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Add hook for supporting RL strategy (#768)

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you-n-g
2021-12-27 12:16:36 +08:00
committed by GitHub
parent a0f49fe2e7
commit 4709909782
1 changed files with 31 additions and 3 deletions
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34
qlib/backtest/executor.py
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@@ -395,9 +395,25 @@ class NestedExecutor(BaseExecutor):
if not self._align_range_limit or start_idx <= sub_cal.get_trade_step() <= end_idx: if not self._align_range_limit or start_idx <= sub_cal.get_trade_step() <= end_idx:
# if force align the range limit, skip the steps outside the decision range limit # if force align the range limit, skip the steps outside the decision range limit
_inner_trade_decision: BaseTradeDecision = self.inner_strategy.generate_trade_decision( res = self.inner_strategy.generate_trade_decision(_inner_execute_result)
_inner_execute_result
) # NOTE: !!!!!
# the two lines below is for a special case in RL
# To solve the confliction below
# - Normally, user will create a strategy and embed it into Qlib's executor and simulator interaction loop
# For a _nested qlib example_, (Qlib Strategy) <=> (Qlib Executor[(inner Qlib Strategy) <=> (inner Qlib Executor)])
# - However, RL-based framework has it's own script to run the loop
# For an _RL learning example_, (RL Policy) <=> (RL Env[(inner Qlib Executor)])
# To make it possible to run _nested qlib example_ and _RL learning example_ together, the solution below is proposed
# - The entry script follow the example of _RL learning example_ to be compatible with all kinds of RL Framework
# - Each step of (RL Env) will make (inner Qlib Executor) one step forward
# - (inner Qlib Strategy) is a proxy strategy, it will give the program control right to (RL Env) by `yield from` and wait for the action from the policy
# So the two lines below is the implementation of yielding control rights
if isinstance(res, GeneratorType):
res = yield from res
_inner_trade_decision: BaseTradeDecision = res
trade_decision.mod_inner_decision(_inner_trade_decision) # propagate part of decision information trade_decision.mod_inner_decision(_inner_trade_decision) # propagate part of decision information
# NOTE sub_cal.get_step_time() must be called before collect_data in case of step shifting # NOTE sub_cal.get_step_time() must be called before collect_data in case of step shifting
@@ -407,6 +423,7 @@ class NestedExecutor(BaseExecutor):
_inner_execute_result = yield from self.inner_executor.collect_data( _inner_execute_result = yield from self.inner_executor.collect_data(
trade_decision=_inner_trade_decision, level=level + 1 trade_decision=_inner_trade_decision, level=level + 1
) )
self.post_inner_exe_step(_inner_execute_result)
execute_result.extend(_inner_execute_result) execute_result.extend(_inner_execute_result)
inner_order_indicators.append( inner_order_indicators.append(
@@ -418,6 +435,17 @@ class NestedExecutor(BaseExecutor):
return execute_result, {"inner_order_indicators": inner_order_indicators, "decision_list": decision_list} return execute_result, {"inner_order_indicators": inner_order_indicators, "decision_list": decision_list}
def post_inner_exe_step(self, inner_exe_res):
"""
A hook for doing sth after each step of inner strategy
Parameters
----------
inner_exe_res :
the execution result of inner task
"""
pass
def get_all_executors(self): def get_all_executors(self):
"""get all executors, including self and inner_executor.get_all_executors()""" """get all executors, including self and inner_executor.get_all_executors()"""
return [self, *self.inner_executor.get_all_executors()] return [self, *self.inner_executor.get_all_executors()]