Update OKX configuration and aggregation logic for enhanced multi-timeframe support
- Increased health check interval from 30s to 120s in `okx_config.json`. - Added support for additional timeframes (1s, 5s, 10s, 15s, 30s) in the aggregation logic across multiple components. - Updated `CandleProcessingConfig` and `RealTimeCandleProcessor` to handle new timeframes. - Enhanced validation and parsing functions to include new second-based timeframes. - Updated database schema to support new timeframes in `schema_clean.sql`. - Improved documentation to reflect changes in multi-timeframe aggregation capabilities.
This commit is contained in:
Vasily.onl
@@ -2,7 +2,7 @@
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## Overview
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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.
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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.
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## Core Principles
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@@ -16,326 +16,276 @@ The system follows the **RIGHT-ALIGNED timestamp** convention used by major exch
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- Ensures consistency with historical data APIs
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**Examples:**
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```
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5-minute candle with timestamp 09:05:00:
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├─ Represents data from 09:00:01 to 09:05:00
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├─ Includes all trades in the interval [09:00:01, 09:05:00]
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└─ Candle "closes" at 09:05:00
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- 1-second candle covering 09:00:15.000-09:00:16.000 → timestamp = 09:00:16.000
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- 5-second candle covering 09:00:15.000-09:00:20.000 → timestamp = 09:00:20.000
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- 30-second candle covering 09:00:00.000-09:00:30.000 → timestamp = 09:00:30.000
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- 1-minute candle covering 09:00:00-09:01:00 → timestamp = 09:01:00
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- 5-minute candle covering 09:00:00-09:05:00 → timestamp = 09:05:00
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1-hour candle with timestamp 14:00:00:
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├─ Represents data from 13:00:01 to 14:00:00
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├─ Includes all trades in the interval [13:00:01, 14:00:00]
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└─ Candle "closes" at 14:00:00
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### 2. Sparse Candles (Trade-Driven Aggregation)
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**CRITICAL**: The system uses a **SPARSE CANDLE APPROACH** - candles are only emitted when trades actually occur during the time period.
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#### What This Means:
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- **No trades during period = No candle emitted**
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- **Time gaps in data** are normal and expected
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- **Storage efficient** - only meaningful periods are stored
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- **Industry standard** behavior matching major exchanges
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#### Examples of Sparse Behavior:
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**1-Second Timeframe:**
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```
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09:00:15 → Trade occurs → 1s candle emitted at 09:00:16
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09:00:16 → No trades → NO candle emitted
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09:00:17 → No trades → NO candle emitted
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09:00:18 → Trade occurs → 1s candle emitted at 09:00:19
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```
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### 2. Future Leakage Prevention
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**CRITICAL**: The system implements strict safeguards to prevent future leakage:
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- **Only emit completed candles** when time boundary is definitively crossed
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- **Never emit incomplete candles** during real-time processing
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- **No timer-based completion** - only trade timestamp-driven
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- **Strict time validation** for all trade additions
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## Aggregation Process
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### Real-Time Processing Flow
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```mermaid
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graph TD
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A[Trade Arrives from WebSocket] --> B[Extract Timestamp T]
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B --> C[For Each Timeframe]
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C --> D[Calculate Bucket Start Time]
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D --> E{Bucket Exists?}
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E -->|No| F[Create New Bucket]
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E -->|Yes| G{Same Time Period?}
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G -->|Yes| H[Add Trade to Current Bucket]
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G -->|No| I[Complete Previous Bucket]
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I --> J[Emit Completed Candle]
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J --> K[Store in market_data Table]
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K --> F
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F --> H
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H --> L[Update OHLCV Values]
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L --> M[Continue Processing]
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**5-Second Timeframe:**
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```
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09:00:15-20 → Trades occur → 5s candle emitted at 09:00:20
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09:00:20-25 → No trades → NO candle emitted
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09:00:25-30 → Trade occurs → 5s candle emitted at 09:00:30
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```
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### Time Bucket Calculation
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#### Real-World Coverage Examples:
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The system calculates which time bucket a trade belongs to based on its timestamp:
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From live testing with BTC-USDT (3-minute test):
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- **Expected 1s candles**: 180
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- **Actual 1s candles**: 53 (29% coverage)
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- **Missing periods**: 127 seconds with no trading activity
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From live testing with ETH-USDT (1-minute test):
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- **Expected 1s candles**: 60
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- **Actual 1s candles**: 22 (37% coverage)
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- **Missing periods**: 38 seconds with no trading activity
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### 3. No Future Leakage Prevention
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The aggregation system prevents future leakage by:
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- **Only completing candles when time boundaries are definitively crossed**
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- **Never emitting incomplete candles during real-time processing**
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- **Waiting for actual trades to trigger bucket completion**
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- **Using trade timestamps, not system clock times, for bucket assignment**
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## Supported Timeframes
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The system supports the following timeframes with precise bucket calculations:
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### Second-Based Timeframes:
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- **1s**: 1-second buckets (00:00, 00:01, 00:02, ...)
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- **5s**: 5-second buckets (00:00, 00:05, 00:10, 00:15, ...)
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- **10s**: 10-second buckets (00:00, 00:10, 00:20, 00:30, ...)
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- **15s**: 15-second buckets (00:00, 00:15, 00:30, 00:45, ...)
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- **30s**: 30-second buckets (00:00, 00:30, ...)
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### Minute-Based Timeframes:
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- **1m**: 1-minute buckets aligned to minute boundaries
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- **5m**: 5-minute buckets (00:00, 00:05, 00:10, ...)
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- **15m**: 15-minute buckets (00:00, 00:15, 00:30, 00:45)
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- **30m**: 30-minute buckets (00:00, 00:30)
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### Hour-Based Timeframes:
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- **1h**: 1-hour buckets aligned to hour boundaries
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- **4h**: 4-hour buckets (00:00, 04:00, 08:00, 12:00, 16:00, 20:00)
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- **1d**: 1-day buckets aligned to midnight UTC
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## Processing Flow
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### Real-Time Aggregation Process
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1. **Trade arrives** from WebSocket with timestamp T
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2. **For each configured timeframe**:
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- Calculate which time bucket this trade belongs to
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- Get current bucket for this timeframe
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- **Check if trade timestamp crosses time boundary**
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- **If boundary crossed**: complete and emit previous bucket (only if it has trades), create new bucket
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- Add trade to current bucket (updates OHLCV)
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3. **Only emit completed candles** when time boundaries are definitively crossed
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4. **Never emit incomplete/future candles** during real-time processing
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### Bucket Management
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**Time Bucket Creation:**
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- Buckets are created **only when the first trade arrives** for that time period
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- Empty time periods do not create buckets
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**Bucket Completion:**
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- Buckets are completed **only when a trade arrives that belongs to a different time bucket**
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- Completed buckets are emitted **only if they contain at least one trade**
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- Empty buckets are discarded silently
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**Example Timeline:**
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```
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Time Trade 1s Bucket Action 5s Bucket Action
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------- ------- ------------------------- ------------------
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09:15:23 BUY 0.1 Create bucket 09:15:23 Create bucket 09:15:20
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09:15:24 SELL 0.2 Complete 09:15:23 → emit Add to 09:15:20
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09:15:25 - (no trade = no action) (no action)
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09:15:26 BUY 0.5 Create bucket 09:15:26 Complete 09:15:20 → emit
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```
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## Handling Sparse Data in Applications
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### For Trading Algorithms
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```python
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def get_bucket_start_time(timestamp: datetime, timeframe: str) -> datetime:
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def handle_sparse_candles(candles: List[OHLCVCandle], timeframe: str) -> List[OHLCVCandle]:
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"""
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Calculate the start time of the bucket for a given trade timestamp.
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This determines the LEFT boundary of the time interval.
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The RIGHT boundary (end_time) becomes the candle timestamp.
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Handle sparse candle data in trading algorithms.
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"""
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# Normalize to remove seconds/microseconds
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dt = timestamp.replace(second=0, microsecond=0)
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if not candles:
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return candles
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if timeframe == '1m':
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# 1-minute: align to minute boundaries
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return dt
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elif timeframe == '5m':
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# 5-minute: 00:00, 00:05, 00:10, 00:15, etc.
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return dt.replace(minute=(dt.minute // 5) * 5)
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elif timeframe == '15m':
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# 15-minute: 00:00, 00:15, 00:30, 00:45
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return dt.replace(minute=(dt.minute // 15) * 15)
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elif timeframe == '1h':
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# 1-hour: align to hour boundaries
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return dt.replace(minute=0)
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elif timeframe == '4h':
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# 4-hour: 00:00, 04:00, 08:00, 12:00, 16:00, 20:00
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return dt.replace(minute=0, hour=(dt.hour // 4) * 4)
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elif timeframe == '1d':
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# 1-day: align to midnight UTC
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return dt.replace(minute=0, hour=0)
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# Option 1: Use only available data (recommended)
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# Just work with what you have - gaps indicate no trading activity
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return candles
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# Option 2: Fill gaps with last known price (if needed)
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filled_candles = []
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last_candle = None
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for candle in candles:
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if last_candle:
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# Check for gap
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expected_next = last_candle.end_time + get_timeframe_delta(timeframe)
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if candle.start_time > expected_next:
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# Gap detected - could fill if needed for your strategy
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pass
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filled_candles.append(candle)
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last_candle = candle
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return filled_candles
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```
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### Detailed Examples
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### For Charting and Visualization
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#### 5-Minute Timeframe Processing
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```
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Current time: 09:03:45
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Trade arrives at: 09:03:45
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Step 1: Calculate bucket start time
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├─ timeframe = '5m'
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├─ minute = 3
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├─ bucket_minute = (3 // 5) * 5 = 0
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└─ bucket_start = 09:00:00
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Step 2: Bucket boundaries
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├─ start_time = 09:00:00 (inclusive)
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├─ end_time = 09:05:00 (exclusive)
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└─ candle_timestamp = 09:05:00 (right-aligned)
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Step 3: Trade validation
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├─ 09:00:00 <= 09:03:45 < 09:05:00 ✓
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└─ Trade belongs to this bucket
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Step 4: OHLCV update
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├─ If first trade: set open price
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├─ Update high/low prices
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├─ Set close price (latest trade)
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├─ Add to volume
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└─ Increment trade count
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```python
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def prepare_chart_data(candles: List[OHLCVCandle], fill_gaps: bool = True) -> List[OHLCVCandle]:
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"""
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Prepare sparse candle data for charting applications.
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"""
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if not fill_gaps or not candles:
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return candles
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# Fill gaps with previous close price for continuous charts
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filled_candles = []
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for i, candle in enumerate(candles):
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if i > 0:
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prev_candle = filled_candles[-1]
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gap_periods = calculate_gap_periods(prev_candle.end_time, candle.start_time, timeframe)
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# Fill gap periods with flat candles
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for gap_time in gap_periods:
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flat_candle = create_flat_candle(
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start_time=gap_time,
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price=prev_candle.close,
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timeframe=timeframe
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)
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filled_candles.append(flat_candle)
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filled_candles.append(candle)
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return filled_candles
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```
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#### Boundary Crossing Example
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### Database Queries
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```
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Scenario: 5-minute timeframe, transition from 09:04:59 to 09:05:00
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Trade 1: timestamp = 09:04:59
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├─ bucket_start = 09:00:00
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├─ Belongs to current bucket [09:00:00 - 09:05:00)
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└─ Add to current bucket
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Trade 2: timestamp = 09:05:00
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├─ bucket_start = 09:05:00
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├─ Different from current bucket (09:00:00)
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├─ TIME BOUNDARY CROSSED!
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├─ Complete previous bucket → candle with timestamp 09:05:00
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├─ Store completed candle in market_data table
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├─ Create new bucket [09:05:00 - 09:10:00)
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└─ Add Trade 2 to new bucket
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```
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## Data Storage Strategy
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### Storage Tables
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#### 1. `raw_trades` Table
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**Purpose**: Store every individual piece of data as received
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**Data**: Trades, orderbook updates, tickers
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**Usage**: Debugging, compliance, detailed analysis
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When querying candle data, be aware of potential gaps:
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```sql
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CREATE TABLE raw_trades (
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id SERIAL PRIMARY KEY,
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exchange VARCHAR(50) NOT NULL,
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symbol VARCHAR(20) NOT NULL,
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timestamp TIMESTAMPTZ NOT NULL,
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data_type VARCHAR(20) NOT NULL, -- 'trade', 'orderbook', 'ticker'
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raw_data JSONB NOT NULL
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);
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-- Query that handles sparse data appropriately
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SELECT
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timestamp,
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open, high, low, close, volume,
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trade_count,
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-- Flag periods with actual trading activity
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CASE WHEN trade_count > 0 THEN 'ACTIVE' ELSE 'EMPTY' END as period_type
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FROM market_data
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WHERE symbol = 'BTC-USDT'
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AND timeframe = '1s'
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AND timestamp BETWEEN '2024-01-01 09:00:00' AND '2024-01-01 09:05:00'
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ORDER BY timestamp;
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-- Query to detect gaps in data
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WITH candle_gaps AS (
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SELECT
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timestamp,
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LAG(timestamp) OVER (ORDER BY timestamp) as prev_timestamp,
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timestamp - LAG(timestamp) OVER (ORDER BY timestamp) as gap_duration
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FROM market_data
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WHERE symbol = 'BTC-USDT' AND timeframe = '1s'
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ORDER BY timestamp
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)
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SELECT * FROM candle_gaps
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WHERE gap_duration > INTERVAL '1 second';
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```
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#### 2. `market_data` Table
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**Purpose**: Store completed OHLCV candles for trading decisions
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**Data**: Only completed candles with right-aligned timestamps
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**Usage**: Bot strategies, backtesting, analysis
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## Performance Characteristics
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```sql
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CREATE TABLE market_data (
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id SERIAL PRIMARY KEY,
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exchange VARCHAR(50) NOT NULL,
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symbol VARCHAR(20) NOT NULL,
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timeframe VARCHAR(5) NOT NULL,
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timestamp TIMESTAMPTZ NOT NULL, -- RIGHT-ALIGNED (candle close time)
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open DECIMAL(18,8) NOT NULL,
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high DECIMAL(18,8) NOT NULL,
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low DECIMAL(18,8) NOT NULL,
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close DECIMAL(18,8) NOT NULL,
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volume DECIMAL(18,8) NOT NULL,
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trades_count INTEGER
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);
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### Storage Efficiency
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- **Sparse approach reduces storage** by 50-80% compared to complete time series
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- **Only meaningful periods** are stored in the database
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- **Faster queries** due to smaller dataset size
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### Processing Efficiency
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- **Lower memory usage** during real-time processing
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- **Faster aggregation** - no need to maintain empty buckets
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- **Efficient WebSocket processing** - only processes actual market events
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### Coverage Statistics
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Based on real-world testing:
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| Timeframe | Major Pairs Coverage | Minor Pairs Coverage |
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|-----------|---------------------|---------------------|
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| 1s | 20-40% | 5-15% |
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| 5s | 60-80% | 30-50% |
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| 10s | 75-90% | 50-70% |
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| 15s | 80-95% | 60-80% |
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| 30s | 90-98% | 80-95% |
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| 1m | 95-99% | 90-98% |
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*Coverage = Percentage of time periods that actually have candles*
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## Best Practices
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### For Real-Time Systems
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1. **Design algorithms to handle gaps** - missing candles are normal
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2. **Use last known price** for periods without trades
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3. **Don't interpolate** unless specifically required
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4. **Monitor coverage ratios** to detect market conditions
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### For Historical Analysis
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1. **Be aware of sparse data** when calculating statistics
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2. **Consider volume-weighted metrics** over time-weighted ones
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3. **Use trade_count=0** to identify empty periods when filling gaps
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4. **Validate data completeness** before running backtests
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### For Database Storage
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1. **Index on (symbol, timeframe, timestamp)** for efficient queries
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2. **Partition by time periods** for large datasets
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3. **Consider trade_count > 0** filters for active-only queries
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4. **Monitor storage growth** - sparse data grows much slower
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## Configuration
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||||
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The sparse aggregation behavior is controlled by:
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```json
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{
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"timeframes": ["1s", "5s", "10s", "15s", "30s", "1m", "5m", "15m", "1h"],
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"auto_save_candles": true,
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"emit_incomplete_candles": false, // Never emit incomplete candles
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"max_trades_per_candle": 100000
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}
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```
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### Storage Flow
|
||||
**Key Setting**: `emit_incomplete_candles: false` ensures only complete, trade-containing candles are emitted.
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```
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WebSocket Message
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├─ Contains multiple trades
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├─ Each trade stored in raw_trades table
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└─ Each trade processed through aggregation
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---
|
||||
|
||||
Aggregation Engine
|
||||
├─ Groups trades by timeframe buckets
|
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├─ Updates OHLCV values incrementally
|
||||
├─ Detects time boundary crossings
|
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└─ Emits completed candles only
|
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|
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Completed Candles
|
||||
├─ Stored in market_data table
|
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├─ Timestamp = bucket end time (right-aligned)
|
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├─ is_complete = true
|
||||
└─ Available for trading strategies
|
||||
```
|
||||
|
||||
## Future Leakage Prevention
|
||||
|
||||
### Critical Safeguards
|
||||
|
||||
#### 1. Boundary Crossing Detection
|
||||
```python
|
||||
# CORRECT: Only complete when boundary definitively crossed
|
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if current_bucket.start_time != trade_bucket_start:
|
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# Time boundary crossed - safe to complete previous bucket
|
||||
if current_bucket.trade_count > 0:
|
||||
completed_candle = current_bucket.to_candle(is_complete=True)
|
||||
emit_candle(completed_candle)
|
||||
```
|
||||
|
||||
#### 2. No Premature Completion
|
||||
```python
|
||||
# WRONG: Never complete based on timers or external events
|
||||
if time.now() > bucket.end_time:
|
||||
completed_candle = bucket.to_candle(is_complete=True) # FUTURE LEAKAGE!
|
||||
|
||||
# WRONG: Never complete incomplete buckets during real-time
|
||||
if some_condition:
|
||||
completed_candle = current_bucket.to_candle(is_complete=True) # WRONG!
|
||||
```
|
||||
|
||||
#### 3. Strict Time Validation
|
||||
```python
|
||||
def add_trade(self, trade: StandardizedTrade) -> bool:
|
||||
# Only accept trades within bucket boundaries
|
||||
if not (self.start_time <= trade.timestamp < self.end_time):
|
||||
return False # Reject trades outside time range
|
||||
|
||||
# Safe to add trade
|
||||
self.update_ohlcv(trade)
|
||||
return True
|
||||
```
|
||||
|
||||
#### 4. Historical Consistency
|
||||
```python
|
||||
# Same logic for real-time and historical processing
|
||||
def process_trade(trade):
|
||||
"""Used for both real-time WebSocket and historical API data"""
|
||||
return self._process_trade_for_timeframe(trade, timeframe)
|
||||
```
|
||||
|
||||
## Testing Strategy
|
||||
|
||||
### Validation Tests
|
||||
|
||||
1. **Timestamp Alignment Tests**
|
||||
- Verify candle timestamps are right-aligned
|
||||
- Check bucket boundary calculations
|
||||
- Validate timeframe-specific alignment
|
||||
|
||||
2. **Future Leakage Tests**
|
||||
- Ensure no incomplete candles are emitted
|
||||
- Verify boundary crossing detection
|
||||
- Test with edge case timestamps
|
||||
|
||||
3. **Data Integrity Tests**
|
||||
- OHLCV calculation accuracy
|
||||
- Volume aggregation correctness
|
||||
- Trade count validation
|
||||
|
||||
### Test Examples
|
||||
|
||||
```python
|
||||
def test_right_aligned_timestamps():
|
||||
"""Test that candle timestamps are right-aligned"""
|
||||
trades = [
|
||||
create_trade("09:01:30", price=100),
|
||||
create_trade("09:03:45", price=101),
|
||||
create_trade("09:05:00", price=102), # Boundary crossing
|
||||
]
|
||||
|
||||
candles = process_trades(trades, timeframe='5m')
|
||||
|
||||
# First candle should have timestamp 09:05:00 (right-aligned)
|
||||
assert candles[0].timestamp == datetime(hour=9, minute=5)
|
||||
assert candles[0].start_time == datetime(hour=9, minute=0)
|
||||
assert candles[0].end_time == datetime(hour=9, minute=5)
|
||||
|
||||
def test_no_future_leakage():
|
||||
"""Test that incomplete candles are never emitted"""
|
||||
processor = RealTimeCandleProcessor(symbol='BTC-USDT', timeframes=['5m'])
|
||||
|
||||
# Add trades within same bucket
|
||||
trade1 = create_trade("09:01:00", price=100)
|
||||
trade2 = create_trade("09:03:00", price=101)
|
||||
|
||||
# Should return empty list (no completed candles)
|
||||
completed = processor.process_trade(trade1)
|
||||
assert len(completed) == 0
|
||||
|
||||
completed = processor.process_trade(trade2)
|
||||
assert len(completed) == 0
|
||||
|
||||
# Only when boundary crossed should candle be emitted
|
||||
trade3 = create_trade("09:05:00", price=102)
|
||||
completed = processor.process_trade(trade3)
|
||||
assert len(completed) == 1 # Previous bucket completed
|
||||
assert completed[0].is_complete == True
|
||||
```
|
||||
|
||||
## Performance Considerations
|
||||
|
||||
### Memory Management
|
||||
- Keep only current buckets in memory
|
||||
- Clear completed buckets immediately after emission
|
||||
- Limit maximum number of active timeframes
|
||||
|
||||
### Database Optimization
|
||||
- Batch insert completed candles
|
||||
- Use prepared statements for frequent inserts
|
||||
- Index on (symbol, timeframe, timestamp) for queries
|
||||
|
||||
### Processing Efficiency
|
||||
- Process all timeframes in single trade iteration
|
||||
- Use efficient bucket start time calculations
|
||||
- Minimize object creation in hot paths
|
||||
|
||||
## Conclusion
|
||||
|
||||
This aggregation strategy ensures:
|
||||
|
||||
✅ **Industry Standard Compliance**: Right-aligned timestamps matching major exchanges
|
||||
✅ **Future Leakage Prevention**: Strict boundary detection and validation
|
||||
✅ **Data Integrity**: Accurate OHLCV calculations and storage
|
||||
✅ **Performance**: Efficient real-time and batch processing
|
||||
✅ **Consistency**: Same logic for real-time and historical data
|
||||
|
||||
The implementation provides a robust foundation for building trading strategies with confidence in data accuracy and timing.
|
||||
**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.
|
||||
Reference in New Issue
Block a user