545 strategy backtests add financial metrics

ape-config.yaml

@@ -8,7 +8,7 @@ default_ecosystem: gnosis
 node:
   gnosis:
     mainnet:
-      uri: https://rpc.gnosischain.com
+      uri: https://rpc.gnosis.gateway.fm #https://rpc.gnosischain.com
 
 
 networks:

examples/monitor/data_models.py

@@ -0,0 +1,24 @@
+from pydantic import BaseModel
+
+from prediction_market_agent_tooling.tools.datetime_utc import DatetimeUTC
+
+
+class SimulationDetail(BaseModel):
+    strategy: str
+    url: str
+    market_p_yes: float
+    agent_p_yes: float
+    agent_conf: float
+    org_bet: float
+    sim_bet: float
+    org_dir: bool
+    sim_dir: bool
+    org_profit: float
+    sim_profit: float
+    timestamp: DatetimeUTC
+
+
+class SharpeOutput(BaseModel):
+    annualized_volatility: float
+    mean_daily_return: float
+    annualized_sharpe_ratio: float

examples/monitor/financial_metrics.py

@@ -0,0 +1,63 @@
+import numpy as np
+import pandas as pd
+
+from examples.monitor.data_models import SharpeOutput, SimulationDetail
+
+
+class SharpeRatioCalculator:
+    def __init__(
+        self, details: list[SimulationDetail], risk_free_rate: float = 0.0
+    ) -> None:
+        self.details = details
+        self.df = pd.DataFrame([d.model_dump() for d in self.details])
+        self.risk_free_rate = risk_free_rate
+
+    def __has_df_valid_columns_else_exception(
+        self, required_columns: list[str]
+    ) -> None:
+        if not set(required_columns).issubset(self.df.columns):
+            raise ValueError("Dataframe doesn't contain all the required columns.")
+
+    def prepare_wallet_daily_balance_df(
+        self, timestamp_col_name: str, profit_col_name: str
+    ) -> pd.DataFrame:
+        self.__has_df_valid_columns_else_exception(
+            [timestamp_col_name, profit_col_name]
+        )
+        df = self.df.copy()
+        df[timestamp_col_name] = pd.to_datetime(df[timestamp_col_name])
+        df.sort_values(timestamp_col_name, ascending=True, inplace=True)
+
+        df["profit_cumsum"] = df[profit_col_name].cumsum()
+        df["profit_cumsum"] = df["profit_cumsum"] + 50
+
+        df = df.drop_duplicates(subset=timestamp_col_name, keep="last")
+        df.set_index(timestamp_col_name, inplace=True)
+        # We generate a new Dataframe with daily wallet balances, derived by the final wallet balance
+        # from the previous day.
+        wallet_balance_daily_df = df[["profit_cumsum"]].resample("D").ffill()
+        wallet_balance_daily_df.dropna(inplace=True)
+        wallet_balance_daily_df["returns"] = wallet_balance_daily_df[
+            "profit_cumsum"
+        ].pct_change()
+        return wallet_balance_daily_df
+
+    def calculate_annual_sharpe_ratio(
+        self, timestamp_col_name: str = "timestamp", profit_col_name: str = "sim_profit"
+    ) -> SharpeOutput:
+        wallet_daily_balance_df = self.prepare_wallet_daily_balance_df(
+            timestamp_col_name=timestamp_col_name, profit_col_name=profit_col_name
+        )
+
+        daily_volatility = wallet_daily_balance_df["returns"].std()
+        annualized_volatility = daily_volatility * np.sqrt(365)
+        mean_daily_return = wallet_daily_balance_df["returns"].mean()
+        daily_sharpe_ratio = (
+            mean_daily_return - self.risk_free_rate
+        ) / daily_volatility
+        annualized_sharpe_ratio = daily_sharpe_ratio * np.sqrt(365)
+        return SharpeOutput(
+            annualized_volatility=annualized_volatility,
+            mean_daily_return=mean_daily_return,
+            annualized_sharpe_ratio=annualized_sharpe_ratio,
+        )