Add metadata attributes

Author: MDUYN

investing_algorithm_framework/domain/backtesting/backtest.py

@@ -91,6 +91,18 @@ def get_backtest_run(
                 return run
         return None
 
+    def get_all_backtest_permutation_tests(
+        self
+    ) -> List[BacktestPermutationTest]:
+        """
+        Retrieve all BacktestPermutationTest instances from the backtest.
+
+        Returns:
+            List[BacktestPermutationTest]: A list of all
+                BacktestPermutationTest instances.
+        """
+        return self.backtest_permutation_tests
+
     def get_backtest_permutation_test(
         self, date_range: BacktestDateRange
     ) -> Union[BacktestPermutationTest, None]:

investing_algorithm_framework/domain/backtesting/backtest_permutation_test.py

@@ -137,7 +137,7 @@ def save(self, path: str) -> None:
         os.makedirs(path, exist_ok=True)
 
         # Save the real metrics
-        self.real_metrics.save(os.path.join(path, "original_metrics"))
+        self.real_metrics.save(os.path.join(path, "original_metrics.json"))
 
         permuted_dir = os.path.join(path, "permuted_metrics")
         os.makedirs(permuted_dir, exist_ok=True)
@@ -154,36 +154,33 @@ def open(path: str) -> "BacktestPermutationTest":
         """
         Load the permutation test results from disk (JSON + Parquet).
         """
-        with open(os.path.join(path, "results.json"), "r") as f:
-            results = json.load(f)
+        original_metrics = os.path.join(path, "original_metrics.json")
 
         # Rehydrate BacktestMetrics
-        real_metrics = BacktestMetrics(**results["real_metrics"])
-        permutated_metrics = [
-            BacktestMetrics(**pm) for pm in results["permutated_metrics"]
-        ]
-
-        # Reload DataFrames
-        ohlcv_original_datasets = {}
-        ohlcv_permutated_datasets = {}
-        for file in os.listdir(path):
-            if file.startswith("original_") and file.endswith(".parquet"):
-                key = file.replace("original_", "").replace(".parquet", "")
-                ohlcv_original_datasets[key] = pd.read_parquet(
-                    os.path.join(path, file)
-                )
-            elif file.startswith("permuted_") and file.endswith(".parquet"):
-                key = file.replace("permuted_", "").replace(".parquet", "")
-                ohlcv_permutated_datasets[key] = pd.read_parquet(
-                    os.path.join(path, file)
-                )
+        real_metrics = BacktestMetrics.open(original_metrics)
+
+        permuted_dir = os.path.join(path, "permuted_metrics")
+
+        permutated_metrics = []
+        if os.path.exists(permuted_dir):
+            for fname in os.listdir(permuted_dir):
+                if fname.startswith("permuted_"):
+                    pm = BacktestMetrics.open(
+                        os.path.join(permuted_dir, fname)
+                    )
+                    permutated_metrics.append(pm)
+
+        p_values_path = os.path.join(path, "p_values.json")
+        p_values = {}
+
+        if os.path.exists(p_values_path):
+            with open(p_values_path, "r") as f:
+                p_values = json.load(f)
 
         return BacktestPermutationTest(
             real_metrics=real_metrics,
             permutated_metrics=permutated_metrics,
-            p_values=results["p_values"],
-            ohlcv_original_datasets=ohlcv_original_datasets,
-            ohlcv_permutated_datasets=ohlcv_permutated_datasets
+            p_values=p_values,
         )
 
     def create_directory_name(self) -> str:

investing_algorithm_framework/domain/backtesting/backtest_run.py

@@ -82,6 +82,7 @@ class BacktestRun:
     number_of_positions: int = 0
     backtest_metrics: BacktestMetrics = None
     backtest_date_range_name: str = None
+    data_sources: List[dict] = field(default_factory=list)
 
     def to_dict(self) -> dict:
         """

investing_algorithm_framework/domain/models/portfolio/portfolio_snapshot.py

@@ -1,5 +1,7 @@
 from dateutil import parser
 from investing_algorithm_framework.domain.models.base_model import BaseModel
+from investing_algorithm_framework.domain.constants import \
+    DEFAULT_DATETIME_FORMAT
 
 
 class PortfolioSnapshot(BaseModel):
@@ -17,7 +19,8 @@ def __init__(
         total_value=None,
         cash_flow=None,
         created_at=None,
-        position_snapshots=None
+        position_snapshots=None,
+        metadata=None,
     ):
         self.portfolio_id = portfolio_id
         self.trading_symbol = trading_symbol
@@ -29,6 +32,7 @@ def __init__(
         self.net_size = net_size
         self.total_cost = total_cost
         self.cash_flow = cash_flow
+        self.metadata = metadata if metadata is not None else {}
 
         if created_at is not None and isinstance(created_at, str):
             self.created_at = parser.parse(created_at)
@@ -144,9 +148,18 @@ def to_dict(self, datetime_format=None):
                 if self.created_at else None
 
         else:
-            created_at = self.created_at
+            created_at = self.created_at.strftime(DEFAULT_DATETIME_FORMAT)
 
         return {
+            "metadata": self.metadata,
+            "portfolio_id": self.portfolio_id,
+            "trading_symbol": self.trading_symbol,
+            "pending_value": self.pending_value,
+            "unallocated": self.unallocated,
+            "total_net_gain": self.total_net_gain,
+            "total_revenue": self.total_revenue,
+            "total_cost": self.total_cost,
+            "cash_flow": self.cash_flow,
             "net_size": self.net_size,
             "created_at": created_at,
             "total_value": self.total_value,
@@ -169,4 +182,15 @@ def from_dict(data):
             net_size=data.get("net_size", 0.0),
             created_at=created_at,
             total_value=data.get("total_value", 0.0),
+            trading_symbol=data.get(
+                "trading_symbol", None
+            ),
+            portfolio_id=data.get("portfolio_id", None),
+            pending_value=data.get("pending_value", 0.0),
+            unallocated=data.get("unallocated", 0.0),
+            total_net_gain=data.get("total_net_gain", 0.0),
+            total_revenue=data.get("total_revenue", 0.0),
+            total_cost=data.get("total_cost", 0.0),
+            cash_flow=data.get("cash_flow", 0.0),
+            metadata=data.get("metadata", {})
         )