more strong model response

crypto/brian/index-deep-new.py

@@ -327,6 +327,8 @@ def update_live_chart(ax, candles, trade_history):
 def simulate_trades(model, env, device, args):
     """
     Run a simulation on the current sliding window using the model's outputs and a decision rule.
+    Here we force the simulation to always take an action by comparing the predicted potentials,
+    ensuring that the model is forced to trade (either BUY or SELL) rather than HOLD.
     This simulation updates env.trade_history and is used for visualization only.
     """
     env.reset()  # resets the window and index
@@ -339,12 +341,11 @@ def simulate_trades(model, env, device, args):
         pred_high, pred_low = model(state_tensor, timeframe_ids)
         pred_high = pred_high.item()
         pred_low = pred_low.item()
-        if (pred_high - current_open) >= (current_open - pred_low) and (pred_high - current_open) > args.threshold:
+        # Force a trade: choose BUY if predicted up-move is higher (or equal), else SELL.
+        if (pred_high - current_open) >= (current_open - pred_low):
             action = 2  # BUY
-        elif (current_open - pred_low) > (pred_high - current_open) and (current_open - pred_low) > args.threshold:
-            action = 0  # SELL
         else:
-            action = 1  # HOLD
+            action = 0  # SELL
         _, _, _, done, _, _ = env.step(action)
         if done:
             break
@@ -481,9 +482,9 @@ def parse_args():
     parser.add_argument('--mode', choices=['train', 'live', 'inference'], default='train')
     parser.add_argument('--epochs', type=int, default=100)
     parser.add_argument('--lr', type=float, default=3e-4)
-    parser.add_argument('--threshold', type=float, default=0.005, help="Minimum predicted move to trigger trade.")
+    parser.add_argument('--threshold', type=float, default=0.005, help="Minimum predicted move to trigger trade (used in loss).")
     parser.add_argument('--lambda_trade', type=float, default=1.0, help="Weight for trade surrogate loss.")
-    parser.add_argument('--penalty_noaction', type=float, default=10.0, help="Penalty if no action is taken.")
+    parser.add_argument('--penalty_noaction', type=float, default=10.0, help="Penalty if no action is taken (used in loss).")
     parser.add_argument('--start_fresh', action='store_true', help="Start training from scratch.")
     return parser.parse_args()
 
@@ -545,7 +546,7 @@ async def main():
         env = BacktestEnvironment(candles_dict, base_tf="1m", timeframes=timeframes, window_size=100)
         preview_thread = threading.Thread(target=live_preview_loop, args=(env.candle_window, env), daemon=True)
         preview_thread.start()
-        print("Starting live trading loop. (Using model-based decision rule.)")
+        print("Starting live trading loop. (Forcing trade actions based on highest potential.)")
         while True:
             state = env.get_state(env.current_index)
             current_open = env.candle_window[env.current_index]["open"]
@@ -554,12 +555,11 @@ async def main():
             pred_high, pred_low = model(state_tensor, timeframe_ids)
             pred_high = pred_high.item()
             pred_low = pred_low.item()
-            if (pred_high - current_open) >= (current_open - pred_low) and (pred_high - current_open) > args.threshold:
+            # Force a trade (choose BUY if upward potential >= downward, else SELL)
+            if (pred_high - current_open) >= (current_open - pred_low):
                 action = 2
-            elif (current_open - pred_low) > (pred_high - current_open) and (current_open - pred_low) > args.threshold:
-                action = 0
             else:
-                action = 1
+                action = 0
             _, _, _, done, _, _ = env.step(action)
             if done:
                 print("Reached end of simulation window; resetting environment.")