Shared Pattern Encoder
fix T training
This commit is contained in:
Dobromir Popov
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docs/HYBRID_MULTI_TIMEFRAME_ARCHITECTURE.md
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docs/HYBRID_MULTI_TIMEFRAME_ARCHITECTURE.md
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# Hybrid Multi-Timeframe Transformer Architecture
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## Overview
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The transformer uses a **hybrid serial-parallel architecture** that:
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1. **SERIAL**: Learns candle patterns ONCE (shared weights across all timeframes)
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2. **PARALLEL**: Captures cross-timeframe dependencies simultaneously
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This design ensures the model learns common patterns efficiently while understanding relationships between timeframes.
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---
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## Architecture Flow
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```
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Input: Multiple Timeframes
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↓
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┌─────────────────────────────────────────┐
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│ STEP 1: SERIAL PROCESSING │
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│ (Shared Pattern Encoder) │
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│ │
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│ 1s data → Shared Encoder → Encoding_1s │
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│ 1m data → Shared Encoder → Encoding_1m │
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│ 1h data → Shared Encoder → Encoding_1h │
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│ 1d data → Shared Encoder → Encoding_1d │
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│ BTC data → Shared Encoder → Encoding_BTC│
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│ │
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│ Same weights learn patterns once! │
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└─────────────────────────────────────────┘
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↓
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┌─────────────────────────────────────────┐
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│ STEP 2: PARALLEL PROCESSING │
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│ (Cross-Timeframe Attention) │
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│ │
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│ Stack all encodings: │
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│ [Enc_1s, Enc_1m, Enc_1h, Enc_1d, Enc_BTC]│
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│ ↓ │
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│ Cross-Timeframe Transformer Layers │
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│ (Captures dependencies between TFs) │
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│ ↓ │
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│ Unified representation │
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└─────────────────────────────────────────┘
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↓
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┌─────────────────────────────────────────┐
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│ STEP 3: PREDICTION │
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│ │
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│ → Action (BUY/SELL/HOLD) │
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│ → Next candle for EACH timeframe │
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│ → BTC next candle │
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│ → Pivot points │
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│ → Trend analysis │
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└─────────────────────────────────────────┘
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```
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---
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## Key Components
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### 1. Shared Pattern Encoder (SERIAL)
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**Purpose**: Learn candle patterns ONCE for all timeframes
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```python
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self.shared_pattern_encoder = nn.Sequential(
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nn.Linear(5, 256), # OHLCV → 256
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nn.LayerNorm(256),
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nn.GELU(),
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nn.Dropout(0.1),
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nn.Linear(256, 512), # 256 → 512
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nn.LayerNorm(512),
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nn.GELU(),
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nn.Dropout(0.1),
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nn.Linear(512, 1024) # 512 → 1024 (d_model)
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)
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```
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**How it works**:
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- Same network processes ALL timeframes
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- Learns universal candle patterns:
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- Doji, hammer, engulfing, etc.
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- Support/resistance bounces
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- Breakout patterns
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- Volume spikes
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- **Efficient**: Patterns learned once, not 5 times
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**Example**:
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```python
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# All timeframes use SAME encoder
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encoding_1s = shared_encoder(price_data_1s) # [batch, 600, 1024]
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encoding_1m = shared_encoder(price_data_1m) # [batch, 600, 1024]
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encoding_1h = shared_encoder(price_data_1h) # [batch, 600, 1024]
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encoding_1d = shared_encoder(price_data_1d) # [batch, 600, 1024]
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encoding_btc = shared_encoder(btc_data_1m) # [batch, 600, 1024]
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# Same weights → learns patterns once!
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```
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### 2. Timeframe Embeddings
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**Purpose**: Help model distinguish which timeframe it's looking at
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```python
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self.timeframe_embeddings = nn.Embedding(5, 1024)
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# 5 timeframes: 1s, 1m, 1h, 1d, BTC
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```
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**How it works**:
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```python
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# Add timeframe identity to shared encoding
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tf_embedding = timeframe_embeddings[tf_index] # [1024]
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encoding = shared_encoding + tf_embedding
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# Now model knows: "This is a 1h candle pattern"
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```
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### 3. Cross-Timeframe Attention (PARALLEL)
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**Purpose**: Capture dependencies BETWEEN timeframes
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```python
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self.cross_timeframe_layers = nn.ModuleList([
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nn.TransformerEncoderLayer(
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d_model=1024,
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nhead=16,
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dim_feedforward=4096,
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dropout=0.1,
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batch_first=True
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) for _ in range(2) # 2 layers
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])
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```
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**How it works**:
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```python
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# Stack all timeframes
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stacked = torch.stack([enc_1s, enc_1m, enc_1h, enc_1d, enc_btc], dim=1)
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# Shape: [batch, 5 timeframes, 600 seq_len, 1024 d_model]
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# Reshape for attention
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# [batch, 5, 600, 1024] → [batch, 3000, 1024]
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cross_input = stacked.reshape(batch, 5*600, 1024)
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# Apply cross-timeframe attention
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# Each position can attend to ALL timeframes simultaneously
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for layer in cross_timeframe_layers:
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cross_input = layer(cross_input)
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# Model learns:
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# - "1s shows breakout, 1h confirms trend"
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# - "1d resistance, but 1m shows accumulation"
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# - "BTC dumping, ETH following"
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```
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**What it captures**:
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- **Trend confirmation**: Signal on 1m confirmed by 1h
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- **Divergences**: 1s bullish but 1d bearish
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- **Correlation**: BTC moves predict ETH moves
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- **Multi-scale patterns**: Fractal patterns across timeframes
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---
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## Benefits of Hybrid Architecture
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### 1. Knowledge Sharing (SERIAL)
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✅ **Efficient Learning**
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```
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Traditional: 5 separate encoders × 656K params = 3.28M params
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Hybrid: 1 shared encoder × 656K params = 656K params
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Savings: 80% fewer parameters!
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```
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✅ **Better Generalization**
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- Patterns learned from ALL timeframes
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- More training data per pattern
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- Stronger pattern recognition
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✅ **Transfer Learning**
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- Pattern learned on 1m helps 1h
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- Pattern learned on 1d helps 1s
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- Cross-timeframe knowledge transfer
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### 2. Dependency Capture (PARALLEL)
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✅ **Cross-Timeframe Validation**
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```python
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# Example: Entry signal validation
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1s: Bullish breakout (local signal)
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1m: Uptrend confirmed (short-term)
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1h: Above support (medium-term)
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1d: Bullish trend (long-term)
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BTC: Also bullish (market-wide)
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→ High confidence entry!
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```
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✅ **Divergence Detection**
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```python
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# Example: Warning signal
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1s: Bullish (noise)
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1m: Bullish (short-term)
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1h: Bearish divergence (warning!)
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1d: Downtrend (macro)
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BTC: Dumping (market-wide)
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→ Don't enter, wait for confirmation
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```
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✅ **Market Correlation**
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```python
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# Example: BTC influence
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BTC: Sharp drop detected
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ETH 1s: Following BTC
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ETH 1m: Correlation confirmed
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ETH 1h: Likely to follow
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ETH 1d: Macro trend affected
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→ Exit positions, BTC leading
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```
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---
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## Input/Output Specification
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### Input Format
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```python
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model(
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# Primary symbol (ETH/USDT) - all timeframes
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price_data_1s=[batch, 600, 5], # 600 × 1s candles (10 min)
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price_data_1m=[batch, 600, 5], # 600 × 1m candles (10 hours)
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price_data_1h=[batch, 600, 5], # 600 × 1h candles (25 days)
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price_data_1d=[batch, 600, 5], # 600 × 1d candles (~2 years)
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# Reference symbol (BTC/USDT)
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btc_data_1m=[batch, 600, 5], # 600 × 1m BTC candles
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# Other features
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cob_data=[batch, 600, 100], # Order book
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tech_data=[batch, 40], # Technical indicators
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market_data=[batch, 30], # Market features
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position_state=[batch, 5] # Position state
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)
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```
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**Notes**:
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- All timeframes optional (handles missing data)
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- Fixed sequence length: 600 candles
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- OHLCV format: [open, high, low, close, volume]
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### Output Format
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```python
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outputs = {
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# Trading decision
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'action_logits': [batch, 3], # BUY/SELL/HOLD logits
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'action_probs': [batch, 3], # Softmax probabilities
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'confidence': [batch, 1], # Prediction confidence
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# Next candle predictions (ALL timeframes)
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'next_candles': {
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'1s': [batch, 5], # Next 1s candle OHLCV
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'1m': [batch, 5], # Next 1m candle OHLCV
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'1h': [batch, 5], # Next 1h candle OHLCV
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'1d': [batch, 5] # Next 1d candle OHLCV
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},
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# BTC prediction
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'btc_next_candle': [batch, 5], # Next BTC 1m candle
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# Auxiliary predictions
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'price_prediction': [batch, 1], # Price target
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'volatility_prediction': [batch, 1], # Expected volatility
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'trend_strength_prediction': [batch, 1], # Trend strength
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# Pivot points (L1-L5)
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'next_pivots': {...},
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# Trend analysis
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'trend_analysis': {...}
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}
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```
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---
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## Training Strategy
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### Multi-Timeframe Loss
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```python
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# Action loss (primary)
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action_loss = CrossEntropyLoss(action_logits, target_action)
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# Next candle losses (auxiliary)
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candle_losses = []
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for tf in ['1s', '1m', '1h', '1d']:
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if f'target_{tf}' in batch:
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pred = outputs['next_candles'][tf]
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target = batch[f'target_{tf}']
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loss = MSELoss(pred, target)
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candle_losses.append(loss)
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# BTC loss
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if 'target_btc' in batch:
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btc_loss = MSELoss(outputs['btc_next_candle'], batch['target_btc'])
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candle_losses.append(btc_loss)
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# Combined loss
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total_candle_loss = sum(candle_losses) / len(candle_losses)
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total_loss = action_loss + 0.1 * total_candle_loss
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```
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**Why this works**:
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- Action loss: Primary objective (trading decisions)
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- Candle losses: Auxiliary tasks (improve representations)
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- Multi-task learning: Better feature learning
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---
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## Usage Examples
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### Example 1: All Timeframes Available
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```python
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outputs = model(
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price_data_1s=eth_1s,
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price_data_1m=eth_1m,
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price_data_1h=eth_1h,
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price_data_1d=eth_1d,
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btc_data_1m=btc_1m,
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position_state=position
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)
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# Get action
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action = torch.argmax(outputs['action_probs'])
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# Get next candle predictions for all timeframes
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next_1s = outputs['next_candles']['1s']
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next_1m = outputs['next_candles']['1m']
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next_1h = outputs['next_candles']['1h']
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next_1d = outputs['next_candles']['1d']
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next_btc = outputs['btc_next_candle']
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```
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### Example 2: Missing 1s Data (Degraded Mode)
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```python
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# 1s data not available
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outputs = model(
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price_data_1m=eth_1m,
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price_data_1h=eth_1h,
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price_data_1d=eth_1d,
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btc_data_1m=btc_1m,
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position_state=position
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)
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# Still works! Model adapts to available timeframes
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action = torch.argmax(outputs['action_probs'])
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# 1s prediction still available (learned from other TFs)
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next_1s = outputs['next_candles']['1s']
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```
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### Example 3: Legacy Single Timeframe
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```python
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# Old code still works
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outputs = model(
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price_data=eth_1m, # Legacy parameter
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position_state=position
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)
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# Automatically uses as 1m data
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action = torch.argmax(outputs['action_probs'])
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```
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---
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## Performance Characteristics
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### Memory Usage
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**Per Sample**:
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```
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5 timeframes × 600 candles × 5 OHLCV = 15,000 values
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15,000 × 4 bytes = 60 KB input
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Shared encoder: 656K params
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Cross-TF layers: ~8M params
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Total: ~9M params for multi-TF processing
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Batch of 5: 300 KB input, manageable
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```
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### Computational Cost
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**Forward Pass**:
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```
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1. Shared encoder: 5 × (600 × 656K) = ~2B ops
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2. Cross-TF attention: 2 layers × (3000 × 3000) = ~18M ops
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3. Main transformer: 12 layers × (600 × 600) = ~4M ops
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Total: ~2B ops (dominated by shared encoder)
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```
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**Compared to Separate Encoders**:
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```
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Traditional: 5 encoders × 2B ops = 10B ops
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Hybrid: 1 encoder × 2B ops = 2B ops
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Speedup: 5x faster!
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```
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---
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## Key Insights
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### 1. Pattern Universality
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Candle patterns are universal across timeframes:
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- Doji on 1s = Doji on 1d (same pattern, different scale)
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- Hammer on 1m = Hammer on 1h (same reversal signal)
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- Shared encoder exploits this universality
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### 2. Scale Invariance
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The model learns scale-invariant features:
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- Normalized OHLCV removes absolute price scale
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- Patterns recognized regardless of timeframe
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- Timeframe embeddings add scale context
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### 3. Cross-Scale Validation
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Multi-timeframe attention enables validation:
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- Micro signals (1s) validated by macro trends (1d)
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- Reduces false signals
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- Increases prediction confidence
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### 4. Market Correlation
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BTC reference captures market-wide moves:
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- BTC leads, altcoins follow
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- Market-wide sentiment
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- Risk-on/risk-off detection
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---
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## Comparison to Alternatives
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### vs. Separate Models per Timeframe
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| Aspect | Separate Models | Hybrid Architecture |
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|--------|----------------|---------------------|
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| Parameters | 5 × 46M = 230M | 46M |
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| Training Time | 5x longer | 1x |
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| Pattern Learning | 5x redundant | Shared |
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| Cross-TF Dependencies | ❌ None | ✅ Captured |
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| Memory Usage | 5x higher | 1x |
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| Inference Speed | 5x slower | 1x |
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### vs. Single Concatenated Input
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| Aspect | Concatenation | Hybrid Architecture |
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|--------|--------------|---------------------|
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| Pattern Sharing | ❌ No | ✅ Yes |
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| Cross-TF Attention | ❌ No | ✅ Yes |
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| Missing Data | ❌ Breaks | ✅ Handles |
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| Interpretability | Low | High |
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| Efficiency | Medium | High |
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---
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## Summary
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The hybrid serial-parallel architecture provides:
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✅ **Efficient Pattern Learning**: Shared encoder learns once
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✅ **Cross-Timeframe Dependencies**: Parallel attention captures relationships
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✅ **Flexible Input**: Handles missing timeframes gracefully
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✅ **Multi-Scale Predictions**: Predicts next candle for ALL timeframes
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✅ **Market Correlation**: BTC reference for market-wide context
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✅ **Backward Compatible**: Legacy code still works
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This design maximizes both efficiency and expressiveness! 🚀
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