feat: Add Phase 4 advanced metrics and components

- Add advanced metrics dashboard with trade analytics
- Add new trading components (EntryTypeAnalysis, MultiDayPositionTracker, NewsEventTracker, etc.)
- Add strategy mode selector and trend confirmation
- Add risk automation panel and slippage correlation analysis
- Add daily trading plan enhancements with modal components
- Add custom hooks (useApi, useLocalStorage, useAdvancedTradeMetrics)
- Add broker service integration and trading API
- Add test setup and vitest configuration
- Include parquet data files for live market data
- Add comprehensive documentation in docs/ folder

backend/app/api/ollama.py

@@ -0,0 +1,125 @@
+"""
+Ollama API endpoints for local AI status and simple tasks.
+"""
+
+from fastapi import APIRouter, HTTPException
+from pydantic import BaseModel
+from typing import Optional, List
+from app.services.ollama_service import ollama_service
+from app.config import settings
+
+router = APIRouter(prefix="/api/ollama", tags=["Local AI"])
+
+
+class OllamaStatus(BaseModel):
+    available: bool
+    model: str
+    embed_model: str
+    base_url: str
+
+
+class GenerateRequest(BaseModel):
+    prompt: str
+    system: Optional[str] = None
+    temperature: float = 0.7
+    max_tokens: int = 500
+
+
+class GenerateResponse(BaseModel):
+    response: Optional[str]
+    model: str
+    success: bool
+
+
+class SentimentRequest(BaseModel):
+    text: str
+
+
+class SentimentResponse(BaseModel):
+    sentiment: Optional[str]
+    confidence: Optional[float]
+    success: bool
+
+
+class ClassifyRequest(BaseModel):
+    text: str
+    categories: List[str]
+
+
+class ClassifyResponse(BaseModel):
+    category: Optional[str]
+    success: bool
+
+
+class SummarizeRequest(BaseModel):
+    text: str
+    max_sentences: int = 2
+
+
+class SummarizeResponse(BaseModel):
+    summary: Optional[str]
+    success: bool
+
+
+@router.get("/status", response_model=OllamaStatus)
+async def get_ollama_status():
+    """Check if Ollama is available and configured."""
+    available = await ollama_service.is_available()
+    return OllamaStatus(
+        available=available,
+        model=settings.OLLAMA_MODEL,
+        embed_model=settings.OLLAMA_MODEL_EMBED,
+        base_url=settings.OLLAMA_BASE_URL
+    )
+
+
+@router.post("/generate", response_model=GenerateResponse)
+async def generate_text(request: GenerateRequest):
+    """Generate text using local Ollama model."""
+    result = await ollama_service.generate(
+        prompt=request.prompt,
+        system=request.system,
+        temperature=request.temperature,
+        max_tokens=request.max_tokens
+    )
+    return GenerateResponse(
+        response=result,
+        model=settings.OLLAMA_MODEL,
+        success=result is not None
+    )
+
+
+@router.post("/sentiment", response_model=SentimentResponse)
+async def analyze_sentiment(request: SentimentRequest):
+    """Quick sentiment analysis using local model."""
+    result = await ollama_service.quick_sentiment(request.text)
+    if result:
+        return SentimentResponse(
+            sentiment=result.get("sentiment"),
+            confidence=result.get("confidence"),
+            success=True
+        )
+    return SentimentResponse(sentiment=None, confidence=None, success=False)
+
+
+@router.post("/classify", response_model=ClassifyResponse)
+async def classify_text(request: ClassifyRequest):
+    """Classify text into one of the provided categories."""
+    result = await ollama_service.quick_classify(request.text, request.categories)
+    return ClassifyResponse(
+        category=result,
+        success=result is not None
+    )
+
+
+@router.post("/summarize", response_model=SummarizeResponse)
+async def summarize_text(request: SummarizeRequest):
+    """Quick text summarization using local model."""
+    result = await ollama_service.quick_summarize(
+        text=request.text,
+        max_sentences=request.max_sentences
+    )
+    return SummarizeResponse(
+        summary=result,
+        success=result is not None
+    )