"""Gemeinsame Fixtures: synthetische Kursverläufe ohne Netzwerkzugriff."""

from __future__ import annotations

import numpy as np
import pytest

from trademind.config import Config, PaperConfig, RiskConfig, RuleConfig
from trademind.models import Candles

BAR_MS = 300_000  # 5 Minuten


def make_candles(
    symbol: str = "BTC/USDT",
    n: int = 600,
    start_price: float = 30_000.0,
    trend: float = 0.0002,
    noise: float = 0.002,
    cycle: float = 0.0,
    cycle_period: int = 80,
    seed: int = 7,
    timeframe: str = "5m",
    start_ts: int = 1_700_000_000_000,
) -> Candles:
    """Erzeugt eine plausible OHLCV-Serie (geometrischer Random Walk plus optionale Welle)."""
    rng = np.random.default_rng(seed)
    steps = rng.normal(loc=trend, scale=noise, size=n)
    if cycle:
        steps += cycle * np.sin(2 * np.pi * np.arange(n) / cycle_period)
    close = start_price * np.exp(np.cumsum(steps))

    open_ = np.concatenate([[start_price], close[:-1]])
    spread = np.abs(rng.normal(0.0, noise * 0.8, size=n)) * close
    high = np.maximum(open_, close) + spread
    low = np.minimum(open_, close) - spread
    low = np.maximum(low, close * 0.5)
    volume = rng.lognormal(mean=3.0, sigma=0.4, size=n)
    timestamp = start_ts + np.arange(n, dtype=np.int64) * BAR_MS

    return Candles(
        symbol=symbol,
        timeframe=timeframe,
        timestamp=timestamp,
        open=open_,
        high=high,
        low=low,
        close=close,
        volume=volume,
    )


@pytest.fixture
def candles() -> Candles:
    return make_candles()


@pytest.fixture
def rules() -> RuleConfig:
    return RuleConfig()


@pytest.fixture
def paper_config() -> PaperConfig:
    return PaperConfig(starting_balance=10_000.0, fee_rate=0.001, slippage_bps=5.0)


@pytest.fixture
def risk_config() -> RiskConfig:
    return RiskConfig(max_open_positions=2, max_position_pct=0.25, cooldown_bars_after_exit=0)


@pytest.fixture
def base_config(tmp_path) -> Config:
    return Config.model_validate(
        {
            "mode": "backtest",
            "market": {"symbols": ["BTC/USDT"], "timeframe": "5m", "history_bars": 300},
            "paper": {"starting_balance": 10_000.0},
            "storage": {"database_path": str(tmp_path / "test.sqlite3")},
            "server": {"enabled": False},
            "strategy": {"learner": {"model_path": str(tmp_path / "model.npz"), "warmup_samples": 20}},
        }
    )