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"""Tests for technical indicator library."""
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
import numpy as np
import pandas as pd
import pytest
from strategies.indicators.momentum import rsi, stochastic
from strategies.indicators.trend import adx, ema, macd, sma
from strategies.indicators.volatility import atr, bollinger_bands
from strategies.indicators.volume import obv, volume_ratio, volume_sma
class TestTrend:
def test_sma_basic(self):
s = pd.Series([1, 2, 3, 4, 5])
result = sma(s, 3)
assert result.iloc[-1] == pytest.approx(4.0)
def test_ema_basic(self):
s = pd.Series([1, 2, 3, 4, 5])
result = ema(s, 3)
assert not np.isnan(result.iloc[-1])
def test_macd_returns_three_series(self):
s = pd.Series(range(50), dtype=float)
macd_line, signal_line, hist = macd(s)
assert len(macd_line) == 50
assert len(signal_line) == 50
assert len(hist) == 50
def test_adx_returns_series(self):
n = 60
highs = pd.Series(np.random.uniform(100, 110, n))
lows = pd.Series(np.random.uniform(90, 100, n))
closes = pd.Series(np.random.uniform(95, 105, n))
result = adx(highs, lows, closes, period=14)
assert len(result) == n
# Last value should be a number (not NaN for 60 bars with period 14)
assert not np.isnan(result.iloc[-1])
def test_adx_trending_market(self):
# Strong uptrend should have high ADX
n = 100
closes = pd.Series([100 + i * 2 for i in range(n)], dtype=float)
highs = closes + 3
lows = closes - 3
result = adx(highs, lows, closes)
assert result.iloc[-1] > 25 # Strong trend
def test_adx_ranging_market(self):
# Sideways market should have low ADX
n = 100
closes = pd.Series([100 + (i % 5) - 2 for i in range(n)], dtype=float)
highs = closes + 1
lows = closes - 1
result = adx(highs, lows, closes)
assert result.iloc[-1] < 25
class TestVolatility:
def test_atr_basic(self):
n = 30
highs = pd.Series([110] * n, dtype=float)
lows = pd.Series([90] * n, dtype=float)
closes = pd.Series([100] * n, dtype=float)
result = atr(highs, lows, closes, period=14)
assert result.iloc[-1] == pytest.approx(20.0, rel=0.01)
def test_bollinger_bands_width(self):
s = pd.Series([100] * 20 + [110, 90] * 5, dtype=float)
upper, mid, lower = bollinger_bands(s, period=20)
assert upper.iloc[-1] > mid.iloc[-1] > lower.iloc[-1]
class TestMomentum:
def test_rsi_oversold(self):
# Declining prices should give low RSI
s = pd.Series([100 - i for i in range(30)], dtype=float)
result = rsi(s, period=14)
assert result.iloc[-1] < 30
def test_rsi_overbought(self):
s = pd.Series([100 + i for i in range(30)], dtype=float)
result = rsi(s, period=14)
assert result.iloc[-1] > 70
def test_stochastic_returns_k_and_d(self):
n = 30
highs = pd.Series(np.random.uniform(100, 110, n))
lows = pd.Series(np.random.uniform(90, 100, n))
closes = pd.Series(np.random.uniform(95, 105, n))
k, d = stochastic(highs, lows, closes)
assert len(k) == n
assert len(d) == n
class TestVolume:
def test_volume_sma(self):
v = pd.Series([100] * 20 + [200] * 5, dtype=float)
result = volume_sma(v, period=20)
assert result.iloc[-1] > 100
def test_volume_ratio_above_average(self):
v = pd.Series([100] * 20 + [300], dtype=float)
result = volume_ratio(v, period=20)
assert result.iloc[-1] > 2.0
def test_obv_up(self):
closes = pd.Series([100, 101, 102, 103], dtype=float)
volumes = pd.Series([10, 10, 10, 10], dtype=float)
result = obv(closes, volumes)
assert result.iloc[-1] > 0 # All up moves = positive OBV
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