#!/usr/bin/env python
"""
Predix CLI - Wrapper for rdagent with LLM model selection.

Usage:
    predix quant                    # Local llama.cpp (default)
    predix quant --model local      # Explicit local
    predix quant --model openrouter # OpenRouter cloud model
    predix quant -d                 # With web dashboard
"""
import os
import sys
from pathlib import Path

from dotenv import load_dotenv
load_dotenv(Path(__file__).parent / ".env")

import typer
from rich.console import Console

app = typer.Typer(help="Predix - AI Quantitative Trading Agent")
console = Console()


@app.command()
def quant(
    model: str = typer.Option(
        "local",
        "--model", "-m",
        help="LLM backend: 'local' (llama.cpp) or 'openrouter' (cloud)",
    ),
    dashboard: bool = typer.Option(
        False,
        "--dashboard/-d",
        help="Start web dashboard",
    ),
    cli_dashboard: bool = typer.Option(
        False,
        "--cli-dashboard/-c",
        help="Start CLI dashboard",
    ),
    log_file: str = typer.Option(
        None,  # None means auto-detect based on run_id
        "--log-file",
        help="Log file path (default: auto-detected). Use 'none' to disable.",
    ),
    step_n: int = typer.Option(None, help="Number of steps to run"),
    loop_n: int = typer.Option(None, help="Number of loops to run"),
    run_id: int = typer.Option(
        0,
        "--run-id",
        help="Parallel run ID (for isolated results). 0 = single run mode.",
    ),
):
    """
    Start EURUSD quantitative trading loop.

    Examples:
        predix quant                          # Local llama.cpp
        predix quant -m openrouter            # OpenRouter cloud model
        predix quant -d                       # With web dashboard
        predix quant -m openrouter -d         # Both
        predix quant --run-id 1               # Parallel run #1 (isolated)
    """
    import subprocess
    import threading
    import time
    import sys

    # ---- Parallel Run Isolation ----
    # When run_id > 0, isolate all outputs (logs, results, workspace)
    if run_id > 0:
        os.environ["PARALLEL_RUN_ID"] = str(run_id)
        console.print(f"\n[bold yellow]🔀 Parallel Run Mode:[/bold yellow] [cyan]ID={run_id}[/cyan]")

        # Auto-detect log file for parallel run
        if log_file is None:
            log_file = f"fin_quant_run{run_id}.log"

        # Isolate results directories
        results_base = Path(__file__).parent / "results" / "runs" / f"run{run_id}"
        results_base.mkdir(parents=True, exist_ok=True)

        # Isolate workspace directory
        workspace_dir = Path(__file__).parent / f"RD-Agent_workspace_run{run_id}"
        os.environ["RD_AGENT_WORKSPACE"] = str(workspace_dir)

        console.print(f"   [dim]Log: {log_file}[/dim]")
        console.print(f"   [dim]Results: results/runs/run{run_id}/[/dim]")
        console.print(f"   [dim]Workspace: {workspace_dir.name}/[/dim]")
    else:
        # Single run mode: default log file
        if log_file is None:
            log_file = "fin_quant.log"

    # ---- Log File Setup ----
    if log_file.lower() != "none":
        log_path = Path(__file__).parent / log_file
        log_path.parent.mkdir(parents=True, exist_ok=True)

        # Open log file for appending
        log_f = open(log_path, "a", encoding="utf-8")

        # Redirect stdout and stderr to both console and log file
        class TeeWriter:
            def __init__(self, *streams):
                self._streams = streams

            def write(self, data):
                for s in self._streams:
                    try:
                        s.write(data)
                        s.flush()
                    except:
                        pass

            def flush(self):
                for s in self._streams:
                    try:
                        s.flush()
                    except:
                        pass

        sys.stdout = TeeWriter(sys.__stdout__, log_f)
        sys.stderr = TeeWriter(sys.__stderr__, log_f)

        console.print(f"\n[dim]📝 Logging to: {log_path}[/dim]")
    else:
        console.print("\n[dim]⚠️  Logging disabled (console only)[/dim]")

    # ---- LLM Model Selection ----
    if model == "openrouter":
        api_key = os.getenv("OPENROUTER_API_KEY", "")
        api_key_2 = os.getenv("OPENROUTER_API_KEY_2", "")
        if not api_key:
            console.print("\n[bold red]❌ OPENROUTER_API_KEY not set in .env[/bold red]")
            console.print("[yellow]Add your API key to .env:[/yellow]")
            console.print('  OPENROUTER_API_KEY=sk-or-your-key-here')
            raise typer.Exit(code=1)

        # Setup both API keys for load balancing
        os.environ["OPENAI_API_BASE"] = "https://openrouter.ai/api/v1"
        os.environ["CHAT_MODEL"] = os.getenv("OPENROUTER_MODEL", "openrouter/qwen/qwen3.6-plus:free")

        # If second key exists, configure LiteLLM for load balancing
        if api_key_2:
            os.environ["OPENAI_API_KEY"] = f"{api_key},{api_key_2}"
            os.environ["LITELLM_PARALLEL_CALLS"] = "2"
            console.print(f"\n[bold blue]🌐 Using OpenRouter (2 API Keys):[/bold blue] [cyan]{os.environ['CHAT_MODEL']}[/cyan]")
            console.print(f"   [dim]Keys: {api_key[:15]}*** + {api_key_2[:15]}***[/dim]")
            console.print(f"   [dim]Parallel: 2 concurrent requests[/dim]")
        else:
            os.environ["OPENAI_API_KEY"] = api_key
            console.print(f"\n[bold blue]🌐 Using OpenRouter:[/bold blue] [cyan]{os.environ['CHAT_MODEL']}[/cyan]")
            console.print(f"   [dim]Key: {api_key[:15]}***[/dim]")
    elif model == "local":
        os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY", "local")
        os.environ["OPENAI_API_BASE"] = os.getenv("OPENAI_API_BASE", "http://localhost:8081/v1")
        os.environ["CHAT_MODEL"] = os.getenv("CHAT_MODEL", "openai/qwen3.5-35b")

        console.print(f"\n[bold green]🏠 Using local LLM:[/bold green] [cyan]{os.environ['CHAT_MODEL']}[/cyan]")
        console.print(f"   [dim]Base: {os.environ['OPENAI_API_BASE']}[/dim]")
    else:
        console.print(f"\n[yellow]⚠️  Unknown model: '{model}'. Using .env settings.[/yellow]")

    # ---- Dashboards ----
    if dashboard:
        def start_web_dashboard():
            console.print(f"\n[bold green]🚀 Web Dashboard: http://localhost:5000[/bold green]")
            subprocess.run(
                ["python", "web/dashboard_api.py"],
                cwd=str(Path(__file__).parent),
                env={**os.environ, "FLASK_ENV": "development"},
            )

        threading.Thread(target=start_web_dashboard, daemon=True).start()
        time.sleep(2)

    if cli_dashboard:
        def start_cli_dash():
            from rdagent.log.ui.predix_dashboard import run_dashboard
            run_dashboard(log_path="fin_quant.log", refresh_interval=3)

        threading.Thread(target=start_cli_dash, daemon=True).start()
        time.sleep(1)

    # ---- Start fin_quant ----
    from rdagent.app.qlib_rd_loop.quant import main as fin_quant

    console.print(f"\n[bold cyan]📊 Starting EURUSD Trading Loop...[/bold cyan]\n")

    fin_quant(
        step_n=step_n,
        loop_n=loop_n,
    )


@app.command()
def evaluate(
    top: int = typer.Option(
        100,
        "--top", "-n",
        help="Number of factors to evaluate (default: 100)",
    ),
    all_factors: bool = typer.Option(
        False,
        "--all", "-a",
        help="Evaluate all undiscovered factors",
    ),
    parallel: int = typer.Option(
        4,
        "--parallel", "-p",
        help="Number of parallel workers (default: 4)",
    ),
    force: bool = typer.Option(
        False,
        "--force", "-f",
        help="Force re-evaluation of ALL factors (even already evaluated)",
    ),
):
    """
    Evaluate existing factors with full 1min data (2020-2026).

    Computes IC, Sharpe, Max DD, Win Rate for each factor.
    Automatically skips already evaluated factors (use --force to re-evaluate).

    Examples:
        predix evaluate                   # Evaluate 100 NEW factors
        predix evaluate --top 500         # Evaluate 500 NEW factors
        predix evaluate --all             # Evaluate all NEW factors
        predix evaluate --force --top 50  # Re-evaluate 50 factors
        predix evaluate -p 8              # Use 8 parallel workers
    """
    console.print(Panel(
        "[bold cyan]📊 Predix Factor Evaluator[/bold cyan]\n"
        "Evaluating factors with FULL 1min data (2020-2026)\n"
        "Skips already evaluated factors automatically",
        border_style="cyan",
    ))

    # Import and run the evaluator
    from predix_full_eval import main as eval_main

    try:
        eval_main(
            top=top,
            all_factors=all_factors,
            parallel=parallel,
            force=force,
        )
    except KeyboardInterrupt:
        console.print("\n[yellow]Evaluation interrupted by user[/yellow]")
    except Exception as e:
        console.print(f"\n[bold red]Evaluation failed: {e}[/bold red]")
        import traceback
        console.print(traceback.format_exc())


@app.command()
def top(
    n: int = typer.Option(
        20,
        "--num", "-n",
        help="Number of top factors to show (default: 20)",
    ),
    metric: str = typer.Option(
        "ic",
        "--metric", "-m",
        help="Sort by metric: 'ic' or 'sharpe'",
    ),
):
    """
    Show top-performing factors by IC or Sharpe.

    Examples:
        predix top                      # Top 20 by IC
        predix top -n 50                # Top 50 by IC
        predix top -m sharpe            # Top 20 by Sharpe
    """
    import json
    import glob as glob_module
    import numpy as np
    from rich.table import Table
    from rich.panel import Panel

    factors_dir = Path(__file__).parent / "results" / "factors"
    if not factors_dir.exists():
        console.print("[red]No results found in results/factors/[/red]")
        return

    # Load all factor JSON files
    results = []
    for f in glob_module.glob(str(factors_dir / "*.json")):
        try:
            with open(f) as fh:
                data = json.load(fh)
            # Only include factors with valid IC
            if data.get("status") == "success" and data.get("ic") is not None:
                results.append(data)
        except Exception:
            continue

    if not results:
        console.print("[yellow]No evaluated factors found with valid IC[/yellow]")
        return

    # Sort by metric
    if metric == "sharpe":
        results.sort(key=lambda x: abs(x.get("sharpe", 0) or 0), reverse=True)
        sort_label = "Sharpe"
    else:
        results.sort(key=lambda x: abs(x.get("ic", 0) or 0), reverse=True)
        sort_label = "IC"

    # Display as table
    table = Table(
        title=f"Top {min(n, len(results))} Factors by {sort_label}",
        show_header=True,
        header_style="bold cyan",
    )
    table.add_column("#", justify="center", width=4)
    table.add_column("Factor", width=40)
    table.add_column("IC", justify="right", width=10)
    table.add_column("Sharpe", justify="right", width=10)
    table.add_column("Ann. Return %", justify="right", width=12)
    table.add_column("Max DD", justify="right", width=10)
    table.add_column("Win Rate", justify="right", width=10)

    for i, r in enumerate(results[:n], 1):
        ic = r.get("ic")
        sharpe = r.get("sharpe")
        ann_ret = r.get("annualized_return")
        max_dd = r.get("max_drawdown")
        win_rate = r.get("win_rate")

        table.add_row(
            str(i),
            r["factor_name"][:38],
            f"{ic:.6f}" if ic is not None else "N/A",
            f"{sharpe:.4f}" if sharpe is not None else "N/A",
            f"{ann_ret:.4f}" if ann_ret is not None else "N/A",
            f"{max_dd:.4f}" if max_dd is not None else "N/A",
            f"{win_rate:.2%}" if win_rate is not None else "N/A",
        )

    console.print(table)

    # Summary
    valid_ic = [r.get("ic") for r in results if r.get("ic") is not None]
    valid_sharpe = [r.get("sharpe") for r in results if r.get("sharpe") is not None]
    # Filter extreme outliers for average
    valid_sharpe_filtered = [s for s in valid_sharpe if abs(s or 0) < 1e6]

    console.print(Panel(
        f"[bold]Summary[/bold]\n"
        f"Total evaluated: {len(results)}\n"
        f"Avg IC: {np.mean(valid_ic):.6f} (n={len(valid_ic)})\n"
        f"Best IC: {max(valid_ic, key=abs, default=0):.6f}\n"
        f"Avg Sharpe: {np.mean(valid_sharpe_filtered):.4f} (n={len(valid_sharpe_filtered)})\n"
        f"Best Sharpe: {max(valid_sharpe, key=abs, default=0):.4f}",
        border_style="green",
    ))


@app.command()
def health():
    """Check system health and configuration."""
    from rdagent.app.utils.health_check import health_check
    health_check()


@app.command()
def status():
    """Show current trading loop status."""
    import sqlite3

    # Process check
    result = subprocess.run(
        ["pgrep", "-f", "fin_quant"],
        capture_output=True, text=True
    )
    if result.returncode == 0:
        console.print("[bold green]✅ Trading Loop: RUNNING[/bold green]")
    else:
        console.print("[bold yellow]⏸️  Trading Loop: STOPPED[/bold yellow]")

    # DB stats
    db_path = Path(__file__).parent / "results" / "db" / "backtest_results.db"
    if db_path.exists():
        conn = sqlite3.connect(str(db_path))
        c = conn.cursor()
        c.execute("SELECT COUNT(*) FROM backtest_runs")
        runs = c.fetchone()[0]
        c.execute("SELECT COUNT(*) FROM factors")
        factors = c.fetchone()[0]
        conn.close()

        console.print(f"\n📊 Results:")
        console.print(f"  Backtest runs: {runs}")
        console.print(f"  Factors: {factors}")


if __name__ == "__main__":
    app()