Trim pre-trade flat period from stats

bt/backtest.py

@@ -202,6 +202,7 @@ def __init__(
 
         self.stats = {}
         self._original_prices = None
+        self._stat_prices = None
         self._weights = None
         self._sweights = None
         self.has_run = False
@@ -353,8 +354,19 @@ def run(self):
                 if self.progress_bar:
                     bar.stop()
 
-        self.stats = self.strategy.prices.calc_perf_stats()
         self._original_prices = self.strategy.prices
+        self._stat_prices = self._compute_stat_prices()
+        self.stats = self._stat_prices.calc_perf_stats()
+
+    def _compute_stat_prices(self):
+        prices = self.strategy.prices
+        transactions = self.strategy.get_transactions()
+
+        if not transactions.empty:
+            first_transaction_date = transactions.index.get_level_values(0)[0]
+            return prices.loc[first_transaction_date:]
+
+        return prices
 
     @property
     def weights(self):
@@ -469,7 +481,7 @@ class Result(ffn.GroupStats):
     """
 
     def __init__(self, *backtests):
-        tmp = [pd.DataFrame({x.name: x.strategy.prices}) for x in backtests]
+        tmp = [pd.DataFrame({x.name: x._stat_prices if x._stat_prices is not None else x.strategy.prices}) for x in backtests]
         super().__init__(*tmp)
         self.backtest_list = backtests
         self.backtests = {x.name: x for x in backtests}
@@ -687,7 +699,12 @@ def __init__(self, normalizing_value, *backtests):
                 raise ValueError(f"Cannot apply RenormalizedFixedIncomeResult because backtest {backtest.name} is not on a fixed income strategy")
         if not isinstance(normalizing_value, dict):
             normalizing_value = {x.name: normalizing_value for x in backtests}
-        tmp = [pd.DataFrame({x.name: self._price(x.strategy, normalizing_value[x.name])}) for x in backtests]
+        tmp = []
+        for backtest in backtests:
+            prices = self._price(backtest.strategy, normalizing_value[backtest.name])
+            if backtest._stat_prices is not None:
+                prices = prices.reindex(backtest._stat_prices.index)
+            tmp.append(pd.DataFrame({backtest.name: prices}))
         super(Result, self).__init__(*tmp)
         self.backtest_list = backtests
         self.backtests = {x.name: x for x in backtests}

tests/test_backtest.py

@@ -316,6 +316,28 @@ def test_nested_strategy_backtest_handles_initial_paper_trade_value():
     assert result.prices["root"].iloc[0] == 100
 
 
+def test_run_after_date_stats_start_on_first_transaction():
+    dates = pd.date_range("2000-01-01", "2002-12-31", freq=pd.tseries.offsets.BDay())
+    prices = pd.DataFrame(index=dates, data={"a": 100.0})
+    prices.loc[dates[260]:, "a"] = np.linspace(100, 150, len(dates[260:]))
+
+    strategy = bt.Strategy(
+        "delayed",
+        [
+            bt.algos.RunAfterDate("2001-01-01"),
+            bt.algos.SelectAll(),
+            bt.algos.WeighEqually(),
+            bt.algos.Rebalance(),
+        ],
+    )
+
+    result = bt.run(bt.Backtest(strategy, prices, progress_bar=False))
+
+    first_transaction_date = result.get_transactions().index.get_level_values(0)[0]
+    assert result.stats["delayed"].start == first_transaction_date
+    assert result.prices.index[0] == first_transaction_date
+
+
 def test_30_min_data():
     names = ["foo"]
     dates = pd.date_range(start="2017-01-01", end="2017-12-31", freq="30min")