add highfreq_backtest

qlib/contrib/backtest/backtest.py

@@ -15,7 +15,7 @@ from ...data.dataset.utils import get_level_index
 LOG = get_module_logger("backtest")
 
 
-def backtest(pred, strategy, trade_exchange, shift, verbose, account, benchmark):
+def backtest(pred, strategy, trade_exchange, shift, verbose, account, benchmark, return_order):
     """Parameters
     ----------
     pred : pandas.DataFrame
@@ -71,7 +71,7 @@ def backtest(pred, strategy, trade_exchange, shift, verbose, account, benchmark)
 
     trade_dates = np.append(predict_dates[shift:], get_date_range(predict_dates[-1], left_shift=1, right_shift=shift))
     executor = SimulatorExecutor(trade_exchange, verbose=verbose)
-
+    order_set = []
     # trading apart
     for pred_date, trade_date in zip(predict_dates, trade_dates):
         # for loop predict date and trading date
@@ -103,6 +103,8 @@ def backtest(pred, strategy, trade_exchange, shift, verbose, account, benchmark)
             )
         else:
             order_list = []
+
+        order_set.append((trade_account, order_list, trade_date))
         # 4. Get result after executing order list
         # NOTE: The following operation will modify order.amount.
         # NOTE: If it is buy and the cash is insufficient, the tradable amount will be recalculated
@@ -111,12 +113,49 @@ def backtest(pred, strategy, trade_exchange, shift, verbose, account, benchmark)
         # 5. Update account information according to transaction
         update_account(trade_account, trade_info, trade_exchange, trade_date)
 
-    # generate backtest report
-    report_df = trade_account.report.generate_report_dataframe()
-    report_df["bench"] = bench
-    positions = trade_account.get_positions()
-    return report_df, positions
+    if return_order:
+        return order_set
+    else:
+        # generate backtest report
+        report_df = trade_account.report.generate_report_dataframe()
+        report_df["bench"] = bench
+        positions = trade_account.get_positions()
+        return report_df, positions
 
+def backtest_highfreq(pred, executor, trade_exchange, shift, order_set, verbose, account, benchmark):
+    if get_level_index(pred, level="datetime") == 1:
+        pred = pred.swaplevel().sort_index()
+
+    trade_account_highfreq = Account(init_cash=account)
+    _pred_dates = pred.index.get_level_values(level="datetime")
+    predict_dates = D.calendar(start_time=_pred_dates.min(), end_time=_pred_dates.max())
+    
+    if isinstance(benchmark, pd.Series):
+        bench = benchmark
+    else:
+        _codes = benchmark if isinstance(benchmark, list) else [benchmark]
+        _temp_result = D.features(
+            _codes,
+            ["$close/Ref($close,1)-1"],
+            predict_dates[0],
+            get_date_by_shift(predict_dates[-1], shift=shift),
+            disk_cache=1,
+        )
+        if len(_temp_result) == 0:
+            raise ValueError(f"The benchmark {_codes} does not exist. Please provide the right benchmark")
+        bench = _temp_result.groupby(level="datetime")[_temp_result.columns.tolist()[0]].mean()
+
+    for trade_account, order_list, trade_date in order_set:
+        if verbose:
+            LOG.info("[I {:%Y-%m-%d}]: highfreq trade begin.".format(trade_date))
+        ## TODO: kanren group need to merge code here
+        trade_info = executor.execute(trade_account, order_list, trade_date)
+        update_account(trade_account_highfreq, trade_info, trade_exchange, trade_date)
+
+    report_df = trade_account_highfreq.report.generate_report_dataframe()
+    report_df["bench"] = bench
+    positions = trade_account_highfreq.get_positions()
+    return report_df, positions
 
 def update_account(trade_account, trade_info, trade_exchange, trade_date):
     """Update the account and strategy