import numpy as np
import pandas as pd

from aitrader.features.indicators import compute_features
from aitrader.features.orderbook import summarize_orderbook


def _fake_ohlcv(n=250, seed=42):
    rng = np.random.default_rng(seed)
    close = 100 + np.cumsum(rng.normal(0, 1, n))
    high = close + rng.uniform(0, 1, n)
    low = close - rng.uniform(0, 1, n)
    open_ = close + rng.normal(0, 0.5, n)
    vol = rng.uniform(1, 10, n)
    ts = pd.date_range("2025-01-01", periods=n, freq="15min", tz="UTC")
    return pd.DataFrame({"ts": ts, "open": open_, "high": high, "low": low, "close": close, "volume": vol})


def test_compute_features_returns_keys():
    df = _fake_ohlcv()
    f = compute_features(df)
    assert {"close", "rsi14", "macd", "ema20", "ema50", "ema200", "atr14"}.issubset(f.keys())
    assert 0 <= f["rsi14"] <= 100


def test_orderbook_summary():
    ob = {"bids": [[100.0, 1.0], [99.5, 2.0]], "asks": [[100.5, 1.5], [101.0, 1.0]]}
    s = summarize_orderbook(ob)
    assert s["best_bid"] == 100.0
    assert s["best_ask"] == 100.5
    assert s["spread_bps"] > 0
    assert -1 <= s["depth_imbalance"] <= 1