feat: technical, news, and social analysis agents

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

tests/test_news_agent.py

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+import pytest
+from agents.news import NewsAgent
+
+def test_score_positive_news():
+    agent = NewsAgent()
+    articles = [
+        {"title": "Bitcoin surges 10%", "sentiment_votes": {"positive": 10, "negative": 1}},
+        {"title": "BTC adoption grows", "sentiment_votes": {"positive": 8, "negative": 2}},
+        {"title": "Bitcoin rally continues", "sentiment_votes": {"positive": 15, "negative": 0}},
+    ]
+    score = agent.analyze(articles)
+    assert score >= 70
+
+def test_score_negative_news():
+    agent = NewsAgent()
+    articles = [
+        {"title": "Bitcoin crashes hard", "sentiment_votes": {"positive": 1, "negative": 10}},
+        {"title": "Crypto market in fear", "sentiment_votes": {"positive": 0, "negative": 15}},
+    ]
+    score = agent.analyze(articles)
+    assert score <= 35
+
+def test_score_no_news_returns_50():
+    agent = NewsAgent()
+    assert agent.analyze([]) == 50
+
+def test_score_mixed_news():
+    agent = NewsAgent()
+    articles = [{"title": "BTC up", "sentiment_votes": {"positive": 5, "negative": 5}}]
+    score = agent.analyze(articles)
+    assert 40 <= score <= 60

tests/test_social_agent.py

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+import pytest
+from agents.social import SocialAgent
+
+def test_score_bullish_social():
+    agent = SocialAgent()
+    sentiment = {"positive": 8, "negative": 1, "neutral": 1, "total": 10}
+    score = agent.analyze(sentiment, mention_trend=2.5)
+    assert score >= 70
+
+def test_score_bearish_social():
+    agent = SocialAgent()
+    sentiment = {"positive": 1, "negative": 8, "neutral": 1, "total": 10}
+    score = agent.analyze(sentiment, mention_trend=0.5)
+    assert score <= 35
+
+def test_no_data_returns_50():
+    agent = SocialAgent()
+    score = agent.analyze({"positive": 0, "negative": 0, "neutral": 0, "total": 0}, 1.0)
+    assert score == 50

tests/test_technical.py

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+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