Update end-to-end example and requirements

examples/nested_decision_execution/requirements.txt

@@ -0,0 +1,2 @@
+tianshou>=0.4.1
+torch>=1.8.0

examples/nested_decision_execution/rl_dummy.py

@@ -4,12 +4,14 @@ from dataclasses import dataclass, asdict
 from pprint import pprint
 from typing import Iterable, Any, Optional, OrderedDict, Tuple, Dict, List
 
+import fire
 import gym
 import numpy as np
 import pandas as pd
 import qlib
 from gym import spaces
-from qlib.backtest import get_exchange, Account, BaseExecutor, CommonInfrastructure, Order, TradeCalendarManager
+from qlib.backtest import get_exchange, Account, BaseExecutor, CommonInfrastructure, Order, TradeCalendarManager, backtest_func
+from qlib.backtest.executor import NestedExecutor, SimulatorExecutor
 from qlib.config import REG_CN
 from qlib.data import D
 from qlib.rl.interpreter import StateInterpreter, ActionInterpreter
@@ -21,6 +23,8 @@ from tianshou.data import Batch, Collector
 from tianshou.env import DummyVectorEnv, SubprocVectorEnv
 from tianshou.policy import BasePolicy
 
+from workflow import NestedDecisonExecutionWorkflow
+
 
 MAX_STEPS = 10
 
@@ -324,79 +328,122 @@ class RLStrategy(BaseStrategy):
         # apply results from the last step
         if execute_result is not None:
             orders = defaultdict(list)
-            for order, _, __, in execute_result:
-                orders[order.stock_id, order.direction].append(order)
+            for e in execute_result:
+                orders[e[0].stock_id, e[0].direction].append(e)
             for (stock_id, direction), state in self.states.items():
-                state.update(orders[stock_id, direction])
-    
+                state.update(orders[stock_id, direction], self.trade_calendar)
+
+        if not self.states:
+            return []
+
         obs_batch = Batch([{"obs": self.observation(state)} for state in self.states.values()])
         act = self.policy(obs_batch)
-        exec_vols = [self.action(a) for a in act.act]
-        return [create_sub_order(v, self.trade_calendar, order) for v in exec_vols]
+        exec_vols = [self.action(a, s) for a, s in zip(act.act, self.states.values())]
+        return [create_sub_order(v, self.trade_calendar, o) for v, o in zip(exec_vols, self.outer_trade_decision)]
 
 
-def _init_qlib():
-    provider_uri = "~/.qlib/qlib_data/cn_data"  # target_dir
-    if not exists_qlib_data(provider_uri):
-        print(f"Qlib data is not found in {provider_uri}")
-        GetData().qlib_data(target_dir=provider_uri, region=REG_CN)
-    qlib.init(provider_uri=provider_uri, region=REG_CN)
+class RlWorkflow(NestedDecisonExecutionWorkflow):
 
+    def tianshou(self):
+        self._init_qlib()
 
-def _main_tianshou():
-    _init_qlib()
-
-    # TODO: why is there a benchmark?
-    trade_start_time = "2017-01-01"
-    trade_end_time = "2020-08-01"
-    benchmark = "SH000300"
-    time_per_step = "day"
-    executor_config = {
-        "class": "SimulatorExecutor",
-        "module_path": "qlib.backtest.executor",
-        "kwargs": {
-            "time_per_step": time_per_step,
-            "verbose": True,
-            "generate_report": False,
+        # TODO: why is there a benchmark?
+        trade_start_time = "2017-01-01"
+        trade_end_time = "2020-08-01"
+        benchmark = "SH000300"
+        time_per_step = "day"
+        executor_config = {
+            "class": "SimulatorExecutor",
+            "module_path": "qlib.backtest.executor",
+            "kwargs": {
+                "time_per_step": time_per_step,
+                "verbose": True,
+                "generate_report": False,
+            }
         }
-    }
-    exchange = get_exchange(
-        freq="day",
-        limit_threshold=0.095,
-        deal_price="close",
-        open_cost=0.0005,
-        close_cost=0.0015,
-        min_cost=5
-    )
-
-    observation = Observation(time_per_step)
-    action = Action()
-    reward_fn = Reward()
-
-    def dummy_env():
-        executor = get_executor(
-            trade_start_time,
-            trade_end_time,
-            executor_config,
-            exchange,
-            benchmark,
-            1000000000,
+        exchange = get_exchange(
+            freq="day",
+            limit_threshold=0.095,
+            deal_price="close",
+            open_cost=0.0005,
+            close_cost=0.0015,
+            min_cost=5
         )
-        return SingleOrderEnv(
-            observation, action, reward_fn,
-            iter(DataLoader(QlibOrderDataset('assets/orders'), batch_size=None, shuffle=True)), executor)
 
-    policy = DummyPolicy()
+        observation = Observation(time_per_step)
+        action = Action()
+        reward_fn = Reward()
 
-    # This can not be replaced with SubprocVectorEnv
-    # File "/xxx/qlib/qlib/data/data.py", line 462, in dataset_processor
-    # p = Pool(processes=workers)
-    # AssertionError: daemonic processes are not allowed to have children
-    envs = DummyVectorEnv([dummy_env for _ in range(4)])
-    test_collector = Collector(policy, envs)
-    policy.eval()
-    # TODO: create a queue for all orders and make it auto-complete when all the orders are processed
-    test_collector.collect(n_episode=10)
+        def dummy_env():
+            executor = get_executor(
+                trade_start_time,
+                trade_end_time,
+                executor_config,
+                exchange,
+                benchmark,
+                1000000000,
+            )
+            return SingleOrderEnv(
+                observation, action, reward_fn,
+                iter(DataLoader(QlibOrderDataset('assets/orders'), batch_size=None, shuffle=True)), executor)
+
+        policy = DummyPolicy()
+
+        # This can not be replaced with SubprocVectorEnv
+        # File "/xxx/qlib/qlib/data/data.py", line 462, in dataset_processor
+        # p = Pool(processes=workers)
+        # AssertionError: daemonic processes are not allowed to have children
+        envs = DummyVectorEnv([dummy_env for _ in range(4)])
+        test_collector = Collector(policy, envs)
+        policy.eval()
+        # TODO: create a queue for all orders and make it auto-complete when all the orders are processed
+        test_collector.collect(n_episode=10)
+
+    def rl_day(self, load_model: Optional[str] = None):
+        self._init_qlib()
+        model = init_instance_by_config(self.task["model"])
+        dataset = init_instance_by_config(self.task["dataset"])
+        if load_model is None:
+            self._train_model(model, dataset)
+        else:
+            model = self._load_model(load_model)
+        trade_start_time = "2017-01-01"
+        trade_end_time = "2020-08-01"
+        trade_account = Account(
+            init_cash=int(1e9),
+            benchmark_config={
+                "benchmark": "SH000300",
+                "start_time": trade_start_time,
+                "end_time": trade_end_time,
+            },
+        )
+        exchange = get_exchange(
+            freq="day",
+            limit_threshold=0.095,
+            deal_price="close",
+            open_cost=0.0005,
+            close_cost=0.0015,
+            min_cost=5
+        )
+        common_infra = CommonInfrastructure(trade_account=trade_account, trade_exchange=exchange)
+        strategy = init_instance_by_config({
+            "class": "TopkDropoutStrategy",
+            "module_path": "qlib.contrib.strategy.model_strategy",
+            "kwargs": {
+                "model": model,
+                "dataset": dataset,
+                "topk": 50,
+                "n_drop": 5,
+            },
+        }, common_infra=common_infra)
+        executor = NestedExecutor(
+            time_per_step="week",
+            inner_executor=SimulatorExecutor(time_per_step="day", verbose=True),
+            inner_strategy=RLStrategy(Observation("day"), Action(), DummyPolicy()),
+            common_infra=common_infra
+        )
+        report_dict = backtest_func(trade_start_time, trade_end_time, strategy, executor)
+        print(report_dict)
 
 
 ### This is a full RL strategy ###
@@ -527,4 +574,4 @@ def _to_float32(val): return np.array(val, dtype=np.float32)
 
 
 if __name__ == '__main__':
-    _main_tianshou()
+    fire.Fire(RlWorkflow)