"""
tests/conftest.py — Shared fixtures for pytest.
"""
from __future__ import annotations

import datetime
import os
import sys
from pathlib import Path

import numpy as np
import pandas as pd
import pytest

# Ensure project root on path
ROOT = Path(__file__).resolve().parent.parent
sys.path.insert(0, str(ROOT))

# Force DRY_RUN for all tests
os.environ.setdefault("DRY_RUN", "true")
os.environ.setdefault("TRADING_MODE", "paper")
os.environ.setdefault("ALPACA_API_KEY", "test_key")
os.environ.setdefault("ALPACA_SECRET_KEY", "test_secret")
os.environ.setdefault("ALPACA_BASE_URL", "https://paper-api.alpaca.markets")
os.environ.setdefault("TELEGRAM_BOT_TOKEN", "000:TEST")
os.environ.setdefault("TELEGRAM_CHAT_ID", "0")
os.environ.setdefault("NEWS_API_KEY", "testkey")
os.environ.setdefault("UNIVERSE", "AAPL,MSFT,NVDA")
os.environ.setdefault("ENV_SCHEMA_VERSION", "2")
os.environ.setdefault("GPU_AVAILABLE", "false")
os.environ.setdefault("ALLOW_SHORT", "true")
os.environ.setdefault("ACCOUNT_BALANCE_ABOVE_25K", "true")
os.environ.setdefault("ALPACA_ACCOUNT_TYPE", "margin")
os.environ.setdefault("ALLOW_OVERNIGHT_POSITIONS", "false")
os.environ.setdefault("DB_BACKEND", "sqlite")


@pytest.fixture
def sample_5m_bars():
    """Generate 200 rows of realistic 5-minute bar data."""
    np.random.seed(42)
    n = 200
    base = 150.0
    timestamps = pd.date_range(
        start="2024-06-01 09:30",
        periods=n,
        freq="5min",
        tz="US/Eastern",
    )
    close = base + np.cumsum(np.random.randn(n) * 0.3)
    high = close + np.abs(np.random.randn(n) * 0.2)
    low = close - np.abs(np.random.randn(n) * 0.2)
    open_ = close + np.random.randn(n) * 0.1
    volume = np.random.randint(10000, 100000, n).astype(float)
    vwap = (high + low + close) / 3.0
    trade_count = np.random.randint(50, 500, n).astype(float)

    df = pd.DataFrame({
        "timestamp": timestamps,
        "open": open_,
        "high": high,
        "low": low,
        "close": close,
        "volume": volume,
        "vwap": vwap,
        "trade_count": trade_count,
    })
    return df


@pytest.fixture
def sample_1h_bars():
    """Generate 100 rows of hourly bar data."""
    np.random.seed(99)
    n = 100
    base = 150.0
    timestamps = pd.date_range(
        start="2024-05-01 09:30",
        periods=n,
        freq="1h",
        tz="US/Eastern",
    )
    close = base + np.cumsum(np.random.randn(n) * 0.5)
    high = close + np.abs(np.random.randn(n) * 0.3)
    low = close - np.abs(np.random.randn(n) * 0.3)
    open_ = close + np.random.randn(n) * 0.15
    volume = np.random.randint(50000, 500000, n).astype(float)
    vwap = (high + low + close) / 3.0
    trade_count = np.random.randint(200, 2000, n).astype(float)

    df = pd.DataFrame({
        "timestamp": timestamps,
        "open": open_,
        "high": high,
        "low": low,
        "close": close,
        "volume": volume,
        "vwap": vwap,
        "trade_count": trade_count,
    })
    return df


@pytest.fixture
def sample_1d_bars():
    """Generate 252 rows of daily bar data (1 year)."""
    np.random.seed(7)
    n = 252
    base = 150.0
    timestamps = pd.bdate_range(
        start="2024-01-01",
        periods=n,
    )
    timestamps = timestamps.tz_localize("US/Eastern")
    close = base + np.cumsum(np.random.randn(n) * 1.5)
    high = close + np.abs(np.random.randn(n) * 1.0)
    low = close - np.abs(np.random.randn(n) * 1.0)
    open_ = close + np.random.randn(n) * 0.5
    volume = np.random.randint(1_000_000, 10_000_000, n).astype(float)
    vwap = (high + low + close) / 3.0
    trade_count = np.random.randint(1000, 10000, n).astype(float)

    df = pd.DataFrame({
        "timestamp": timestamps,
        "open": open_,
        "high": high,
        "low": low,
        "close": close,
        "volume": volume,
        "vwap": vwap,
        "trade_count": trade_count,
    })
    return df