59 lines
1.9 KiB
Python
59 lines
1.9 KiB
Python
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import pytest
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import pandas as pd
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import numpy as np
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from agents.technical import TechnicalAgent
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def make_ohlcv(n=100, base_price=100.0):
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np.random.seed(42)
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closes = base_price + np.cumsum(np.random.randn(n) * 2)
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df = pd.DataFrame({
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"timestamp": pd.date_range("2026-01-01", periods=n, freq="1h"),
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"open": closes - np.random.rand(n),
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"high": closes + np.abs(np.random.randn(n) * 2),
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"low": closes - np.abs(np.random.randn(n) * 2),
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"close": closes,
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"volume": np.random.randint(100, 10000, n).astype(float),
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"quote_volume": np.random.randint(100000, 1000000, n).astype(float),
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})
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return df
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def test_score_returns_0_to_100():
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agent = TechnicalAgent()
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df = make_ohlcv(100)
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score = agent.analyze(df)
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assert 0 <= score <= 100
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def test_score_with_uptrend():
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agent = TechnicalAgent()
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n = 100
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closes = np.linspace(100, 200, n)
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df = pd.DataFrame({
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"timestamp": pd.date_range("2026-01-01", periods=n, freq="1h"),
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"open": closes - 1, "high": closes + 2,
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"low": closes - 2, "close": closes,
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"volume": np.full(n, 5000.0),
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"quote_volume": np.full(n, 500000.0),
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})
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score = agent.analyze(df)
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assert score >= 55 # uptrend should score above average
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def test_score_with_downtrend():
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agent = TechnicalAgent()
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n = 100
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closes = np.linspace(200, 100, n)
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df = pd.DataFrame({
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"timestamp": pd.date_range("2026-01-01", periods=n, freq="1h"),
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"open": closes + 1, "high": closes + 2,
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"low": closes - 2, "close": closes,
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"volume": np.full(n, 5000.0),
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"quote_volume": np.full(n, 500000.0),
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})
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score = agent.analyze(df)
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assert score <= 45 # downtrend should score below average
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def test_insufficient_data_returns_50():
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agent = TechnicalAgent()
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df = make_ohlcv(5)
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score = agent.analyze(df)
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assert score == 50
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