TCPDashboard/docs/reference/aggregation-strategy.md

# Data Aggregation Strategy

## Overview

This document describes the comprehensive data aggregation strategy used in the TCP Trading Platform for converting real-time trade data into OHLCV (Open, High, Low, Close, Volume) candles across multiple timeframes, including sub-minute precision.

## Core Principles

### 1. Right-Aligned Timestamps (Industry Standard)

The system follows the **RIGHT-ALIGNED timestamp** convention used by major exchanges:

- **Candle timestamp = end time of the interval (close time)**
- This represents when the candle period **closes**, not when it opens
- Aligns with Binance, OKX, Coinbase, and other major exchanges
- Ensures consistency with historical data APIs

**Examples:**
- 1-second candle covering 09:00:15.000-09:00:16.000 → timestamp = 09:00:16.000
- 5-second candle covering 09:00:15.000-09:00:20.000 → timestamp = 09:00:20.000
- 30-second candle covering 09:00:00.000-09:00:30.000 → timestamp = 09:00:30.000
- 1-minute candle covering 09:00:00-09:01:00 → timestamp = 09:01:00
- 5-minute candle covering 09:00:00-09:05:00 → timestamp = 09:05:00

### 2. Sparse Candles (Trade-Driven Aggregation)

**CRITICAL**: The system uses a **SPARSE CANDLE APPROACH** - candles are only emitted when trades actually occur during the time period.

#### What This Means:
- **No trades during period = No candle emitted**
- **Time gaps in data** are normal and expected
- **Storage efficient** - only meaningful periods are stored
- **Industry standard** behavior matching major exchanges

#### Examples of Sparse Behavior:

**1-Second Timeframe:**
```
09:00:15 → Trade occurs → 1s candle emitted at 09:00:16
09:00:16 → No trades → NO candle emitted
09:00:17 → No trades → NO candle emitted  
09:00:18 → Trade occurs → 1s candle emitted at 09:00:19
```

**5-Second Timeframe:**
```
09:00:15-20 → Trades occur → 5s candle emitted at 09:00:20
09:00:20-25 → No trades → NO candle emitted
09:00:25-30 → Trade occurs → 5s candle emitted at 09:00:30
```

#### Real-World Coverage Examples:

From live testing with BTC-USDT (3-minute test):
- **Expected 1s candles**: 180
- **Actual 1s candles**: 53 (29% coverage)
- **Missing periods**: 127 seconds with no trading activity

From live testing with ETH-USDT (1-minute test):
- **Expected 1s candles**: 60  
- **Actual 1s candles**: 22 (37% coverage)
- **Missing periods**: 38 seconds with no trading activity

### 3. No Future Leakage Prevention

The aggregation system prevents future leakage by:

- **Only completing candles when time boundaries are definitively crossed**
- **Never emitting incomplete candles during real-time processing**
- **Waiting for actual trades to trigger bucket completion**
- **Using trade timestamps, not system clock times, for bucket assignment**

## Supported Timeframes

The system supports the following timeframes with precise bucket calculations:

### Second-Based Timeframes:
- **1s**: 1-second buckets (00:00, 00:01, 00:02, ...)
- **5s**: 5-second buckets (00:00, 00:05, 00:10, 00:15, ...)  
- **10s**: 10-second buckets (00:00, 00:10, 00:20, 00:30, ...)
- **15s**: 15-second buckets (00:00, 00:15, 00:30, 00:45, ...)
- **30s**: 30-second buckets (00:00, 00:30, ...)

### Minute-Based Timeframes:
- **1m**: 1-minute buckets aligned to minute boundaries
- **5m**: 5-minute buckets (00:00, 00:05, 00:10, ...)
- **15m**: 15-minute buckets (00:00, 00:15, 00:30, 00:45)
- **30m**: 30-minute buckets (00:00, 00:30)

### Hour-Based Timeframes:
- **1h**: 1-hour buckets aligned to hour boundaries
- **4h**: 4-hour buckets (00:00, 04:00, 08:00, 12:00, 16:00, 20:00)
- **1d**: 1-day buckets aligned to midnight UTC

## Processing Flow

### Real-Time Aggregation Process

1. **Trade arrives** from WebSocket with timestamp T
2. **For each configured timeframe**:
   - Calculate which time bucket this trade belongs to
   - Get current bucket for this timeframe
   - **Check if trade timestamp crosses time boundary**
   - **If boundary crossed**: complete and emit previous bucket (only if it has trades), create new bucket
   - Add trade to current bucket (updates OHLCV)
3. **Only emit completed candles** when time boundaries are definitively crossed
4. **Never emit incomplete/future candles** during real-time processing

### Bucket Management

**Time Bucket Creation:**
- Buckets are created **only when the first trade arrives** for that time period
- Empty time periods do not create buckets

**Bucket Completion:**
- Buckets are completed **only when a trade arrives that belongs to a different time bucket**
- Completed buckets are emitted **only if they contain at least one trade**
- Empty buckets are discarded silently

**Example Timeline:**
```
Time     Trade    1s Bucket Action           5s Bucket Action
-------  -------  -------------------------  ------------------
09:15:23 BUY 0.1  Create bucket 09:15:23     Create bucket 09:15:20
09:15:24 SELL 0.2 Complete 09:15:23 → emit   Add to 09:15:20
09:15:25 -        (no trade = no action)     (no action)
09:15:26 BUY 0.5  Create bucket 09:15:26     Complete 09:15:20 → emit
```

## Handling Sparse Data in Applications

### For Trading Algorithms

```python
def handle_sparse_candles(candles: List[OHLCVCandle], timeframe: str) -> List[OHLCVCandle]:
    """
    Handle sparse candle data in trading algorithms.
    """
    if not candles:
        return candles
    
    # Option 1: Use only available data (recommended)
    # Just work with what you have - gaps indicate no trading activity
    return candles
    
    # Option 2: Fill gaps with last known price (if needed)
    filled_candles = []
    last_candle = None
    
    for candle in candles:
        if last_candle:
            # Check for gap
            expected_next = last_candle.end_time + get_timeframe_delta(timeframe)
            if candle.start_time > expected_next:
                # Gap detected - could fill if needed for your strategy
                pass
        
        filled_candles.append(candle)
        last_candle = candle
    
    return filled_candles
```

### For Charting and Visualization

```python
def prepare_chart_data(candles: List[OHLCVCandle], fill_gaps: bool = True) -> List[OHLCVCandle]:
    """
    Prepare sparse candle data for charting applications.
    """
    if not fill_gaps or not candles:
        return candles
    
    # Fill gaps with previous close price for continuous charts
    filled_candles = []
    
    for i, candle in enumerate(candles):
        if i > 0:
            prev_candle = filled_candles[-1]
            gap_periods = calculate_gap_periods(prev_candle.end_time, candle.start_time, timeframe)
            
            # Fill gap periods with flat candles
            for gap_time in gap_periods:
                flat_candle = create_flat_candle(
                    start_time=gap_time,
                    price=prev_candle.close,
                    timeframe=timeframe
                )
                filled_candles.append(flat_candle)
        
        filled_candles.append(candle)
    
    return filled_candles
```

### Database Queries

When querying candle data, be aware of potential gaps:

```sql
-- Query that handles sparse data appropriately
SELECT 
    timestamp,
    open, high, low, close, volume,
    trade_count,
    -- Flag periods with actual trading activity
    CASE WHEN trade_count > 0 THEN 'ACTIVE' ELSE 'EMPTY' END as period_type
FROM market_data 
WHERE symbol = 'BTC-USDT' 
  AND timeframe = '1s'
  AND timestamp BETWEEN '2024-01-01 09:00:00' AND '2024-01-01 09:05:00'
ORDER BY timestamp;

-- Query to detect gaps in data
WITH candle_gaps AS (
    SELECT 
        timestamp,
        LAG(timestamp) OVER (ORDER BY timestamp) as prev_timestamp,
        timestamp - LAG(timestamp) OVER (ORDER BY timestamp) as gap_duration
    FROM market_data
    WHERE symbol = 'BTC-USDT' AND timeframe = '1s'
    ORDER BY timestamp
)
SELECT * FROM candle_gaps 
WHERE gap_duration > INTERVAL '1 second';
```

## Performance Characteristics

### Storage Efficiency
- **Sparse approach reduces storage** by 50-80% compared to complete time series
- **Only meaningful periods** are stored in the database
- **Faster queries** due to smaller dataset size

### Processing Efficiency  
- **Lower memory usage** during real-time processing
- **Faster aggregation** - no need to maintain empty buckets
- **Efficient WebSocket processing** - only processes actual market events

### Coverage Statistics
Based on real-world testing:

| Timeframe | Major Pairs Coverage | Minor Pairs Coverage |
|-----------|---------------------|---------------------|
| 1s        | 20-40%              | 5-15%              |
| 5s        | 60-80%              | 30-50%             |
| 10s       | 75-90%              | 50-70%             |
| 15s       | 80-95%              | 60-80%             |
| 30s       | 90-98%              | 80-95%             |
| 1m        | 95-99%              | 90-98%             |

*Coverage = Percentage of time periods that actually have candles*

## Best Practices

### For Real-Time Systems
1. **Design algorithms to handle gaps** - missing candles are normal
2. **Use last known price** for periods without trades
3. **Don't interpolate** unless specifically required
4. **Monitor coverage ratios** to detect market conditions

### For Historical Analysis
1. **Be aware of sparse data** when calculating statistics
2. **Consider volume-weighted metrics** over time-weighted ones
3. **Use trade_count=0** to identify empty periods when filling gaps
4. **Validate data completeness** before running backtests

### For Database Storage
1. **Index on (symbol, timeframe, timestamp)** for efficient queries
2. **Partition by time periods** for large datasets
3. **Consider trade_count > 0** filters for active-only queries
4. **Monitor storage growth** - sparse data grows much slower

## Configuration

The sparse aggregation behavior is controlled by:

```json
{
  "timeframes": ["1s", "5s", "10s", "15s", "30s", "1m", "5m", "15m", "1h"],
  "auto_save_candles": true,
  "emit_incomplete_candles": false,  // Never emit incomplete candles
  "max_trades_per_candle": 100000
}
```

**Key Setting**: `emit_incomplete_candles: false` ensures only complete, trade-containing candles are emitted.

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**Note**: This sparse approach is the **industry standard** used by major exchanges and trading platforms. It provides the most accurate representation of actual market activity while maintaining efficiency and preventing data artifacts.