backend/app/routers/patterns.py

"""
Pattern Intelligence Router.

Endpoints for candlestick pattern detection, ML-based prediction,
pattern catalog, and historical accuracy analysis.
"""

from __future__ import annotations

import logging
from typing import List, Optional

from fastapi import APIRouter, Depends, HTTPException
from pydantic import BaseModel, Field

from app.dependencies import get_current_user
from app.models.user import User

logger = logging.getLogger(__name__)

router = APIRouter(prefix="/patterns", tags=["Pattern Intelligence"])


# ── Schemas ──────────────────────────────────────────────────────────────

class PatternAnalyzeRequest(BaseModel):
    ticker: str = Field(..., min_length=1, max_length=20)
    period: str = Field("2y", description="Historical data period")
    horizon: int = Field(5, ge=1, le=30, description="Prediction horizon in days")


class MultiAnalyzeRequest(BaseModel):
    tickers: List[str] = Field(..., min_items=1, max_items=10)
    period: str = Field("2y")
    horizon: int = Field(5, ge=1, le=30)


class BacktestAccuracyRequest(BaseModel):
    ticker: str = Field(..., min_length=1, max_length=20)
    period: str = Field("5y")
    horizon: int = Field(5, ge=1, le=30)


# ── Endpoints ────────────────────────────────────────────────────────────

@router.post("/analyze")
async def analyze_patterns(
    data: PatternAnalyzeRequest,
    user: User = Depends(get_current_user),
):
    """
    Detect candlestick patterns and predict price direction using
    pattern-aware LightGBM model with advanced mathematical features.

    Returns:
    - Predicted direction (strong_up / neutral / strong_down)
    - Confidence and probability distribution
    - Detected patterns with reliability scores
    - Top feature importances
    - Advanced feature values (Hurst, Fractal, Entropy, etc.)
    """
    from app.services.ml.pattern_recognition.predictor import predict_with_patterns

    try:
        result = await predict_with_patterns(
            ticker=data.ticker,
            period=data.period,
            horizon=data.horizon,
        )
        return result
    except ValueError as e:
        raise HTTPException(status_code=400, detail=str(e))
    except Exception as e:
        logger.error("Pattern analysis failed for %s: %s", data.ticker, e, exc_info=True)
        raise HTTPException(status_code=500, detail="Pattern analysis failed")


@router.post("/multi-analyze")
async def multi_analyze(
    data: MultiAnalyzeRequest,
    user: User = Depends(get_current_user),
):
    """Analyze multiple tickers and return comparative results."""
    from app.services.ml.pattern_recognition.predictor import analyze_multiple

    try:
        results = await analyze_multiple(
            tickers=data.tickers,
            period=data.period,
            horizon=data.horizon,
        )
        return results
    except Exception as e:
        logger.error("Multi-analysis failed: %s", e, exc_info=True)
        raise HTTPException(status_code=500, detail="Multi-analysis failed")


@router.get("/catalog")
async def pattern_catalog(
    user: User = Depends(get_current_user),
):
    """
    Get the full catalog of all 35+ supported candlestick
    and chart patterns with descriptions and reliability ratings.
    """
    from app.services.ml.pattern_recognition.pattern_detector import pattern_detector
    return {
        "total_patterns": len(pattern_detector.get_pattern_catalog()),
        "patterns": pattern_detector.get_pattern_catalog(),
    }


@router.post("/backtest-accuracy")
async def backtest_accuracy(
    data: BacktestAccuracyRequest,
    user: User = Depends(get_current_user),
):
    """
    Backtest pattern detection accuracy on historical data.
    For each pattern type, returns occurrences, win rate,
    average return, and actual vs theoretical reliability.
    """
    from app.services.ml.pattern_recognition.predictor import backtest_pattern_accuracy

    try:
        result = await backtest_pattern_accuracy(
            ticker=data.ticker,
            period=data.period,
            horizon=data.horizon,
        )
        return result
    except ValueError as e:
        raise HTTPException(status_code=400, detail=str(e))
    except Exception as e:
        logger.error("Backtest accuracy failed for %s: %s", data.ticker, e, exc_info=True)
        raise HTTPException(status_code=500, detail="Backtest accuracy failed")


@router.post("/clear-cache")
async def clear_pattern_cache(
    user: User = Depends(get_current_user),
):
    """Clear the pattern predictor model cache."""
    from app.services.ml.pattern_recognition.predictor import clear_cache
    count = clear_cache()
    return {"cleared": count}