mirror of
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1d65d28b28
* Use dict-like configuration * Rename from_neutrader to integration * SAOE strategy * Optimize file structure * Optimize code * Format code * create_state_maintainer_recursive * Remove explicit time_per_step * CI test passed * Resolve PR comments * Pass all CI * Minor test issue * Refine SAOE adapter logic * Minor bugfix * Cherry pick updates * Resolve PR comments * CI issues * Refine adapter & saoe_data logic * Resolve PR comments * Resolve PR comments * Rename ONE_SEC to EPS_T; complete backtest loop * CI issue * Resolve Yuge's PR comments
205 lines
7.6 KiB
Python
205 lines
7.6 KiB
Python
# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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import sys
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from pathlib import Path
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from typing import Tuple
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import pandas as pd
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import pytest
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from qlib.backtest.decision import Order, OrderDir, TradeRangeByTime
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from qlib.backtest.executor import SimulatorExecutor
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from qlib.rl.order_execution import CategoricalActionInterpreter
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from qlib.rl.order_execution.simulator_qlib import SingleAssetOrderExecution
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TOTAL_POSITION = 2100.0
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python_version_request = pytest.mark.skipif(sys.version_info < (3, 8), reason="requires python3.8 or higher")
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def is_close(a: float, b: float, epsilon: float = 1e-4) -> bool:
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return abs(a - b) <= epsilon
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def get_order() -> Order:
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return Order(
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stock_id="SH600000",
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amount=TOTAL_POSITION,
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direction=OrderDir.BUY,
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start_time=pd.Timestamp("2019-03-04 09:30:00"),
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end_time=pd.Timestamp("2019-03-04 14:29:00"),
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)
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def get_configs(order: Order) -> Tuple[dict, dict, dict]:
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strategy_config = {
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"class": "SingleOrderStrategy",
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"module_path": "qlib.rl.strategy.single_order",
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"kwargs": {
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"order": order,
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"trade_range": TradeRangeByTime(order.start_time.time(), order.end_time.time()),
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},
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}
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executor_config = {
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"class": "NestedExecutor",
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"module_path": "qlib.backtest.executor",
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"kwargs": {
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"time_per_step": "1day",
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"inner_strategy": {"class": "ProxySAOEStrategy", "module_path": "qlib.rl.order_execution.strategy"},
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"track_data": True,
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"inner_executor": {
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"class": "NestedExecutor",
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"module_path": "qlib.backtest.executor",
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"kwargs": {
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"time_per_step": "30min",
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"inner_strategy": {
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"class": "TWAPStrategy",
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"module_path": "qlib.contrib.strategy.rule_strategy",
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},
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"inner_executor": {
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"class": "SimulatorExecutor",
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"module_path": "qlib.backtest.executor",
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"kwargs": {
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"time_per_step": "1min",
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"verbose": False,
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"trade_type": SimulatorExecutor.TT_SERIAL,
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"generate_report": False,
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"track_data": True,
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},
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},
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"track_data": True,
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},
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},
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"start_time": pd.Timestamp(order.start_time.date()),
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"end_time": pd.Timestamp(order.start_time.date()),
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},
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}
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exchange_config = {
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"freq": "1min",
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"codes": [order.stock_id],
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"limit_threshold": ("$ask == 0", "$bid == 0"),
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"deal_price": ("If($ask == 0, $bid, $ask)", "If($bid == 0, $ask, $bid)"),
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"volume_threshold": {
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"all": ("cum", "0.2 * DayCumsum($volume, '9:30', '14:29')"),
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"buy": ("current", "$askV1"),
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"sell": ("current", "$bidV1"),
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},
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"open_cost": 0.0005,
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"close_cost": 0.0015,
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"min_cost": 5.0,
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"trade_unit": None,
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}
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return strategy_config, executor_config, exchange_config
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def get_simulator(order: Order) -> SingleAssetOrderExecution:
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DATA_ROOT_DIR = Path(__file__).parent.parent / ".data" / "rl" / "qlib_simulator"
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# fmt: off
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qlib_config = {
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"provider_uri_day": DATA_ROOT_DIR / "qlib_1d",
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"provider_uri_1min": DATA_ROOT_DIR / "qlib_1min",
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"feature_root_dir": DATA_ROOT_DIR / "qlib_handler_stock",
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"feature_columns_today": [
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"$open", "$high", "$low", "$close", "$vwap", "$bid", "$ask", "$volume",
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"$bidV", "$bidV1", "$bidV3", "$bidV5", "$askV", "$askV1", "$askV3", "$askV5",
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],
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"feature_columns_yesterday": [
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"$open_1", "$high_1", "$low_1", "$close_1", "$vwap_1", "$bid_1", "$ask_1", "$volume_1",
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"$bidV_1", "$bidV1_1", "$bidV3_1", "$bidV5_1", "$askV_1", "$askV1_1", "$askV3_1", "$askV5_1",
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],
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}
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# fmt: on
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strategy_config, executor_config, exchange_config = get_configs(order)
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return SingleAssetOrderExecution(
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order=order,
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qlib_config=qlib_config,
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strategy_config=strategy_config,
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executor_config=executor_config,
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exchange_config=exchange_config,
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)
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@python_version_request
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def test_simulator_first_step():
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order = get_order()
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simulator = get_simulator(order)
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state = simulator.get_state()
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assert state.cur_time == pd.Timestamp("2019-03-04 09:30:00")
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assert state.position == TOTAL_POSITION
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AMOUNT = 300.0
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simulator.step(AMOUNT)
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state = simulator.get_state()
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assert state.cur_time == pd.Timestamp("2019-03-04 10:00:00")
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assert state.position == TOTAL_POSITION - AMOUNT
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assert len(state.history_exec) == 30
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assert state.history_exec.index[0] == pd.Timestamp("2019-03-04 09:30:00")
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assert is_close(state.history_exec["market_volume"].iloc[0], 109382.382812)
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assert is_close(state.history_exec["market_price"].iloc[0], 149.566483)
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assert (state.history_exec["amount"] == AMOUNT / 30).all()
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assert (state.history_exec["deal_amount"] == AMOUNT / 30).all()
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assert is_close(state.history_exec["trade_price"].iloc[0], 149.566483)
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assert is_close(state.history_exec["trade_value"].iloc[0], 1495.664825)
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assert is_close(state.history_exec["position"].iloc[0], TOTAL_POSITION - AMOUNT / 30)
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assert is_close(state.history_exec["ffr"].iloc[0], AMOUNT / TOTAL_POSITION / 30)
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assert is_close(state.history_steps["market_volume"].iloc[0], 1254848.5756835938)
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assert state.history_steps["amount"].iloc[0] == AMOUNT
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assert state.history_steps["deal_amount"].iloc[0] == AMOUNT
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assert state.history_steps["ffr"].iloc[0] == AMOUNT / TOTAL_POSITION
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assert is_close(
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state.history_steps["pa"].iloc[0] * (1.0 if order.direction == OrderDir.SELL else -1.0),
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(state.history_steps["trade_price"].iloc[0] / simulator.twap_price - 1) * 10000,
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)
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@python_version_request
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def test_simulator_stop_twap() -> None:
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order = get_order()
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simulator = get_simulator(order)
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NUM_STEPS = 7
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for i in range(NUM_STEPS):
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simulator.step(TOTAL_POSITION / NUM_STEPS)
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HISTORY_STEP_LENGTH = 30 * NUM_STEPS
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state = simulator.get_state()
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assert len(state.history_exec) == HISTORY_STEP_LENGTH
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assert (state.history_exec["deal_amount"] == TOTAL_POSITION / HISTORY_STEP_LENGTH).all()
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assert is_close(state.history_steps["position"].iloc[0], TOTAL_POSITION * (NUM_STEPS - 1) / NUM_STEPS)
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assert is_close(state.history_steps["position"].iloc[-1], 0.0)
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assert is_close(state.position, 0.0)
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assert is_close(state.metrics["ffr"], 1.0)
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assert is_close(state.metrics["market_price"], state.backtest_data.get_deal_price().mean())
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assert is_close(state.metrics["market_volume"], state.backtest_data.get_volume().sum())
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assert is_close(state.metrics["trade_price"], state.metrics["market_price"])
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assert is_close(state.metrics["pa"], 0.0)
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assert simulator.done()
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@python_version_request
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def test_interpreter() -> None:
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NUM_EXECUTION = 3
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order = get_order()
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simulator = get_simulator(order)
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interpreter_action = CategoricalActionInterpreter(values=NUM_EXECUTION)
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NUM_STEPS = 7
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state = simulator.get_state()
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position_history = []
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for i in range(NUM_STEPS):
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simulator.step(interpreter_action(state, 1))
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state = simulator.get_state()
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position_history.append(state.position)
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assert position_history[-1] == max(TOTAL_POSITION - TOTAL_POSITION / NUM_EXECUTION * (i + 1), 0.0)
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