indent fixes

autonomous_bot.py

@@ -963,30 +963,30 @@ class AutonomousTradingBot:
         logger.info(f"Running {len(self.slots)} slot(s): " +
                     ", ".join(f"{s.coin}/{s.timeframe}({s.strategy_name})" for s in self.slots))
 
-         # ── Keep-alive + auto-reconnect ───────────────────────────────
-         reconnect_delay = 5
-         try:
-             while True:
-                 # Check if shutdown was requested (e.g., via /api/restart)
-                 if self._shutdown_event.is_set():
-                     logger.info("Shutdown event received. Exiting gracefully...")
-                     break
-
-                 time.sleep(1)
-                 ws_alive = info.ws_manager is not None and info.ws_manager.is_alive()
-                 if not ws_alive:
-                     logger.warning("WebSocket disconnected. Reconnecting…")
-                     time.sleep(reconnect_delay)
-                     try:
-                         clients  = self.api.create_live_execution_clients(
-                             private_key, account_address=account_address, skip_ws=False
-                         )
-                         info = clients["info"]
-                         subscribed.clear()
-                         subscribe_all()
-                         reconnect_delay = 5
-                         logger.info("Reconnected.")
-                     except Exception as e:
+        # ── Keep-alive + auto-reconnect ───────────────────────────────
+        reconnect_delay = 5
+        try:
+            while True:
+                # Check if shutdown was requested (e.g., via /api/restart)
+                if self._shutdown_event.is_set():
+                    logger.info("Shutdown event received. Exiting gracefully...")
+                    break
+
+                time.sleep(1)
+                ws_alive = info.ws_manager is not None and info.ws_manager.is_alive()
+                if not ws_alive:
+                    logger.warning("WebSocket disconnected. Reconnecting…")
+                    time.sleep(reconnect_delay)
+                    try:
+                        clients  = self.api.create_live_execution_clients(
+                            private_key, account_address=account_address, skip_ws=False
+                        )
+                        info = clients["info"]
+                        subscribed.clear()
+                        subscribe_all()
+                        reconnect_delay = 5
+                        logger.info("Reconnected.")
+                    except Exception as e:
                         reconnect_delay = min(reconnect_delay * 2, 60)
                         logger.error(f"Reconnect failed: {e}. Retry in {reconnect_delay}s…")
                         self.tg.send_error("WebSocket reconnect", str(e))
@@ -1251,113 +1251,113 @@ def _start_health_server(port: int = 7860, bot_instance: Optional[AutonomousBot]
             except Exception as e:
                 return 500, json.dumps({"error": str(e)}).encode()
 
-         elif path == "/api/config":
-             try:
-                 from pathlib import Path
-                 config_path = Path(__file__).parent / "bot_config.json"
-                 if config_path.exists():
-                     with open(config_path) as f:
-                         config = json.load(f)
-                     return 200, json.dumps(config).encode()
-                 return 404, json.dumps({"error": "Config not found"}).encode()
-             except Exception as e:
-                 return 500, json.dumps({"error": str(e)}).encode()
-
-         elif path == "/api/portfolio/backtest":
-             try:
-                 from pathlib import Path
-                 backtest_file = Path(__file__).parent / "reports" / "backtest_results.json"
-                 if backtest_file.exists():
-                     with open(backtest_file) as f:
-                         results = json.load(f)
-                     return 200, json.dumps(results).encode()
-                 return 200, json.dumps({"status": "No backtest results yet", "results": []}).encode()
-             except Exception as e:
-                 return 500, json.dumps({"error": str(e)}).encode()
-
-         elif path == "/api/scripts/ml-learn":
-             # POST endpoint to start ML tracker - runs track_ml_learning.py
-             try:
-                 logger.info("ML learning started via API - executing track_ml_learning.py")
-                 import subprocess
-                 script_path = os.path.join(os.path.dirname(__file__), "track_ml_learning.py")
-                 # Run in background
-                 subprocess.Popen([sys.executable, script_path],
-                                stdout=subprocess.DEVNULL,
-                                stderr=subprocess.DEVNULL)
-                 return 200, json.dumps({"status": "ML learning started", "script": "track_ml_learning.py"}).encode()
-             except Exception as e:
-                 logger.error(f"ML learning failed: {e}")
-                 return 500, json.dumps({"error": str(e)}).encode()
-
-         elif path == "/api/scripts/ml-optimize":
-             # POST endpoint to find best coins - runs _scan_coins.py then optimize_ml_coins.py
-             try:
-                 logger.info("ML optimization started via API - executing _scan_coins.py then optimize_ml_coins.py")
-                 import subprocess
-
-                 # First run _scan_coins.py
-                 scan_path = os.path.join(os.path.dirname(__file__), "_scan_coins.py")
-                 logger.info(f"Starting _scan_coins.py")
-                 subprocess.Popen([sys.executable, scan_path],
-                                stdout=subprocess.DEVNULL,
-                                stderr=subprocess.DEVNULL)
-
-                 # Then run optimize_ml_coins.py (will run after scan completes)
-                 optimize_path = os.path.join(os.path.dirname(__file__), "optimize_ml_coins.py")
-                 logger.info(f"Optimize will start after scan completes")
-
-                 return 200, json.dumps({
-                     "status": "ML optimization started",
-                     "scripts": ["_scan_coins.py", "optimize_ml_coins.py"],
-                     "note": "optimize_ml_coins.py will run after _scan_coins.py completes"
-                 }).encode()
-             except Exception as e:
-                 logger.error(f"ML optimization failed: {e}")
-                 return 500, json.dumps({"error": str(e)}).encode()
-
-         elif path == "/api/scripts/backtest":
-             # POST endpoint to start backtest - runs backtest.py then track_ml_learning.py
-             try:
-                 logger.info("Backtest started via API - executing backtest.py")
-                 import subprocess
-
-                 # Run backtest.py
-                 backtest_path = os.path.join(os.path.dirname(__file__), "backtest.py")
-                 logger.info(f"Starting backtest.py")
-                 subprocess.Popen([sys.executable, backtest_path],
-                                stdout=subprocess.DEVNULL,
-                                stderr=subprocess.DEVNULL)
-
-                 return 200, json.dumps({
-                     "status": "Backtest started",
-                     "scripts": ["backtest.py"],
-                     "note": "track_ml_learning.py can be run separately with /api/scripts/ml-learn"
-                 }).encode()
-             except Exception as e:
-                 logger.error(f"Backtest failed: {e}")
-                 return 500, json.dumps({"error": str(e)}).encode()
-
-         elif path == "/api/restart":
-             # POST endpoint to gracefully restart the bot with new config
-             try:
-                 logger.info("Bot restart requested via API")
-                 logger.info("Shutting down gracefully...")
-                 # Signal to exit the live loop
-                 if bot_instance and hasattr(bot_instance, '_shutdown_event'):
-                     bot_instance._shutdown_event.set()
-                 # Give it a moment to shut down
-                 import time as time_module
-                 time_module.sleep(2)
-                 # Exit the process so it can be restarted by HF Spaces or supervisor
-                 logger.info("Exiting bot process for restart")
-                 os._exit(0)
-             except Exception as e:
-                 logger.error(f"Restart failed: {e}")
-                 return 500, json.dumps({"error": str(e)}).encode()
-
-         else:
-             return 404, json.dumps({"error": "Endpoint not found"}).encode()
+        elif path == "/api/config":
+            try:
+                from pathlib import Path
+                config_path = Path(__file__).parent / "bot_config.json"
+                if config_path.exists():
+                    with open(config_path) as f:
+                        config = json.load(f)
+                    return 200, json.dumps(config).encode()
+                return 404, json.dumps({"error": "Config not found"}).encode()
+            except Exception as e:
+                return 500, json.dumps({"error": str(e)}).encode()
+
+        elif path == "/api/portfolio/backtest":
+            try:
+                from pathlib import Path
+                backtest_file = Path(__file__).parent / "reports" / "backtest_results.json"
+                if backtest_file.exists():
+                    with open(backtest_file) as f:
+                        results = json.load(f)
+                    return 200, json.dumps(results).encode()
+                return 200, json.dumps({"status": "No backtest results yet", "results": []}).encode()
+            except Exception as e:
+                return 500, json.dumps({"error": str(e)}).encode()
+
+        elif path == "/api/scripts/ml-learn":
+            # POST endpoint to start ML tracker - runs track_ml_learning.py
+            try:
+                logger.info("ML learning started via API - executing track_ml_learning.py")
+                import subprocess
+                script_path = os.path.join(os.path.dirname(__file__), "track_ml_learning.py")
+                # Run in background
+                subprocess.Popen([sys.executable, script_path],
+                               stdout=subprocess.DEVNULL,
+                               stderr=subprocess.DEVNULL)
+                return 200, json.dumps({"status": "ML learning started", "script": "track_ml_learning.py"}).encode()
+            except Exception as e:
+                logger.error(f"ML learning failed: {e}")
+                return 500, json.dumps({"error": str(e)}).encode()
+
+        elif path == "/api/scripts/ml-optimize":
+            # POST endpoint to find best coins - runs _scan_coins.py then optimize_ml_coins.py
+            try:
+                logger.info("ML optimization started via API - executing _scan_coins.py then optimize_ml_coins.py")
+                import subprocess
+
+                # First run _scan_coins.py
+                scan_path = os.path.join(os.path.dirname(__file__), "_scan_coins.py")
+                logger.info(f"Starting _scan_coins.py")
+                subprocess.Popen([sys.executable, scan_path],
+                               stdout=subprocess.DEVNULL,
+                               stderr=subprocess.DEVNULL)
+
+                # Then run optimize_ml_coins.py (will run after scan completes)
+                optimize_path = os.path.join(os.path.dirname(__file__), "optimize_ml_coins.py")
+                logger.info(f"Optimize will start after scan completes")
+
+                return 200, json.dumps({
+                    "status": "ML optimization started",
+                    "scripts": ["_scan_coins.py", "optimize_ml_coins.py"],
+                    "note": "optimize_ml_coins.py will run after _scan_coins.py completes"
+                }).encode()
+            except Exception as e:
+                logger.error(f"ML optimization failed: {e}")
+                return 500, json.dumps({"error": str(e)}).encode()
+
+        elif path == "/api/scripts/backtest":
+            # POST endpoint to start backtest - runs backtest.py then track_ml_learning.py
+            try:
+                logger.info("Backtest started via API - executing backtest.py")
+                import subprocess
+
+                # Run backtest.py
+                backtest_path = os.path.join(os.path.dirname(__file__), "backtest.py")
+                logger.info(f"Starting backtest.py")
+                subprocess.Popen([sys.executable, backtest_path],
+                               stdout=subprocess.DEVNULL,
+                               stderr=subprocess.DEVNULL)
+
+                return 200, json.dumps({
+                    "status": "Backtest started",
+                    "scripts": ["backtest.py"],
+                    "note": "track_ml_learning.py can be run separately with /api/scripts/ml-learn"
+                }).encode()
+            except Exception as e:
+                logger.error(f"Backtest failed: {e}")
+                return 500, json.dumps({"error": str(e)}).encode()
+
+        elif path == "/api/restart":
+            # POST endpoint to gracefully restart the bot with new config
+            try:
+                logger.info("Bot restart requested via API")
+                logger.info("Shutting down gracefully...")
+                # Signal to exit the live loop
+                if bot_instance and hasattr(bot_instance, '_shutdown_event'):
+                    bot_instance._shutdown_event.set()
+                # Give it a moment to shut down
+                import time as time_module
+                time_module.sleep(2)
+                # Exit the process so it can be restarted by HF Spaces or supervisor
+                logger.info("Exiting bot process for restart")
+                os._exit(0)
+            except Exception as e:
+                logger.error(f"Restart failed: {e}")
+                return 500, json.dumps({"error": str(e)}).encode()
+
+        else:
+            return 404, json.dumps({"error": "Endpoint not found"}).encode()
 
     class _Handler(BaseHTTPRequestHandler):
         def do_GET(self):