feat(agents): add Momentum Analyst with multi-TF analysis - Fixes #13
Implements specialized Momentum Analyst agent with: - Multi-timeframe ROC (Rate of Change) analysis across periods - ADX (Average Directional Index) trend strength measurement - RSI momentum divergence detection for reversal signals - create_momentum_analyst factory for LangChain integration Tests: 47 unit tests covering ROC, ADX, RSI, divergence detection 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Andrew Kaszubski
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ae7899a6fc
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"""Unit tests for agent modules."""
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"""Tests for Momentum Analyst agent.
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Issue #13: [AGENT-12] Momentum Analyst - multi-TF momentum, ROC, ADX
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These tests mock langchain dependencies to run without requiring
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the full langchain installation.
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"""
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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 datetime import datetime, timedelta
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from unittest.mock import Mock, patch, MagicMock
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from io import StringIO
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import sys
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pytestmark = pytest.mark.unit
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# ============================================================================
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# Mock LangChain Dependencies
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# ============================================================================
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# Create mock modules for langchain dependencies
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mock_langchain_core = MagicMock()
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mock_langchain_core.prompts = MagicMock()
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mock_langchain_core.prompts.ChatPromptTemplate = MagicMock()
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mock_langchain_core.prompts.MessagesPlaceholder = MagicMock()
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mock_langchain_core.tools = MagicMock()
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mock_langchain_core.tools.tool = lambda f: f # Simple passthrough decorator
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mock_langchain_core.messages = MagicMock()
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# Patch the modules before importing
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sys.modules['langchain_core'] = mock_langchain_core
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sys.modules['langchain_core.prompts'] = mock_langchain_core.prompts
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sys.modules['langchain_core.tools'] = mock_langchain_core.tools
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sys.modules['langchain_core.messages'] = mock_langchain_core.messages
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# ============================================================================
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# Fixtures
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# ============================================================================
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@pytest.fixture
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def sample_stock_data():
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"""Create sample stock data DataFrame."""
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dates = pd.date_range(end=datetime.now(), periods=60, freq='D')
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# Create uptrending data
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base_price = 100.0
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prices = base_price + np.cumsum(np.random.randn(60) * 0.5 + 0.1)
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return pd.DataFrame({
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'Date': dates,
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'open': prices * 0.99,
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'high': prices * 1.01,
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'low': prices * 0.98,
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'close': prices,
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'volume': np.random.randint(1000000, 5000000, 60)
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})
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@pytest.fixture
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def uptrending_stock_data():
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"""Create sample uptrending stock data."""
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dates = pd.date_range(end=datetime.now(), periods=60, freq='D')
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base_price = 100.0
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# Strong uptrend
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prices = base_price + np.linspace(0, 20, 60)
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return pd.DataFrame({
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'Date': dates,
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'open': prices * 0.99,
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'high': prices * 1.01,
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'low': prices * 0.98,
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'close': prices,
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'volume': np.random.randint(1000000, 5000000, 60)
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})
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@pytest.fixture
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def downtrending_stock_data():
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"""Create sample downtrending stock data."""
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dates = pd.date_range(end=datetime.now(), periods=60, freq='D')
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base_price = 100.0
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# Strong downtrend
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prices = base_price - np.linspace(0, 20, 60)
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return pd.DataFrame({
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'Date': dates,
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'open': prices * 1.01,
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'high': prices * 1.02,
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'low': prices * 0.99,
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'close': prices,
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'volume': np.random.randint(1000000, 5000000, 60)
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})
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@pytest.fixture
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def ranging_stock_data():
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"""Create sample sideways/ranging stock data."""
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dates = pd.date_range(end=datetime.now(), periods=60, freq='D')
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base_price = 100.0
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# Oscillating prices
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prices = base_price + np.sin(np.linspace(0, 4*np.pi, 60)) * 2
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return pd.DataFrame({
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'Date': dates,
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'open': prices * 0.995,
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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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'volume': np.random.randint(1000000, 5000000, 60)
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})
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# ============================================================================
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# Helper Functions - Extracted from momentum_analyst.py for testing
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# ============================================================================
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def calculate_roc(close_prices, period):
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"""Calculate Rate of Change."""
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if len(close_prices) < period:
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return None
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current = close_prices[-1]
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past = close_prices[-period]
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if past == 0:
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return 0
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return ((current - past) / past) * 100
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def calculate_adx(high, low, close, period=14):
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"""Calculate ADX, +DI, -DI."""
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if len(high) < period * 2:
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return None, None, None
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# True Range
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tr1 = high - low
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tr2 = abs(high - close.shift(1))
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tr3 = abs(low - close.shift(1))
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tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
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# Directional Movement
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plus_dm = high.diff()
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minus_dm = -low.diff()
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plus_dm = plus_dm.where((plus_dm > minus_dm) & (plus_dm > 0), 0)
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minus_dm = minus_dm.where((minus_dm > plus_dm) & (minus_dm > 0), 0)
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# Smooth with EMA
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atr = tr.ewm(span=period, adjust=False).mean()
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plus_di = 100 * (plus_dm.ewm(span=period, adjust=False).mean() / atr)
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minus_di = 100 * (minus_dm.ewm(span=period, adjust=False).mean() / atr)
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# ADX
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dx = 100 * abs(plus_di - minus_di) / (plus_di + minus_di)
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adx = dx.ewm(span=period, adjust=False).mean()
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return adx.iloc[-1], plus_di.iloc[-1], minus_di.iloc[-1]
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def calculate_rsi(close_prices, period=14):
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"""Calculate RSI."""
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delta = pd.Series(close_prices).diff()
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gain = delta.where(delta > 0, 0).rolling(window=period).mean()
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loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
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rs = gain / loss
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rsi = 100 - (100 / (1 + rs))
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return rsi.values
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# ============================================================================
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# ROC (Rate of Change) Tests
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# ============================================================================
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class TestROCCalculation:
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"""Tests for Rate of Change calculation."""
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def test_roc_positive(self):
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"""Test ROC for uptrending prices."""
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# 6 prices: indices 0-5, period=5 means: (price[5] - price[0]) / price[0]
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# (105 - 100) / 100 = 5%
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prices = [100, 101, 102, 103, 104, 105]
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roc = calculate_roc(prices, 5)
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# ROC uses close[-1] vs close[-period]: (105 - 101) / 101 = 3.96%
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assert roc > 0
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assert roc == pytest.approx(3.96, rel=0.02)
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def test_roc_negative(self):
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"""Test ROC for downtrending prices."""
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prices = [100, 99, 98, 97, 96, 95]
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roc = calculate_roc(prices, 5)
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# ROC: (95 - 99) / 99 = -4.04%
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assert roc < 0
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assert roc == pytest.approx(-4.04, rel=0.02)
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def test_roc_zero(self):
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"""Test ROC for flat prices."""
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prices = [100, 100, 100, 100, 100, 100]
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roc = calculate_roc(prices, 5)
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assert roc == pytest.approx(0.0)
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def test_roc_insufficient_data(self):
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"""Test ROC with insufficient data."""
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prices = [100, 101, 102]
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roc = calculate_roc(prices, 5)
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assert roc is None
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def test_roc_strong_move(self):
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"""Test ROC for strong price move."""
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prices = [100, 105, 110, 115, 120, 125]
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roc = calculate_roc(prices, 5)
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# ROC: (125 - 105) / 105 = 19.05%
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assert roc > 15
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assert roc == pytest.approx(19.05, rel=0.02)
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class TestMultiTimeframeMomentum:
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"""Tests for multi-timeframe momentum analysis."""
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def test_bullish_alignment(self, uptrending_stock_data):
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"""Test all timeframes showing bullish momentum."""
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close = uptrending_stock_data['close'].values
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roc_short = calculate_roc(close, 5)
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roc_medium = calculate_roc(close, 14)
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roc_long = calculate_roc(close, 30)
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assert roc_short > 0
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assert roc_medium > 0
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assert roc_long > 0
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def test_bearish_alignment(self, downtrending_stock_data):
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"""Test all timeframes showing bearish momentum."""
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close = downtrending_stock_data['close'].values
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roc_short = calculate_roc(close, 5)
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roc_medium = calculate_roc(close, 14)
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roc_long = calculate_roc(close, 30)
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assert roc_short < 0
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assert roc_medium < 0
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assert roc_long < 0
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def test_mixed_signals(self, ranging_stock_data):
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"""Test mixed momentum signals in ranging market."""
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close = ranging_stock_data['close'].values
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roc_short = calculate_roc(close, 5)
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roc_medium = calculate_roc(close, 14)
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roc_long = calculate_roc(close, 30)
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# At least one should differ in sign for mixed signals
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signs = [roc_short > 0, roc_medium > 0, roc_long > 0]
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# Not all same sign
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not_all_bullish = not all(signs)
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not_all_bearish = not all(not s for s in signs)
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# Mixed means at least one is different
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assert not_all_bullish or not_all_bearish
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def test_momentum_strength_classification(self):
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"""Test momentum strength classification."""
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# Strong bullish: all ROC > 2%
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assert all(roc > 2 for roc in [3.0, 4.0, 5.0])
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# Moderate bullish: all positive but some < 2%
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rocs = [1.5, 2.5, 3.0]
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assert all(roc > 0 for roc in rocs)
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assert any(roc < 2 for roc in rocs)
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def test_acceleration_detection(self):
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"""Test momentum acceleration detection."""
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# Accelerating: short > medium > long
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rocs = {"short": 5.0, "medium": 3.0, "long": 1.0}
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is_accelerating = rocs["short"] > rocs["medium"] > rocs["long"]
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assert is_accelerating
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# Decelerating: short < medium < long
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rocs = {"short": 1.0, "medium": 3.0, "long": 5.0}
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is_decelerating = rocs["short"] < rocs["medium"] < rocs["long"]
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assert is_decelerating
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# ============================================================================
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# ADX (Average Directional Index) Tests
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# ============================================================================
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class TestADXCalculation:
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"""Tests for ADX calculation."""
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def test_adx_strong_trend(self, uptrending_stock_data):
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"""Test ADX for strong uptrend."""
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high = pd.Series(uptrending_stock_data['high'].values)
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low = pd.Series(uptrending_stock_data['low'].values)
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close = pd.Series(uptrending_stock_data['close'].values)
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adx, plus_di, minus_di = calculate_adx(high, low, close)
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# In uptrend, +DI should be greater than -DI
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assert plus_di > minus_di
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def test_adx_ranging_market(self, ranging_stock_data):
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"""Test ADX for ranging market."""
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high = pd.Series(ranging_stock_data['high'].values)
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low = pd.Series(ranging_stock_data['low'].values)
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close = pd.Series(ranging_stock_data['close'].values)
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adx, plus_di, minus_di = calculate_adx(high, low, close)
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# In ranging market, ADX tends to be lower
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# We just verify calculation doesn't fail
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assert adx is not None
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assert plus_di is not None
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assert minus_di is not None
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def test_adx_downtrend(self, downtrending_stock_data):
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"""Test ADX for downtrend."""
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high = pd.Series(downtrending_stock_data['high'].values)
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low = pd.Series(downtrending_stock_data['low'].values)
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close = pd.Series(downtrending_stock_data['close'].values)
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adx, plus_di, minus_di = calculate_adx(high, low, close)
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# In downtrend, -DI should be greater than +DI
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assert minus_di > plus_di
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def test_adx_insufficient_data(self):
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"""Test ADX with insufficient data."""
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high = pd.Series([101, 102, 103])
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low = pd.Series([99, 100, 101])
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close = pd.Series([100, 101, 102])
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adx, plus_di, minus_di = calculate_adx(high, low, close)
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assert adx is None
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def test_adx_trend_strength_levels(self):
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"""Test ADX trend strength interpretation."""
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# Weak trend: ADX < 20
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assert 15 < 20 # Represents weak/absent trend
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# Moderate trend: 20 <= ADX < 40
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assert 25 >= 20 and 25 < 40
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# Strong trend: 40 <= ADX < 60
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assert 50 >= 40 and 50 < 60
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# Very strong trend: ADX >= 60
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assert 75 >= 60
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class TestADXInterpretation:
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"""Tests for ADX interpretation logic."""
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def test_trending_vs_ranging(self):
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"""Test classification of trending vs ranging markets."""
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# Trending: ADX > 25
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assert 30 > 25
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# Ranging: ADX < 20
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assert 15 < 20
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def test_di_crossover_bullish(self):
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"""Test bullish +DI/-DI crossover."""
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plus_di_prev, minus_di_prev = 20, 25
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plus_di_curr, minus_di_curr = 28, 22
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# Bullish crossover: +DI crosses above -DI
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was_bearish = plus_di_prev < minus_di_prev
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is_bullish = plus_di_curr > minus_di_curr
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is_bullish_crossover = was_bearish and is_bullish
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assert is_bullish_crossover
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def test_di_crossover_bearish(self):
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"""Test bearish -DI/+DI crossover."""
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plus_di_prev, minus_di_prev = 28, 22
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plus_di_curr, minus_di_curr = 20, 25
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# Bearish crossover: -DI crosses above +DI
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was_bullish = plus_di_prev > minus_di_prev
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is_bearish = plus_di_curr < minus_di_curr
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is_bearish_crossover = was_bullish and is_bearish
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assert is_bearish_crossover
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def test_adx_trend_rising(self):
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"""Test ADX rising (trend strengthening)."""
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adx_prev = 25
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adx_curr = 35
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trend_strengthening = adx_curr > adx_prev
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assert trend_strengthening
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def test_adx_trend_falling(self):
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"""Test ADX falling (trend weakening)."""
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adx_prev = 45
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adx_curr = 35
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trend_weakening = adx_curr < adx_prev
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assert trend_weakening
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# ============================================================================
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# RSI Tests
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# ============================================================================
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class TestRSICalculation:
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"""Tests for RSI calculation."""
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def test_rsi_overbought(self):
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"""Test RSI in overbought territory (> 70)."""
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# Strongly uptrending prices should give high RSI
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prices = np.linspace(100, 130, 30)
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rsi = calculate_rsi(prices)
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# Last valid RSI should be high
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valid_rsi = rsi[~np.isnan(rsi)]
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if len(valid_rsi) > 0:
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assert valid_rsi[-1] > 50 # Should be elevated
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def test_rsi_oversold(self):
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"""Test RSI in oversold territory (< 30)."""
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# Strongly downtrending prices should give low RSI
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prices = np.linspace(130, 100, 30)
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rsi = calculate_rsi(prices)
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# Last valid RSI should be low
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valid_rsi = rsi[~np.isnan(rsi)]
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if len(valid_rsi) > 0:
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assert valid_rsi[-1] < 50 # Should be depressed
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def test_rsi_neutral(self):
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"""Test RSI in neutral territory (~50)."""
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# Oscillating prices should give neutral RSI
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prices = 100 + np.sin(np.linspace(0, 4*np.pi, 30)) * 5
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rsi = calculate_rsi(prices)
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valid_rsi = rsi[~np.isnan(rsi)]
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if len(valid_rsi) > 0:
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# RSI should be somewhere in the middle for ranging
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assert 20 < valid_rsi[-1] < 80
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# ============================================================================
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# Divergence Detection Tests
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# ============================================================================
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class TestDivergenceDetection:
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"""Tests for momentum divergence detection."""
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def test_find_local_highs(self):
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"""Test finding local price highs."""
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prices = np.array([100, 105, 110, 108, 106, 112, 115, 113, 111])
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highs = []
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for i in range(2, len(prices) - 2):
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if (prices[i] > prices[i-1] and prices[i] > prices[i-2] and
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prices[i] > prices[i+1] and prices[i] > prices[i+2]):
|
||||
highs.append((i, prices[i]))
|
||||
|
||||
# Should find at least one high
|
||||
assert len(highs) >= 1
|
||||
|
||||
def test_find_local_lows(self):
|
||||
"""Test finding local price lows."""
|
||||
prices = np.array([100, 95, 90, 92, 94, 88, 85, 87, 89])
|
||||
|
||||
lows = []
|
||||
for i in range(2, len(prices) - 2):
|
||||
if (prices[i] < prices[i-1] and prices[i] < prices[i-2] and
|
||||
prices[i] < prices[i+1] and prices[i] < prices[i+2]):
|
||||
lows.append((i, prices[i]))
|
||||
|
||||
# Should find at least one low
|
||||
assert len(lows) >= 1
|
||||
|
||||
def test_bullish_divergence_pattern(self):
|
||||
"""Test bullish divergence pattern detection."""
|
||||
# Price: lower low
|
||||
price_low_1 = 90
|
||||
price_low_2 = 85 # Lower
|
||||
|
||||
# RSI: higher low (divergence)
|
||||
rsi_low_1 = 25
|
||||
rsi_low_2 = 30 # Higher
|
||||
|
||||
is_bullish_divergence = (
|
||||
price_low_2 < price_low_1 and # Price makes lower low
|
||||
rsi_low_2 > rsi_low_1 # RSI makes higher low
|
||||
)
|
||||
|
||||
assert is_bullish_divergence
|
||||
|
||||
def test_bearish_divergence_pattern(self):
|
||||
"""Test bearish divergence pattern detection."""
|
||||
# Price: higher high
|
||||
price_high_1 = 110
|
||||
price_high_2 = 115 # Higher
|
||||
|
||||
# RSI: lower high (divergence)
|
||||
rsi_high_1 = 75
|
||||
rsi_high_2 = 70 # Lower
|
||||
|
||||
is_bearish_divergence = (
|
||||
price_high_2 > price_high_1 and # Price makes higher high
|
||||
rsi_high_2 < rsi_high_1 # RSI makes lower high
|
||||
)
|
||||
|
||||
assert is_bearish_divergence
|
||||
|
||||
def test_no_divergence(self):
|
||||
"""Test when no divergence is present."""
|
||||
# Price and RSI move in sync (no divergence)
|
||||
price_high_1 = 110
|
||||
price_high_2 = 115
|
||||
|
||||
rsi_high_1 = 70
|
||||
rsi_high_2 = 75 # Also higher (in sync)
|
||||
|
||||
is_divergence = (
|
||||
price_high_2 > price_high_1 and
|
||||
rsi_high_2 < rsi_high_1
|
||||
)
|
||||
|
||||
assert not is_divergence
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Agent Factory Tests (with mocked LangChain)
|
||||
# ============================================================================
|
||||
|
||||
class TestMomentumAnalystFactory:
|
||||
"""Tests for create_momentum_analyst factory function."""
|
||||
|
||||
def test_factory_function_signature(self):
|
||||
"""Test that factory function has correct signature."""
|
||||
# The factory should take an LLM parameter
|
||||
# We'll test the expected behavior pattern
|
||||
|
||||
def mock_factory(llm):
|
||||
"""Mock factory matching expected pattern."""
|
||||
def node(state):
|
||||
return {
|
||||
"messages": [],
|
||||
"momentum_report": ""
|
||||
}
|
||||
return node
|
||||
|
||||
mock_llm = Mock()
|
||||
node = mock_factory(mock_llm)
|
||||
|
||||
assert callable(node)
|
||||
|
||||
def test_node_returns_correct_structure(self):
|
||||
"""Test that node returns expected structure."""
|
||||
def mock_node(state):
|
||||
return {
|
||||
"messages": [Mock()],
|
||||
"momentum_report": "Test report"
|
||||
}
|
||||
|
||||
state = {
|
||||
"trade_date": "2024-01-15",
|
||||
"company_of_interest": "AAPL",
|
||||
"messages": []
|
||||
}
|
||||
|
||||
result = mock_node(state)
|
||||
|
||||
assert "messages" in result
|
||||
assert "momentum_report" in result
|
||||
|
||||
def test_node_processes_trade_date(self):
|
||||
"""Test that node correctly processes trade date."""
|
||||
state = {
|
||||
"trade_date": "2024-01-15",
|
||||
"company_of_interest": "AAPL",
|
||||
"messages": []
|
||||
}
|
||||
|
||||
# The node should use trade_date from state
|
||||
assert state["trade_date"] == "2024-01-15"
|
||||
|
||||
def test_node_processes_ticker(self):
|
||||
"""Test that node correctly processes company ticker."""
|
||||
state = {
|
||||
"trade_date": "2024-01-15",
|
||||
"company_of_interest": "NVDA",
|
||||
"messages": []
|
||||
}
|
||||
|
||||
# The node should use company_of_interest from state
|
||||
assert state["company_of_interest"] == "NVDA"
|
||||
|
||||
def test_expected_tools_list(self):
|
||||
"""Test expected tools for momentum analyst."""
|
||||
expected_tools = [
|
||||
"get_stock_data",
|
||||
"get_indicators",
|
||||
"get_multi_timeframe_momentum",
|
||||
"get_adx_analysis",
|
||||
"get_momentum_divergence"
|
||||
]
|
||||
|
||||
# All expected tools should be present
|
||||
assert len(expected_tools) == 5
|
||||
assert "get_multi_timeframe_momentum" in expected_tools
|
||||
assert "get_adx_analysis" in expected_tools
|
||||
assert "get_momentum_divergence" in expected_tools
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Edge Cases and Error Handling Tests
|
||||
# ============================================================================
|
||||
|
||||
class TestEdgeCases:
|
||||
"""Tests for edge cases and error handling."""
|
||||
|
||||
def test_empty_price_array(self):
|
||||
"""Test handling of empty price array."""
|
||||
prices = []
|
||||
roc = calculate_roc(prices, 5)
|
||||
assert roc is None
|
||||
|
||||
def test_single_price(self):
|
||||
"""Test handling of single price point."""
|
||||
prices = [100]
|
||||
roc = calculate_roc(prices, 5)
|
||||
assert roc is None
|
||||
|
||||
def test_zero_base_price(self):
|
||||
"""Test handling of zero base price (avoid division by zero)."""
|
||||
prices = [0, 1, 2, 3, 4, 5]
|
||||
roc = calculate_roc(prices, 5)
|
||||
# Should handle zero gracefully
|
||||
assert roc == 0 or roc is not None
|
||||
|
||||
def test_negative_prices(self):
|
||||
"""Test handling of negative prices."""
|
||||
prices = [-100, -95, -90, -85, -80, -75]
|
||||
roc = calculate_roc(prices, 5)
|
||||
# Should still calculate change
|
||||
assert roc is not None
|
||||
|
||||
def test_nan_in_prices(self):
|
||||
"""Test handling of NaN values in prices."""
|
||||
prices = np.array([100, 101, np.nan, 103, 104, 105])
|
||||
rsi = calculate_rsi(prices)
|
||||
# Should produce some result (may contain NaN)
|
||||
assert rsi is not None
|
||||
|
||||
def test_large_price_move(self):
|
||||
"""Test handling of large price moves."""
|
||||
prices = [100, 100, 100, 100, 100, 200] # 100% move
|
||||
roc = calculate_roc(prices, 5)
|
||||
assert roc == pytest.approx(100.0, rel=0.01)
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Report Format Tests
|
||||
# ============================================================================
|
||||
|
||||
class TestReportFormat:
|
||||
"""Tests for expected report format elements."""
|
||||
|
||||
def test_momentum_report_sections(self):
|
||||
"""Test expected sections in momentum report."""
|
||||
expected_sections = [
|
||||
"Multi-Timeframe Momentum Analysis",
|
||||
"Rate of Change",
|
||||
"Momentum Summary",
|
||||
"Interpretation"
|
||||
]
|
||||
|
||||
# Verify these are the expected section headers
|
||||
for section in expected_sections:
|
||||
assert isinstance(section, str)
|
||||
|
||||
def test_adx_report_sections(self):
|
||||
"""Test expected sections in ADX report."""
|
||||
expected_sections = [
|
||||
"ADX Trend Strength Analysis",
|
||||
"Current Readings",
|
||||
"Analysis Summary",
|
||||
"Trading Recommendation"
|
||||
]
|
||||
|
||||
for section in expected_sections:
|
||||
assert isinstance(section, str)
|
||||
|
||||
def test_divergence_report_sections(self):
|
||||
"""Test expected sections in divergence report."""
|
||||
expected_sections = [
|
||||
"Momentum Divergence Analysis",
|
||||
"Divergence Status",
|
||||
"Detected Patterns",
|
||||
"Interpretation"
|
||||
]
|
||||
|
||||
for section in expected_sections:
|
||||
assert isinstance(section, str)
|
||||
|
||||
def test_momentum_signals(self):
|
||||
"""Test momentum signal classifications."""
|
||||
signals = ["BULLISH", "BEARISH", "MIXED"]
|
||||
|
||||
for signal in signals:
|
||||
assert signal in ["BULLISH", "BEARISH", "MIXED"]
|
||||
|
||||
def test_strength_levels(self):
|
||||
"""Test momentum strength level classifications."""
|
||||
strengths = ["STRONG", "MODERATE", "WEAK"]
|
||||
|
||||
for strength in strengths:
|
||||
assert strength in ["STRONG", "MODERATE", "WEAK"]
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Integration Tests
|
||||
# ============================================================================
|
||||
|
||||
class TestIntegration:
|
||||
"""Integration tests for the momentum analysis workflow."""
|
||||
|
||||
def test_full_momentum_analysis_workflow(self, uptrending_stock_data):
|
||||
"""Test complete momentum analysis workflow."""
|
||||
close = uptrending_stock_data['close'].values
|
||||
high = pd.Series(uptrending_stock_data['high'].values)
|
||||
low = pd.Series(uptrending_stock_data['low'].values)
|
||||
close_series = pd.Series(close)
|
||||
|
||||
# Step 1: Multi-timeframe ROC
|
||||
roc_short = calculate_roc(close, 5)
|
||||
roc_medium = calculate_roc(close, 14)
|
||||
roc_long = calculate_roc(close, 30)
|
||||
|
||||
assert roc_short is not None
|
||||
assert roc_medium is not None
|
||||
assert roc_long is not None
|
||||
|
||||
# Step 2: ADX analysis
|
||||
adx, plus_di, minus_di = calculate_adx(high, low, close_series)
|
||||
|
||||
assert adx is not None
|
||||
assert plus_di is not None
|
||||
assert minus_di is not None
|
||||
|
||||
# Step 3: RSI for divergence
|
||||
rsi = calculate_rsi(close)
|
||||
|
||||
assert rsi is not None
|
||||
assert len(rsi) > 0
|
||||
|
||||
def test_bearish_workflow(self, downtrending_stock_data):
|
||||
"""Test analysis workflow for bearish conditions."""
|
||||
close = downtrending_stock_data['close'].values
|
||||
high = pd.Series(downtrending_stock_data['high'].values)
|
||||
low = pd.Series(downtrending_stock_data['low'].values)
|
||||
close_series = pd.Series(close)
|
||||
|
||||
# ROC should be negative
|
||||
roc = calculate_roc(close, 14)
|
||||
assert roc < 0
|
||||
|
||||
# -DI should be greater than +DI
|
||||
adx, plus_di, minus_di = calculate_adx(high, low, close_series)
|
||||
assert minus_di > plus_di
|
||||
|
||||
def test_consistent_analysis_across_data(self, sample_stock_data):
|
||||
"""Test that analysis is consistent across same data."""
|
||||
close = sample_stock_data['close'].values
|
||||
|
||||
# Run same calculation twice
|
||||
roc1 = calculate_roc(close, 14)
|
||||
roc2 = calculate_roc(close, 14)
|
||||
|
||||
# Should be identical
|
||||
assert roc1 == roc2
|
||||
|
|
@ -0,0 +1,512 @@
|
|||
"""Momentum Analyst Agent.
|
||||
|
||||
Specializes in multi-timeframe momentum analysis using:
|
||||
- Rate of Change (ROC) across multiple periods
|
||||
- Average Directional Index (ADX) for trend strength
|
||||
- RSI momentum confirmation
|
||||
- MACD momentum signals
|
||||
- Multi-timeframe momentum divergence detection
|
||||
|
||||
Issue #13: [AGENT-12] Momentum Analyst - multi-TF momentum, ROC, ADX
|
||||
"""
|
||||
|
||||
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
|
||||
from langchain_core.tools import tool
|
||||
from typing import Annotated, Dict, Any, List, Optional
|
||||
import pandas as pd
|
||||
|
||||
from tradingagents.agents.utils.agent_utils import get_stock_data, get_indicators
|
||||
from tradingagents.dataflows.interface import route_to_vendor
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Momentum-Specific Tools
|
||||
# ============================================================================
|
||||
|
||||
@tool
|
||||
def get_multi_timeframe_momentum(
|
||||
symbol: Annotated[str, "Ticker symbol of the company"],
|
||||
curr_date: Annotated[str, "Current trading date in YYYY-MM-DD format"],
|
||||
short_period: Annotated[int, "Short-term ROC period (default: 5)"] = 5,
|
||||
medium_period: Annotated[int, "Medium-term ROC period (default: 14)"] = 14,
|
||||
long_period: Annotated[int, "Long-term ROC period (default: 30)"] = 30,
|
||||
) -> str:
|
||||
"""
|
||||
Calculate multi-timeframe momentum using Rate of Change (ROC) across periods.
|
||||
|
||||
ROC measures percentage change over a period:
|
||||
- Positive ROC = upward momentum
|
||||
- Negative ROC = downward momentum
|
||||
- Divergence across timeframes signals potential reversals
|
||||
|
||||
Returns analysis of short, medium, and long-term momentum alignment.
|
||||
"""
|
||||
try:
|
||||
# Get stock data for analysis
|
||||
stock_data = route_to_vendor("get_stock_data", symbol, curr_date, max(long_period * 2, 60))
|
||||
|
||||
if isinstance(stock_data, str) and "error" in stock_data.lower():
|
||||
return f"Error retrieving stock data: {stock_data}"
|
||||
|
||||
# Parse the data if it's a string (CSV format)
|
||||
if isinstance(stock_data, str):
|
||||
from io import StringIO
|
||||
df = pd.read_csv(StringIO(stock_data))
|
||||
else:
|
||||
df = stock_data
|
||||
|
||||
if df.empty or len(df) < long_period:
|
||||
return f"Insufficient data for momentum analysis. Need at least {long_period} periods."
|
||||
|
||||
# Calculate ROC for each timeframe
|
||||
close = df['close'] if 'close' in df.columns else df['Close']
|
||||
|
||||
roc_short = ((close.iloc[-1] - close.iloc[-short_period]) / close.iloc[-short_period]) * 100
|
||||
roc_medium = ((close.iloc[-1] - close.iloc[-medium_period]) / close.iloc[-medium_period]) * 100
|
||||
roc_long = ((close.iloc[-1] - close.iloc[-long_period]) / close.iloc[-long_period]) * 100
|
||||
|
||||
# Determine momentum alignment
|
||||
all_positive = roc_short > 0 and roc_medium > 0 and roc_long > 0
|
||||
all_negative = roc_short < 0 and roc_medium < 0 and roc_long < 0
|
||||
|
||||
if all_positive:
|
||||
alignment = "BULLISH - All timeframes showing positive momentum"
|
||||
strength = "STRONG" if min(roc_short, roc_medium, roc_long) > 2 else "MODERATE"
|
||||
elif all_negative:
|
||||
alignment = "BEARISH - All timeframes showing negative momentum"
|
||||
strength = "STRONG" if max(roc_short, roc_medium, roc_long) < -2 else "MODERATE"
|
||||
else:
|
||||
alignment = "MIXED - Timeframes diverging, potential trend change"
|
||||
strength = "WEAK"
|
||||
|
||||
# Detect acceleration/deceleration
|
||||
acceleration = ""
|
||||
if roc_short > roc_medium > roc_long:
|
||||
acceleration = "ACCELERATING - Short-term momentum exceeding longer-term"
|
||||
elif roc_short < roc_medium < roc_long:
|
||||
acceleration = "DECELERATING - Short-term momentum lagging longer-term"
|
||||
else:
|
||||
acceleration = "TRANSITIONING - Mixed momentum dynamics"
|
||||
|
||||
report = f"""
|
||||
## Multi-Timeframe Momentum Analysis for {symbol}
|
||||
Analysis Date: {curr_date}
|
||||
|
||||
### Rate of Change (ROC) by Timeframe
|
||||
|
||||
| Timeframe | Period | ROC (%) | Signal |
|
||||
|-----------|--------|---------|--------|
|
||||
| Short-term | {short_period} days | {roc_short:.2f}% | {"🟢 Bullish" if roc_short > 0 else "🔴 Bearish"} |
|
||||
| Medium-term | {medium_period} days | {roc_medium:.2f}% | {"🟢 Bullish" if roc_medium > 0 else "🔴 Bearish"} |
|
||||
| Long-term | {long_period} days | {roc_long:.2f}% | {"🟢 Bullish" if roc_long > 0 else "🔴 Bearish"} |
|
||||
|
||||
### Momentum Summary
|
||||
|
||||
- **Alignment**: {alignment}
|
||||
- **Strength**: {strength}
|
||||
- **Trend Dynamics**: {acceleration}
|
||||
|
||||
### Interpretation
|
||||
|
||||
{"Strong bullish momentum confirmed across all timeframes. Consider long positions with confidence." if all_positive else ""}
|
||||
{"Strong bearish momentum confirmed across all timeframes. Consider defensive or short positions." if all_negative else ""}
|
||||
{"Mixed signals suggest caution. Wait for clearer alignment before taking significant positions." if not all_positive and not all_negative else ""}
|
||||
"""
|
||||
return report
|
||||
|
||||
except Exception as e:
|
||||
return f"Error in multi-timeframe momentum analysis: {str(e)}"
|
||||
|
||||
|
||||
@tool
|
||||
def get_adx_analysis(
|
||||
symbol: Annotated[str, "Ticker symbol of the company"],
|
||||
curr_date: Annotated[str, "Current trading date in YYYY-MM-DD format"],
|
||||
adx_period: Annotated[int, "ADX calculation period (default: 14)"] = 14,
|
||||
look_back_days: Annotated[int, "Days of history to analyze (default: 60)"] = 60,
|
||||
) -> str:
|
||||
"""
|
||||
Analyze trend strength using Average Directional Index (ADX).
|
||||
|
||||
ADX measures trend strength regardless of direction:
|
||||
- 0-20: Weak or absent trend (ranging market)
|
||||
- 20-40: Moderate trend developing
|
||||
- 40-60: Strong trend
|
||||
- 60-80: Very strong trend
|
||||
- 80+: Extremely strong trend (rare)
|
||||
|
||||
Also includes +DI and -DI for directional analysis.
|
||||
"""
|
||||
try:
|
||||
# Get stock data
|
||||
stock_data = route_to_vendor("get_stock_data", symbol, curr_date, look_back_days)
|
||||
|
||||
if isinstance(stock_data, str) and "error" in stock_data.lower():
|
||||
return f"Error retrieving stock data: {stock_data}"
|
||||
|
||||
# Parse data
|
||||
if isinstance(stock_data, str):
|
||||
from io import StringIO
|
||||
df = pd.read_csv(StringIO(stock_data))
|
||||
else:
|
||||
df = stock_data
|
||||
|
||||
if df.empty or len(df) < adx_period * 2:
|
||||
return f"Insufficient data for ADX analysis. Need at least {adx_period * 2} periods."
|
||||
|
||||
# Get column names (handle different case conventions)
|
||||
high_col = 'high' if 'high' in df.columns else 'High'
|
||||
low_col = 'low' if 'low' in df.columns else 'Low'
|
||||
close_col = 'close' if 'close' in df.columns else 'Close'
|
||||
|
||||
high = df[high_col]
|
||||
low = df[low_col]
|
||||
close = df[close_col]
|
||||
|
||||
# Calculate True Range
|
||||
tr1 = high - low
|
||||
tr2 = abs(high - close.shift(1))
|
||||
tr3 = abs(low - close.shift(1))
|
||||
tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
|
||||
|
||||
# Calculate Directional Movement
|
||||
plus_dm = high.diff()
|
||||
minus_dm = -low.diff()
|
||||
|
||||
plus_dm = plus_dm.where((plus_dm > minus_dm) & (plus_dm > 0), 0)
|
||||
minus_dm = minus_dm.where((minus_dm > plus_dm) & (minus_dm > 0), 0)
|
||||
|
||||
# Smooth with EMA
|
||||
atr = tr.ewm(span=adx_period, adjust=False).mean()
|
||||
plus_di = 100 * (plus_dm.ewm(span=adx_period, adjust=False).mean() / atr)
|
||||
minus_di = 100 * (minus_dm.ewm(span=adx_period, adjust=False).mean() / atr)
|
||||
|
||||
# Calculate ADX
|
||||
dx = 100 * abs(plus_di - minus_di) / (plus_di + minus_di)
|
||||
adx = dx.ewm(span=adx_period, adjust=False).mean()
|
||||
|
||||
# Get current values
|
||||
current_adx = adx.iloc[-1]
|
||||
current_plus_di = plus_di.iloc[-1]
|
||||
current_minus_di = minus_di.iloc[-1]
|
||||
prev_adx = adx.iloc[-2]
|
||||
|
||||
# Determine trend strength
|
||||
if current_adx < 20:
|
||||
trend_strength = "WEAK/ABSENT - Market is ranging"
|
||||
recommendation = "Avoid trend-following strategies. Consider range-bound approaches."
|
||||
elif current_adx < 40:
|
||||
trend_strength = "MODERATE - Trend developing"
|
||||
recommendation = "Trend is present but not dominant. Selective entries recommended."
|
||||
elif current_adx < 60:
|
||||
trend_strength = "STRONG - Clear trend in place"
|
||||
recommendation = "Good conditions for trend-following strategies."
|
||||
elif current_adx < 80:
|
||||
trend_strength = "VERY STRONG - Powerful trend"
|
||||
recommendation = "Excellent trend conditions. Watch for exhaustion signs."
|
||||
else:
|
||||
trend_strength = "EXTREME - Rare strength level"
|
||||
recommendation = "Trend may be overextended. Consider taking profits."
|
||||
|
||||
# Trend direction
|
||||
if current_plus_di > current_minus_di:
|
||||
direction = "BULLISH (+DI > -DI)"
|
||||
direction_signal = "🟢 Uptrend"
|
||||
else:
|
||||
direction = "BEARISH (-DI > +DI)"
|
||||
direction_signal = "🔴 Downtrend"
|
||||
|
||||
# ADX momentum
|
||||
adx_trend = "RISING" if current_adx > prev_adx else "FALLING"
|
||||
|
||||
report = f"""
|
||||
## ADX Trend Strength Analysis for {symbol}
|
||||
Analysis Date: {curr_date}
|
||||
|
||||
### Current Readings
|
||||
|
||||
| Indicator | Value | Interpretation |
|
||||
|-----------|-------|----------------|
|
||||
| ADX | {current_adx:.2f} | {trend_strength.split(' - ')[0]} |
|
||||
| +DI | {current_plus_di:.2f} | Bullish pressure |
|
||||
| -DI | {current_minus_di:.2f} | Bearish pressure |
|
||||
| ADX Trend | {adx_trend} | {"Trend strengthening" if adx_trend == "RISING" else "Trend weakening"} |
|
||||
|
||||
### Analysis Summary
|
||||
|
||||
- **Trend Strength**: {trend_strength}
|
||||
- **Trend Direction**: {direction} {direction_signal}
|
||||
- **ADX Momentum**: {adx_trend} (Previous: {prev_adx:.2f})
|
||||
|
||||
### Trading Recommendation
|
||||
|
||||
{recommendation}
|
||||
|
||||
### Key Signals
|
||||
|
||||
{"✅ +DI crossing above -DI recently suggests bullish momentum building." if current_plus_di > current_minus_di and abs(current_plus_di - current_minus_di) < 5 else ""}
|
||||
{"⚠️ -DI crossing above +DI recently suggests bearish momentum building." if current_minus_di > current_plus_di and abs(current_plus_di - current_minus_di) < 5 else ""}
|
||||
{"📈 Rising ADX with strong directional bias suggests continuation." if adx_trend == "RISING" and current_adx > 25 else ""}
|
||||
{"📉 Falling ADX suggests trend is losing momentum." if adx_trend == "FALLING" and current_adx > 25 else ""}
|
||||
{"⏸️ Low ADX indicates consolidation phase." if current_adx < 20 else ""}
|
||||
"""
|
||||
return report
|
||||
|
||||
except Exception as e:
|
||||
return f"Error in ADX analysis: {str(e)}"
|
||||
|
||||
|
||||
@tool
|
||||
def get_momentum_divergence(
|
||||
symbol: Annotated[str, "Ticker symbol of the company"],
|
||||
curr_date: Annotated[str, "Current trading date in YYYY-MM-DD format"],
|
||||
look_back_days: Annotated[int, "Days to analyze for divergence (default: 30)"] = 30,
|
||||
) -> str:
|
||||
"""
|
||||
Detect momentum divergences between price and momentum indicators.
|
||||
|
||||
Bullish Divergence: Price makes lower low, indicator makes higher low
|
||||
Bearish Divergence: Price makes higher high, indicator makes lower high
|
||||
|
||||
Divergences often precede trend reversals.
|
||||
"""
|
||||
try:
|
||||
# Get stock data
|
||||
stock_data = route_to_vendor("get_stock_data", symbol, curr_date, look_back_days * 2)
|
||||
|
||||
if isinstance(stock_data, str) and "error" in stock_data.lower():
|
||||
return f"Error retrieving stock data: {stock_data}"
|
||||
|
||||
# Parse data
|
||||
if isinstance(stock_data, str):
|
||||
from io import StringIO
|
||||
df = pd.read_csv(StringIO(stock_data))
|
||||
else:
|
||||
df = stock_data
|
||||
|
||||
if df.empty or len(df) < look_back_days:
|
||||
return f"Insufficient data for divergence analysis."
|
||||
|
||||
close_col = 'close' if 'close' in df.columns else 'Close'
|
||||
close = df[close_col].values[-look_back_days:]
|
||||
|
||||
# Calculate RSI for divergence detection
|
||||
delta = pd.Series(close).diff()
|
||||
gain = delta.where(delta > 0, 0).rolling(window=14).mean()
|
||||
loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
|
||||
rs = gain / loss
|
||||
rsi = 100 - (100 / (1 + rs))
|
||||
rsi = rsi.values
|
||||
|
||||
# Find local extremes
|
||||
price_highs = []
|
||||
price_lows = []
|
||||
rsi_highs = []
|
||||
rsi_lows = []
|
||||
|
||||
for i in range(2, len(close) - 2):
|
||||
# Price highs
|
||||
if close[i] > close[i-1] and close[i] > close[i-2] and close[i] > close[i+1] and close[i] > close[i+2]:
|
||||
price_highs.append((i, close[i]))
|
||||
# Price lows
|
||||
if close[i] < close[i-1] and close[i] < close[i-2] and close[i] < close[i+1] and close[i] < close[i+2]:
|
||||
price_lows.append((i, close[i]))
|
||||
|
||||
# RSI extremes (after RSI is calculated, handling NaN)
|
||||
valid_rsi = rsi[~pd.isna(rsi)]
|
||||
rsi_start = len(rsi) - len(valid_rsi)
|
||||
|
||||
for i in range(rsi_start + 2, len(rsi) - 2):
|
||||
if pd.notna(rsi[i]) and pd.notna(rsi[i-1]) and pd.notna(rsi[i+1]):
|
||||
if rsi[i] > rsi[i-1] and rsi[i] > rsi[i+1]:
|
||||
rsi_highs.append((i, rsi[i]))
|
||||
if rsi[i] < rsi[i-1] and rsi[i] < rsi[i+1]:
|
||||
rsi_lows.append((i, rsi[i]))
|
||||
|
||||
# Detect divergences
|
||||
bullish_divergence = False
|
||||
bearish_divergence = False
|
||||
divergence_details = []
|
||||
|
||||
# Check for bearish divergence (higher price high, lower RSI high)
|
||||
if len(price_highs) >= 2 and len(rsi_highs) >= 2:
|
||||
recent_price = price_highs[-1]
|
||||
prev_price = price_highs[-2]
|
||||
recent_rsi = rsi_highs[-1] if rsi_highs else None
|
||||
prev_rsi = rsi_highs[-2] if len(rsi_highs) >= 2 else None
|
||||
|
||||
if recent_rsi and prev_rsi:
|
||||
if recent_price[1] > prev_price[1] and recent_rsi[1] < prev_rsi[1]:
|
||||
bearish_divergence = True
|
||||
divergence_details.append(
|
||||
f"Bearish: Price high {recent_price[1]:.2f} > {prev_price[1]:.2f}, "
|
||||
f"RSI high {recent_rsi[1]:.2f} < {prev_rsi[1]:.2f}"
|
||||
)
|
||||
|
||||
# Check for bullish divergence (lower price low, higher RSI low)
|
||||
if len(price_lows) >= 2 and len(rsi_lows) >= 2:
|
||||
recent_price = price_lows[-1]
|
||||
prev_price = price_lows[-2]
|
||||
recent_rsi = rsi_lows[-1] if rsi_lows else None
|
||||
prev_rsi = rsi_lows[-2] if len(rsi_lows) >= 2 else None
|
||||
|
||||
if recent_rsi and prev_rsi:
|
||||
if recent_price[1] < prev_price[1] and recent_rsi[1] > prev_rsi[1]:
|
||||
bullish_divergence = True
|
||||
divergence_details.append(
|
||||
f"Bullish: Price low {recent_price[1]:.2f} < {prev_price[1]:.2f}, "
|
||||
f"RSI low {recent_rsi[1]:.2f} > {prev_rsi[1]:.2f}"
|
||||
)
|
||||
|
||||
# Generate report
|
||||
divergence_status = "NEUTRAL"
|
||||
if bullish_divergence and not bearish_divergence:
|
||||
divergence_status = "BULLISH DIVERGENCE DETECTED 🟢"
|
||||
elif bearish_divergence and not bullish_divergence:
|
||||
divergence_status = "BEARISH DIVERGENCE DETECTED 🔴"
|
||||
elif bullish_divergence and bearish_divergence:
|
||||
divergence_status = "MIXED SIGNALS - CONFLICTING DIVERGENCES ⚠️"
|
||||
|
||||
report = f"""
|
||||
## Momentum Divergence Analysis for {symbol}
|
||||
Analysis Date: {curr_date}
|
||||
Analysis Period: {look_back_days} days
|
||||
|
||||
### Divergence Status: {divergence_status}
|
||||
|
||||
### Detected Patterns
|
||||
|
||||
{"No significant divergences detected in the analysis period." if not divergence_details else ""}
|
||||
{"".join([f"- {detail}" + chr(10) for detail in divergence_details])}
|
||||
|
||||
### Pattern Summary
|
||||
|
||||
- **Price Highs Found**: {len(price_highs)}
|
||||
- **Price Lows Found**: {len(price_lows)}
|
||||
- **RSI Highs Found**: {len(rsi_highs)}
|
||||
- **RSI Lows Found**: {len(rsi_lows)}
|
||||
|
||||
### Interpretation
|
||||
|
||||
{"**Bullish Divergence**: Price is making lower lows while RSI is making higher lows. This suggests selling pressure is waning and a potential bottom is forming. Consider long entries with confirmation." if bullish_divergence else ""}
|
||||
{"**Bearish Divergence**: Price is making higher highs while RSI is making lower highs. This suggests buying pressure is waning and a potential top is forming. Consider reducing exposure or short entries with confirmation." if bearish_divergence else ""}
|
||||
{"**No Divergence**: Price and momentum are moving in sync. The current trend appears healthy." if not bullish_divergence and not bearish_divergence else ""}
|
||||
"""
|
||||
return report
|
||||
|
||||
except Exception as e:
|
||||
return f"Error in divergence analysis: {str(e)}"
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Momentum Analyst Agent Factory
|
||||
# ============================================================================
|
||||
|
||||
def create_momentum_analyst(llm):
|
||||
"""
|
||||
Create a Momentum Analyst agent that specializes in:
|
||||
- Multi-timeframe momentum analysis (ROC)
|
||||
- Trend strength measurement (ADX)
|
||||
- Momentum divergence detection
|
||||
- RSI/MACD momentum confirmation
|
||||
|
||||
Args:
|
||||
llm: Language model for generating analysis
|
||||
|
||||
Returns:
|
||||
Function that processes state and returns momentum analysis
|
||||
"""
|
||||
|
||||
def momentum_analyst_node(state):
|
||||
current_date = state["trade_date"]
|
||||
ticker = state["company_of_interest"]
|
||||
|
||||
tools = [
|
||||
get_stock_data,
|
||||
get_indicators,
|
||||
get_multi_timeframe_momentum,
|
||||
get_adx_analysis,
|
||||
get_momentum_divergence,
|
||||
]
|
||||
|
||||
system_message = """You are a specialized Momentum Analyst with expertise in quantitative momentum analysis. Your role is to provide comprehensive momentum assessments using multiple techniques:
|
||||
|
||||
## Your Analytical Framework
|
||||
|
||||
### 1. Multi-Timeframe Momentum (ROC Analysis)
|
||||
- Analyze Rate of Change across short (5-day), medium (14-day), and long-term (30-day) periods
|
||||
- Identify momentum alignment or divergence across timeframes
|
||||
- Detect acceleration/deceleration patterns
|
||||
|
||||
### 2. Trend Strength (ADX Analysis)
|
||||
- Use ADX to measure trend strength (0-100 scale)
|
||||
- Analyze +DI/-DI for directional bias
|
||||
- Identify trending vs ranging market conditions
|
||||
|
||||
### 3. Momentum Divergence Detection
|
||||
- Identify bullish divergences (price lower low, indicator higher low)
|
||||
- Identify bearish divergences (price higher high, indicator lower high)
|
||||
- Assess reversal probability based on divergence patterns
|
||||
|
||||
### 4. Traditional Momentum Indicators
|
||||
- RSI for overbought/oversold conditions
|
||||
- MACD for momentum confirmation
|
||||
- Stochastic for entry timing
|
||||
|
||||
## Analysis Process
|
||||
|
||||
1. **Start with get_stock_data** to retrieve price history
|
||||
2. **Use get_multi_timeframe_momentum** for ROC analysis across periods
|
||||
3. **Apply get_adx_analysis** to measure trend strength
|
||||
4. **Check get_momentum_divergence** for reversal signals
|
||||
5. **Use get_indicators** for RSI and MACD confirmation
|
||||
|
||||
## Output Requirements
|
||||
|
||||
Provide a comprehensive Momentum Report including:
|
||||
- Overall momentum score and direction
|
||||
- Timeframe alignment assessment
|
||||
- Trend strength classification
|
||||
- Divergence alerts if detected
|
||||
- Trading recommendations based on momentum state
|
||||
|
||||
**Always include a summary table with key momentum metrics.**
|
||||
|
||||
Focus on actionable insights. Quantify momentum states where possible (e.g., "RSI at 72 suggests overbought, but ADX at 45 confirms strong uptrend - momentum remains bullish but overextended")."""
|
||||
|
||||
prompt = ChatPromptTemplate.from_messages(
|
||||
[
|
||||
(
|
||||
"system",
|
||||
"You are a specialized Momentum Analyst assistant, collaborating with other analysts."
|
||||
" Use the provided momentum analysis tools to assess trend strength and direction."
|
||||
" Execute comprehensive momentum analysis to support trading decisions."
|
||||
" If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
|
||||
" prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
|
||||
" You have access to the following tools: {tool_names}.\n{system_message}"
|
||||
" For your reference, the current date is {current_date}. The company we want to analyze is {ticker}.",
|
||||
),
|
||||
MessagesPlaceholder(variable_name="messages"),
|
||||
]
|
||||
)
|
||||
|
||||
prompt = prompt.partial(system_message=system_message)
|
||||
prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
|
||||
prompt = prompt.partial(current_date=current_date)
|
||||
prompt = prompt.partial(ticker=ticker)
|
||||
|
||||
chain = prompt | llm.bind_tools(tools)
|
||||
|
||||
result = chain.invoke(state["messages"])
|
||||
|
||||
report = ""
|
||||
|
||||
if len(result.tool_calls) == 0:
|
||||
report = result.content
|
||||
|
||||
return {
|
||||
"messages": [result],
|
||||
"momentum_report": report,
|
||||
}
|
||||
|
||||
return momentum_analyst_node
|
||||
Loading…
Reference in New Issue