finish custom indicators timeframe

config/indicators/user_indicators/ema_bfbf3a1d.json

@@ -16,5 +16,5 @@
   "timeframe": "5m",
   "visible": true,
   "created_date": "2025-06-06T07:02:34.613543+00:00",
-  "modified_date": "2025-06-06T07:02:34.613543+00:00"
+  "modified_date": "2025-06-06T07:23:10.757978+00:00"
 }

tasks/tasks-indicator-timeframe-feature.md

@@ -33,6 +33,6 @@
   - [x] 4.2 In `dashboard/callbacks/indicators.py`, update the save indicator callback to read the timeframe value.
   - [x] 4.3 Pass the selected timeframe to `indicator_manager.create_indicator` or `update_indicator`.
 - [ ] 5.0 Testing and Validation
-  - [ ] 5.1 Write unit tests for custom timeframe data fetching and alignment.
-  - [ ] 5.2 Manually test creating and viewing indicators with various timeframes (higher, lower, and same as chart).
-  - [ ] 5.3 Verify visual correctness and data integrity on the chart. 
+  - [x] 5.1 Write unit tests for custom timeframe data fetching and alignment.
+  - [xx] 5.2 Manually test creating and viewing indicators with various timeframes (higher, lower, and same as chart).
+  - [x] 5.3 Verify visual correctness and data integrity on the chart. 

tests/test_data_integration.py

@@ -0,0 +1,121 @@
+import pytest
+import pandas as pd
+from unittest.mock import Mock, patch
+from datetime import datetime
+
+from components.charts.data_integration import MarketDataIntegrator
+from components.charts.indicator_manager import IndicatorManager
+from components.charts.layers.indicators import IndicatorLayerConfig
+
+@pytest.fixture
+def market_data_integrator_components():
+    """Provides a complete setup for testing MarketDataIntegrator."""
+    
+    # 1. Main DataFrame (e.g., 1h)
+    main_timestamps = pd.to_datetime(['2024-01-01 10:00', '2024-01-01 11:00', '2024-01-01 12:00', '2024-01-01 13:00'], utc=True)
+    main_df = pd.DataFrame({'close': [100, 102, 101, 103]}, index=main_timestamps)
+    
+    # 2. Higher-timeframe DataFrame (e.g., 4h)
+    indicator_timestamps = pd.to_datetime(['2024-01-01 08:00', '2024-01-01 12:00'], utc=True)
+    indicator_df_raw = [{'timestamp': ts, 'close': val} for ts, val in zip(indicator_timestamps, [98, 101.5])]
+
+    # 3. Mock IndicatorManager and configs
+    indicator_manager = Mock(spec=IndicatorManager)
+    user_indicator = Mock()
+    user_indicator.id = 'rsi_4h'
+    user_indicator.name = 'RSI'
+    user_indicator.timeframe = '4h'
+    user_indicator.type = 'rsi'
+    user_indicator.parameters = {'period': 14}
+    
+    indicator_manager.load_indicator.return_value = user_indicator
+    
+    indicator_config = Mock(spec=IndicatorLayerConfig)
+    indicator_config.id = 'rsi_4h'
+
+    # 4. DataIntegrator instance
+    integrator = MarketDataIntegrator()
+
+    # 5. Mock internal fetching and calculation
+    # Mock get_market_data_for_indicators to return raw candles
+    integrator.get_market_data_for_indicators = Mock(return_value=(indicator_df_raw, []))
+    
+    # Mock indicator calculation result
+    indicator_result_values = [{'timestamp': indicator_timestamps[1], 'rsi': 55.0}] # Only one valid point
+    indicator_pkg = {'data': [Mock(timestamp=r['timestamp'], values={'rsi': r['rsi']}) for r in indicator_result_values]}
+    integrator.indicators.calculate = Mock(return_value=indicator_pkg)
+
+    return integrator, main_df, indicator_config, indicator_manager, user_indicator
+
+def test_multi_timeframe_alignment(market_data_integrator_components):
+    """
+    Tests that indicator data from a higher timeframe is correctly aligned
+    with the main chart's data.
+    """
+    integrator, main_df, indicator_config, indicator_manager, user_indicator = market_data_integrator_components
+
+    # Execute the method to test
+    indicator_data_map = integrator.get_indicator_data(
+        main_df=main_df,
+        main_timeframe='1h',
+        indicator_configs=[indicator_config],
+        indicator_manager=indicator_manager,
+        symbol='BTC-USDT'
+    )
+    
+    # --- Assertions ---
+    assert user_indicator.id in indicator_data_map
+    aligned_data = indicator_data_map[user_indicator.id]
+    
+    # Expected series after reindexing and forward-filling
+    expected_series = pd.Series(
+        [None, None, 55.0, 55.0],
+        index=main_df.index,
+        name='rsi'
+    )
+    
+    result_series = aligned_data['rsi']
+    pd.testing.assert_series_equal(result_series, expected_series, check_index_type=False)
+
+@patch('components.charts.utils.prepare_chart_data', lambda x: pd.DataFrame(x).set_index('timestamp'))
+def test_no_custom_timeframe_uses_main_df(market_data_integrator_components):
+    """
+    Tests that if an indicator has no custom timeframe, it uses the main
+    DataFrame for calculation.
+    """
+    integrator, main_df, indicator_config, indicator_manager, user_indicator = market_data_integrator_components
+
+    # Override indicator to have no timeframe
+    user_indicator.timeframe = None
+    indicator_manager.load_indicator.return_value = user_indicator
+    
+    # Mock calculation result on main_df
+    result_timestamps = main_df.index[1:]
+    indicator_result_values = [{'timestamp': ts, 'sma': val} for ts, val in zip(result_timestamps, [101.0, 101.5, 102.0])]
+    indicator_pkg = {'data': [Mock(timestamp=r['timestamp'], values={'sma': r['sma']}) for r in indicator_result_values]}
+    integrator.indicators.calculate = Mock(return_value=indicator_pkg)
+
+    # Execute
+    indicator_data_map = integrator.get_indicator_data(
+        main_df=main_df,
+        main_timeframe='1h',
+        indicator_configs=[indicator_config],
+        indicator_manager=indicator_manager,
+        symbol='BTC-USDT'
+    )
+
+    # Assert that get_market_data_for_indicators was NOT called
+    integrator.get_market_data_for_indicators.assert_not_called()
+    
+    # Assert that calculate was called with main_df
+    integrator.indicators.calculate.assert_called_with('rsi', main_df, period=14)
+
+    # Assert the result is what we expect
+    assert user_indicator.id in indicator_data_map
+    result_series = indicator_data_map[user_indicator.id]['sma']
+    expected_series = pd.Series([101.0, 101.5, 102.0], index=result_timestamps, name='sma')
+
+    # Reindex expected to match the result's index for comparison
+    expected_series = expected_series.reindex(main_df.index)
+    
+    pd.testing.assert_series_equal(result_series, expected_series, check_index_type=False)