Rename analytics/bankroll.txt to analytics/bankroll.py

analytics/bankroll.py

@@ -0,0 +1,59 @@
+from __future__ import annotations
+
+import pandas as pd
+
+from analytics.edge import american_to_decimal
+
+
+def grade_profit(stake: float, odds: int, result: str) -> float:
+    result = result.lower()
+    if result == "win":
+        decimal_odds = american_to_decimal(odds)
+        return stake * (decimal_odds - 1.0)
+    if result == "loss":
+        return -stake
+    return 0.0
+
+
+def bankroll_curve(bets_df: pd.DataFrame) -> pd.DataFrame:
+    if bets_df.empty:
+        return pd.DataFrame(columns=["bet_id", "cum_profit"])
+
+    df = bets_df.sort_values("bet_id").copy()
+    df["cum_profit"] = df["profit"].fillna(0.0).cumsum()
+    return df[["bet_id", "cum_profit"]]
+
+
+def summary_metrics(bets_df: pd.DataFrame) -> dict[str, float]:
+    if bets_df.empty:
+        return {
+            "bets": 0,
+            "stake": 0.0,
+            "profit": 0.0,
+            "roi": 0.0,
+            "win_rate": 0.0,
+        }
+
+    graded = bets_df[bets_df["result"].isin(["win", "loss"])].copy()
+    if graded.empty:
+        return {
+            "bets": 0,
+            "stake": 0.0,
+            "profit": 0.0,
+            "roi": 0.0,
+            "win_rate": 0.0,
+        }
+
+    total_stake = float(graded["stake"].sum())
+    total_profit = float(graded["profit"].sum())
+    wins = int((graded["result"] == "win").sum())
+    losses = int((graded["result"] == "loss").sum())
+    total = wins + losses
+
+    return {
+        "bets": total,
+        "stake": total_stake,
+        "profit": total_profit,
+        "roi": (total_profit / total_stake) if total_stake else 0.0,
+        "win_rate": (wins / total) if total else 0.0,
+    }