Fix vector backtest capital allocation

Author: MDUYN

investing_algorithm_framework/__init__.py

@@ -7,7 +7,7 @@
     get_rolling_sharpe_ratio_chart, rank_results, \
     get_monthly_returns_heatmap_chart, create_weights, \
     get_yearly_returns_bar_chart, get_entry_and_exit_signals, \
-    get_ohlcv_data_completeness_chart
+    get_ohlcv_data_completeness_chart, get_equity_curve_chart
 from .domain import ApiException, combine_backtests, PositionSize, \
     OrderType, OperationalException, OrderStatus, OrderSide, \
     TimeUnit, TimeInterval, Order, Portfolio, Backtest, \
@@ -174,5 +174,6 @@
     "get_cumulative_return_series",
     "get_total_loss",
     "get_total_growth",
-    "generate_backtest_summary_metrics"
+    "generate_backtest_summary_metrics",
+    "get_equity_curve_chart"
 ]

investing_algorithm_framework/app/__init__.py

@@ -11,7 +11,7 @@
     get_equity_curve_with_drawdown_chart, \
     get_rolling_sharpe_ratio_chart, \
     get_monthly_returns_heatmap_chart, \
-    get_yearly_returns_bar_chart, \
+    get_yearly_returns_bar_chart, get_equity_curve_chart, \
     get_ohlcv_data_completeness_chart, get_entry_and_exit_signals
 from .analysis import select_backtest_date_ranges, rank_results, \
     create_weights
@@ -40,5 +40,6 @@
     "get_ohlcv_data_completeness_chart",
     "rank_results",
     "create_weights",
-    "get_entry_and_exit_signals"
+    "get_entry_and_exit_signals",
+    "get_equity_curve_chart"
 ]

investing_algorithm_framework/app/app.py

@@ -798,7 +798,9 @@ def run_vector_backtests(
         snapshot_interval: SnapshotInterval = SnapshotInterval.DAILY,
         risk_free_rate: Optional[float] = None,
         skip_data_sources_initialization: bool = False,
-        show_progress: bool = True
+        show_progress: bool = True,
+        market: Optional[str] = None,
+        trading_symbol: Optional[str] = None,
     ) -> List[Backtest]:
         """
         Run vectorized backtests for a set of strategies. The provided
@@ -840,6 +842,12 @@ def run_vector_backtests(
             show_progress (bool): Whether to show progress bars during
                 data source initialization. This is useful for long-running
                 initialization processes.
+            market (str): The market to use for the backtest. This is used
+                to create a portfolio configuration if no portfolio
+                configuration is provided in the strategy.
+            trading_symbol (str): The trading symbol to use for the backtest.
+                This is used to create a portfolio configuration if no
+                portfolio configuration is provided in the strategy.
 
         Returns:
             List[Backtest]: List of Backtest instances for each strategy
@@ -887,7 +895,9 @@ def run_vector_backtests(
                     strategy=strategy,
                     snapshot_interval=snapshot_interval,
                     risk_free_rate=risk_free_rate,
-                    skip_data_sources_initialization=True
+                    skip_data_sources_initialization=True,
+                    market=market,
+                    trading_symbol=trading_symbol
                 )
                 backtests.append(backtest)
         else:
@@ -938,13 +948,15 @@ def run_vector_backtests(
     def run_vector_backtest(
         self,
         backtest_date_range: BacktestDateRange,
-        initial_amount,
         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,
-        show_data_initialization_progress: bool = True
+        show_data_initialization_progress: bool = True,
+        initial_amount: float = None,
+        market: str = None,
+        trading_symbol: str = None
     ) -> Backtest:
         """
         Run vectorized backtests for a strategy. The provided
@@ -982,6 +994,20 @@ def run_vector_backtest(
                 initialized before calling this method.
             show_data_initialization_progress (bool): Whether to show the
                 progress bar when initializing data sources.
+            market (str): The market to use for the backtest. This is used
+                to create a portfolio configuration if no portfolio
+                configuration is found for the strategy. If not provided,
+                the first portfolio configuration found will be used.
+            trading_symbol (str): The trading symbol to use for the backtest.
+                This is used to create a portfolio configuration if no
+                portfolio configuration is found for the strategy. If not
+                provided, the first trading symbol found in the portfolio
+                configuration will be used.
+            initial_amount (float): The initial amount to start the
+                backtest with. This will be the amount of trading currency
+                that the portfolio will start with. If not provided,
+                the initial amount from the portfolio configuration will
+                be used.
 
         Returns:
             Backtest: Instance of Backtest
@@ -1017,8 +1043,10 @@ def run_vector_backtest(
         run = backtest_service.create_vector_backtest(
             strategy=strategy,
             backtest_date_range=backtest_date_range,
-            initial_amount=initial_amount,
-            risk_free_rate=risk_free_rate
+            risk_free_rate=risk_free_rate,
+            market=market,
+            trading_symbol=trading_symbol,
+            initial_amount=initial_amount
         )
         backtest = Backtest(
             backtest_runs=[run],
@@ -1256,6 +1284,8 @@ def run_permutation_test(
         backtest_date_range: BacktestDateRange,
         number_of_permutations: int = 100,
         initial_amount: float = 1000.0,
+        market: str = None,
+        trading_symbol: str = None,
         risk_free_rate: Optional[float] = None
     ) -> BacktestPermutationTest:
         """
@@ -1281,6 +1311,15 @@ def run_permutation_test(
             risk_free_rate (Optional[float]): The risk-free rate to use for
                 the backtest metrics. If not provided, it will try to fetch
                 the risk-free rate from the US Treasury website.
+            market (str): The market to use for the backtest. This is used
+                to create a portfolio configuration if no portfolio
+                configuration is found for the strategy. If not provided,
+                the first portfolio configuration found will be used.
+            trading_symbol (str): The trading symbol to use for the backtest.
+                This is used to create a portfolio configuration if no
+                portfolio configuration is found for the strategy. If not
+                provided, the first trading symbol found in the portfolio
+                configuration will be used.
 
         Raises:
             OperationalException: If the risk-free rate cannot be retrieved.
@@ -1309,6 +1348,8 @@ def run_permutation_test(
             strategy=strategy,
             snapshot_interval=SnapshotInterval.DAILY,
             risk_free_rate=risk_free_rate,
+            market=market,
+            trading_symbol=trading_symbol
         )
         backtest_metrics = backtest.get_backtest_metrics(backtest_date_range)
 
@@ -1388,7 +1429,9 @@ def run_permutation_test(
                 strategy=strategy,
                 snapshot_interval=SnapshotInterval.DAILY,
                 risk_free_rate=risk_free_rate,
-                skip_data_sources_initialization=True
+                skip_data_sources_initialization=True,
+                market=market,
+                trading_symbol=trading_symbol
             )
 
             # Add the results of the permuted backtest to the main backtest

investing_algorithm_framework/app/reporting/__init__.py

@@ -7,7 +7,8 @@
     get_monthly_returns_heatmap_chart, \
     get_yearly_returns_bar_chart, \
     get_ohlcv_data_completeness_chart, \
-    get_entry_and_exit_signals
+    get_entry_and_exit_signals, \
+    get_equity_curve_chart
 
 __all__ = [
     "add_html_report",
@@ -21,5 +22,6 @@
     "get_monthly_returns_heatmap_chart",
     "get_yearly_returns_bar_chart",
     "get_ohlcv_data_completeness_chart",
-    "get_entry_and_exit_signals"
+    "get_entry_and_exit_signals",
+    "get_equity_curve_chart"
 ]

investing_algorithm_framework/app/reporting/charts/__init__.py

@@ -1,4 +1,5 @@
 from .equity_curve_drawdown import get_equity_curve_with_drawdown_chart
+from .equity_curve import get_equity_curve_chart
 from .rolling_sharp_ratio import get_rolling_sharpe_ratio_chart
 from .monthly_returns_heatmap import get_monthly_returns_heatmap_chart
 from .yearly_returns_barchart import get_yearly_returns_bar_chart
@@ -14,4 +15,5 @@
     "get_ohlcv_data_completeness_chart",
     "get_entry_and_exit_signals",
     "create_line_scatter",
+    "get_equity_curve_chart"
 ]

investing_algorithm_framework/services/backtesting/backtest_service.py

@@ -13,7 +13,8 @@
 from investing_algorithm_framework.domain import BacktestRun, OrderType, \
     TimeUnit, Trade, OperationalException, BacktestDateRange, TimeFrame, \
     Backtest, TradeStatus, PortfolioSnapshot, Order, OrderStatus, OrderSide, \
-    Portfolio, DataType, generate_backtest_summary_metrics
+    Portfolio, DataType, generate_backtest_summary_metrics, \
+    PortfolioConfiguration
 from investing_algorithm_framework.services.data_providers import \
     DataProviderService
 from investing_algorithm_framework.services.portfolios import \
@@ -114,8 +115,10 @@ def create_vector_backtest(
         self,
         strategy,
         backtest_date_range: BacktestDateRange,
-        initial_amount: float,
         risk_free_rate: float = 0.027,
+        initial_amount: float = None,
+        trading_symbol: str = None,
+        market: str = None,
     ) -> BacktestRun:
         """
         Vectorized backtest for multiple assets using strategy
@@ -124,9 +127,16 @@ def create_vector_backtest(
         Args:
             strategy: The strategy to backtest.
             backtest_date_range: The date range for the backtest.
-            initial_amount: The initial amount to use for the backtest.
             risk_free_rate: The risk-free rate to use for the backtest
                 metrics. Default is 0.027 (2.7%).
+            initial_amount: The initial amount to use for the backtest.
+                If None, the initial amount will be taken from the first
+                portfolio configuration.
+            trading_symbol: The trading symbol to use for the backtest.
+                If None, the trading symbol will be taken from the first
+                portfolio configuration.
+            market: The market to use for the backtest. If None, the market
+                will be taken from the first portfolio configuration.
 
         Returns:
             BacktestRun: The backtest run containing the results and metrics.
@@ -135,12 +145,26 @@ def create_vector_backtest(
             .get_all()
 
         if (portfolio_configurations is None
-                or len(portfolio_configurations) == 0):
+                or len(portfolio_configurations) == 0
+                and initial_amount is None
+                or trading_symbol is None
+                or market is None):
             raise OperationalException(
                 "No portfolio configurations found, please register a "
                 "portfolio configuration before running a backtest."
             )
 
+        if portfolio_configurations is None \
+                or len(portfolio_configurations) == 0:
+            portfolio_configurations = []
+            portfolio_configurations.append(
+                PortfolioConfiguration(
+                    market=market,
+                    trading_symbol=trading_symbol,
+                    initial_balance=initial_amount
+                )
+            )
+
         trading_symbol = portfolio_configurations[0].trading_symbol
 
         # Load vectorized backtest data
@@ -188,8 +212,6 @@ def create_vector_backtest(
                 total_net_gain=0.0
             )
         ]
-        unallocated = initial_amount
-        total_values = pd.Series(0.0, index=index)
 
         for symbol in buy_signals.keys():
             full_symbol = f"{symbol}/{trading_symbol}"
@@ -221,7 +243,6 @@ def create_vector_backtest(
             returns = close.pct_change().fillna(0)
             returns = returns.astype(float)
             signal = signal.astype(float)
-            strategy_returns = signal * returns
 
             if pos_size_obj is None:
                 raise OperationalException(
@@ -242,9 +263,6 @@ def create_vector_backtest(
                 asset_price=close.iloc[0]
             )
 
-            holdings = (strategy_returns + 1).cumprod() * capital_for_trade
-            total_values += holdings
-
             # Trade generation
             last_trade = None
 
@@ -296,7 +314,6 @@ def create_vector_backtest(
                     )
                     last_trade = trade
                     trades.append(trade)
-                    unallocated -= capital_for_trade
 
                 # If we are in a position, and we get a sell signal
                 if current_signal == -1 and last_trade is not None:
@@ -327,42 +344,50 @@ def create_vector_backtest(
                             "net_gain": net_gain_val
                         }
                     )
-                    unallocated += last_trade.available_amount * current_price
                     last_trade = None
 
+        unallocated = initial_amount
+        total_net_gain = 0.0
+        open_trades = []
+
         # Create portfolio snapshots
         for ts in index:
-            invested_value = 0.0
+            allocated = 0
+            interval_datetime = pd.Timestamp(ts).to_pydatetime()
+            interval_datetime = interval_datetime.replace(tzinfo=timezone.utc)
 
             for trade in trades:
-                if trade.opened_at <= ts and (
-                        trade.closed_at is None or trade.closed_at >= ts):
-
-                    # Trade is still open at this time
-                    ohlcv = granular_ohlcv_data_order_by_symbol[trade.symbol]
-
-                    # Datetime is the index for pandas DataFrame, find the
-                    # closest timestamp that is less than or equal to ts
-                    # prices = ohlcv.loc[ohlcv.index <= ts, "Close"].values
-                    #
-                    # if len(prices) == 0:
-                    #     # No price data for this timestamp
-                    #     price = trade.open_price
-                    # else:
-                    #     price = prices[-1]
-                    try:
-                        price = ohlcv.loc[:ts, "Close"].iloc[-1]
-                    except IndexError:
-                        continue  # skip if no price yet
-
-                    invested_value += trade.filled_amount * price
-            total_value = invested_value + unallocated
-            total_net_gain = total_value - initial_amount
+
+                if trade.opened_at == interval_datetime:
+                    # Snapshot taken at the moment a trade is opened
+                    unallocated -= trade.cost
+                    open_trades.append(trade)
+
+                if trade.closed_at == interval_datetime:
+                    # Snapshot taken at the moment a trade is closed
+                    unallocated += trade.cost + trade.net_gain
+                    total_net_gain += trade.net_gain
+                    open_trades.remove(trade)
+
+            for open_trade in open_trades:
+                ohlcv = granular_ohlcv_data_order_by_symbol[
+                    f"{open_trade.target_symbol}/{trading_symbol}"
+                ]
+
+                try:
+                    price = ohlcv.loc[:ts, "Close"].iloc[-1]
+                except IndexError:
+                    continue  # skip if no price yet
+
+                allocated += open_trade.filled_amount * price
+
+            # total_value = invested_value + unallocated
+            # total_net_gain = total_value - initial_amount
             snapshots.append(
                 PortfolioSnapshot(
-                    created_at=pd.Timestamp(ts),
+                    created_at=interval_datetime,
                     unallocated=unallocated,
-                    total_value=total_value,
+                    total_value=unallocated + allocated,
                     total_net_gain=total_net_gain
                 )
             )

tests/scenarios/permutation_tests/test_permutation_test.py

@@ -260,6 +260,8 @@ def test_run(self):
         )
         backtests = app.run_permutation_test(
             initial_amount=1000,
+            market="bitvavo",
+            trading_symbol="EUR",
             backtest_date_range=date_range,
             strategy=strategy,
             number_of_permutations=50

tests/scenarios/test_vector_vs_event_backtest_results.py

@@ -271,7 +271,9 @@ def test_run(self):
             backtest_date_range=date_range,
             strategy=strategy,
             snapshot_interval=SnapshotInterval.DAILY,
-            risk_free_rate=0.027
+            risk_free_rate=0.027,
+            trading_symbol="EUR",
+            market="BITVAVO"
         )
         run = vector_backtests.backtest_runs[0]
         end_time = time.time()