Support multiple vector backtests

Author: MDUYN

investing_algorithm_framework/app/app.py

@@ -381,6 +381,10 @@ def initialize_data_sources(
             None
         """
         logger.info("Initializing data sources")
+
+        if data_sources is None or len(data_sources) == 0:
+            return
+
         data_provider_service = self.container.data_provider_service()
         data_provider_service.reset()
 
@@ -415,6 +419,10 @@ def initialize_data_sources_backtest(
             None
         """
         logger.info("Initializing data sources for backtest")
+
+        if data_sources is None or len(data_sources) == 0:
+            return
+
         data_provider_service = self.container.data_provider_service()
         data_provider_service.reset()
 
@@ -900,18 +908,90 @@ def run_backtest(
 
         return backtest
 
+    def run_vector_backtests(
+        self,
+        backtest_date_range: BacktestDateRange,
+        initial_amount,
+        strategies: List[TradingStrategy],
+        snapshot_interval: SnapshotInterval = SnapshotInterval.DAILY,
+        risk_free_rate: Optional[float] = None,
+        skip_data_sources_initialization: bool = False
+    ):
+        """
+        Run vectorized backtests for a set of strategies. The provided
+        set of strategies need to have their 'buy_signal_vectorized' and
+        'sell_signal_vectorized' methods implemented to support vectorized
+        backtesting.
+
+        Args:
+            backtest_date_range: The date range to run the backtest for
+                (instance of BacktestDateRange)
+            initial_amount: The initial amount to start the backtest with.
+                This will be the amount of trading currency that the backtest
+                portfolio will start with.
+            strategies (List[TradingStrategy]): List of strategy objects
+                that need to be backtested. Each strategy should implement
+                the 'buy_signal_vectorized' and 'sell_signal_vectorized'
+                methods to support vectorized backtesting.
+            snapshot_interval (SnapshotInterval): The snapshot
+                interval to use for the backtest. This is used to determine
+                how often the portfolio snapshot should be taken during the
+                backtest. The default is TRADE_CLOSE, which means that the
+                portfolio snapshot will be taken at the end of each trade.
+            risk_free_rate (Optional[float]): The risk-free rate to use for
+                the backtest. This is used to calculate the Sharpe ratio
+                and other performance metrics. If not provided, the default
+                risk-free rate will be tried to be fetched from the
+                US Treasury website.
+            skip_data_sources_initialization (bool): Whether to skip the
+                initialization of data sources. This is useful when the data
+                sources are already initialized, and you want to skip the
+                initialization step. This will speed up the backtesting
+                process, but make sure that the data sources are already
+                initialized before calling this method.
+
+        Returns:
+            List[Backtest]: List of Backtest instances for each strategy
+                that was backtested.
+        """
+        backtests = []
+        data_sources = []
+
+        for strategy in strategies:
+            data_sources.extend(strategy.data_sources)
+
+        if not skip_data_sources_initialization:
+            self.initialize_data_sources_backtest(
+                data_sources, backtest_date_range
+            )
+
+        for strategy in tqdm(strategies):
+            backtests.append(
+                self.run_vector_backtest(
+                    backtest_date_range=backtest_date_range,
+                    initial_amount=initial_amount,
+                    strategy=strategy,
+                    snapshot_interval=snapshot_interval,
+                    risk_free_rate=risk_free_rate,
+                    skip_data_sources_initialization=True
+                )
+            )
+
+        return backtests
+
     def run_vector_backtest(
         self,
         backtest_date_range: BacktestDateRange,
         initial_amount,
-        strategy,
+        strategy: TradingStrategy,
         snapshot_interval: SnapshotInterval = SnapshotInterval.DAILY,
         metadata: Optional[Dict[str, str]] = None,
         risk_free_rate: Optional[float] = None,
+        skip_data_sources_initialization: bool = False
     ) -> Backtest:
         """
-        Run a vectorized backtest for an algorithm. The provided algorithm
-        or set of strategies need to have their 'buy_signal_vectorized' and
+        Run vectorized backtests for a strategy. The provided
+        strategy needs to have its 'buy_signal_vectorized' and
         'sell_signal_vectorized' methods implemented to support vectorized
         backtesting.
 
@@ -937,6 +1017,12 @@ def run_vector_backtest(
                 backtest report. This can be used to store additional
                 information about the backtest, such as the author, version,
                 parameters or any other relevant information.
+            skip_data_sources_initialization (bool): Whether to skip the
+                initialization of data sources. This is useful when the data
+                sources are already initialized, and you want to skip the
+                initialization step. This will speed up the backtesting
+                process, but make sure that the data sources are already
+                initialized before calling this method.
 
         Returns:
             Backtest: Instance of Backtest
@@ -947,9 +1033,11 @@ def run_vector_backtest(
             snapshot_interval=snapshot_interval,
             initial_amount=initial_amount
         )
-        self.initialize_data_sources_backtest(
-            strategy.data_sources, backtest_date_range
-        )
+
+        if not skip_data_sources_initialization:
+            self.initialize_data_sources_backtest(
+                strategy.data_sources, backtest_date_range
+            )
 
         if risk_free_rate is None:
             logger.info("No risk free rate provided, retrieving it...")
@@ -971,7 +1059,17 @@ def run_vector_backtest(
             initial_amount=initial_amount,
             risk_free_rate=risk_free_rate
         )
-        backtest.metadata = metadata if metadata is not None else {}
+
+        # Add the metadata to the backtest
+        if metadata is None:
+
+            if strategy.metadata is not None:
+                backtest.metadata = {}
+            else:
+                backtest.metadata = strategy.metadata
+        else:
+            backtest.metadata = metadata
+
         return backtest
 
     def run_backtests(

investing_algorithm_framework/app/strategy.py

@@ -27,22 +27,28 @@ class TradingStrategy:
             sources to use for the strategy. The data sources will be used
             to indentify data providers that will be called to gather data
             and pass to the strategy before its run.
+        metadata (optional): Dict[str, Any] - a dictionary
+            containing metadata about the strategy. This can be used to
+            store additional information about the strategy, such as its
+            author, version, description, params etc.
     """
-    time_unit: str = None
+    time_unit: TimeUnit = None
     interval: int = None
     worker_id: str = None
     strategy_id: str = None
     decorated = None
     data_sources: List[DataSource] = None
     traces = None
     context: Context = None
+    metadata: Dict[str, Any] = None
 
     def __init__(
         self,
         strategy_id=None,
         time_unit=None,
         interval=None,
         data_sources=None,
+        metadata=None,
         worker_id=None,
         decorated=None
     ):
@@ -65,6 +71,8 @@ def __init__(
         if data_sources is not None:
             self.data_sources = data_sources
 
+        self.metadata = metadata if metadata is not None else {}
+
         if decorated is not None:
             self.decorated = decorated
 

tests/resources/backtest_report/results.json

@@ -34,5 +34,5 @@
             "total_value": 1200
         }
     ],
-    "created_at": "2025-08-21 06:16:46"
+    "created_at": "2025-08-22 08:08:54"
 }

tests/resources/backtest_reports_for_testing/test_algorithm_backtest/results.json

@@ -34,5 +34,5 @@
             "total_value": 1000.0
         }
     ],
-    "created_at": "2025-08-21 06:16:37"
+    "created_at": "2025-08-22 08:08:44"
 }