From: Jérôme Benoit <jerome.benoit@piment-noir.org>
Date: Tue, 9 Sep 2025 16:02:16 +0000 (+0200)
Subject: docs(reforcexy): align make_env() docstring
X-Git-Url: https://git.piment-noir.org/?a=commitdiff_plain;h=38f9dc35186a9e0c5ae947ebe0d210dea980acdb;p=freqai-strategies.git

docs(reforcexy): align make_env() docstring

Signed-off-by: Jérôme Benoit <jerome.benoit@piment-noir.org>
---

diff --git a/ReforceXY/user_data/freqaimodels/ReforceXY.py b/ReforceXY/user_data/freqaimodels/ReforceXY.py
index 9a7a3b6..66021b0 100644
--- a/ReforceXY/user_data/freqaimodels/ReforceXY.py
+++ b/ReforceXY/user_data/freqaimodels/ReforceXY.py
@@ -25,7 +25,6 @@ from freqtrade.freqai.RL.BaseReinforcementLearningModel import (
 )
 from freqtrade.freqai.tensorboard.TensorboardCallback import TensorboardCallback
 from freqtrade.strategy import timeframe_to_minutes
-from gymnasium import Env
 from gymnasium.spaces import Box
 from numpy.typing import NDArray
 from optuna import Trial, TrialPruned, create_study
@@ -1579,23 +1578,24 @@ def make_env(
     train_df: DataFrame,
     price: DataFrame,
     env_info: Dict[str, Any],
-) -> Callable:
+) -> Callable[[], BaseEnvironment]:
     """
     Utility function for multiprocessed env.
 
+    :param MyRLEnv: (Type[BaseEnvironment]) environment class to instantiate
     :param env_id: (str) the environment ID
-    :param num_env: (int) the number of environment you wish to have in subprocesses
-    :param seed: (int) the inital seed for RNG
     :param rank: (int) index of the subprocess
+    :param seed: (int) the initial seed for RNG
+    :param train_df: (DataFrame) feature dataframe for the environment
+    :param price: (DataFrame) aligned price dataframe
     :param env_info: (dict) all required arguments to instantiate the environment.
-    :return: (Callable)
+    :return: (Callable[[], BaseEnvironment]) closure that when called instantiates and returns the environment.
     """
 
-    def _init() -> Env:
-        env = MyRLEnv(
+    def _init() -> BaseEnvironment:
+        return MyRLEnv(
             df=train_df, prices=price, id=env_id, seed=seed + rank, **env_info
         )
-        return env
 
     set_random_seed(seed)
     return _init