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| .. _highfreq: | ||
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| ============================================ | ||
| Design of hierarchical order execution framework | ||
| Design of Nested Decision Execution Framework for High-Frequency Trading | ||
| ============================================ | ||
| .. currentmodule:: qlib | ||
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| Introduction | ||
| =================== | ||
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| In order to support reinforcement learning algorithms for high-frequency trading, a corresponding framework is required. None of the publicly available high-frequency trading frameworks now consider multi-layer trading mechanisms, and the currently designed algorithms cannot directly use existing frameworks. | ||
| In addition to supporting the basic intraday multi-layer trading, the linkage with the day-ahead strategy is also a factor that affects the performance evaluation of the strategy. Different day strategies generate different order distributions and different patterns on different stocks. To verify that high-frequency trading strategies perform well on real trading orders, it is necessary to support day-frequency and high-frequency multi-level linkage trading. In addition to more accurate backtesting of high-frequency trading algorithms, if the distribution of day-frequency orders is considered when training a high-frequency trading model, the algorithm can also be optimized more for product-specific day-frequency orders. | ||
| Therefore, innovation in the high-frequency trading framework is necessary to solve the various problems mentioned above, for which we designed a hierarchical order execution framework that can link daily-frequency and intra-day trading at different granularities. | ||
| Daily trading (e.g. portfolio management) and intraday trading (e.g. orders execution) are two hot topics in Quant investment and usually studied separately. | ||
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| .. image:: ../_static/img/framework.svg | ||
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| The design of the framework is shown in the figure above. At each layer consists of Trading Agent and Execution Env. The Trading Agent has its own data processing module (Information Extractor), forecasting module (Forecast Model) and decision generator (Decision Generator). The trading algorithm generates the corresponding decisions by the Decision Generator based on the forecast signals output by the Forecast Module, and the decisions generated by the trading algorithm are passed to the Execution Env, which returns the execution results. Here the frequency of trading algorithm, decision content and execution environment can be customized by users (e.g. intra-day trading, daily-frequency trading, weekly-frequency trading), and the execution environment can be nested with finer-grained trading algorithm and execution environment inside (i.e. sub-workflow in the figure, e.g. daily-frequency orders can be turned into finer-grained decisions by splitting orders within the day). The hierarchical order execution framework is user-defined in terms of hierarchy division and decision frequency, making it easy for users to explore the effects of combining different levels of trading algorithms and breaking down the barriers between different levels of trading algorithm optimization. | ||
| In addition to the innovation in the framework, the hierarchical order execution framework also takes into account various details of the real backtesting environment, minimizing the differences with the final real environment as much as possible. At the same time, the framework is designed to unify the interface between online and offline (e.g. data pre-processing level supports using the same set of code to process both offline and online data) to reduce the cost of strategy go-live as much as possible. | ||
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| Prepare Data | ||
| =================== | ||
| .. _data:: ../../examples/highfreq/README.md | ||
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| Example | ||
| =========================== | ||
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| Here is an example of highfreq execution. | ||
| To get the join trading performance of daily and intraday trading, they must interact with each other and run backtest jointly. | ||
| In order to support the joint backtest strategies in multiple levels, a corresponding framework is required. None of the publicly available high-frequency trading frameworks considers multi-level joint trading, which make the backtesting aforementioned inaccurate. | ||
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| .. code-block:: python | ||
| Besides backtesting, the optimization of strategies from different levels is not standalone and can be affected by each other. | ||
| For example, the best portfolio management strategy may change with the performance of order executions(e.g. a portfolio with higher turnover may becomes a better choice when we imporve the order execution strategies). | ||
| To achieve the overall good performance , it is necessary to consider the interaction of strategies in different level. | ||
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| import qlib | ||
| # init qlib | ||
| provider_uri_day = "~/.qlib/qlib_data/cn_data" | ||
| provider_uri_1min = "~/.qlib/qlib_data/cn_data_1min" | ||
| provider_uri_map = {"1min": provider_uri_1min, "day": provider_uri_day} | ||
| qlib.init(provider_uri=provider_uri_day, expression_cache=None, dataset_cache=None) | ||
| Therefore, building a new framework for trading in multiple levels becomes necessary to solve the various problems mentioned above, for which we designed a nested decision execution framework that consider the interaction of strategies. | ||
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| # data freq and backtest time | ||
| freq = "1min" | ||
| inst_list = D.list_instruments(D.instruments("all"), as_list=True) | ||
| start_time = "2020-01-01" | ||
| start_time = "2020-01-31" | ||
| When initializing qlib, if the default data is used, then both daily and minute frequency data need to be passed in. | ||
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| .. code-block:: python | ||
| # random order strategy config | ||
| strategy_config = { | ||
| "class": "RandomOrderStrategy", | ||
| "module_path": "qlib.contrib.strategy.rule_strategy", | ||
| "kwargs": { | ||
| "trade_range": TradeRangeByTime("9:30", "15:00"), | ||
| "sample_ratio": 1.0, | ||
| "volume_ratio": 0.01, | ||
| "market": market, | ||
| }, | ||
| } | ||
| .. code-block:: python | ||
| # backtest config | ||
| backtest_config = { | ||
| "start_time": start_time, | ||
| "end_time": end_time, | ||
| "account": 100000000, | ||
| "benchmark": None, | ||
| "exchange_kwargs": { | ||
| "freq": freq, | ||
| "limit_threshold": 0.095, | ||
| "deal_price": "close", | ||
| "open_cost": 0.0005, | ||
| "close_cost": 0.0015, | ||
| "min_cost": 5, | ||
| "codes": market, | ||
| }, | ||
| "pos_type": "InfPosition", # Position with infinitive position | ||
| } | ||
| please refer to "../../qlib/backtest". | ||
| .. image:: ../_static/img/framework.svg | ||
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| .. code-block:: python | ||
| # excutor config | ||
| executor_config = { | ||
| "class": "NestedExecutor", | ||
| "module_path": "qlib.backtest.executor", | ||
| "kwargs": { | ||
| "time_per_step": "day", | ||
| "inner_executor": { | ||
| "class": "SimulatorExecutor", | ||
| "module_path": "qlib.backtest.executor", | ||
| "kwargs": { | ||
| "time_per_step": freq, | ||
| "generate_portfolio_metrics": True, | ||
| "verbose": False, | ||
| # "verbose": True, | ||
| "indicator_config": { | ||
| "show_indicator": False, | ||
| }, | ||
| }, | ||
| }, | ||
| "inner_strategy": { | ||
| "class": "TWAPStrategy", | ||
| "module_path": "qlib.contrib.strategy.rule_strategy", | ||
| }, | ||
| "track_data": True, | ||
| "generate_portfolio_metrics": True, | ||
| "indicator_config": { | ||
| "show_indicator": True, | ||
| }, | ||
| }, | ||
| } | ||
| The design of the framework is shown in the yellow part in the middle of the figure above. Each level consists of ``Trading Agent`` and ``Execution Env``. ``Trading Agent`` has its own data processing module (``Information Extractor``), forecasting module (``Forecast Model``) and decision generator (``Decision Generator``). The trading algorithm generates the decisions by the ``Decision Generator`` based on the forecast signals output by the ``Forecast Module``, and the decisions generated by the trading algorithm are passed to the ``Execution Env``, which returns the execution results. | ||
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| NestedExecutor represents not the innermost layer, the initialization parameters should contain inner_executor and inner_strategy. simulatorExecutor represents the current excutor is the innermost layer, the innermost strategy used here is the TWAP strategy, the framework currently also supports the VWAP strategy | ||
| The frequency of trading algorithm, decision content and execution environment can be customized by users (e.g. intraday trading, daily-frequency trading, weekly-frequency trading), and the execution environment can be nested with finer-grained trading algorithm and execution environment inside (i.e. sub-workflow in the figure, e.g. daily-frequency orders can be turned into finer-grained decisions by splitting orders within the day). The flexibility of nested decision execution framework makes it easy for users to explore the effects of combining different levels of trading strategies and break down the optimization barriers between different levels of trading algorithm. | ||
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| .. code-block:: python | ||
| # backtest | ||
| portfolio_metrics_dict, indicator_dict = backtest(executor=executor_config, strategy=strategy_config, **backtest_config) | ||
| Example | ||
| =========================== | ||
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| The metrics of backtest are included in the portfolio_metrics_dict and indicator_dict. | ||
| An example of nested decision execution framework for high-frequency can be found `here <https://github.com/microsoft/qlib/blob/main/examples/nested_decision_execution/workflow.py>`_. |
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