import numpy as np
import pandas as pd
from src.live.tsmom_worker import TsmomWorker


def _df(n=300, slope=1.0):
    c = np.linspace(100, 100 + slope * n, n)
    ts = (pd.date_range("2023-01-01", periods=n, freq="1D", tz="UTC").astype("int64") // 10**6)
    return pd.DataFrame({"timestamp": ts, "open": c, "high": c, "low": c, "close": c, "volume": 1.0})


def test_tsmom_selects_full_consensus_uptrend(tmp_path):
    # tutti gli orizzonti positivi -> score=1>=thr; BTC su -> risk_on
    w = TsmomWorker(universe=["BTC", "AAA"], horizons=(63, 126, 252), thr=1.0,
                    gross=0.30, data_dir=tmp_path)
    data = {"BTC": _df(slope=1.0), "AAA": _df(slope=2.0)}
    w.tick(data)
    assert w.weights["BTC"] > 0 and w.weights["AAA"] > 0
    assert abs(sum(w.weights.values()) - 0.30) < 1e-9


def test_tsmom_flat_when_risk_off(tmp_path):
    w = TsmomWorker(universe=["BTC", "AAA"], thr=1.0, gross=0.30, data_dir=tmp_path)
    data = {"BTC": _df(slope=-1.0), "AAA": _df(slope=2.0)}
    w.tick(data)
    assert sum(w.weights.values()) == 0.0


def test_tsmom_persists_and_resumes(tmp_path):
    w = TsmomWorker(universe=["BTC", "AAA"], gross=0.30, data_dir=tmp_path)
    w.tick({"BTC": _df(slope=1.0), "AAA": _df(slope=2.0)})
    w2 = TsmomWorker(universe=["BTC", "AAA"], gross=0.30, data_dir=tmp_path)
    assert w2.weights == w.weights