commenets

crypto/brian/design.md

@@ -0,0 +1,67 @@
+The proposed architecture effectively addresses synchronizing multiple timeframes and indicators using channel-specific processing and transformer-based attention. Here's how to enhance it further and key considerations:
+
+Key Enhancements & Considerations:
+Timeframe Embeddings:
+Why: Helps the model distinguish between different resolutions (1m vs 1h).
+How: Add an embedding layer that encodes timeframe IDs (e.g., 0 for 1m, 1 for 5m). Inject this into each channel's processed features.
+python
+
+Run
+
+Copy
+self.timeframe_embed = nn.Embedding(num_timeframes, hidden_dim)
+# In forward:
+timeframe_ids = torch.arange(num_timeframes).to(device)  # During init or passed as input
+embeddings = self.timeframe_embed(timeframe_ids).unsqueeze(0)  # (1, num_timeframes, hidden_dim)
+channels_concat = channels_concat + embeddings  # Add to branch outputs
+Positional Encodings in Transformer:
+Why: Inform the model about the order/hierarchy of timeframes.
+How: Apply sinusoidal positional encodings to transformer_input before passing to the encoder.
+Dynamic Aggregation:
+Why: Mean aggregation may dilute important signals.
+How: Use attention pooling to weight channels dynamically:
+python
+
+Run
+
+Copy
+# After transformer_output of shape (num_timeframes, batch_size, hidden_dim)
+attn_weights = torch.softmax(self.attn_pool(transformer_output), dim=0)  # Learnable linear layer
+aggregated_channels = (transformer_output * attn_weights).sum(dim=0)
+Multi-Step Prediction:
+Why: Predicting multiple future candles requires temporal coherence.
+How: Replace the final fc layer with an LSTM or transformer decoder to output sequences.
+Meta Feature Interaction:
+Why: Allow meta features to modulate channel processing.
+How: Use cross-attention between meta features and channel data:
+python
+
+Run
+
+Copy
+# Compute cross-attention where meta_out attends to aggregated_channels
+cross_attn = nn.MultiheadAttention(hidden_dim, n_heads)
+attn_output, _ = cross_attn(meta_out.unsqueeze(0), aggregated_channels.unsqueeze(0), aggregated_channels.unsqueeze(0))
+combined = torch.cat([attn_output.squeeze(0), meta_out], dim=1)
+Data Alignment Strategy:
+Preprocessing: Resample higher timeframes to match the lowest resolution's timestamps (e.g., 1m), forward-filling 5m/1h data. This ensures all channels have values at every 1m timestamp.
+Example: For a 1h channel, each 1m timestamp in the same hour window shares the same 1h candle values until the next hour.
+Loss Function:
+Enhancement: Incorporate a custom loss that penalizes prediction errors in peaks/valleys more heavily:
+python
+
+Run
+
+Copy
+def peak_valley_loss(pred_highs, true_highs, pred_lows, true_lows):
+    high_error = torch.abs(pred_highs - true_highs) * 2  # Emphasize peaks
+    low_error = torch.abs(pred_lows - true_lows) * 2     # Emphasize valleys
+    return (high_error + low_error).mean()
+Implementation Adjustments:
+Transformer Layers: Increase depth (num_layers=2) for richer interactions.
+Regularization: Add dropout in channel branches and meta encoder to prevent overfitting.
+Input Normalization: Apply instance normalization per channel to handle varying indicator scales.
+RL Integration (Future Step):
+Use the model's predictions as part of the state representation in an RL agent (e.g., PPO or DQN).
+Design a reward function based on trading profitability (e.g., Sharpe ratio, portfolio returns).
+Include transaction costs and slippage in the RL environment for realistic backtesting.