forced actions

.gitignore

@@ -31,3 +31,4 @@ app_data.db
 crypto/sol/.vs/*
 crypto/brian/models/best/*
 crypto/brian/models/last/*
+crypto/brian/live_chart.html

crypto/brian/index-deep-new.py

@@ -186,7 +186,6 @@ def save_checkpoint(model, optimizer, epoch, loss, last_dir=LAST_DIR, best_dir=B
     if len(best_models) < 10:
         add_to_best = True
     else:
-        # The worst saved checkpoint will have the highest loss.
         worst_loss, worst_file = max(best_models, key=lambda x: x[0])
         if loss < worst_loss:
             add_to_best = True
@@ -219,7 +218,7 @@ def load_best_checkpoint(model, best_dir=BEST_DIR):
 def update_live_html(candles, trade_history, epoch):
     """
     Generate a chart image with buy/sell markers and a dotted line between open and close,
-    then embed it in a simple HTML page that auto-refreshes.
+    then embed it in a simple HTML page that auto-refreshes every 10 seconds.
     """
     from io import BytesIO
     import base64
@@ -301,6 +300,25 @@ def update_live_chart(ax, candles, trade_history):
     ax.legend()
     ax.grid(True)
 
+# --- Forced Action Helper ---
+def get_forced_action(env):
+    """
+    Force at least one trade per episode:
+      - At the very first step, force a BUY (action 2) if no position is open.
+      - At the penultimate step, if a position is open, force a SELL (action 0).
+      - Otherwise, default to HOLD (action 1).
+    """
+    total = len(env)
+    if env.current_index == 0:
+        return 2  # BUY
+    elif env.current_index >= total - 2:
+        if env.position is not None:
+            return 0  # SELL
+        else:
+            return 1  # HOLD
+    else:
+        return 1  # HOLD
+
 # --- Backtest Environment ---
 class BacktestEnvironment:
     def __init__(self, candles_dict, base_tf, timeframes):
@@ -343,11 +361,11 @@ class BacktestEnvironment:
 
         # Action mapping: 0 -> SELL, 1 -> HOLD, 2 -> BUY.
         if self.position is None:
-            if action == 2:  # BUY: enter at next candle's open.
+            if action == 2:  # BUY signal: enter at next candle's open.
                 entry_price = next_candle["open"]
                 self.position = {"entry_price": entry_price, "entry_index": self.current_index}
         else:
-            if action == 0:  # SELL: exit at next candle's open.
+            if action == 0:  # SELL signal: exit at next candle's open.
                 exit_price = next_candle["open"]
                 reward = exit_price - self.position["entry_price"]
                 trade = {
@@ -376,11 +394,13 @@ def train_on_historical_data(env, model, device, args, start_epoch, optimizer, s
         total_loss = 0
         model.train()
         while True:
+            # Use forced action policy to guarantee at least one trade per episode
+            action = get_forced_action(env)
             state_tensor = torch.FloatTensor(state).unsqueeze(0).to(device)
             timeframe_ids = torch.arange(state.shape[0]).to(device)
             pred_high, pred_low = model(state_tensor, timeframe_ids)
-            # Get targets from environment (dummy high/low from next candle)
+            # Use the forced action in the environment step.
-            _, _, next_state, done, actual_high, actual_low = env.step(None)
+            _, reward, next_state, done, actual_high, actual_low = env.step(action)
             target_high = torch.FloatTensor([actual_high]).to(device)
             target_low  = torch.FloatTensor([actual_low]).to(device)
             high_loss = torch.abs(pred_high - target_high) * 2
@@ -398,7 +418,7 @@ def train_on_historical_data(env, model, device, args, start_epoch, optimizer, s
         epoch_loss = total_loss / len(env)
         print(f"Epoch {epoch+1} Loss: {epoch_loss:.4f}")
         save_checkpoint(model, optimizer, epoch, total_loss)
-        # Update the live HTML file with the current epoch chart
+        # Update the live HTML file with the current epoch chart.
         update_live_html(base_candles, env.trade_history, epoch+1)
 
 # --- Live Plotting Functions (For live mode) ---
@@ -465,7 +485,7 @@ async def main():
                 print("Loaded optimizer state from checkpoint.")
             else:
                 print("No valid optimizer state found in checkpoint; starting fresh optimizer state.")
-        # Pass base candles from the base timeframe for HTML chart updates.
+        # Pass the base timeframe candles for the live HTML chart update.
         train_on_historical_data(env, model, device, args, start_epoch, optimizer, scheduler, candles_dict[base_tf])
 
     elif args.mode == 'live':
@@ -477,9 +497,11 @@ async def main():
         env = BacktestEnvironment(candles_dict, base_tf="1m", timeframes=timeframes)
         preview_thread = threading.Thread(target=live_preview_loop, args=(candles_dict["1m"], env), daemon=True)
         preview_thread.start()
-        print("Starting live trading loop. (Using random actions for simulation.)")
+        print("Starting live trading loop. (Using forced action policy for simulation.)")
+        # Here we use the forced-action policy as in training.
         while True:
-            state, reward, next_state, done, _, _ = env.step(random_action())
+            action = get_forced_action(env)
+            state, reward, next_state, done, _, _ = env.step(action)
             if done:
                 print("Reached end of simulated data, resetting environment.")
                 state = env.reset()
@@ -487,7 +509,7 @@ async def main():
     elif args.mode == 'inference':
         load_best_checkpoint(model)
         print("Running inference...")
-        # Your inference logic goes here.
+        # Here you can apply a similar forced-action policy or use a learned policy.
     else:
         print("Invalid mode specified.")