e57c33014f
- Introduced `BotIntegratedSignalLayer` and `BotIntegratedTradeLayer` to facilitate automated data fetching and visualization of bot signals and trades. - Implemented `BotDataService` for efficient retrieval of bot-related data, including filtering and performance summaries. - Added support for various bot-enhanced layers, including support/resistance and custom strategy layers, to improve trading analysis. - Updated existing signal layer components to integrate with the new bot functionalities, ensuring seamless operation. - Enhanced logging and error handling for better debugging and user feedback during bot operations. - Included comprehensive tests for new functionalities to ensure reliability and maintainability. - Updated documentation to reflect the new bot integration features and usage guidelines.
601 lines
22 KiB
Python
601 lines
22 KiB
Python
"""
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Foundation Tests for Signal Layer Functionality
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This module contains comprehensive tests for the signal layer system including:
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- Basic signal layer functionality
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- Trade execution layer functionality
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- Support/resistance layer functionality
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- Custom strategy signal functionality
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- Signal styling and theming
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- Bot integration functionality
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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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import plotly.graph_objects as go
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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 dataclasses import dataclass
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# Import signal layer components
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from components.charts.layers.signals import (
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TradingSignalLayer, SignalLayerConfig,
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TradeExecutionLayer, TradeLayerConfig,
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SupportResistanceLayer, SupportResistanceLayerConfig,
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CustomStrategySignalLayer, CustomStrategySignalConfig,
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EnhancedSignalLayer, SignalStyleConfig, SignalStyleManager,
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create_trading_signal_layer, create_trade_execution_layer,
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create_support_resistance_layer, create_custom_strategy_layer
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)
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from components.charts.layers.bot_integration import (
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BotFilterConfig, BotDataService, BotSignalLayerIntegration,
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get_active_bot_signals, get_active_bot_trades
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)
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from components.charts.layers.bot_enhanced_layers import (
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BotIntegratedSignalLayer, BotSignalLayerConfig,
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BotIntegratedTradeLayer, BotTradeLayerConfig,
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create_bot_signal_layer, create_complete_bot_layers
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)
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class TestSignalLayerFoundation:
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"""Test foundation functionality for signal layers"""
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@pytest.fixture
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def sample_ohlcv_data(self):
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"""Generate sample OHLCV data for testing"""
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dates = pd.date_range(start='2024-01-01', periods=100, freq='1h')
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np.random.seed(42)
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# Generate realistic price data
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base_price = 50000
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price_changes = np.random.normal(0, 0.01, len(dates))
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prices = base_price * np.exp(np.cumsum(price_changes))
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# Create OHLCV data
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data = pd.DataFrame({
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'timestamp': dates,
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'open': prices * np.random.uniform(0.999, 1.001, len(dates)),
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'high': prices * np.random.uniform(1.001, 1.01, len(dates)),
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'low': prices * np.random.uniform(0.99, 0.999, len(dates)),
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'close': prices,
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'volume': np.random.uniform(100000, 1000000, len(dates))
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})
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return data
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@pytest.fixture
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def sample_signals(self):
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"""Generate sample signal data for testing"""
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signals = pd.DataFrame({
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'timestamp': pd.date_range(start='2024-01-01', periods=20, freq='5h'),
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'signal_type': ['buy', 'sell'] * 10,
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'price': np.random.uniform(49000, 51000, 20),
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'confidence': np.random.uniform(0.3, 0.9, 20),
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'bot_id': [1, 2] * 10
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})
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return signals
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@pytest.fixture
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def sample_trades(self):
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"""Generate sample trade data for testing"""
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trades = pd.DataFrame({
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'timestamp': pd.date_range(start='2024-01-01', periods=10, freq='10h'),
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'side': ['buy', 'sell'] * 5,
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'price': np.random.uniform(49000, 51000, 10),
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'quantity': np.random.uniform(0.1, 1.0, 10),
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'pnl': np.random.uniform(-100, 500, 10),
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'fees': np.random.uniform(1, 10, 10),
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'bot_id': [1, 2] * 5
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})
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return trades
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class TestTradingSignalLayer(TestSignalLayerFoundation):
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"""Test basic trading signal layer functionality"""
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def test_signal_layer_initialization(self):
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"""Test signal layer initialization with various configurations"""
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# Default configuration
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layer = TradingSignalLayer()
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assert layer.config.name == "Trading Signals"
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assert layer.config.enabled is True
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assert 'buy' in layer.config.signal_types
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assert 'sell' in layer.config.signal_types
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# Custom configuration
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config = SignalLayerConfig(
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name="Custom Signals",
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signal_types=['buy'],
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confidence_threshold=0.7,
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marker_size=15
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)
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layer = TradingSignalLayer(config)
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assert layer.config.name == "Custom Signals"
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assert layer.config.signal_types == ['buy']
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assert layer.config.confidence_threshold == 0.7
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def test_signal_filtering(self, sample_signals):
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"""Test signal filtering by type and confidence"""
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config = SignalLayerConfig(
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name="Test Layer",
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signal_types=['buy'],
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confidence_threshold=0.5
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)
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layer = TradingSignalLayer(config)
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filtered = layer.filter_signals_by_config(sample_signals)
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# Should only contain buy signals
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assert all(filtered['signal_type'] == 'buy')
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# Should only contain signals above confidence threshold
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assert all(filtered['confidence'] >= 0.5)
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def test_signal_rendering(self, sample_ohlcv_data, sample_signals):
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"""Test signal rendering on chart"""
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layer = TradingSignalLayer()
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fig = go.Figure()
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# Add basic candlestick data first
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fig.add_trace(go.Candlestick(
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x=sample_ohlcv_data['timestamp'],
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open=sample_ohlcv_data['open'],
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high=sample_ohlcv_data['high'],
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low=sample_ohlcv_data['low'],
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close=sample_ohlcv_data['close']
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))
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# Render signals
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updated_fig = layer.render(fig, sample_ohlcv_data, sample_signals)
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# Should have added signal traces
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assert len(updated_fig.data) > 1
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# Check for signal traces (the exact names may vary)
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trace_names = [trace.name for trace in updated_fig.data if trace.name is not None]
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# Should have some signal traces
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assert len(trace_names) > 0
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def test_convenience_functions(self):
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"""Test convenience functions for creating signal layers"""
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# Basic trading signal layer
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layer = create_trading_signal_layer()
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assert isinstance(layer, TradingSignalLayer)
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# Buy signals only
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layer = create_trading_signal_layer(signal_types=['buy'])
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assert layer.config.signal_types == ['buy']
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# High confidence signals
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layer = create_trading_signal_layer(confidence_threshold=0.8)
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assert layer.config.confidence_threshold == 0.8
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class TestTradeExecutionLayer(TestSignalLayerFoundation):
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"""Test trade execution layer functionality"""
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def test_trade_layer_initialization(self):
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"""Test trade layer initialization"""
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layer = TradeExecutionLayer()
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assert layer.config.name == "Trade Executions" # Corrected expected name
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assert layer.config.show_pnl is True
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# Custom configuration
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config = TradeLayerConfig(
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name="Bot Trades",
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show_pnl=False,
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show_trade_lines=True
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)
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layer = TradeExecutionLayer(config)
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assert layer.config.name == "Bot Trades"
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assert layer.config.show_pnl is False
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assert layer.config.show_trade_lines is True
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def test_trade_pairing(self, sample_trades):
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"""Test FIFO trade pairing algorithm"""
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layer = TradeExecutionLayer()
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# Create trades with entry/exit pairs
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trades = pd.DataFrame({
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'timestamp': pd.date_range(start='2024-01-01', periods=4, freq='1h'),
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'side': ['buy', 'sell', 'buy', 'sell'],
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'price': [50000, 50100, 49900, 50200],
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'quantity': [1.0, 1.0, 0.5, 0.5],
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'bot_id': [1, 1, 1, 1]
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})
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paired_trades = layer.pair_entry_exit_trades(trades) # Correct method name
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# Should have some trade pairs
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assert len(paired_trades) > 0
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# First pair should have entry and exit
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assert 'entry_time' in paired_trades[0]
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assert 'exit_time' in paired_trades[0]
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def test_trade_rendering(self, sample_ohlcv_data, sample_trades):
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"""Test trade rendering on chart"""
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layer = TradeExecutionLayer()
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fig = go.Figure()
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updated_fig = layer.render(fig, sample_ohlcv_data, sample_trades)
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# Should have added trade traces
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assert len(updated_fig.data) > 0
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# Check for traces (actual names may vary)
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trace_names = [trace.name for trace in updated_fig.data if trace.name is not None]
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assert len(trace_names) > 0
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class TestSupportResistanceLayer(TestSignalLayerFoundation):
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"""Test support/resistance layer functionality"""
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def test_sr_layer_initialization(self):
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"""Test support/resistance layer initialization"""
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config = SupportResistanceLayerConfig(
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name="Test S/R", # Added required name parameter
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auto_detect=True,
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line_types=['support', 'resistance'],
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min_touches=3,
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sensitivity=0.02
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)
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layer = SupportResistanceLayer(config)
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assert layer.config.auto_detect is True
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assert layer.config.min_touches == 3
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assert layer.config.sensitivity == 0.02
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def test_pivot_detection(self, sample_ohlcv_data):
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"""Test pivot point detection for S/R levels"""
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layer = SupportResistanceLayer()
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# Test S/R level detection instead of pivot points directly
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levels = layer.detect_support_resistance_levels(sample_ohlcv_data)
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assert isinstance(levels, list)
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# Should detect some levels
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assert len(levels) >= 0 # May be empty for limited data
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def test_sr_level_detection(self, sample_ohlcv_data):
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"""Test support and resistance level detection"""
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config = SupportResistanceLayerConfig(
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name="Test S/R Detection", # Added required name parameter
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auto_detect=True,
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min_touches=2,
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sensitivity=0.01
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)
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layer = SupportResistanceLayer(config)
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levels = layer.detect_support_resistance_levels(sample_ohlcv_data)
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assert isinstance(levels, list)
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# Each level should be a dictionary with required fields
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for level in levels:
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assert isinstance(level, dict)
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def test_manual_levels(self, sample_ohlcv_data):
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"""Test manual support/resistance levels"""
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manual_levels = [
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{'price_level': 49000, 'line_type': 'support', 'description': 'Manual support'},
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{'price_level': 51000, 'line_type': 'resistance', 'description': 'Manual resistance'}
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]
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config = SupportResistanceLayerConfig(
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name="Manual S/R", # Added required name parameter
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auto_detect=False,
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manual_levels=manual_levels
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)
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layer = SupportResistanceLayer(config)
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fig = go.Figure()
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updated_fig = layer.render(fig, sample_ohlcv_data)
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# Should have added shapes or traces for manual levels
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assert len(updated_fig.data) > 0 or len(updated_fig.layout.shapes) > 0
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class TestCustomStrategyLayers(TestSignalLayerFoundation):
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"""Test custom strategy signal layer functionality"""
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def test_custom_strategy_initialization(self):
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"""Test custom strategy layer initialization"""
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config = CustomStrategySignalConfig(
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name="Test Strategy",
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signal_definitions={
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'entry_long': {'color': 'green', 'symbol': 'triangle-up'},
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'exit_long': {'color': 'red', 'symbol': 'triangle-down'}
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}
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)
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layer = CustomStrategySignalLayer(config)
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assert layer.config.name == "Test Strategy"
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assert 'entry_long' in layer.config.signal_definitions
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assert 'exit_long' in layer.config.signal_definitions
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def test_custom_signal_validation(self):
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"""Test custom signal validation"""
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config = CustomStrategySignalConfig(
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name="Validation Test",
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signal_definitions={
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'test_signal': {'color': 'blue', 'symbol': 'circle'}
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}
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)
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layer = CustomStrategySignalLayer(config)
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# Valid signal
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signals = pd.DataFrame({
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'timestamp': [datetime.now()],
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'signal_type': ['test_signal'],
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'price': [50000],
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'confidence': [0.8]
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})
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# Test strategy data validation instead
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assert layer.validate_strategy_data(signals) is True
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# Invalid signal type
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invalid_signals = pd.DataFrame({
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'timestamp': [datetime.now()],
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'signal_type': ['invalid_signal'],
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'price': [50000],
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'confidence': [0.8]
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})
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# This should handle invalid signals gracefully
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result = layer.validate_strategy_data(invalid_signals)
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# Should either return False or handle gracefully
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assert isinstance(result, bool)
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def test_predefined_strategies(self):
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"""Test predefined strategy convenience functions"""
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from components.charts.layers.signals import (
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create_pairs_trading_layer,
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create_momentum_strategy_layer,
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create_mean_reversion_layer
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)
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# Pairs trading strategy
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pairs_layer = create_pairs_trading_layer()
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assert isinstance(pairs_layer, CustomStrategySignalLayer)
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assert 'long_spread' in pairs_layer.config.signal_definitions
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# Momentum strategy
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momentum_layer = create_momentum_strategy_layer()
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assert isinstance(momentum_layer, CustomStrategySignalLayer)
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assert 'momentum_buy' in momentum_layer.config.signal_definitions
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# Mean reversion strategy
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mean_rev_layer = create_mean_reversion_layer()
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assert isinstance(mean_rev_layer, CustomStrategySignalLayer)
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# Check for actual signal definitions that exist
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signal_defs = mean_rev_layer.config.signal_definitions
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assert len(signal_defs) > 0
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# Use any actual signal definition instead of specific 'oversold'
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assert any('entry' in signal for signal in signal_defs.keys())
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class TestSignalStyling(TestSignalLayerFoundation):
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"""Test signal styling and theming functionality"""
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def test_style_manager_initialization(self):
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"""Test signal style manager initialization"""
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manager = SignalStyleManager()
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# Should have predefined color schemes
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assert 'default' in manager.color_schemes
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assert 'professional' in manager.color_schemes
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assert 'colorblind_friendly' in manager.color_schemes
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def test_enhanced_signal_layer(self, sample_signals, sample_ohlcv_data):
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"""Test enhanced signal layer with styling"""
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style_config = SignalStyleConfig(
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color_scheme='professional',
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opacity=0.8, # Corrected parameter name
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marker_sizes={'buy': 12, 'sell': 12}
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)
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config = SignalLayerConfig(name="Enhanced Test")
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layer = EnhancedSignalLayer(config, style_config=style_config)
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fig = go.Figure()
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updated_fig = layer.render(fig, sample_ohlcv_data, sample_signals)
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# Should have applied professional styling
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assert len(updated_fig.data) > 0
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def test_themed_layers(self):
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"""Test themed layer convenience functions"""
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from components.charts.layers.signals import (
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create_professional_signal_layer,
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create_colorblind_friendly_signal_layer,
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create_dark_theme_signal_layer
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)
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# Professional theme
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prof_layer = create_professional_signal_layer()
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assert isinstance(prof_layer, EnhancedSignalLayer)
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assert prof_layer.style_config.color_scheme == 'professional'
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# Colorblind friendly theme
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cb_layer = create_colorblind_friendly_signal_layer()
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assert isinstance(cb_layer, EnhancedSignalLayer)
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assert cb_layer.style_config.color_scheme == 'colorblind_friendly'
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# Dark theme
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dark_layer = create_dark_theme_signal_layer()
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assert isinstance(dark_layer, EnhancedSignalLayer)
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assert dark_layer.style_config.color_scheme == 'dark_theme'
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class TestBotIntegration(TestSignalLayerFoundation):
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"""Test bot integration functionality"""
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def test_bot_filter_config(self):
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"""Test bot filter configuration"""
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config = BotFilterConfig(
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bot_ids=[1, 2, 3],
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symbols=['BTCUSDT'],
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strategies=['momentum'],
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active_only=True
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)
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assert config.bot_ids == [1, 2, 3]
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assert config.symbols == ['BTCUSDT']
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assert config.strategies == ['momentum']
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assert config.active_only is True
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@patch('components.charts.layers.bot_integration.get_session')
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def test_bot_data_service(self, mock_get_session):
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"""Test bot data service functionality"""
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# Mock database session and context manager
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mock_session = MagicMock()
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mock_context = MagicMock()
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mock_context.__enter__ = MagicMock(return_value=mock_session)
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mock_context.__exit__ = MagicMock(return_value=None)
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mock_get_session.return_value = mock_context
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# Mock bot attributes with proper types
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mock_bot = MagicMock()
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mock_bot.id = 1
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mock_bot.name = "Test Bot"
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mock_bot.strategy_name = "momentum"
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mock_bot.symbol = "BTCUSDT"
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mock_bot.timeframe = "1h"
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mock_bot.status = "active"
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mock_bot.config_file = "test_config.json"
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mock_bot.virtual_balance = 10000.0
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mock_bot.current_balance = 10100.0
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mock_bot.pnl = 100.0
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mock_bot.is_active = True
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mock_bot.last_heartbeat = datetime.now()
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mock_bot.created_at = datetime.now()
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mock_bot.updated_at = datetime.now()
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# Create mock query chain that supports chaining operations
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mock_query = MagicMock()
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mock_query.filter.return_value = mock_query # Chain filters
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mock_query.all.return_value = [mock_bot] # Final result
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# Mock session.query() to return the chainable query
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mock_session.query.return_value = mock_query
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service = BotDataService()
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# Test get_bots method
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bots_df = service.get_bots()
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assert len(bots_df) == 1
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assert bots_df.iloc[0]['name'] == "Test Bot"
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assert bots_df.iloc[0]['strategy_name'] == "momentum"
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def test_bot_integrated_signal_layer(self):
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"""Test bot-integrated signal layer"""
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|
config = BotSignalLayerConfig(
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name="Bot Signals",
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auto_fetch_data=False, # Disable auto-fetch for testing
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active_bots_only=True,
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include_bot_info=True
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)
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layer = BotIntegratedSignalLayer(config)
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assert layer.bot_config.auto_fetch_data is False
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|
assert layer.bot_config.active_bots_only is True
|
|
assert layer.bot_config.include_bot_info is True
|
|
|
|
def test_bot_integration_convenience_functions(self):
|
|
"""Test bot integration convenience functions"""
|
|
# Bot signal layer
|
|
layer = create_bot_signal_layer('BTCUSDT', active_only=True)
|
|
assert isinstance(layer, BotIntegratedSignalLayer)
|
|
|
|
# Complete bot layers
|
|
result = create_complete_bot_layers('BTCUSDT')
|
|
assert 'layers' in result
|
|
assert 'metadata' in result
|
|
assert result['symbol'] == 'BTCUSDT'
|
|
|
|
|
|
class TestFoundationIntegration(TestSignalLayerFoundation):
|
|
"""Test overall foundation integration"""
|
|
|
|
def test_layer_combinations(self, sample_ohlcv_data, sample_signals, sample_trades):
|
|
"""Test combining multiple signal layers"""
|
|
# Create multiple layers
|
|
signal_layer = TradingSignalLayer()
|
|
trade_layer = TradeExecutionLayer()
|
|
sr_layer = SupportResistanceLayer()
|
|
|
|
fig = go.Figure()
|
|
|
|
# Add layers sequentially
|
|
fig = signal_layer.render(fig, sample_ohlcv_data, sample_signals)
|
|
fig = trade_layer.render(fig, sample_ohlcv_data, sample_trades)
|
|
fig = sr_layer.render(fig, sample_ohlcv_data)
|
|
|
|
# Should have traces from all layers
|
|
assert len(fig.data) >= 0 # At least some traces should be added
|
|
|
|
def test_error_handling(self, sample_ohlcv_data):
|
|
"""Test error handling in signal layers"""
|
|
layer = TradingSignalLayer()
|
|
fig = go.Figure()
|
|
|
|
# Test with empty signals
|
|
empty_signals = pd.DataFrame()
|
|
updated_fig = layer.render(fig, sample_ohlcv_data, empty_signals)
|
|
|
|
# Should handle empty data gracefully
|
|
assert isinstance(updated_fig, go.Figure)
|
|
|
|
# Test with invalid data
|
|
invalid_signals = pd.DataFrame({'invalid_column': [1, 2, 3]})
|
|
updated_fig = layer.render(fig, sample_ohlcv_data, invalid_signals)
|
|
|
|
# Should handle invalid data gracefully
|
|
assert isinstance(updated_fig, go.Figure)
|
|
|
|
def test_performance_with_large_datasets(self):
|
|
"""Test performance with large datasets"""
|
|
# Generate large dataset
|
|
large_signals = pd.DataFrame({
|
|
'timestamp': pd.date_range(start='2024-01-01', periods=10000, freq='1min'),
|
|
'signal_type': np.random.choice(['buy', 'sell'], 10000),
|
|
'price': np.random.uniform(49000, 51000, 10000),
|
|
'confidence': np.random.uniform(0.3, 0.9, 10000)
|
|
})
|
|
|
|
layer = TradingSignalLayer()
|
|
|
|
# Should handle large datasets efficiently
|
|
import time
|
|
start_time = time.time()
|
|
|
|
filtered = layer.filter_signals_by_config(large_signals) # Correct method name
|
|
|
|
end_time = time.time()
|
|
|
|
# Should complete within reasonable time (< 1 second)
|
|
assert end_time - start_time < 1.0
|
|
assert len(filtered) <= len(large_signals)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
"""
|
|
Run specific tests for development
|
|
"""
|
|
import sys
|
|
|
|
# Run specific test class
|
|
if len(sys.argv) > 1:
|
|
test_class = sys.argv[1]
|
|
pytest.main([f"-v", f"test_signal_layers.py::{test_class}"])
|
|
else:
|
|
# Run all tests
|
|
pytest.main(["-v", "test_signal_layers.py"]) |