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https://github.com/NicolasBohn/NexQuant.git
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TPTBusiness
117 lines
3.8 KiB
Python
117 lines
3.8 KiB
Python
"""Tests for rl/indicators.py — pure technical indicator functions."""
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from __future__ import annotations
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import sys
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from pathlib import Path
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import numpy as np
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import pandas as pd
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import pytest
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PROJECT_ROOT = Path(__file__).parent.parent.parent
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sys.path.insert(0, str(PROJECT_ROOT))
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def _load_indicators():
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import importlib.util
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spec = importlib.util.spec_from_file_location(
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"indicators",
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PROJECT_ROOT / "rdagent/components/coder/rl/indicators.py",
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)
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mod = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(mod)
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return mod
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@pytest.fixture(scope="module")
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def indicators():
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return _load_indicators()
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@pytest.fixture
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def prices():
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rng = np.random.default_rng(42)
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return pd.Series(100 + rng.normal(0, 1, 200).cumsum())
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class TestRSI:
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def test_returns_series(self, indicators, prices):
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rsi = indicators.calculate_rsi(prices, period=14)
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assert isinstance(rsi, pd.Series)
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assert len(rsi) == len(prices)
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def test_first_period_is_nan(self, indicators, prices):
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rsi = indicators.calculate_rsi(prices, period=14)
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assert rsi.iloc[:13].isna().all()
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assert not np.isnan(rsi.iloc[14])
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def test_range_between_0_and_100(self, indicators, prices):
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rsi = indicators.calculate_rsi(prices, period=14)
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valid = rsi.dropna()
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assert (valid >= 0).all()
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assert (valid <= 100).all()
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def test_constant_prices_gives_neutral_rsi(self, indicators):
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const = pd.Series([100.0] * 50)
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rsi = indicators.calculate_rsi(const, period=14)
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# With no change, gain=loss=0 → RSI = NaN (division by zero)
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valid = rsi.dropna()
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assert len(valid) == 0 # all NaN when no movement
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class TestMACD:
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def test_returns_dataframe(self, indicators, prices):
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macd = indicators.calculate_macd(prices)
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assert isinstance(macd, pd.DataFrame)
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assert list(macd.columns) == ["macd", "signal", "histogram"]
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def test_histogram_is_macd_minus_signal(self, indicators, prices):
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macd = indicators.calculate_macd(prices)
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computed = macd["macd"] - macd["signal"]
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pd.testing.assert_series_equal(macd["histogram"], computed, check_names=False)
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class TestBollinger:
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def test_returns_dataframe(self, indicators, prices):
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bb = indicators.calculate_bollinger_bands(prices, period=20)
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assert isinstance(bb, pd.DataFrame)
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assert list(bb.columns) == ["upper", "middle", "lower"]
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def test_upper_above_middle_lower_below(self, indicators, prices):
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bb = indicators.calculate_bollinger_bands(prices, period=20)
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valid = bb.dropna()
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assert (valid["upper"] > valid["middle"]).all()
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assert (valid["lower"] < valid["middle"]).all()
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class TestATR:
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def test_returns_series(self, indicators, prices):
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high = prices * 1.01
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low = prices * 0.99
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close = prices
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atr = indicators.calculate_atr(high, low, close, period=14)
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assert isinstance(atr, pd.Series)
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assert len(atr) == len(prices)
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def test_non_negative(self, indicators, prices):
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high = prices * 1.01
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low = prices * 0.99
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atr = indicators.calculate_atr(high, low, prices, period=14)
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valid = atr.dropna()
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assert (valid >= 0).all()
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class TestCCI:
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def test_returns_series(self, indicators, prices):
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cci = indicators.calculate_cci(prices, prices * 1.01, prices * 0.99, period=20)
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assert isinstance(cci, pd.Series)
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class TestPrepareFeatures:
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def test_returns_dataframe_with_columns(self, indicators, prices):
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df_input = pd.DataFrame({"close": prices})
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df = indicators.prepare_features(df_input, ["rsi", "macd"])
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assert isinstance(df, pd.DataFrame)
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assert len(df.columns) >= 3 # close + at least rsi + macd columns
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