benchmark*

examples/monitor/match_bets_with_langfuse_traces.py

@@ -1,3 +1,4 @@
+from pathlib import Path
 from typing import Any
 
 import pandas as pd
@@ -89,6 +90,9 @@ def get_outcome_for_trace(
 
 
 if __name__ == "__main__":
+    output_directory = Path("bet_strategy_benchmark")
+    output_directory.mkdir(parents=True, exist_ok=True)
+
     # Get the private keys for the agents from GCP Secret Manager
     agent_gcp_secret_map = {
         "DeployablePredictionProphetGPT4TurboFinalAgent": "pma-prophetgpt4turbo-final",
@@ -240,7 +244,8 @@ def get_outcome_for_trace(
 
             details.sort(key=lambda x: x["sim_profit"], reverse=True)
             pd.DataFrame.from_records(details).to_csv(
-                f"{agent_name} - {strategy} - all bets.csv", index=False
+                output_directory / f"{agent_name} - {strategy} - all bets.csv",
+                index=False,
             )
 
             sum_squared_errors = 0.0
@@ -297,7 +302,9 @@ def get_outcome_for_trace(
             + simulations_df.to_markdown(index=False)
         )
         # export details per agent
-        pd.DataFrame.from_records(details).to_csv(f"{agent_name}_details.csv")
+        pd.DataFrame.from_records(details).to_csv(
+            output_directory / f"{agent_name}_details.csv"
+        )
 
     print(f"Correlation between p_yes mse and total profit:")
     for strategy_name, mse_profit in strat_mse_profits.items():
@@ -306,5 +313,7 @@ def get_outcome_for_trace(
         correlation = pd.Series(mse).corr(pd.Series(profit))
         print(f"{strategy_name}: {correlation=}")
 
-    with open("match_bets_with_langfuse_traces_overall.md", "w") as overall_f:
+    with open(
+        output_directory / "match_bets_with_langfuse_traces_overall.md", "w"
+    ) as overall_f:
         overall_f.write(overall_md)

prediction_market_agent_tooling/deploy/betting_strategy.py

@@ -4,7 +4,7 @@
 
 from prediction_market_agent_tooling.gtypes import xDai
 from prediction_market_agent_tooling.loggers import logger
-from prediction_market_agent_tooling.markets.agent_market import AgentMarket
+from prediction_market_agent_tooling.markets.agent_market import AgentMarket, MarketFees
 from prediction_market_agent_tooling.markets.data_models import (
     Currency,
     Position,
@@ -19,7 +19,6 @@
 )
 from prediction_market_agent_tooling.tools.betting_strategies.kelly_criterion import (
     get_kelly_bet_full,
-    get_kelly_bet_simplified,
 )
 from prediction_market_agent_tooling.tools.betting_strategies.utils import SimpleBet
 from prediction_market_agent_tooling.tools.utils import check_not_none
@@ -153,32 +152,24 @@ def calculate_trades(
         market: AgentMarket,
     ) -> list[Trade]:
         outcome_token_pool = check_not_none(market.outcome_token_pool)
-        kelly_bet = (
-            get_kelly_bet_full(
-                yes_outcome_pool_size=outcome_token_pool[
-                    market.get_outcome_str_from_bool(True)
-                ],
-                no_outcome_pool_size=outcome_token_pool[
-                    market.get_outcome_str_from_bool(False)
-                ],
-                estimated_p_yes=answer.p_yes,
-                max_bet=self.max_bet_amount,
-                confidence=answer.confidence,
-            )
-            if market.has_token_pool()
-            else get_kelly_bet_simplified(
-                self.max_bet_amount,
-                market.current_p_yes,
-                answer.p_yes,
-                answer.confidence,
-            )
+        kelly_bet = get_kelly_bet_full(
+            yes_outcome_pool_size=outcome_token_pool[
+                market.get_outcome_str_from_bool(True)
+            ],
+            no_outcome_pool_size=outcome_token_pool[
+                market.get_outcome_str_from_bool(False)
+            ],
+            estimated_p_yes=answer.p_yes,
+            max_bet=self.max_bet_amount,
+            confidence=answer.confidence,
+            fees=market.fees,
         )
 
         kelly_bet_size = kelly_bet.size
         if self.max_price_impact:
             # Adjust amount
             max_price_impact_bet_amount = self.calculate_bet_amount_for_price_impact(
-                market, kelly_bet, 0
+                market, kelly_bet
             )
 
             # We just don't want Kelly size to extrapolate price_impact - hence we take the min.
@@ -196,15 +187,20 @@ def calculate_trades(
         return trades
 
     def calculate_price_impact_for_bet_amount(
-        self, buy_direction: bool, bet_amount: float, yes: float, no: float, fee: float
+        self,
+        buy_direction: bool,
+        bet_amount: float,
+        yes: float,
+        no: float,
+        fees: MarketFees,
     ) -> float:
         total_outcome_tokens = yes + no
         expected_price = (
             no / total_outcome_tokens if buy_direction else yes / total_outcome_tokens
         )
 
         tokens_to_buy = get_buy_outcome_token_amount(
-            bet_amount, buy_direction, yes, no, fee
+            bet_amount, buy_direction, yes, no, fees
         )
 
         actual_price = bet_amount / tokens_to_buy
@@ -216,7 +212,6 @@ def calculate_bet_amount_for_price_impact(
         self,
         market: AgentMarket,
         kelly_bet: SimpleBet,
-        fee: float,
     ) -> float:
         def calculate_price_impact_deviation_from_target_price_impact(
             bet_amount: xDai,
@@ -226,7 +221,7 @@ def calculate_price_impact_deviation_from_target_price_impact(
                 bet_amount,
                 yes_outcome_pool_size,
                 no_outcome_pool_size,
-                fee,
+                market.fees,
             )
             # We return abs for the algorithm to converge to 0 instead of the min (and possibly negative) value.
 
@@ -285,25 +280,17 @@ def calculate_trades(
         estimated_p_yes = float(answer.p_yes > 0.5)
         confidence = 1.0
 
-        kelly_bet = (
-            get_kelly_bet_full(
-                yes_outcome_pool_size=outcome_token_pool[
-                    market.get_outcome_str_from_bool(True)
-                ],
-                no_outcome_pool_size=outcome_token_pool[
-                    market.get_outcome_str_from_bool(False)
-                ],
-                estimated_p_yes=estimated_p_yes,
-                max_bet=adjusted_bet_amount,
-                confidence=confidence,
-            )
-            if market.has_token_pool()
-            else get_kelly_bet_simplified(
-                adjusted_bet_amount,
-                market.current_p_yes,
-                estimated_p_yes,
-                confidence,
-            )
+        kelly_bet = get_kelly_bet_full(
+            yes_outcome_pool_size=outcome_token_pool[
+                market.get_outcome_str_from_bool(True)
+            ],
+            no_outcome_pool_size=outcome_token_pool[
+                market.get_outcome_str_from_bool(False)
+            ],
+            estimated_p_yes=estimated_p_yes,
+            max_bet=adjusted_bet_amount,
+            confidence=confidence,
+            fees=market.fees,
         )
 
         amounts = {

prediction_market_agent_tooling/jobs/omen/omen_jobs.py

@@ -75,7 +75,7 @@ def from_omen_agent_market(market: OmenAgentMarket) -> "OmenJobAgentMarket":
             market_maker_contract_address_checksummed=market.market_maker_contract_address_checksummed,
             condition=market.condition,
             finalized_time=market.finalized_time,
-            fee=market.fee,
+            fees=market.fees,
         )
 
 

prediction_market_agent_tooling/markets/agent_market.py

@@ -2,7 +2,7 @@
 from enum import Enum
 
 from eth_typing import ChecksumAddress
-from pydantic import BaseModel, field_validator
+from pydantic import BaseModel, field_validator, model_validator
 from pydantic_core.core_schema import FieldValidationInfo
 
 from prediction_market_agent_tooling.config import APIKeys
@@ -16,6 +16,7 @@
     ResolvedBet,
     TokenAmount,
 )
+from prediction_market_agent_tooling.markets.market_fees import MarketFees
 from prediction_market_agent_tooling.tools.utils import (
     DatetimeUTC,
     check_not_none,
@@ -60,6 +61,7 @@ class AgentMarket(BaseModel):
     current_p_yes: Probability
     url: str
     volume: float | None  # Should be in currency of `currency` above.
+    fees: MarketFees
 
     @field_validator("outcome_token_pool")
     def validate_outcome_token_pool(
@@ -77,6 +79,14 @@ def validate_outcome_token_pool(
                 )
         return outcome_token_pool
 
+    @model_validator(mode="before")
+    def handle_legacy_fee(cls, data: dict[str, t.Any]) -> dict[str, t.Any]:
+        # Backward compatibility for older `AgentMarket` without `fees`.
+        if "fees" not in data and "fee" in data:
+            data["fees"] = MarketFees(absolute=0.0, bet_proportion=data["fee"])
+            del data["fee"]
+        return data
+
     @property
     def current_p_no(self) -> Probability:
         return Probability(1 - self.current_p_yes)

prediction_market_agent_tooling/markets/manifold/manifold.py

@@ -6,6 +6,7 @@
 from prediction_market_agent_tooling.markets.agent_market import (
     AgentMarket,
     FilterBy,
+    MarketFees,
     SortBy,
 )
 from prediction_market_agent_tooling.markets.data_models import BetAmount, Currency
@@ -33,6 +34,13 @@ class ManifoldAgentMarket(AgentMarket):
     currency: t.ClassVar[Currency] = Currency.Mana
     base_url: t.ClassVar[str] = MANIFOLD_BASE_URL
 
+    # Manifold has additional fees than `platform_absolute`, but they don't expose them in the API before placing the bet, see https://docs.manifold.markets/api.
+    # So we just consider them as 0, which anyway is true for all markets I randomly checked on Manifold.
+    fees: MarketFees = MarketFees(
+        bet_proportion=0,
+        absolute=0.25,  # For doing trades via API.
+    )
+
     def get_last_trade_p_yes(self) -> Probability:
         """On Manifold, probablities aren't updated after the closure, so we can just use the current probability"""
         return self.current_p_yes

prediction_market_agent_tooling/markets/market_fees.py

@@ -0,0 +1,36 @@
+from pydantic import BaseModel, Field
+
+
+class MarketFees(BaseModel):
+    bet_proportion: float = Field(
+        ..., ge=0.0, lt=1.0
+    )  # proportion of the bet, from 0 to 1
+    absolute: float  # absolute value paid in the currency of the market
+
+    @staticmethod
+    def get_zero_fees(
+        bet_proportion: float = 0.0,
+        absolute: float = 0.0,
+    ) -> "MarketFees":
+        return MarketFees(
+            bet_proportion=bet_proportion,
+            absolute=absolute,
+        )
+
+    def total_fee_absolute_value(self, bet_amount: float) -> float:
+        """
+        Returns the total fee in absolute terms, including both proportional and fixed fees.
+        """
+        return self.bet_proportion * bet_amount + self.absolute
+
+    def total_fee_relative_value(self, bet_amount: float) -> float:
+        """
+        Returns the total fee relative to the bet amount, including both proportional and fixed fees.
+        """
+        if bet_amount == 0:
+            return 0.0
+        total_fee = self.total_fee_absolute_value(bet_amount)
+        return total_fee / bet_amount
+
+    def get_bet_size_after_fees(self, bet_amount: float) -> float:
+        return bet_amount * (1 - self.bet_proportion) - self.absolute

prediction_market_agent_tooling/markets/metaculus/metaculus.py

@@ -5,6 +5,7 @@
 from prediction_market_agent_tooling.markets.agent_market import (
     AgentMarket,
     FilterBy,
+    MarketFees,
     SortBy,
 )
 from prediction_market_agent_tooling.markets.metaculus.api import (
@@ -29,6 +30,7 @@ class MetaculusAgentMarket(AgentMarket):
     description: str | None = (
         None  # Metaculus markets don't have a description, so just default to None.
     )
+    fees: MarketFees = MarketFees.get_zero_fees()  # No fees on Metaculus.
 
     @staticmethod
     def from_data_model(model: MetaculusQuestion) -> "MetaculusAgentMarket":