docs: update user-guide docs for AI-Trader-Server rebrand

Update all user-guide documentation files:
- configuration.md: Update title and container name references
- using-the-api.md: Update title
- integration-examples.md: Update title, class names
  (AsyncAITraderServerClient), container names, DAG names, and log paths
- troubleshooting.md: Update title, container names (ai-trader to
  ai-trader-server), GitHub issues URL

All Docker commands and code examples now reference ai-trader-server
container name.

Part of Phase 3: Developer & Deployment Documentation

docs/user-guide/integration-examples.md

@@ -1,6 +1,6 @@
 # Integration Examples
 
-Examples for integrating AI-Trader with external systems.
+Examples for integrating AI-Trader-Server with external systems.
 
 ---
 
@@ -14,7 +14,7 @@ See complete Python client in [API_REFERENCE.md](../../API_REFERENCE.md#client-l
 import aiohttp
 import asyncio
 
-class AsyncAITraderClient:
+class AsyncAITraderServerClient:
     def __init__(self, base_url="http://localhost:8080"):
         self.base_url = base_url
 
@@ -48,7 +48,7 @@ class AsyncAITraderClient:
 
 # Usage
 async def main():
-    client = AsyncAITraderClient()
+    client = AsyncAITraderServerClient()
     job = await client.trigger_simulation("2025-01-16", models=["gpt-4"])
     result = await client.wait_for_completion(job["job_id"])
     print(f"Simulation completed: {result['status']}")
@@ -104,7 +104,7 @@ echo "Results saved to results_$DATE.json"
 
 Add to crontab:
 ```bash
-0 6 * * * /path/to/daily_simulation.sh >> /var/log/ai-trader.log 2>&1
+0 6 * * * /path/to/daily_simulation.sh >> /var/log/ai-trader-server.log 2>&1
 ```
 
 ---
@@ -120,7 +120,7 @@ import time
 
 def trigger_simulation(**context):
     response = requests.post(
-        "http://ai-trader:8080/simulate/trigger",
+        "http://ai-trader-server:8080/simulate/trigger",
         json={"start_date": "{{ ds }}", "models": ["gpt-4"]}
     )
     response.raise_for_status()
@@ -128,19 +128,19 @@ def trigger_simulation(**context):
 
 def wait_for_completion(**context):
     job_id = context["task_instance"].xcom_pull(task_ids="trigger")
-    
+
     while True:
-        response = requests.get(f"http://ai-trader:8080/simulate/status/{job_id}")
+        response = requests.get(f"http://ai-trader-server:8080/simulate/status/{job_id}")
         status = response.json()
-        
+
         if status["status"] in ["completed", "partial", "failed"]:
             return status
-        
+
         time.sleep(30)
 
 def fetch_results(**context):
     job_id = context["task_instance"].xcom_pull(task_ids="trigger")
-    response = requests.get(f"http://ai-trader:8080/results?job_id={job_id}")
+    response = requests.get(f"http://ai-trader-server:8080/results?job_id={job_id}")
     return response.json()
 
 default_args = {
@@ -152,7 +152,7 @@ default_args = {
 }
 
 dag = DAG(
-    "ai_trader_simulation",
+    "ai_trader_server_simulation",
     default_args=default_args,
     schedule_interval="0 6 * * *",  # Daily at 6 AM
     catchup=False
@@ -183,7 +183,7 @@ trigger_task >> wait_task >> fetch_task
 
 ## Generic Workflow Automation
 
-Any HTTP-capable automation service can integrate with AI-Trader:
+Any HTTP-capable automation service can integrate with AI-Trader-Server:
 
 1. **Trigger:** POST to `/simulate/trigger`
 2. **Poll:** GET `/simulate/status/{job_id}` every 10-30 seconds