Update scripts/app.py

scripts/app.py

@@ -85,7 +85,7 @@ def evaluate_agent_pro(env, model):
     """
     obs, info = env.reset()
     terminated, truncated = False, False
-    portfolio_values = [env.initial_amount]
+    portfolio_values = [env.initial_balance]
 
     while not (terminated or truncated):
         action, _states = model.predict(obs, deterministic=True)
@@ -268,7 +268,7 @@ def run_historical_simulation(start_date_str, end_date_str):
         status_msg = "Running RL Agent simulation..."
         yield go.Figure(), status_msg, gr.update(visible=False)
 
-        env = PortfolioEnv(df_slice, WINDOW_SIZE, initial_amount=10000)
+        env = PortfolioEnv(df_slice, WINDOW_SIZE, initial_balance=10000)
 
         if not os.path.exists(MODEL_PATH):
              raise FileNotFoundError(f"Model not found: {MODEL_PATH}")
@@ -282,13 +282,13 @@ def run_historical_simulation(start_date_str, end_date_str):
         yield go.Figure(), status_msg, gr.update(visible=False)
 
         # Pass only asset columns to baseline functions
-        bnh_portfolio_series = buy_and_hold(df_slice[asset_cols_only], initial_amount=10000)
+        bnh_portfolio_series = buy_and_hold(df_slice[asset_cols_only], initial_balance=10000)
         # Realign B&H index to match RL agent's start date
         bnh_portfolio_series = bnh_portfolio_series.loc[rl_portfolio_series.index[0]:]
         # Normalize B&H starting value to match RL agent's start
         bnh_portfolio_series = bnh_portfolio_series / bnh_portfolio_series.iloc[0] * 10000
 
-        eq_portfolio_series = equally_weighted_rebalanced(df_slice[asset_cols_only], initial_amount=10000)
+        eq_portfolio_series = equally_weighted_rebalanced(df_slice[asset_cols_only], initial_balance=10000)
         eq_portfolio_series = eq_portfolio_series.loc[rl_portfolio_series.index[0]:]
         eq_portfolio_series = eq_portfolio_series / eq_portfolio_series.iloc[0] * 10000