feat: add allow_short_selling parameter to StockTradingEnv

README.md

@@ -2,7 +2,7 @@
 <img align="center" width="30%" alt="image" src="https://github.com/AI4Finance-Foundation/FinGPT/assets/31713746/e0371951-1ce1-488e-aa25-0992dafcc139">
 </div>
 
-# FinRL: Financial Reinforcement Learning → FinRL-X 
+# FinRL: Financial Reinforcement Learning → FinRL-X
 
 <div align="center">
 <img align="center" src=figs/logo_transparent_background.png width="55%"/>
@@ -24,7 +24,7 @@
 
 > [!IMPORTANT]
 > **FinRL-X** is the next-generation evolution of FinRL, designed for AI-native, modular, and production-oriented quantitative trading.
-> 
+>
 > - **This repository (`FinRL`)** preserves the original end-to-end educational and research framework.
 > - **For the latest architecture, live trading deployment, and production-focused development, please use [`FinRL-X / FinRL-Trading`](https://github.com/AI4Finance-Foundation/FinRL-Trading).**
 
@@ -87,7 +87,7 @@ Key contributors include:
 
 - [**Hongyang (Bruce) Yang**](https://www.linkedin.com/in/brucehy/) – research and development on financial reinforcement learning frameworks, market environments, and quantitative trading applications
 - [other contributors…]
-  
+
 ## Overview
 
 FinRL is the original open-source framework for financial reinforcement learning, organized around three core layers:

examples/FinRL_StockTrading_2026_1_data.py

@@ -6,14 +6,21 @@
 Introduce how to use FinRL to fetch and process data that we need for ML/RL trading.
 """
 
+from __future__ import annotations
+
 import itertools
 
 import pandas as pd
 import yfinance as yf
 
 from finrl import config_tickers
-from finrl.config import INDICATORS, TRAIN_START_DATE, TRAIN_END_DATE, TRADE_START_DATE, TRADE_END_DATE
-from finrl.meta.preprocessor.preprocessors import FeatureEngineer, data_split
+from finrl.config import INDICATORS
+from finrl.config import TRADE_END_DATE
+from finrl.config import TRADE_START_DATE
+from finrl.config import TRAIN_END_DATE
+from finrl.config import TRAIN_START_DATE
+from finrl.meta.preprocessor.preprocessors import data_split
+from finrl.meta.preprocessor.preprocessors import FeatureEngineer
 from finrl.meta.preprocessor.yahoodownloader import YahooDownloader
 
 # %% Part 1. Fetch data - Single ticker

examples/FinRL_StockTrading_2026_2_train.py

@@ -7,11 +7,15 @@
 Introduce how to use FinRL to make data into the gym form environment, and train DRL agents on it.
 """
 
+from __future__ import annotations
+
 import pandas as pd
 from stable_baselines3.common.logger import configure
 
 from finrl.agents.stablebaselines3.models import DRLAgent
-from finrl.config import INDICATORS, TRAINED_MODEL_DIR, RESULTS_DIR
+from finrl.config import INDICATORS
+from finrl.config import RESULTS_DIR
+from finrl.config import TRAINED_MODEL_DIR
 from finrl.main import check_and_make_directories
 from finrl.meta.env_stock_trading.env_stocktrading import StockTradingEnv
 

examples/FinRL_StockTrading_2026_3_Backtest.py

@@ -8,7 +8,10 @@
 Mean Variance Optimization and DJIA index.
 """
 
+from __future__ import annotations
+
 import matplotlib
+
 matplotlib.use("Agg")
 import matplotlib.pyplot as plt
 import numpy as np

finrl/config_tickers.py

@@ -140,7 +140,7 @@
     "TRI",
     "WBD",
     "WMT",
-    "ZS"
+    "ZS",
 ]
 
 # SP 500 constituents at 2019

finrl/meta/env_stock_trading/env_stocktrading.py

@@ -28,6 +28,9 @@ class StockTradingEnv(gym.Env):
         sell_cost_pct (float, array): Cost for selling shares, each index corresponds to each asset
         turbulence_threshold (float): Maximum turbulence allowed in market for purchases to occur. If exceeded, positions are liquidated
         print_verbosity(int): When iterating (step), how often to print stats about state of env
+    allow_short_selling (bool): If False, the agent cannot take short
+        positions. The action space is restricted to [0, 1] and negative
+        actions are clipped. Default is True for backward compatibility.
     """
 
     metadata = {"render.modes": ["human"]}
@@ -55,6 +58,7 @@ def __init__(
         model_name="",
         mode="",
         iteration="",
+        allow_short_selling: bool = True,
     ):
         self.day = day
         self.df = df
@@ -68,7 +72,10 @@ def __init__(
         self.state_space = state_space
         self.action_space = action_space
         self.tech_indicator_list = tech_indicator_list
-        self.action_space = spaces.Box(low=-1, high=1, shape=(self.action_space,))
+        if self.allow_short_selling:
+            self.action_space = spaces.Box(low=-1, high=1, shape=(self.action_space,))
+        else:
+            self.action_space = spaces.Box(low=0, high=1, shape=(self.action_space,))
         self.observation_space = spaces.Box(
             low=-np.inf, high=np.inf, shape=(self.state_space,)
         )
@@ -83,6 +90,7 @@ def __init__(
         self.model_name = model_name
         self.mode = mode
         self.iteration = iteration
+        self.allow_short_selling = allow_short_selling
         # initalize state
         self.state = self._initiate_state()
 
@@ -315,6 +323,9 @@ def step(self, actions):
             actions = actions.astype(
                 int
             )  # convert into integer because we can't by fraction of shares
+            # Prevent short selling: clip negative actions to 0
+            if not self.allow_short_selling:
+                actions = np.where(actions < 0, 0, actions)
             if self.turbulence_threshold is not None:
                 if self.turbulence >= self.turbulence_threshold:
                     actions = np.array([-self.hmax] * self.stock_dim)