From 8defa11f88fdd2069ccc939c3cd7468088beec68 Mon Sep 17 00:00:00 2001
From: Mika <kontakt@donau2space.de>
Date: Wed, 28 Jan 2026 16:22:42 +0000
Subject: [PATCH] Add python_analysis_script/src/python_analysis_script/core.py

---
 .../src/python_analysis_script/core.py        | 64 +++++++++++++++++++
 1 file changed, 64 insertions(+)
 create mode 100644 python_analysis_script/src/python_analysis_script/core.py

diff --git a/python_analysis_script/src/python_analysis_script/core.py b/python_analysis_script/src/python_analysis_script/core.py
new file mode 100644
index 0000000..ada573d
--- /dev/null
+++ b/python_analysis_script/src/python_analysis_script/core.py
@@ -0,0 +1,64 @@
+from __future__ import annotations
+import pandas as pd
+from dataclasses import dataclass
+from datetime import datetime
+from typing import List
+
+
+@dataclass
+class DecisionResult:
+    """Repräsentiert ein einzelnes CI-Entscheidungsresultat."""
+
+    timestamp: datetime
+    decision: str
+    margin: float
+    flaky_flag: bool
+    subset_flip_count: int
+    mischfenster_p95: float
+
+
+def _validate_input_columns(df: pd.DataFrame) -> None:
+    required_cols = {"margin", "flaky_flag", "subset_flip_count", "mischfenster_p95"}
+    missing = required_cols - set(df.columns)
+    assert not missing, f"Missing required columns: {missing}"
+
+
+def _apply_policy(row: pd.Series) -> str:
+    margin = row.get("margin", 0.0)
+    flaky = row.get("flaky_flag", False)
+    # CI-Policy v0.1: einfache Entscheidungslogik
+    if pd.isna(margin):
+        return "FAIL"
+    if flaky:
+        return "WARN"
+    if margin >= 0.0:
+        return "PASS"
+    elif -0.05 <= margin < 0.0:
+        return "WARN"
+    else:
+        return "FAIL"
+
+
+def analyze_backtest(csv_file: str) -> List[DecisionResult]:
+    """Analysiert Backtest-Ergebnisse und erstellt eine Liste DecisionResult."""
+    assert isinstance(csv_file, str) and csv_file.endswith('.csv'), "csv_file must be a CSV filepath string"
+
+    df = pd.read_csv(csv_file)
+    _validate_input_columns(df)
+
+    results: List[DecisionResult] = []
+    now = datetime.utcnow()
+
+    for _, row in df.iterrows():
+        decision = _apply_policy(row)
+        result = DecisionResult(
+            timestamp=now,
+            decision=decision,
+            margin=float(row.get("margin", 0.0)),
+            flaky_flag=bool(row.get("flaky_flag", False)),
+            subset_flip_count=int(row.get("subset_flip_count", 0)),
+            mischfenster_p95=float(row.get("mischfenster_p95", 0.0)),
+        )
+        results.append(result)
+
+    return results