import pytest
import pandas as pd
import numpy as np
from agents.technical import TechnicalAgent

def make_ohlcv(n=100, base_price=100.0):
    np.random.seed(42)
    closes = base_price + np.cumsum(np.random.randn(n) * 2)
    df = pd.DataFrame({
        "timestamp": pd.date_range("2026-01-01", periods=n, freq="1h"),
        "open": closes - np.random.rand(n),
        "high": closes + np.abs(np.random.randn(n) * 2),
        "low": closes - np.abs(np.random.randn(n) * 2),
        "close": closes,
        "volume": np.random.randint(100, 10000, n).astype(float),
        "quote_volume": np.random.randint(100000, 1000000, n).astype(float),
    })
    return df

def test_score_returns_0_to_100():
    agent = TechnicalAgent()
    df = make_ohlcv(100)
    score = agent.analyze(df)
    assert 0 <= score <= 100

def test_score_with_uptrend():
    agent = TechnicalAgent()
    n = 100
    closes = np.linspace(100, 200, n)
    df = pd.DataFrame({
        "timestamp": pd.date_range("2026-01-01", periods=n, freq="1h"),
        "open": closes - 1, "high": closes + 2,
        "low": closes - 2, "close": closes,
        "volume": np.full(n, 5000.0),
        "quote_volume": np.full(n, 500000.0),
    })
    score = agent.analyze(df)
    assert score >= 55  # uptrend should score above average

def test_score_with_downtrend():
    agent = TechnicalAgent()
    n = 100
    closes = np.linspace(200, 100, n)
    df = pd.DataFrame({
        "timestamp": pd.date_range("2026-01-01", periods=n, freq="1h"),
        "open": closes + 1, "high": closes + 2,
        "low": closes - 2, "close": closes,
        "volume": np.full(n, 5000.0),
        "quote_volume": np.full(n, 500000.0),
    })
    score = agent.analyze(df)
    assert score <= 45  # downtrend should score below average

def test_insufficient_data_returns_50():
    agent = TechnicalAgent()
    df = make_ohlcv(5)
    score = agent.analyze(df)
    assert score == 50