Add CI

tests/rl/test_qlib_simulator.py

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+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+from pathlib import Path
+
+import pandas as pd
+
+from qlib.backtest.decision import Order, OrderDir
+from qlib.backtest.executor import NestedExecutor, SimulatorExecutor
+from qlib.backtest.utils import CommonInfrastructure
+from qlib.contrib.strategy import TWAPStrategy
+from qlib.rl.order_execution import CategoricalActionInterpreter
+from qlib.rl.order_execution.simulator_qlib import ExchangeConfig, SingleAssetQlibSimulator
+
+TOTAL_POSITION = 2100.0
+
+
+def is_close(a: float, b: float, epsilon: float = 1e-4) -> bool:
+    return abs(a - b) <= epsilon
+
+
+def get_order() -> Order:
+    return Order(
+        stock_id="SH600000",
+        amount=TOTAL_POSITION,
+        direction=OrderDir.BUY,
+        start_time=pd.Timestamp("2019-03-04 09:30:00"),
+        end_time=pd.Timestamp("2019-03-04 14:29:00"),
+    )
+
+
+def get_simulator(order: Order) -> SingleAssetQlibSimulator:
+    def _inner_executor_fn(time_per_step: str, common_infra: CommonInfrastructure) -> NestedExecutor:
+        return NestedExecutor(
+            time_per_step=time_per_step,
+            inner_strategy=TWAPStrategy(),
+            inner_executor=SimulatorExecutor(
+                time_per_step="1min",
+                verbose=False,
+                trade_type=SimulatorExecutor.TT_SERIAL,
+                generate_report=False,
+                common_infra=common_infra,
+                track_data=True,
+            ),
+            common_infra=common_infra,
+            track_data=True,
+        )
+
+    DATA_ROOT_DIR = Path(__file__).parent.parent / ".data" / "rl" / "qlib_simulator"
+
+    # fmt: off
+    qlib_config = {
+        "provider_uri_day": DATA_ROOT_DIR / "qlib_amc_1d",
+        "provider_uri_1min": DATA_ROOT_DIR / "qlib_amc_1min",
+        "feature_root_dir": DATA_ROOT_DIR / "qlib_amc_handler_stock",
+        "feature_columns_today": [
+            "$open", "$high", "$low", "$close", "$vwap", "$bid", "$ask", "$volume",
+            "$bidV", "$bidV1", "$bidV3", "$bidV5", "$askV", "$askV1", "$askV3", "$askV5",
+        ],
+        "feature_columns_yesterday": [
+            "$open_1", "$high_1", "$low_1", "$close_1", "$vwap_1", "$bid_1", "$ask_1", "$volume_1",
+            "$bidV_1", "$bidV1_1", "$bidV3_1", "$bidV5_1", "$askV_1", "$askV1_1", "$askV3_1", "$askV5_1",
+        ],
+    }
+    # fmt: on
+
+    exchange_config = ExchangeConfig(
+        limit_threshold=("$ask == 0", "$bid == 0"),
+        deal_price=("If($ask == 0, $bid, $ask)", "If($bid == 0, $ask, $bid)"),
+        volume_threshold={
+            "all": ("cum", "0.2 * DayCumsum($volume, '9:30', '14:29')"),
+            "buy": ("current", "$askV1"),
+            "sell": ("current", "$bidV1"),
+        },
+        open_cost=0.0005,
+        close_cost=0.0015,
+        min_cost=5.0,
+        trade_unit=None,
+        cash_limit=None,
+        generate_report=False,
+    )
+
+    return SingleAssetQlibSimulator(
+        order=order,
+        time_per_step="30min",
+        qlib_config=qlib_config,
+        inner_executor_fn=_inner_executor_fn,
+        exchange_config=exchange_config,
+    )
+
+
+def test_simulator_first_step():
+    order = get_order()
+    simulator = get_simulator(order)
+    state = simulator.get_state()
+    assert state.cur_time == pd.Timestamp("2019-03-04 09:30:00")
+    assert state.position == TOTAL_POSITION
+
+    AMOUNT = 300.0
+    simulator.step(AMOUNT)
+    state = simulator.get_state()
+    assert state.cur_time == pd.Timestamp("2019-03-04 10:00:00")
+    assert state.position == TOTAL_POSITION - AMOUNT
+    assert len(state.history_exec) == 30
+    assert state.history_exec.index[0] == pd.Timestamp("2019-03-04 09:30:00")
+
+    assert is_close(state.history_exec["market_volume"].iloc[0], 109382.382812)
+    assert is_close(state.history_exec["market_price"].iloc[0], 149.566483)
+    assert (state.history_exec["amount"] == AMOUNT / 30).all()
+    assert (state.history_exec["deal_amount"] == AMOUNT / 30).all()
+    assert is_close(state.history_exec["trade_price"].iloc[0], 149.566483)
+    assert is_close(state.history_exec["trade_value"].iloc[0], 1495.664825)
+    assert is_close(state.history_exec["position"].iloc[0], TOTAL_POSITION - AMOUNT / 30)
+    # assert state.history_exec["ffr"].iloc[0] == 1 / 60  # FIXME
+
+    assert is_close(state.history_steps["market_volume"].iloc[0], 1254848.5756835938)
+    assert state.history_steps["amount"].iloc[0] == AMOUNT
+    assert state.history_steps["deal_amount"].iloc[0] == AMOUNT
+    assert state.history_steps["ffr"].iloc[0] == 1.0
+    assert is_close(
+        state.history_steps["pa"].iloc[0] * (1.0 if order.direction == OrderDir.SELL else -1.0),
+        (state.history_steps["trade_price"].iloc[0] / simulator.twap_price - 1) * 10000,
+    )
+
+
+def test_simulator_stop_twap() -> None:
+    order = get_order()
+    simulator = get_simulator(order)
+    NUM_STEPS = 7
+    for i in range(NUM_STEPS):
+        simulator.step(TOTAL_POSITION / NUM_STEPS)
+
+    HISTORY_STEP_LENGTH = 30 * NUM_STEPS
+    state = simulator.get_state()
+    assert len(state.history_exec) == HISTORY_STEP_LENGTH
+
+    assert (state.history_exec["deal_amount"] == TOTAL_POSITION / HISTORY_STEP_LENGTH).all()
+    assert is_close(state.history_steps["position"].iloc[0], TOTAL_POSITION * (NUM_STEPS - 1) / NUM_STEPS)
+    assert is_close(state.history_steps["position"].iloc[-1], 0.0)
+    assert is_close(state.position, 0.0)
+    assert is_close(state.metrics["ffr"], 1.0)
+
+    assert is_close(state.metrics["market_price"], state.backtest_data.get_deal_price().mean())
+    assert is_close(state.metrics["market_volume"], state.backtest_data.get_volume().sum())
+    assert is_close(state.metrics["trade_price"], state.metrics["market_price"])
+    assert is_close(state.metrics["pa"], 0.0)
+
+    assert simulator.done()
+
+
+def test_interpreter() -> None:
+    NUM_EXECUTION = 3
+    order = get_order()
+    simulator = get_simulator(order)
+    interpreter_action = CategoricalActionInterpreter(values=NUM_EXECUTION)
+
+    NUM_STEPS = 7
+    state = simulator.get_state()
+    position_history = []
+    for i in range(NUM_STEPS):
+        simulator.step(interpreter_action(state, 1))
+        state = simulator.get_state()
+        position_history.append(state.position)
+
+        assert position_history[-1] == max(TOTAL_POSITION - TOTAL_POSITION / NUM_EXECUTION * (i + 1), 0.0)
+
+
+if __name__ == "__main__":
+    test_simulator_first_step()
+    test_simulator_stop_twap()
+    test_interpreter()