Multi_Swarm_Coevolutive/src/multi_swarm/dashboard/data.py

from __future__ import annotations

import json
import sqlite3
from pathlib import Path
from typing import Any

import pandas as pd  # type: ignore[import-untyped]

from ..persistence.repository import Repository


def get_repo(db_path: str | Path) -> Repository:
    return Repository(db_path=db_path)


def list_runs_df(repo: Repository) -> pd.DataFrame:
    return pd.DataFrame(repo.list_runs())


def get_run_overview(repo: Repository, run_id: str) -> dict[str, Any]:
    run = repo.get_run(run_id)
    return {
        "name": run["name"],
        "started_at": run["started_at"],
        "completed_at": run["completed_at"],
        "status": run["status"],
        "total_cost_usd": run["total_cost_usd"],
        "config": json.loads(run["config_json"]),
    }


def generations_df(repo: Repository, run_id: str) -> pd.DataFrame:
    return pd.DataFrame(repo.list_generations(run_id))


def evaluations_df(repo: Repository, run_id: str) -> pd.DataFrame:
    return pd.DataFrame(repo.list_evaluations(run_id))


def genomes_df(
    repo: Repository, run_id: str, generation_idx: int | None = None
) -> pd.DataFrame:
    rows = repo.list_genomes(run_id, generation_idx)
    flat: list[dict[str, Any]] = []
    for r in rows:
        payload = json.loads(r["payload_json"])
        flat.append(
            {
                "id": r["id"],
                "generation_idx": r["generation_idx"],
                **payload,
            }
        )
    return pd.DataFrame(flat)


def _paper_conn(db_path: str | Path) -> sqlite3.Connection:
    conn = sqlite3.connect(str(db_path))
    conn.row_factory = sqlite3.Row
    return conn


def paper_runs_df(db_path: str | Path) -> pd.DataFrame:
    with _paper_conn(db_path) as conn:
        rows = conn.execute(
            "SELECT id, name, started_at, stopped_at, status, initial_capital, config_json "
            "FROM paper_trading_runs ORDER BY started_at DESC"
        ).fetchall()
    return pd.DataFrame([dict(r) for r in rows])


def paper_equity_df(db_path: str | Path, run_id: str) -> pd.DataFrame:
    with _paper_conn(db_path) as conn:
        rows = conn.execute(
            "SELECT ts, equity, cash, positions_value FROM paper_trading_equity "
            "WHERE paper_run_id=? ORDER BY ts ASC",
            (run_id,),
        ).fetchall()
    return pd.DataFrame([dict(r) for r in rows])


def paper_positions_df(db_path: str | Path, run_id: str) -> pd.DataFrame:
    with _paper_conn(db_path) as conn:
        rows = conn.execute(
            "SELECT symbol, side, qty, entry_price, entry_ts "
            "FROM paper_trading_positions WHERE paper_run_id=? ORDER BY symbol",
            (run_id,),
        ).fetchall()
    return pd.DataFrame([dict(r) for r in rows])


def paper_trades_df(db_path: str | Path, run_id: str, limit: int = 100) -> pd.DataFrame:
    with _paper_conn(db_path) as conn:
        rows = conn.execute(
            "SELECT symbol, side, qty, entry_price, exit_price, entry_ts, exit_ts, pnl, fees "
            "FROM paper_trading_trades WHERE paper_run_id=? ORDER BY exit_ts DESC LIMIT ?",
            (run_id, limit),
        ).fetchall()
    return pd.DataFrame([dict(r) for r in rows])


def paper_ticks_df(db_path: str | Path, run_id: str, limit: int = 50) -> pd.DataFrame:
    with _paper_conn(db_path) as conn:
        rows = conn.execute(
            "SELECT ts, bar_ts, symbol, close_price, signal, action_taken "
            "FROM paper_trading_ticks WHERE paper_run_id=? ORDER BY ts DESC LIMIT ?",
            (run_id, limit),
        ).fetchall()
    return pd.DataFrame([dict(r) for r in rows])


def paper_run_summary(db_path: str | Path, run_id: str) -> dict[str, Any]:
    """Aggrega metriche sintetiche per la pagina paper trading."""
    with _paper_conn(db_path) as conn:
        run = conn.execute(
            "SELECT id, name, started_at, stopped_at, status, initial_capital, config_json "
            "FROM paper_trading_runs WHERE id=?",
            (run_id,),
        ).fetchone()
        if run is None:
            return {}
        run = dict(run)

        eq_row = conn.execute(
            "SELECT equity, cash, positions_value, ts FROM paper_trading_equity "
            "WHERE paper_run_id=? ORDER BY ts DESC LIMIT 1",
            (run_id,),
        ).fetchone()

        trades_agg = conn.execute(
            "SELECT COUNT(*) AS n, COALESCE(SUM(pnl),0) AS sum_pnl, "
            "COALESCE(SUM(fees),0) AS sum_fees FROM paper_trading_trades "
            "WHERE paper_run_id=?",
            (run_id,),
        ).fetchone()

        tick_agg = conn.execute(
            "SELECT COUNT(*) AS n, MAX(ts) AS last_ts FROM paper_trading_ticks "
            "WHERE paper_run_id=?",
            (run_id,),
        ).fetchone()

        positions_n = conn.execute(
            "SELECT COUNT(*) AS n FROM paper_trading_positions WHERE paper_run_id=?",
            (run_id,),
        ).fetchone()["n"]

    initial = float(run["initial_capital"])
    current_equity = float(eq_row["equity"]) if eq_row is not None else initial
    pnl_pct = (current_equity - initial) / initial * 100.0 if initial else 0.0

    return {
        "id": run["id"],
        "name": run["name"],
        "status": run["status"],
        "started_at": run["started_at"],
        "stopped_at": run["stopped_at"],
        "initial_capital": initial,
        "config": json.loads(run["config_json"]),
        "current_equity": current_equity,
        "current_cash": float(eq_row["cash"]) if eq_row is not None else initial,
        "current_positions_value": float(eq_row["positions_value"]) if eq_row is not None else 0.0,
        "last_equity_ts": eq_row["ts"] if eq_row is not None else None,
        "pnl_abs": current_equity - initial,
        "pnl_pct": pnl_pct,
        "n_trades": int(trades_agg["n"]),
        "trades_pnl": float(trades_agg["sum_pnl"]),
        "trades_fees": float(trades_agg["sum_fees"]),
        "n_ticks": int(tick_agg["n"]),
        "last_tick_ts": tick_agg["last_ts"],
        "n_open_positions": int(positions_n),
    }