added volume. better training

crypto/brian/index-deep-new.py

@@ -44,10 +44,8 @@ CACHE_FILE = "candles_cache.json"
 # --- Constants ---
 NUM_TIMEFRAMES = 5        # e.g., ["1m", "5m", "15m", "1h", "1d"]
 NUM_INDICATORS = 20       # e.g., 20 technical indicators
-# Each channel input will have 7 features.
+FEATURES_PER_CHANNEL = 7  # Each channel input will have 7 features.
-FEATURES_PER_CHANNEL = 7  
+ORDER_CHANNELS = 1        # One extra channel for order info.
-# We add one extra channel for order information.
-ORDER_CHANNELS = 1
 
 # --- Positional Encoding Module ---
 class PositionalEncoding(nn.Module):
@@ -68,7 +66,6 @@ class PositionalEncoding(nn.Module):
 class TradingModel(nn.Module):
     def __init__(self, num_channels, num_timeframes, hidden_dim=128):
         super().__init__()
-        # Create one branch per channel.
         self.channel_branches = nn.ModuleList([
             nn.Sequential(
                 nn.Linear(FEATURES_PER_CHANNEL, hidden_dim),
@@ -77,13 +74,11 @@ class TradingModel(nn.Module):
                 nn.Dropout(0.1)
             ) for _ in range(num_channels)
         ])
-        # Embedding for channels 0..num_channels-1.
         self.timeframe_embed = nn.Embedding(num_channels, hidden_dim)
         self.pos_encoder = PositionalEncoding(hidden_dim)
-        # Set batch_first=True to avoid the nested tensor warning.
         encoder_layers = TransformerEncoderLayer(
             d_model=hidden_dim, nhead=4, dim_feedforward=512,
-            dropout=0.1, activation='gelu', batch_first=True
+            dropout=0.1, activation='gelu', batch_first=True  # Use batch_first to avoid nested tensor warning.
         )
         self.transformer = TransformerEncoder(encoder_layers, num_layers=2)
         self.attn_pool = nn.Linear(hidden_dim, 1)
@@ -104,11 +99,9 @@ class TradingModel(nn.Module):
         for i in range(num_channels):
             channel_out = self.channel_branches[i](x[:, i, :])
             channel_outs.append(channel_out)
-        stacked = torch.stack(channel_outs, dim=1)  # shape: [batch, channels, hidden]
+        stacked = torch.stack(channel_outs, dim=1)  # [batch, channels, hidden]
-        # With batch_first=True, the expected input is [batch, seq_len, hidden]
         tf_embeds = self.timeframe_embed(timeframe_ids)  # shape: [num_channels, hidden]
-        # Expand tf_embeds to match the batch dimension.
+        stacked = stacked + tf_embeds.unsqueeze(0)  # broadcast along batch dimension.
-        stacked = stacked + tf_embeds.unsqueeze(0)
         transformer_out = self.transformer(stacked)
         attn_weights = torch.softmax(self.attn_pool(transformer_out), dim=1)
         aggregated = (transformer_out * attn_weights).sum(dim=1)
@@ -225,32 +218,78 @@ def load_best_checkpoint(model, best_dir=BEST_DIR):
     checkpoint = torch.load(path)
     old_state = checkpoint["model_state_dict"]
     new_state = model.state_dict()
-
-    # Fix the size mismatch for timeframe_embed.weight.
     if "timeframe_embed.weight" in old_state:
         old_embed = old_state["timeframe_embed.weight"]
         new_embed = new_state["timeframe_embed.weight"]
         if old_embed.shape[0] < new_embed.shape[0]:
             new_embed[:old_embed.shape[0]] = old_embed
             old_state["timeframe_embed.weight"] = new_embed
-
-    # For channel_branches, missing keys are handled by strict=False.
     model.load_state_dict(old_state, strict=False)
     return checkpoint
 
+# --- Function for Manual Trade Override ---
+def manual_trade(env):
+    """
+    When no sufficient action is taken by the model, manually decide the trade.
+    Find the maximum high and minimum low in the remaining window.
+    If maximum occurs before minimum, we short; otherwise we long.
+    The trade is closed at the candle where the chosen extreme occurs.
+    """
+    current_index = env.current_index
+    if current_index >= len(env.candle_window) - 1:
+        env.current_index = len(env.candle_window) - 1
+        return
+    max_val = -float('inf')
+    min_val = float('inf')
+    i_max = current_index
+    i_min = current_index
+    for j in range(current_index + 1, len(env.candle_window)):
+        high_j = env.candle_window[j]["high"]
+        low_j = env.candle_window[j]["low"]
+        if high_j > max_val:
+            max_val = high_j
+            i_max = j
+        if low_j < min_val:
+            min_val = low_j
+            i_min = j
+    # If maximum occurs before minimum, we interpret that as short (price will drop).
+    if i_max < i_min:
+        entry_price = env.candle_window[current_index]["open"]
+        exit_price = env.candle_window[i_min]["open"]
+        reward = entry_price - exit_price
+        trade = {
+            "entry_index": current_index,
+            "entry_price": entry_price,
+            "exit_index": i_min,
+            "exit_price": exit_price,
+            "pnl": reward
+        }
+    else:
+        entry_price = env.candle_window[current_index]["open"]
+        exit_price = env.candle_window[i_max]["open"]
+        reward = exit_price - entry_price
+        trade = {
+            "entry_index": current_index,
+            "entry_price": entry_price,
+            "exit_index": i_max,
+            "exit_price": exit_price,
+            "pnl": reward
+        }
+    env.trade_history.append(trade)
+    env.current_index = trade["exit_index"]
+
 # --- Live HTML Chart Update ---
 def update_live_html(candles, trade_history, epoch):
     """
-    Generate a chart image that uses actual timestamps on the x-axis
+    Generate a chart image with actual timestamps on the x-axis and cumulative epoch PnL.
-    and shows a cumulative epoch PnL. The chart (with buy/sell markers and dotted lines)
+    The chart now also plots volume as a bar chart on a secondary y-axis.
-    is embedded in an HTML page that auto-refreshes every 1 seconds.
+    The HTML page auto-refreshes every 10 seconds.
     """
     from io import BytesIO
     import base64
 
     fig, ax = plt.subplots(figsize=(12, 6))
     update_live_chart(ax, candles, trade_history)
-    # Compute cumulative epoch PnL.
     epoch_pnl = sum(trade["pnl"] for trade in trade_history)
     ax.set_title(f"Live Trading Chart - Epoch {epoch} | PnL: {epoch_pnl:.2f}")
     buf = BytesIO()
@@ -263,7 +302,7 @@ def update_live_html(candles, trade_history, epoch):
 <html>
 <head>
   <meta charset="utf-8">
-  <meta http-equiv="refresh" content="1">
+  <meta http-equiv="refresh" content="10">
   <title>Live Trading Chart - Epoch {epoch}</title>
   <style>
     body {{
@@ -298,27 +337,34 @@ def update_live_html(candles, trade_history, epoch):
 # --- Chart Drawing Helpers ---
 def update_live_chart(ax, candles, trade_history):
     """
-    Plot the price chart with actual timestamps on the x-axis.
+    Plot the price chart with proper timestamp conversion.
-    Mark BUY (green) and SELL (red) actions, and draw dotted lines between entry and exit.
+    Mark BUY (green) and SELL (red) actions (with dotted lines between),
+    and plot volume as a bar chart on a secondary y-axis.
     """
     ax.clear()
-    # Use the helper to convert timestamps safely.
     times = [convert_timestamp(candle["timestamp"]) for candle in candles]
     close_prices = [candle["close"] for candle in candles]
     ax.plot(times, close_prices, label="Close Price", color="black", linewidth=1)
-    # Format x-axis date labels.
-    ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M:%S'))
-    for trade in trade_history:
-        entry_time = convert_timestamp(candles[trade["entry_index"]]["timestamp"])
-        exit_time = convert_timestamp(candles[trade["exit_index"]]["timestamp"])
-        in_price = trade["entry_price"]
-        out_price = trade["exit_price"]
-        ax.plot(entry_time, in_price, marker="^", color="green", markersize=10, label="BUY")
-        ax.plot(exit_time, out_price, marker="v", color="red", markersize=10, label="SELL")
-        ax.plot([entry_time, exit_time], [in_price, out_price], linestyle="dotted", color="blue")
     ax.set_xlabel("Time")
     ax.set_ylabel("Price")
-    ax.legend()
+    ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M:%S'))
+    
+    # Plot volume on secondary axis.
+    ax2 = ax.twinx()
+    volumes = [candle["volume"] for candle in candles]
+    # Compute bar width in days.
+    if len(times) > 1:
+        times_num = mdates.date2num(times)
+        bar_width = (times_num[-1] - times_num[0]) / len(times) * 0.8
+    else:
+        bar_width = 0.01
+    ax2.bar(times, volumes, width=bar_width, alpha=0.3, color="grey", label="Volume")
+    ax2.set_ylabel("Volume")
+    
+    # Combine legends.
+    lines, labels = ax.get_legend_handles_labels()
+    lines2, labels2 = ax2.get_legend_handles_labels()
+    ax.legend(lines + lines2, labels + labels2)
     ax.grid(True)
     fig = ax.get_figure()
     fig.autofmt_xdate()
@@ -326,14 +372,15 @@ def update_live_chart(ax, candles, trade_history):
 # --- Simulation of Trades for Visualization ---
 def simulate_trades(model, env, device, args):
     """
-    Run a simulation on the current sliding window using the model's outputs and a decision rule.
+    Run a simulation on the current sliding window.
-    Here we force the simulation to always take an action by comparing the predicted potentials,
+    If the model produces a sufficiently strong signal (based on threshold), use its action.
-    ensuring that the model is forced to trade (either BUY or SELL) rather than HOLD.
+    Otherwise, manually compute the trade by scanning for max/min prices.
-    This simulation updates env.trade_history and is used for visualization only.
     """
-    env.reset()  # resets the window and index
+    env.reset()
     while True:
         i = env.current_index
+        if i >= len(env.candle_window) - 1:
+            break
         state = env.get_state(i)
         current_open = env.candle_window[i]["open"]
         state_tensor = torch.FloatTensor(state).unsqueeze(0).to(device)
@@ -341,19 +388,23 @@ 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()
-        # Force a trade: choose BUY if predicted up-move is higher (or equal), else SELL.
+        # If either upward potential or downward potential exceeds the threshold, use model decision.
+        if (pred_high - current_open) > args.threshold or (current_open - pred_low) > args.threshold:
             if (pred_high - current_open) >= (current_open - pred_low):
                 action = 2  # BUY
             else:
                 action = 0  # SELL
             _, _, _, done, _, _ = env.step(action)
-        if done:
+        else:
+            # No significant signal; use manual trade computation.
+            manual_trade(env)
+        if env.current_index >= len(env.candle_window) - 1:
             break
 
 # --- Backtest Environment with Sliding Window and Order Info ---
 class BacktestEnvironment:
     def __init__(self, candles_dict, base_tf, timeframes, window_size=None):
-        self.candles_dict = candles_dict  # full candles dict across timeframes
+        self.candles_dict = candles_dict
         self.base_tf = base_tf
         self.timeframes = timeframes
         self.full_candles = candles_dict[base_tf]
@@ -361,7 +412,6 @@ class BacktestEnvironment:
             window_size = 100 if len(self.full_candles) >= 100 else len(self.full_candles)
         self.window_size = window_size
         self.reset()
-
     def reset(self):
         self.start_index = random.randint(0, len(self.full_candles) - self.window_size)
         self.candle_window = self.full_candles[self.start_index: self.start_index + self.window_size]
@@ -369,10 +419,8 @@ class BacktestEnvironment:
         self.trade_history = []
         self.position = None
         return self.get_state(self.current_index)
-
     def __len__(self):
         return self.window_size
-
     def get_order_features(self, index):
         candle = self.candle_window[index]
         if self.position is None:
@@ -381,7 +429,6 @@ class BacktestEnvironment:
             flag = 1.0
             diff = (candle["open"] - self.position["entry_price"]) / candle["open"]
             return [flag, diff] + [0.0] * (FEATURES_PER_CHANNEL - 2)
-
     def get_state(self, index):
         state_features = []
         base_ts = self.candle_window[index]["timestamp"]
@@ -398,7 +445,6 @@ class BacktestEnvironment:
         for _ in range(NUM_INDICATORS):
             state_features.append([0.0] * FEATURES_PER_CHANNEL)
         return np.array(state_features, dtype=np.float32)
-
     def step(self, action):
         base = self.candle_window
         if self.current_index >= len(base) - 1:
@@ -410,10 +456,10 @@ class BacktestEnvironment:
         next_candle = base[next_index]
         reward = 0.0
         if self.position is None:
-            if action == 2:
+            if action == 2:  # BUY (open long)
                 self.position = {"entry_price": next_candle["open"], "entry_index": self.current_index}
         else:
-            if action == 0:
+            if action == 0:  # SELL (close long / exit trade)
                 exit_price = next_candle["open"]
                 reward = exit_price - self.position["entry_price"]
                 trade = {
@@ -480,11 +526,14 @@ def live_preview_loop(candles, env):
 def parse_args():
     parser = argparse.ArgumentParser()
     parser.add_argument('--mode', choices=['train', 'live', 'inference'], default='train')
-    parser.add_argument('--epochs', type=int, default=100)
+    parser.add_argument('--epochs', type=int, default=1000)
     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 (used in loss).")
+    parser.add_argument('--threshold', type=float, default=0.005, 
-    parser.add_argument('--lambda_trade', type=float, default=1.0, help="Weight for trade surrogate loss.")
+                        help="Minimum predicted move to trigger trade (used in loss; model may override with manual trade).")
-    parser.add_argument('--penalty_noaction', type=float, default=10.0, help="Penalty if no action is taken (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 (used in loss).")
     parser.add_argument('--start_fresh', action='store_true', help="Start training from scratch.")
     return parser.parse_args()
 
@@ -546,7 +595,7 @@ 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. (Forcing trade actions based on highest potential.)")
+        print("Starting live trading loop. (Using model, with manual override for HOLD actions.)")
         while True:
             state = env.get_state(env.current_index)
             current_open = env.candle_window[env.current_index]["open"]
@@ -555,13 +604,15 @@ async def main():
             pred_high, pred_low = model(state_tensor, timeframe_ids)
             pred_high = pred_high.item()
             pred_low = pred_low.item()
-            # Force a trade (choose BUY if upward potential >= downward, else SELL)
+            if (pred_high - current_open) > args.threshold or (current_open - pred_low) > args.threshold:
                 if (pred_high - current_open) >= (current_open - pred_low):
-                action = 2
+                    action = 2  # BUY
                 else:
-                action = 0
+                    action = 0  # SELL
                 _, _, _, done, _, _ = env.step(action)
-            if done:
+            else:
+                manual_trade(env)
+            if env.current_index >= len(env.candle_window)-1:
                 print("Reached end of simulation window; resetting environment.")
                 env.reset()
             await asyncio.sleep(1)