wip

crypto/brian/index-deep-new.py

@@ -239,7 +239,7 @@ class BacktestEnvironment:
         
     def step(self, action):
         base_candles = self.candles_dict[self.base_tf]
-        # Handle end-of-data scenario: return dummy high/low values if needed.
+        # End-of-data: return dummy high/low targets
         if self.current_index >= len(base_candles) - 1:
             current_state = self.get_state(self.current_index)
             return current_state, 0.0, None, True, 0.0, 0.0
@@ -247,7 +247,6 @@ class BacktestEnvironment:
         current_state = self.get_state(self.current_index)
         next_index = self.current_index + 1
         next_state = self.get_state(next_index)
-        current_candle = base_candles[self.current_index]
         next_candle = base_candles[next_index]
         reward = 0.0
 
@@ -272,24 +271,24 @@ class BacktestEnvironment:
 
         self.current_index = next_index
         done = (self.current_index >= len(base_candles) - 1)
-        # Return the high and low of the next candle as the targets.
         actual_high = next_candle["high"]
         actual_low = next_candle["low"]
         return current_state, reward, next_state, done, actual_high, actual_low
+
     def __len__(self):
         return len(self.candles_dict[self.base_tf])
 
 # --- Enhanced Training Loop ---
-def train_on_historical_data(env, model, device, args):
+def train_on_historical_data(env, model, device, args, start_epoch=0):
     optimizer = optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-5)
-    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.epochs)
+    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.epochs - start_epoch)
-    for epoch in range(args.epochs):
+    for epoch in range(start_epoch, args.epochs):
         state = env.reset()
         total_loss = 0
         model.train()
         while True:
             state_tensor = torch.FloatTensor(state).unsqueeze(0).to(device)
-            timeframe_ids = torch.arange(state.shape[0]).to(device)  # Expect shape[0]==num_channels
+            timeframe_ids = torch.arange(state.shape[0]).to(device)  # shape[0] == num_channels
             pred_high, pred_low = model(state_tensor, timeframe_ids)
             # Dummy targets from next candle's high/low
             _, _, next_state, done, actual_high, actual_low = env.step(None)
@@ -355,6 +354,7 @@ def parse_args():
     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)
+    # New flag: if set, we start training from scratch (ignoring any saved checkpoints)
     parser.add_argument('--start_fresh', action='store_true', help='Start training from scratch ignoring saved checkpoints.')
     return parser.parse_args()
 
@@ -364,12 +364,11 @@ def random_action():
 # --- Main Function ---
 async def main():
     args = parse_args()
+    # Use GPU if available, else fallback to CPU
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print("Using device:", device)
     timeframes = ["1m", "5m", "15m", "1h", "1d"]
-    input_dim = len(timeframes) * 7  # 7 features per timeframe
     hidden_dim = 128
-    output_dim = 3  # SELL, HOLD, BUY
-    # Set total number of channels = NUM_TIMEFRAMES + NUM_INDICATORS.
     total_channels = NUM_TIMEFRAMES + NUM_INDICATORS
     model = TradingModel(total_channels, NUM_TIMEFRAMES).to(device)
 
@@ -380,6 +379,7 @@ async def main():
             return
         base_tf = "1m"
         env = BacktestEnvironment(candles_dict, base_tf, timeframes)
+
         start_epoch = 0
         if not args.start_fresh:
             checkpoint = load_best_checkpoint(model)
@@ -403,10 +403,10 @@ async def main():
         preview_thread.start()
         print("Starting live trading loop. (Using random actions for simulation.)")
         while True:
-            state, reward, next_state, done = env.step(random_action())
+            state, reward, next_state, done, _, _ = env.step(random_action())
             if done:
                 print("Reached end of simulated data, resetting environment.")
-                state = env.reset(clear_trade_history=False)
+                state = env.reset()
             await asyncio.sleep(1)
     elif args.mode == 'inference':
         load_best_checkpoint(model)