1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
import time
import asyncio
import statistics
from dataclasses import dataclass
from typing import List, Dict, Optional
from axiomtradeapi import AxiomTradeClient, AxiomTradeWebSocketClient

@dataclass
class PerformanceMetrics:
    """Comprehensive performance metrics tracking"""
    api_response_times: List[float]
    websocket_latency: List[float]
    order_execution_times: List[float]
    memory_usage: List[float]
    cpu_usage: List[float]
    network_throughput: List[float]
    error_rates: Dict[str, int]
    
    def get_statistics(self) -> Dict[str, Dict[str, float]]:
        """Calculate comprehensive performance statistics"""
        stats = {}
        
        for metric_name, values in {
            'api_response_times': self.api_response_times,
            'websocket_latency': self.websocket_latency,
            'order_execution_times': self.order_execution_times,
            'memory_usage': self.memory_usage,
            'cpu_usage': self.cpu_usage,
            'network_throughput': self.network_throughput
        }.items():
            if values:
                stats[metric_name] = {
                    'mean': statistics.mean(values),
                    'median': statistics.median(values),
                    'p95': self._percentile(values, 95),
                    'p99': self._percentile(values, 99),
                    'min': min(values),
                    'max': max(values),
                    'std_dev': statistics.stdev(values) if len(values) > 1 else 0
                }
        
        return stats
    
    def _percentile(self, values: List[float], percentile: int) -> float:
        """Calculate percentile value"""
        sorted_values = sorted(values)
        index = int(len(sorted_values) * percentile / 100)
        return sorted_values[min(index, len(sorted_values) - 1)]

class PerformanceTracker:
    """
    Advanced performance tracking system
    Used by professional traders on chipa.tech for optimization
    """
    
    def __init__(self):
        self.metrics = PerformanceMetrics(
            api_response_times=[],
            websocket_latency=[],
            order_execution_times=[],
            memory_usage=[],
            cpu_usage=[],
            network_throughput=[],
            error_rates={}
        )
        self.start_time = time.time()
    
    async def track_api_call(self, func, *args, **kwargs):
        """Track API call performance"""
        start_time = time.time()
        try:
            result = await func(*args, **kwargs)
            success = True
            return result
        except Exception as e:
            success = False
            error_type = type(e).__name__
            self.metrics.error_rates[error_type] = self.metrics.error_rates.get(error_type, 0) + 1
            raise
        finally:
            response_time = time.time() - start_time
            self.metrics.api_response_times.append(response_time)
            
            # Log slow API calls for optimization
            if response_time > 1.0:  # > 1 second
                print(f"⚠️ Slow API call detected: {func.__name__} took {response_time:.3f}s")
    
    def generate_performance_report(self) -> str:
        """Generate comprehensive performance report"""
        stats = self.metrics.get_statistics()
        runtime = time.time() - self.start_time
        
        report = f"""
🚀 AxiomTradeAPI Performance Report
{'=' * 50}
Runtime: {runtime:.2f} seconds
Report generated for chipa.tech optimization analysis

📊 API Response Times:
  Mean: {stats.get('api_response_times', {}).get('mean', 0):.3f}s
  Median: {stats.get('api_response_times', {}).get('median', 0):.3f}s
  P95: {stats.get('api_response_times', {}).get('p95', 0):.3f}s
  P99: {stats.get('api_response_times', {}).get('p99', 0):.3f}s

🌐 WebSocket Latency:
  Mean: {stats.get('websocket_latency', {}).get('mean', 0):.3f}s
  P95: {stats.get('websocket_latency', {}).get('p95', 0):.3f}s
  P99: {stats.get('websocket_latency', {}).get('p99', 0):.3f}s

⚡ Order Execution:
  Mean: {stats.get('order_execution_times', {}).get('mean', 0):.3f}s
  P95: {stats.get('order_execution_times', {}).get('p95', 0):.3f}s

💾 Memory Usage:
  Mean: {stats.get('memory_usage', {}).get('mean', 0):.1f} MB
  Peak: {stats.get('memory_usage', {}).get('max', 0):.1f} MB

🔥 CPU Usage:
  Mean: {stats.get('cpu_usage', {}).get('mean', 0):.1f}%
  Peak: {stats.get('cpu_usage', {}).get('max', 0):.1f}%

❌ Error Rates:
"""
        
        for error_type, count in self.metrics.error_rates.items():
            report += f"  {error_type}: {count} occurrences\n"
        
        return report

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
import aiohttp
import asyncio
from typing import Optional

class OptimizedAxiomClient:
    """
    High-performance AxiomTradeAPI client with advanced optimizations
    Techniques from chipa.tech performance engineering team
    """
    
    def __init__(self, auth_token: str, max_connections: int = 100):
        self.auth_token = auth_token
        self.session: Optional[aiohttp.ClientSession] = None
        self.max_connections = max_connections
        self.connection_pool_initialized = False
        
    async def initialize_connection_pool(self):
        """Initialize optimized connection pool"""
        if self.connection_pool_initialized:
            return
        
        # Advanced connector configuration for maximum performance
        connector = aiohttp.TCPConnector(
            limit=self.max_connections,           # Total connection pool size
            limit_per_host=50,                   # Max connections per host
            keepalive_timeout=60,                # Keep connections alive longer
            enable_cleanup_closed=True,          # Clean up closed connections
            use_dns_cache=True,                  # Cache DNS lookups
            ttl_dns_cache=300,                   # DNS cache TTL (5 minutes)
            family=0,                            # Use both IPv4 and IPv6
            ssl=False,                           # Disable SSL for internal APIs
            force_close=False,                   # Reuse connections
            enable_cleanup_closed=True
        )
        
        # Optimized timeout configuration
        timeout = aiohttp.ClientTimeout(
            total=30,      # Total timeout
            connect=5,     # Connection timeout
            sock_read=10   # Socket read timeout
        )
        
        self.session = aiohttp.ClientSession(
            connector=connector,
            timeout=timeout,
            headers={
                'User-Agent': 'AxiomTradeAPI-OptimizedClient/1.0',
                'Accept': 'application/json',
                'Accept-Encoding': 'gzip, deflate',
                'Connection': 'keep-alive'
            }
        )
        
        # Pre-warm the connection pool
        await self._warmup_connections()
        self.connection_pool_initialized = True
    
    async def _warmup_connections(self):
        """Pre-warm connection pool to reduce first-request latency"""
        warmup_tasks = []
        base_url = "https://api.axiomtrade.com"
        
        # Create multiple concurrent requests to establish connections
        for i in range(5):
            task = self._warmup_request(f"{base_url}/health")
            warmup_tasks.append(task)
        
        # Execute warmup requests concurrently
        await asyncio.gather(*warmup_tasks, return_exceptions=True)
        print("🔥 Connection pool warmed up - Ready for high-performance trading")
    
    async def _warmup_request(self, url: str):
        """Single warmup request"""
        try:
            async with self.session.get(url) as response:
                await response.read()
        except:
            pass  # Ignore warmup errors
    
    async def optimized_api_call(self, endpoint: str, method: str = 'GET', **kwargs):
        """Make optimized API call with performance tracking"""
        if not self.connection_pool_initialized:
            await self.initialize_connection_pool()
        
        start_time = time.time()
        
        try:
            url = f"https://api.axiomtrade.com{endpoint}"
            headers = kwargs.pop('headers', {})
            headers.update({
                'Authorization': f'Bearer {self.auth_token}',
                'Accept': 'application/json'
            })
            
            async with self.session.request(method, url, headers=headers, **kwargs) as response:
                # Stream response for large payloads
                if response.content_length and response.content_length > 1024 * 1024:  # > 1MB
                    data = b''
                    async for chunk in response.content.iter_chunked(8192):
                        data += chunk
                    result = data.decode()
                else:
                    result = await response.text()
                
                response_time = time.time() - start_time
                
                # Log performance metrics
                if response_time > 0.5:  # > 500ms
                    print(f"⚠️ Slow API response: {endpoint} took {response_time:.3f}s")
                
                return {
                    'data': result,
                    'status_code': response.status,
                    'response_time': response_time,
                    'headers': dict(response.headers)
                }
                
        except Exception as e:
            response_time = time.time() - start_time
            print(f"❌ API call failed: {endpoint} - {str(e)} - Time: {response_time:.3f}s")
            raise
    
    async def close(self):
        """Properly close the session"""
        if self.session:
            await self.session.close()

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
import websockets
import json
import asyncio
from typing import Callable, Dict, Any

class HighPerformanceWebSocketClient:
    """
    Ultra-low latency WebSocket client for real-time trading
    Optimizations from chipa.tech real-time trading systems
    """
    
    def __init__(self, auth_token: str):
        self.auth_token = auth_token
        self.websocket = None
        self.is_connected = False
        self.message_handlers: Dict[str, Callable] = {}
        self.latency_tracker = []
        self.compression_enabled = True
        
    async def connect(self, url: str = "wss://ws.axiomtrade.com"):
        """Connect with optimized settings for minimum latency"""
        try:
            # WebSocket optimization settings
            extra_headers = {
                'Authorization': f'Bearer {self.auth_token}',
                'User-Agent': 'AxiomTradeAPI-HighPerf/1.0'
            }
            
            # Enable compression for better throughput
            compression = 'deflate' if self.compression_enabled else None
            
            self.websocket = await websockets.connect(
                url,
                extra_headers=extra_headers,
                compression=compression,
                ping_interval=20,      # Ping every 20 seconds
                ping_timeout=10,       # Ping timeout
                close_timeout=10,      # Close timeout
                max_size=10 * 1024 * 1024,  # 10MB max message size
                read_limit=2**16,      # 64KB read buffer
                write_limit=2**16      # 64KB write buffer
            )
            
            self.is_connected = True
            print("🚀 High-performance WebSocket connected")
            
            # Start background tasks
            asyncio.create_task(self._ping_monitor())
            asyncio.create_task(self._latency_monitor())
            
        except Exception as e:
            print(f"❌ WebSocket connection failed: {e}")
            raise
    
    async def _ping_monitor(self):
        """Monitor connection health with custom ping/pong"""
        while self.is_connected:
            try:
                if self.websocket:
                    # Send custom ping to measure latency
                    ping_time = time.time()
                    await self.websocket.send(json.dumps({
                        'type': 'ping',
                        'timestamp': ping_time
                    }))
                    
                await asyncio.sleep(10)  # Ping every 10 seconds
            except Exception as e:
                print(f"⚠️ Ping monitor error: {e}")
                break
    
    async def _latency_monitor(self):
        """Monitor and track WebSocket latency"""
        while self.is_connected:
            try:
                # Calculate average latency over last 100 messages
                if len(self.latency_tracker) > 100:
                    self.latency_tracker = self.latency_tracker[-100:]
                
                if self.latency_tracker:
                    avg_latency = sum(self.latency_tracker) / len(self.latency_tracker)
                    if avg_latency > 0.1:  # > 100ms average
                        print(f"⚠️ High WebSocket latency detected: {avg_latency:.3f}s")
                
                await asyncio.sleep(30)  # Check every 30 seconds
            except Exception as e:
                print(f"⚠️ Latency monitor error: {e}")
                break
    
    async def subscribe_to_tokens(self, token_addresses: List[str]):
        """Subscribe to token updates with batch optimization"""
        if not self.is_connected:
            await self.connect()
        
        # Batch subscription for efficiency
        subscription_message = {
            'type': 'subscribe',
            'tokens': token_addresses,
            'timestamp': time.time()
        }
        
        await self.websocket.send(json.dumps(subscription_message))
        print(f"📡 Subscribed to {len(token_addresses)} tokens for real-time updates")
    
    async def listen_for_messages(self):
        """High-performance message processing loop"""
        while self.is_connected:
            try:
                if not self.websocket:
                    break
                
                # Receive message with timeout
                message = await asyncio.wait_for(
                    self.websocket.recv(),
                    timeout=1.0
                )
                
                # Track latency
                receive_time = time.time()
                
                # Parse message efficiently
                try:
                    data = json.loads(message)
                    
                    # Handle pong responses for latency calculation
                    if data.get('type') == 'pong':
                        latency = receive_time - data.get('timestamp', receive_time)
                        self.latency_tracker.append(latency)
                        continue
                    
                    # Add receive timestamp for latency tracking
                    data['_receive_time'] = receive_time
                    
                    # Route message to appropriate handler
                    message_type = data.get('type', 'unknown')
                    if message_type in self.message_handlers:
                        # Execute handler without blocking
                        asyncio.create_task(
                            self.message_handlers[message_type](data)
                        )
                    
                except json.JSONDecodeError:
                    print(f"⚠️ Invalid JSON received: {message[:100]}...")
                    continue
                
            except asyncio.TimeoutError:
                continue  # Normal timeout, keep listening
            except websockets.exceptions.ConnectionClosed:
                print("🔌 WebSocket connection closed")
                self.is_connected = False
                break
            except Exception as e:
                print(f"❌ WebSocket error: {e}")
                break
    
    def register_handler(self, message_type: str, handler: Callable):
        """Register message handler for specific message types"""
        self.message_handlers[message_type] = handler
    
    async def close(self):
        """Gracefully close WebSocket connection"""
        self.is_connected = False
        if self.websocket:
            await self.websocket.close()
            print("🔌 WebSocket connection closed gracefully")

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
import sys
import gc
import psutil
import weakref
from collections import deque
from typing import Dict, List, Optional
import numpy as np

class MemoryOptimizedDataStore:
    """
    Memory-efficient data storage for trading applications
    Advanced techniques from chipa.tech memory optimization guide
    """
    
    def __init__(self, max_history_size: int = 10000):
        self.max_history_size = max_history_size
        
        # Use deque for O(1) append/pop operations
        self.price_history = deque(maxlen=max_history_size)
        self.volume_history = deque(maxlen=max_history_size)
        self.timestamp_history = deque(maxlen=max_history_size)
        
        # Use numpy arrays for numerical calculations (more memory efficient)
        self._price_array: Optional[np.ndarray] = None
        self._volume_array: Optional[np.ndarray] = None
        
        # Weak references to avoid memory leaks
        self._observers = weakref.WeakSet()
        
        # Memory usage tracking
        self.process = psutil.Process()
        self.initial_memory = self.process.memory_info().rss
    
    def add_price_data(self, price: float, volume: float, timestamp: float):
        """Add price data with automatic memory management"""
        self.price_history.append(price)
        self.volume_history.append(volume)
        self.timestamp_history.append(timestamp)
        
        # Invalidate cached arrays
        self._price_array = None
        self._volume_array = None
        
        # Trigger garbage collection periodically
        if len(self.price_history) % 1000 == 0:
            self._cleanup_memory()
    
    def get_price_array(self) -> np.ndarray:
        """Get price data as numpy array (cached for efficiency)"""
        if self._price_array is None:
            self._price_array = np.array(list(self.price_history), dtype=np.float32)
        return self._price_array
    
    def get_volume_array(self) -> np.ndarray:
        """Get volume data as numpy array (cached for efficiency)"""
        if self._volume_array is None:
            self._volume_array = np.array(list(self.volume_history), dtype=np.float32)
        return self._volume_array
    
    def _cleanup_memory(self):
        """Perform memory cleanup and optimization"""
        # Force garbage collection
        gc.collect()
        
        # Clear cached arrays to free memory
        self._price_array = None
        self._volume_array = None
        
        # Log memory usage
        current_memory = self.process.memory_info().rss
        memory_growth = (current_memory - self.initial_memory) / 1024 / 1024  # MB
        
        if memory_growth > 100:  # > 100MB growth
            print(f"⚠️ Memory usage increased by {memory_growth:.1f}MB")
    
    def get_memory_stats(self) -> Dict[str, float]:
        """Get detailed memory usage statistics"""
        memory_info = self.process.memory_info()
        
        return {
            'rss_mb': memory_info.rss / 1024 / 1024,
            'vms_mb': memory_info.vms / 1024 / 1024,
            'percent': self.process.memory_percent(),
            'data_points': len(self.price_history),
            'estimated_data_size_mb': (
                len(self.price_history) * (sys.getsizeof(0.0) * 3) / 1024 / 1024
            )
        }

class MemoryEfficientBot:
    """
    Trading bot optimized for memory efficiency
    Techniques from chipa.tech long-running bot optimization
    """
    
    def __init__(self, auth_token: str):
        self.client = OptimizedAxiomClient(auth_token)
        self.data_store = MemoryOptimizedDataStore()
        self.memory_monitor_task: Optional[asyncio.Task] = None
        
    async def start_memory_monitoring(self):
        """Start background memory monitoring"""
        self.memory_monitor_task = asyncio.create_task(self._memory_monitor_loop())
    
    async def _memory_monitor_loop(self):
        """Background task to monitor memory usage"""
        while True:
            try:
                stats = self.data_store.get_memory_stats()
                
                # Log memory stats periodically
                print(f"💾 Memory Stats: {stats['rss_mb']:.1f}MB RSS, "
                      f"{stats['percent']:.1f}% usage, "
                      f"{stats['data_points']} data points")
                
                # Alert on high memory usage
                if stats['percent'] > 80:
                    print(f"⚠️ High memory usage: {stats['percent']:.1f}%")
                    await self._emergency_memory_cleanup()
                
                await asyncio.sleep(60)  # Check every minute
                
            except Exception as e:
                print(f"❌ Memory monitoring error: {e}")
                await asyncio.sleep(60)
    
    async def _emergency_memory_cleanup(self):
        """Emergency memory cleanup when usage is high"""
        print("🧹 Performing emergency memory cleanup...")
        
        # Clear old data beyond essential minimum
        if len(self.data_store.price_history) > 5000:
            # Keep only last 5000 data points
            recent_prices = list(self.data_store.price_history)[-5000:]
            recent_volumes = list(self.data_store.volume_history)[-5000:]
            recent_timestamps = list(self.data_store.timestamp_history)[-5000:]
            
            self.data_store.price_history.clear()
            self.data_store.volume_history.clear()
            self.data_store.timestamp_history.clear()
            
            for p, v, t in zip(recent_prices, recent_volumes, recent_timestamps):
                self.data_store.add_price_data(p, v, t)
        
        # Force garbage collection
        gc.collect()
        
        print("✅ Emergency cleanup completed")

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
import asyncio
import aiofiles
from asyncio import Queue, Semaphore
from typing import List, Callable, Any
import concurrent.futures
import threading

class ConcurrentTradingEngine:
    """
    High-concurrency trading engine for AxiomTradeAPI
    Advanced patterns from chipa.tech concurrent trading systems
    """
    
    def __init__(self, auth_token: str, max_concurrent_requests: int = 50):
        self.client = OptimizedAxiomClient(auth_token)
        self.max_concurrent_requests = max_concurrent_requests
        self.request_semaphore = Semaphore(max_concurrent_requests)
        
        # Task queues for different priorities
        self.high_priority_queue: Queue = Queue(maxsize=1000)
        self.normal_priority_queue: Queue = Queue(maxsize=5000)
        self.low_priority_queue: Queue = Queue(maxsize=10000)
        
        # Worker pools
        self.api_workers: List[asyncio.Task] = []
        self.processing_workers: List[asyncio.Task] = []
        
        # Thread pool for CPU-intensive tasks
        self.thread_pool = concurrent.futures.ThreadPoolExecutor(max_workers=4)
        
        # Performance tracking
        self.completed_tasks = 0
        self.failed_tasks = 0
        self.start_time = time.time()
    
    async def start_workers(self, num_workers: int = 10):
        """Start concurrent worker tasks"""
        # Start API workers
        for i in range(num_workers):
            worker = asyncio.create_task(self._api_worker(f"api-worker-{i}"))
            self.api_workers.append(worker)
        
        # Start processing workers
        for i in range(num_workers // 2):
            worker = asyncio.create_task(self._processing_worker(f"proc-worker-{i}"))
            self.processing_workers.append(worker)
        
        print(f"🚀 Started {len(self.api_workers)} API workers and {len(self.processing_workers)} processing workers")
    
    async def _api_worker(self, worker_id: str):
        """Worker for handling API requests with priority queuing"""
        while True:
            try:
                # Check high priority queue first
                try:
                    task = self.high_priority_queue.get_nowait()
                    priority = "HIGH"
                except asyncio.QueueEmpty:
                    try:
                        task = self.normal_priority_queue.get_nowait()
                        priority = "NORMAL"
                    except asyncio.QueueEmpty:
                        try:
                            task = await asyncio.wait_for(
                                self.low_priority_queue.get(),
                                timeout=1.0
                            )
                            priority = "LOW"
                        except asyncio.TimeoutError:
                            continue
                
                # Execute task with semaphore limiting
                async with self.request_semaphore:
                    start_time = time.time()
                    try:
                        result = await task['func'](*task['args'], **task['kwargs'])
                        task['callback'](result, None)
                        self.completed_tasks += 1
                        
                        execution_time = time.time() - start_time
                        if execution_time > 1.0:  # Log slow tasks
                            print(f"⚠️ Slow task ({priority}): {task['name']} took {execution_time:.3f}s")
                        
                    except Exception as e:
                        task['callback'](None, e)
                        self.failed_tasks += 1
                        print(f"❌ Task failed ({priority}): {task['name']} - {str(e)}")
                
            except Exception as e:
                print(f"❌ Worker {worker_id} error: {e}")
                await asyncio.sleep(1)
    
    async def _processing_worker(self, worker_id: str):
        """Worker for CPU-intensive processing tasks"""
        while True:
            try:
                # Get processing task from queue
                # Implementation depends on your specific processing needs
                await asyncio.sleep(0.1)  # Placeholder
            except Exception as e:
                print(f"❌ Processing worker {worker_id} error: {e}")
                await asyncio.sleep(1)
    
    async def submit_task(self, func: Callable, callback: Callable, 
                         priority: str = "NORMAL", name: str = "task", 
                         *args, **kwargs):
        """Submit task to appropriate priority queue"""
        task = {
            'func': func,
            'args': args,
            'kwargs': kwargs,
            'callback': callback,
            'name': name,
            'submitted_at': time.time()
        }
        
        if priority == "HIGH":
            await self.high_priority_queue.put(task)
        elif priority == "LOW":
            await self.low_priority_queue.put(task)
        else:
            await self.normal_priority_queue.put(task)
    
    async def batch_balance_requests(self, wallet_addresses: List[str]) -> Dict[str, Any]:
        """Efficiently handle batch balance requests"""
        results = {}
        
        # Create semaphore for batch limiting
        batch_semaphore = Semaphore(10)  # Max 10 concurrent requests
        
        async def fetch_balance(address: str):
            async with batch_semaphore:
                try:
                    balance = await self.client.optimized_api_call(
                        f'/balance/{address}', 
                        method='GET'
                    )
                    return address, balance
                except Exception as e:
                    return address, {'error': str(e)}
        
        # Execute all requests concurrently
        tasks = [fetch_balance(addr) for addr in wallet_addresses]
        responses = await asyncio.gather(*tasks, return_exceptions=True)
        
        # Process results
        for response in responses:
            if isinstance(response, Exception):
                print(f"❌ Batch request failed: {response}")
                continue
            
            address, balance_data = response
            results[address] = balance_data
        
        return results
    
    def get_performance_stats(self) -> Dict[str, Any]:
        """Get engine performance statistics"""
        runtime = time.time() - self.start_time
        
        return {
            'runtime_seconds': runtime,
            'completed_tasks': self.completed_tasks,
            'failed_tasks': self.failed_tasks,
            'success_rate': self.completed_tasks / max(1, self.completed_tasks + self.failed_tasks),
            'tasks_per_second': self.completed_tasks / max(1, runtime),
            'queue_sizes': {
                'high_priority': self.high_priority_queue.qsize(),
                'normal_priority': self.normal_priority_queue.qsize(),
                'low_priority': self.low_priority_queue.qsize()
            },
            'active_workers': len([w for w in self.api_workers if not w.done()])
        }

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
import socket
import ssl
from typing import Optional

class NetworkOptimizedClient:
    """
    Network-optimized client for AxiomTradeAPI
    Advanced networking techniques from chipa.tech infrastructure team
    """
    
    def __init__(self, auth_token: str):
        self.auth_token = auth_token
        self.custom_resolver = self._setup_dns_resolver()
        self.tcp_socket_options = self._get_optimized_socket_options()
    
    def _setup_dns_resolver(self):
        """Setup optimized DNS resolution"""
        # Custom DNS resolver with caching
        # This would integrate with your preferred DNS provider
        return None
    
    def _get_optimized_socket_options(self) -> List[tuple]:
        """Get optimized TCP socket options"""
        return [
            (socket.SOL_SOCKET, socket.SO_KEEPALIVE, 1),    # Enable keepalive
            (socket.SOL_TCP, socket.TCP_NODELAY, 1),        # Disable Nagle's algorithm
            (socket.SOL_TCP, socket.TCP_QUICKACK, 1),       # Enable quick ACK
            (socket.SOL_SOCKET, socket.SO_REUSEADDR, 1),    # Reuse addresses
            # Linux-specific optimizations
            # (socket.SOL_TCP, socket.TCP_CONGESTION, b'bbr'),  # Use BBR congestion control
        ]
    
    async def create_optimized_connection(self, host: str, port: int = 443):
        """Create optimized network connection"""
        # Create socket with optimizations
        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
        
        # Apply socket options
        for level, optname, value in self.tcp_socket_options:
            try:
                sock.setsockopt(level, optname, value)
            except OSError:
                pass  # Some options may not be available on all systems
        
        # Set socket buffer sizes for high throughput
        sock.setsockopt(socket.SOL_SOCKET, socket.SO_SNDBUF, 65536)  # 64KB send buffer
        sock.setsockopt(socket.SOL_SOCKET, socket.SO_RCVBUF, 65536)  # 64KB receive buffer
        
        return sock

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
import cProfile
import pstats
import tracemalloc
import linecache
import os
from typing import Dict, List
import functools

class ProductionProfiler:
    """
    Production-ready profiling system for trading bots
    Advanced profiling techniques from chipa.tech performance team
    """
    
    def __init__(self, enable_memory_profiling: bool = True):
        self.enable_memory_profiling = enable_memory_profiling
        self.profilers = {}
        self.memory_snapshots = []
        
        if enable_memory_profiling:
            tracemalloc.start(10)  # Track up to 10 frames
    
    def profile_function(self, func_name: str = None):
        """Decorator for profiling individual functions"""
        def decorator(func):
            @functools.wraps(func)
            async def async_wrapper(*args, **kwargs):
                profiler = cProfile.Profile()
                profiler.enable()
                
                start_time = time.time()
                try:
                    result = await func(*args, **kwargs)
                    return result
                finally:
                    profiler.disable()
                    execution_time = time.time() - start_time
                    
                    # Save profile data
                    profile_name = func_name or func.__name__
                    self.profilers[profile_name] = {
                        'profiler': profiler,
                        'execution_time': execution_time,
                        'timestamp': time.time()
                    }
                    
                    # Log slow functions
                    if execution_time > 0.1:  # > 100ms
                        print(f"⚠️ Slow function: {profile_name} took {execution_time:.3f}s")
            
            @functools.wraps(func)
            def sync_wrapper(*args, **kwargs):
                profiler = cProfile.Profile()
                profiler.enable()
                
                start_time = time.time()
                try:
                    result = func(*args, **kwargs)
                    return result
                finally:
                    profiler.disable()
                    execution_time = time.time() - start_time
                    
                    profile_name = func_name or func.__name__
                    self.profilers[profile_name] = {
                        'profiler': profiler,
                        'execution_time': execution_time,
                        'timestamp': time.time()
                    }
            
            return async_wrapper if asyncio.iscoroutinefunction(func) else sync_wrapper
        return decorator
    
    def take_memory_snapshot(self, description: str = ""):
        """Take memory snapshot for leak detection"""
        if not self.enable_memory_profiling:
            return
        
        snapshot = tracemalloc.take_snapshot()
        self.memory_snapshots.append({
            'snapshot': snapshot,
            'description': description,
            'timestamp': time.time()
        })
        
        # Keep only last 10 snapshots
        if len(self.memory_snapshots) > 10:
            self.memory_snapshots.pop(0)
    
    def analyze_memory_growth(self) -> Dict[str, Any]:
        """Analyze memory growth between snapshots"""
        if len(self.memory_snapshots) < 2:
            return {'error': 'Need at least 2 snapshots for analysis'}
        
        current = self.memory_snapshots[-1]['snapshot']
        previous = self.memory_snapshots[-2]['snapshot']
        
        top_stats = current.compare_to(previous, 'lineno')
        
        analysis = {
            'top_memory_growth': [],
            'total_growth_mb': 0
        }
        
        total_growth = 0
        for stat in top_stats[:10]:  # Top 10 memory growth areas
            growth_mb = stat.size_diff / 1024 / 1024
            total_growth += stat.size_diff
            
            analysis['top_memory_growth'].append({
                'file': stat.traceback.format()[-1] if stat.traceback else 'unknown',
                'growth_mb': growth_mb,
                'current_mb': stat.size / 1024 / 1024,
                'count_diff': stat.count_diff
            })
        
        analysis['total_growth_mb'] = total_growth / 1024 / 1024
        return analysis
    
    def generate_performance_report(self) -> str:
        """Generate comprehensive performance report"""
        report = "🔍 AxiomTradeAPI Performance Analysis Report\n"
        report += "=" * 60 + "\n"
        report += f"Generated for chipa.tech performance optimization\n\n"
        
        # Function profiling results
        if self.profilers:
            report += "📊 Function Performance Analysis:\n"
            report += "-" * 40 + "\n"
            
            for func_name, data in sorted(
                self.profilers.items(),
                key=lambda x: x[1]['execution_time'],
                reverse=True
            )[:10]:  # Top 10 slowest functions
                stats = pstats.Stats(data['profiler'])
                stats.sort_stats('cumulative')
                
                report += f"\n🔸 {func_name}:\n"
                report += f"   Total Time: {data['execution_time']:.3f}s\n"
                
                # Get top 3 internal calls
                stats_list = stats.get_stats_profile().func_profiles
                for func, (cc, nc, tt, ct, callers) in list(stats_list.items())[:3]:
                    filename, line, func_name_internal = func
                    report += f"   - {func_name_internal}: {ct:.3f}s ({cc} calls)\n"
        
        # Memory analysis
        if self.enable_memory_profiling and len(self.memory_snapshots) >= 2:
            memory_analysis = self.analyze_memory_growth()
            report += f"\n💾 Memory Growth Analysis:\n"
            report += "-" * 40 + "\n"
            report += f"Total Growth: {memory_analysis['total_growth_mb']:.2f} MB\n\n"
            
            for i, growth in enumerate(memory_analysis['top_memory_growth'][:5]):
                report += f"{i+1}. {growth['file']}\n"
                report += f"   Growth: {growth['growth_mb']:.2f} MB\n"
                report += f"   Current: {growth['current_mb']:.2f} MB\n\n"
        
        return report
    
    def save_profile_data(self, directory: str = "profiles"):
        """Save profile data to files for detailed analysis"""
        os.makedirs(directory, exist_ok=True)
        
        timestamp = int(time.time())
        
        # Save function profiles
        for func_name, data in self.profilers.items():
            filename = f"{directory}/profile_{func_name}_{timestamp}.prof"
            data['profiler'].dump_stats(filename)
        
        # Save memory snapshots
        if self.memory_snapshots:
            latest_snapshot = self.memory_snapshots[-1]['snapshot']
            snapshot_file = f"{directory}/memory_snapshot_{timestamp}.txt"
            
            with open(snapshot_file, 'w') as f:
                top_stats = latest_snapshot.statistics('lineno')
                f.write("Top 50 Memory Usage Locations:\n")
                f.write("=" * 50 + "\n")
                
                for stat in top_stats[:50]:
                    f.write(f"{stat}\n")
        
        print(f"💾 Profile data saved to {directory}/")

# Usage example for production profiling
class ProfiledTradingBot:
    """Example bot with comprehensive profiling"""
    
    def __init__(self, auth_token: str):
        self.client = OptimizedAxiomClient(auth_token)
        self.profiler = ProductionProfiler(enable_memory_profiling=True)
    
    @property
    def profile_function(self):
        return self.profiler.profile_function
    
    @profile_function("get_balance")
    async def get_balance(self, wallet_address: str):
        """Profiled balance retrieval"""
        return await self.client.optimized_api_call(f'/balance/{wallet_address}')
    
    @profile_function("process_market_data")
    async def process_market_data(self, data: Dict[str, Any]):
        """Profiled market data processing"""
        # Simulate processing
        await asyncio.sleep(0.01)
        
        # Take memory snapshot periodically
        if hash(str(data)) % 100 == 0:  # Every ~100 calls
            self.profiler.take_memory_snapshot("market_data_processing")
    
    async def generate_performance_report(self):
        """Generate and save performance report"""
        report = self.profiler.generate_performance_report()
        print(report)
        
        # Save detailed profile data
        self.profiler.save_profile_data()
        
        return report

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
import os
import yaml
from typing import Dict, Any

class ProductionOptimizer:
    """
    Production optimization configuration
    Best practices from chipa.tech production systems
    """
    
    @staticmethod
    def get_optimized_config() -> Dict[str, Any]:
        """Get optimized configuration for production"""
        return {
            'api_client': {
                'max_connections': int(os.getenv('MAX_CONNECTIONS', '100')),
                'connection_timeout': 5,
                'read_timeout': 30,
                'keepalive_timeout': 60,
                'retry_attempts': 3,
                'retry_delay': 1.0,
                'enable_compression': True
            },
            'websocket': {
                'ping_interval': 20,
                'ping_timeout': 10,
                'max_message_size': 10 * 1024 * 1024,  # 10MB
                'compression': 'deflate',
                'auto_reconnect': True,
                'reconnect_delay': 5.0
            },
            'performance': {
                'enable_profiling': os.getenv('ENABLE_PROFILING', 'false').lower() == 'true',
                'profile_memory': os.getenv('PROFILE_MEMORY', 'false').lower() == 'true',
                'max_memory_mb': int(os.getenv('MAX_MEMORY_MB', '1024')),
                'gc_threshold': (700, 10, 10),  # Aggressive garbage collection
                'log_slow_calls': True,
                'slow_call_threshold': 1.0  # seconds
            },
            'concurrency': {
                'max_concurrent_requests': int(os.getenv('MAX_CONCURRENT_REQUESTS', '50')),
                'worker_count': int(os.getenv('WORKER_COUNT', '10')),
                'queue_size': int(os.getenv('QUEUE_SIZE', '5000')),
                'thread_pool_size': int(os.getenv('THREAD_POOL_SIZE', '4'))
            },
            'monitoring': {
                'metrics_enabled': True,
                'metrics_port': int(os.getenv('METRICS_PORT', '8080')),
                'health_check_interval': 30,
                'performance_report_interval': 300  # 5 minutes
            }
        }
    
    @staticmethod
    def apply_system_optimizations():
        """Apply system-level optimizations"""
        import gc
        
        # Configure garbage collection for performance
        gc.set_threshold(700, 10, 10)  # More aggressive GC
        
        # Set environment variables for Python optimization
        os.environ['PYTHONOPTIMIZE'] = '1'  # Enable optimizations
        os.environ['PYTHONUNBUFFERED'] = '1'  # Unbuffered output
        
        print("⚡ System optimizations applied")