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

qlib/contrib/evaluate.py

@@ -11,7 +11,7 @@ from ..log import get_module_logger
 from . import strategy as strategy_pool
 from .strategy.strategy import BaseStrategy
 from .backtest.exchange import Exchange
-from .backtest.backtest import backtest as backtest_func, get_date_range
+from .backtest.backtest import backtest as backtest_func, get_date_range, backtest_highfreq as backtest_highfreq_func
 
 from ..data import D
 from ..config import C
@@ -272,19 +272,46 @@ def backtest(pred, account=1e9, shift=1, benchmark="SH000905", verbose=True, **k
     ex_args = {k: v for k, v in kwargs.items() if k in spec.args}
     trade_exchange = get_exchange(pred, **ex_args)
 
-    # run backtest
-    report_df, positions = backtest_func(
-        pred=pred,
-        strategy=strategy,
-        trade_exchange=trade_exchange,
-        shift=shift,
-        verbose=verbose,
-        account=account,
-        benchmark=benchmark,
-    )
-    # for  compatibility of the old API. return the dict positions
-    positions = {k: p.position for k, p in positions.items()}
-    return report_df, positions
+    
+    if kwargs.get('highfreq_executor', False):
+        order_set = backtest_func(
+            pred=pred,
+            strategy=strategy,
+            trade_exchange=trade_exchange,
+            shift=shift,
+            verbose=verbose,
+            account=account,
+            benchmark=benchmark,
+            return_order=True,
+        )
+        executor = init_instance_by_config(kwargs.get('highfreq_executor'))
+        report_df, positions = backtest_highfreq_func(
+            pred=pred,
+            executor=executor,
+            trade_exchange=trade_exchange,
+            shift=shift,
+            order_set=order_set,
+            verbose=verbose,
+            account=account,
+            benchmark=benchmark
+        )
+        positions = {k: p.position for k, p in positions.items()}
+        return report_df, positions
+    else:
+        # run backtest
+        report_df, positions = backtest_func(
+            pred=pred,
+            strategy=strategy,
+            trade_exchange=trade_exchange,
+            shift=shift,
+            verbose=verbose,
+            account=account,
+            benchmark=benchmark,
+            return_order=False,
+        )
+        # for  compatibility of the old API. return the dict positions
+        positions = {k: p.position for k, p in positions.items()}
+        return report_df, positions
 
 
 def long_short_backtest(