diff --git a/.github/workflows/branch_deployments.yml b/.github/workflows/branch_deployments.yml index 2ae0b94..1159137 100644 --- a/.github/workflows/branch_deployments.yml +++ b/.github/workflows/branch_deployments.yml @@ -65,7 +65,7 @@ jobs: pip install dbt-core dbt-duckdb cd project-repo/macro_agents/dbt_project dbt deps || true - dbt parse --target prod || dbt parse + dbt parse - name: Python Executable Deploy if: steps.prerun.outputs.result == 'pex-deploy' diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index cc42546..f11f590 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -34,7 +34,7 @@ jobs: - name: Install dependencies with uv run: | cd macro_agents - uv sync --dev + uv sync --extra dev - name: Lint with ruff continue-on-error: true diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml index 33122dd..e93e436 100644 --- a/.github/workflows/deploy.yml +++ b/.github/workflows/deploy.yml @@ -68,7 +68,7 @@ jobs: pip install dbt-core dbt-duckdb cd project-repo/macro_agents/dbt_project dbt deps || true - dbt parse --target prod || dbt parse + dbt parse - name: Python Executable Deploy if: steps.prerun.outputs.result == 'pex-deploy' diff --git a/macro_agents/pyproject.toml b/macro_agents/pyproject.toml index 3d83a14..049e612 100644 --- a/macro_agents/pyproject.toml +++ b/macro_agents/pyproject.toml @@ -3,34 +3,32 @@ name = "macro_agents" requires-python = ">=3.10.19,<3.14" version = "0.1.0" dependencies = [ - "dagster>=1.11.4", + "dagster>=1.11.4,<2.0.0", # Add upper bound "dagster-cloud", "dagster-duckdb", - "polars", + "polars>=0.20.0,<1.0.0", # Pin polars "dagster-dbt", - "requests", - "dbt-core", - "google-api-core", - "google-auth", - "google-cloud-bigquery", - "dbt-duckdb", - "dagster-shared>=1.11.4", - "dagster-pipes>=1.11.4", - "dagster-dg-cli>=1.11.4", - "dagster-webserver>=1.11.4", - "pyarrow>=21.0.0", - "beautifulsoup4", - "lxml", - "ruff>=0.12.7", - "tenacity>=9.1.2", - "dspy>=2.6.27", - "sqlfluff>=3.4.2", - "sqlfluff-templater-dbt>=3.4.2", + "requests>=2.31.0,<3.0.0", + "dbt-core>=1.10.0,<2.0.0", # Pin dbt-core + "google-api-core>=2.0.0,<3.0.0", + "google-auth>=2.0.0,<3.0.0", + "google-cloud-bigquery>=3.0.0,<4.0.0", + "dbt-duckdb>=1.10.0,<2.0.0", + "dagster-shared>=1.11.4,<2.0.0", + "dagster-pipes>=1.11.4,<2.0.0", + "dagster-dg-cli>=1.11.4,<2.0.0", + "dagster-webserver>=1.11.4,<2.0.0", + "pyarrow>=21.0.0,<22.0.0", # Pin pyarrow + "beautifulsoup4>=4.12.0,<5.0.0", + "lxml>=5.0.0,<6.0.0", + # Move dev tools to optional-dependencies + "tenacity>=9.1.2,<10.0.0", + "dspy>=2.6.27,<3.0.0", "dagster-sling", - "google-cloud-storage>=2.10.0", - "openai>=1.0.0", - "anthropic>=0.18.0", - "google-generativeai>=0.3.0", + "google-cloud-storage>=2.10.0,<3.0.0", + "openai>=1.0.0,<2.0.0", + "anthropic>=0.18.0,<1.0.0", + "google-generativeai>=0.3.0,<1.0.0", ] [project.optional-dependencies] @@ -40,7 +38,9 @@ dev = [ "pytest-cov", "pytest-xdist", "mypy", - "ruff", + "ruff>=0.12.7,<1.0.0", # Move ruff here + "sqlfluff>=3.4.2,<4.0.0", # Move sqlfluff here + "sqlfluff-templater-dbt>=3.4.2,<4.0.0", # Move here ] [build-system] diff --git a/macro_agents/src/macro_agents/defs/agents/backtest_economy_state_analyzer.py b/macro_agents/src/macro_agents/defs/agents/backtest_economy_state_analyzer.py index a07e4cb..a571837 100644 --- a/macro_agents/src/macro_agents/defs/agents/backtest_economy_state_analyzer.py +++ b/macro_agents/src/macro_agents/defs/agents/backtest_economy_state_analyzer.py @@ -40,12 +40,17 @@ class BacktestConfig(dg.Config): description="Backtest economy state analysis with historical data cutoff", deps=[ dg.AssetKey(["fred_series_latest_aggregates_snapshot"]), + dg.AssetKey(["fred_monthly_diff"]), dg.AssetKey(["leading_econ_return_indicator_snapshot"]), dg.AssetKey(["us_sector_summary_snapshot"]), + dg.AssetKey(["major_indicies_summary"]), dg.AssetKey(["energy_commodities_summary_snapshot"]), dg.AssetKey(["input_commodities_summary_snapshot"]), dg.AssetKey(["agriculture_commodities_summary_snapshot"]), dg.AssetKey(["financial_conditions_index"]), + dg.AssetKey(["housing_inventory_latest_aggregates"]), + dg.AssetKey(["housing_mortgage_rates"]), + dg.AssetKey(["stg_treasury_yields"]), ], ) def backtest_analyze_economy_state( @@ -97,6 +102,21 @@ def backtest_analyze_economy_state( md, cutoff_date=config.backtest_date ) + context.log.info("Gathering housing market data with cutoff date...") + housing_data = economic_analysis.get_housing_data( + md, cutoff_date=config.backtest_date + ) + + context.log.info("Gathering yield curve data with cutoff date...") + yield_curve_data = economic_analysis.get_yield_curve_data( + md, cutoff_date=config.backtest_date + ) + + context.log.info("Gathering economic trends data with cutoff date...") + economic_trends = economic_analysis.get_economic_trends( + md, cutoff_date=config.backtest_date + ) + optimized_analyzer = None if config.use_optimized_models and economic_analysis.use_optimized_models: optimized_analyzer = economic_analysis.load_optimized_module( @@ -126,12 +146,15 @@ def backtest_analyze_economy_state( ) context.log.info( - f"Running economy state analysis with historical data including FCI (personality: {config.personality})..." + f"Running economy state analysis with historical data including FCI, housing, yield curve, and trends (personality: {config.personality})..." ) analysis_result = analyzer_to_use( economic_data=economic_data, commodity_data=commodity_data, financial_conditions_index=fci_data, + housing_data=housing_data, + yield_curve_data=yield_curve_data, + economic_trends=economic_trends, personality=config.personality, ) @@ -155,6 +178,16 @@ def backtest_analyze_economy_state( "agriculture_commodities_summary_snapshot", ], "financial_conditions_index_table": "financial_conditions_index", + "housing_data_tables": [ + "housing_inventory_latest_aggregates", + "housing_mortgage_rates", + ], + "yield_curve_table": "stg_treasury_yields", + "economic_trends_table": "fred_monthly_diff", + "market_data_tables": [ + "us_sector_summary_snapshot", + "major_indicies_summary", + ], }, } diff --git a/macro_agents/src/macro_agents/defs/agents/economy_state_analyzer.py b/macro_agents/src/macro_agents/defs/agents/economy_state_analyzer.py index 65418b2..a2891aa 100644 --- a/macro_agents/src/macro_agents/defs/agents/economy_state_analyzer.py +++ b/macro_agents/src/macro_agents/defs/agents/economy_state_analyzer.py @@ -34,6 +34,18 @@ class EconomyStateAnalysisSignature(dspy.Signature): desc="CSV data containing full historical Financial Conditions Index (FCI) values with date, FCI score, and component scores. FCI values above zero indicate expansionary conditions, below zero indicate contractionary conditions. Use the full history to identify trends, cycles, and current position relative to historical norms." ) + housing_data: str = dspy.InputField( + desc="CSV data containing housing market indicators including inventory levels (with 3m, 6m, 1y changes) and mortgage rates with affordability metrics (median prices, monthly payments). Housing is a leading economic indicator - rising inventory suggests slowing demand, while affordability metrics indicate consumer purchasing power." + ) + + yield_curve_data: str = dspy.InputField( + desc="CSV data containing Treasury yield curve data with key spreads (10Y-2Y, 10Y-3M) and curve shape classification (Steep/Normal/Flat/Inverted). Inverted yield curves (10Y-2Y < 0) historically precede recessions. Normal curves indicate healthy economic expectations." + ) + + economic_trends: str = dspy.InputField( + desc="CSV data containing month-over-month changes for key economic indicators (GDP, CPI, unemployment, payrolls, Fed funds rate, industrial production, retail sales, housing starts). These trends show momentum and direction of economic activity." + ) + personality: str = dspy.InputField( desc="Analytical personality: 'skeptical' (default, bearish, focuses on risks and downside), 'neutral' (balanced, objective), or 'bullish' (optimistic, focuses on opportunities and upside)" ) @@ -63,11 +75,13 @@ class EconomyStateAnalysisSignature(dspy.Signature): - Commodity price trends as leading indicators of economic activity - Supply chain and cost pressure signals from commodity markets 5. Leading Indicators Assessment: - - Yield curve analysis (normal, flat, inverted) + - Yield curve analysis (normal, flat, inverted) - use yield_curve_data to assess curve shape and spreads (10Y-2Y, 10Y-3M). Inverted curves signal recession risk. - Credit spreads and financial conditions (integrate with FCI analysis) - Business confidence indicators - Leading economic index components - Commodity price momentum as economic signals + - Housing market trends from housing_data - inventory levels, affordability, mortgage rates + - Economic momentum from economic_trends - month-over-month changes in key indicators 6. Economic Cycle Phase Characteristics: - Current phase identification with supporting evidence (use FCI to confirm expansion/contraction) - Phase duration and typical progression patterns @@ -103,6 +117,9 @@ def forward( economic_data: str, commodity_data: str, financial_conditions_index: str, + housing_data: str = "", + yield_curve_data: str = "", + economic_trends: str = "", personality: str = None, ): personality_to_use = personality or self.personality @@ -110,6 +127,9 @@ def forward( economic_data=economic_data, commodity_data=commodity_data, financial_conditions_index=financial_conditions_index, + housing_data=housing_data or "No housing data available", + yield_curve_data=yield_curve_data or "No yield curve data available", + economic_trends=economic_trends or "No economic trends data available", personality=personality_to_use, ) @@ -439,7 +459,7 @@ def get_economic_data( def get_market_data( self, md_resource: MotherDuckResource, cutoff_date: Optional[str] = None ) -> str: - """Get latest market performance data. + """Get latest market performance data including US sectors and major indices. Args: md_resource: MotherDuck resource @@ -469,11 +489,42 @@ def get_market_data( negative_days, neutral_days, period_start_price, - period_end_price + period_end_price, + 'sector' as market_category FROM us_sector_summary_snapshot WHERE snapshot_date = '{cutoff_date}' AND time_period IN ('12_weeks', '6_months', '1_year') - ORDER BY asset_type, time_period, total_return_pct DESC + + UNION ALL + + SELECT + symbol, + asset_type, + time_period, + exchange, + name, + period_start_date, + period_end_date, + trading_days, + total_return_pct, + avg_daily_return_pct, + volatility_pct, + win_rate_pct, + total_price_change, + avg_daily_price_change, + worst_day_change, + best_day_change, + positive_days, + negative_days, + neutral_days, + period_start_price, + period_end_price, + 'major_index' as market_category + FROM major_indicies_summary + WHERE time_period IN ('12_weeks', '6_months', '1_year') + AND period_end_date <= '{cutoff_date}' + + ORDER BY market_category, asset_type, time_period, total_return_pct DESC """ else: query = """ @@ -498,10 +549,40 @@ def get_market_data( negative_days, neutral_days, period_start_price, - period_end_price + period_end_price, + 'sector' as market_category FROM us_sector_summary WHERE time_period IN ('12_weeks', '6_months', '1_year') - ORDER BY asset_type, time_period, total_return_pct DESC + + UNION ALL + + SELECT + symbol, + asset_type, + time_period, + exchange, + name, + period_start_date, + period_end_date, + trading_days, + total_return_pct, + avg_daily_return_pct, + volatility_pct, + win_rate_pct, + total_price_change, + avg_daily_price_change, + worst_day_change, + best_day_change, + positive_days, + negative_days, + neutral_days, + period_start_price, + period_end_price, + 'major_index' as market_category + FROM major_indicies_summary + WHERE time_period IN ('12_weeks', '6_months', '1_year') + + ORDER BY market_category, asset_type, time_period, total_return_pct DESC """ df = md_resource.execute_query(query) @@ -820,6 +901,217 @@ def get_correlation_data( df = md_resource.execute_query(query) return df.write_csv() + def get_housing_data( + self, md_resource: MotherDuckResource, cutoff_date: Optional[str] = None + ) -> str: + """Get housing market data including inventory and mortgage rates. + + Args: + md_resource: MotherDuck resource + cutoff_date: Optional date string (YYYY-MM-DD) for backtesting. + If provided, filters data up to that date. + """ + # Get housing inventory data + inventory_query = """ + SELECT + series_code, + series_name, + month, + current_value, + pct_change_3m, + pct_change_6m, + pct_change_1y, + date_grain + FROM housing_inventory_latest_aggregates + WHERE current_value IS NOT NULL + ORDER BY series_name, month DESC + """ + + # Get mortgage rates data (latest available) + mortgage_query = """ + SELECT + date, + mortgage_rate, + median_price_no_down_payment, + median_price_20_pct_down_payment, + monthly_payment_no_down_payment, + monthly_payment_20_pct_down_payment + FROM housing_mortgage_rates + WHERE date = (SELECT MAX(date) FROM housing_mortgage_rates) + """ + + if cutoff_date: + inventory_query = f""" + SELECT + series_code, + series_name, + month, + current_value, + pct_change_3m, + pct_change_6m, + pct_change_1y, + date_grain + FROM housing_inventory_latest_aggregates + WHERE month <= '{cutoff_date}' + AND current_value IS NOT NULL + ORDER BY series_name, month DESC + """ + mortgage_query = f""" + SELECT + date, + mortgage_rate, + median_price_no_down_payment, + median_price_20_pct_down_payment, + monthly_payment_no_down_payment, + monthly_payment_20_pct_down_payment + FROM housing_mortgage_rates + WHERE date <= '{cutoff_date}' + AND date = ( + SELECT MAX(date) + FROM housing_mortgage_rates + WHERE date <= '{cutoff_date}' + ) + """ + + inventory_df = md_resource.execute_query(inventory_query, read_only=True) + mortgage_df = md_resource.execute_query(mortgage_query, read_only=True) + + # Combine into single CSV + inventory_csv = inventory_df.write_csv() if not inventory_df.is_empty() else "" + mortgage_csv = mortgage_df.write_csv() if not mortgage_df.is_empty() else "" + + # Combine with separator + if inventory_csv and mortgage_csv: + return f"Housing Inventory Data:\n{inventory_csv}\n\nMortgage Rates Data:\n{mortgage_csv}" + elif inventory_csv: + return f"Housing Inventory Data:\n{inventory_csv}" + elif mortgage_csv: + return f"Mortgage Rates Data:\n{mortgage_csv}" + else: + return "" + + def get_yield_curve_data( + self, md_resource: MotherDuckResource, cutoff_date: Optional[str] = None + ) -> str: + """Get yield curve data and calculate spreads. + + Args: + md_resource: MotherDuck resource + cutoff_date: Optional date string (YYYY-MM-DD) for backtesting. + If provided, filters data up to that date. + """ + date_filter = f"AND date <= '{cutoff_date}'" if cutoff_date else "" + + query = f""" + WITH pivoted_yields AS ( + SELECT + date, + MAX(CASE WHEN yield_type = 'BC_1MONTH' THEN value END) as yield_1m, + MAX(CASE WHEN yield_type = 'BC_3MONTH' THEN value END) as yield_3m, + MAX(CASE WHEN yield_type = 'BC_6MONTH' THEN value END) as yield_6m, + MAX(CASE WHEN yield_type = 'BC_1YEAR' THEN value END) as yield_1y, + MAX(CASE WHEN yield_type = 'BC_2YEAR' THEN value END) as yield_2y, + MAX(CASE WHEN yield_type = 'BC_3YEAR' THEN value END) as yield_3y, + MAX(CASE WHEN yield_type = 'BC_5YEAR' THEN value END) as yield_5y, + MAX(CASE WHEN yield_type = 'BC_7YEAR' THEN value END) as yield_7y, + MAX(CASE WHEN yield_type = 'BC_10YEAR' THEN value END) as yield_10y, + MAX(CASE WHEN yield_type = 'BC_20YEAR' THEN value END) as yield_20y, + MAX(CASE WHEN yield_type = 'BC_30YEAR' THEN value END) as yield_30y + FROM stg_treasury_yields + WHERE date IS NOT NULL {date_filter} + GROUP BY date + ), + latest_date AS ( + SELECT MAX(date) as max_date + FROM pivoted_yields + ), + current_yields AS ( + SELECT * + FROM pivoted_yields + WHERE date = (SELECT max_date FROM latest_date) + ), + yield_spreads AS ( + SELECT + date, + yield_10y, + yield_2y, + yield_3m, + yield_1y, + yield_30y, + -- Calculate key spreads + yield_10y - yield_2y as spread_10y_2y, + yield_10y - yield_3m as spread_10y_3m, + yield_2y - yield_3m as spread_2y_3m, + yield_30y - yield_2y as spread_30y_2y, + -- Yield curve shape classification + CASE + WHEN yield_10y - yield_2y > 0.5 THEN 'Steep' + WHEN yield_10y - yield_2y > 0 THEN 'Normal' + WHEN yield_10y - yield_2y > -0.5 THEN 'Flat' + ELSE 'Inverted' + END as curve_shape, + -- Inversion status + CASE + WHEN yield_10y - yield_2y < 0 THEN 'Inverted' + WHEN yield_10y - yield_3m < 0 THEN 'Inverted (10Y-3M)' + ELSE 'Normal' + END as inversion_status + FROM current_yields + ) + SELECT * FROM yield_spreads + """ + + df = md_resource.execute_query(query, read_only=True) + if df.is_empty(): + return "" + return df.write_csv() + + def get_economic_trends( + self, md_resource: MotherDuckResource, cutoff_date: Optional[str] = None + ) -> str: + """Get month-over-month changes for key economic indicators using fred_monthly_diff. + + Args: + md_resource: MotherDuck resource + cutoff_date: Optional date string (YYYY-MM-DD) for backtesting. + If provided, filters data up to that date. + """ + # Key indicators to track trends for + key_indicators = [ + "GDPC1", # Real GDP + "CPIAUCSL", # CPI + "UNRATE", # Unemployment Rate + "PAYEMS", # Nonfarm Payrolls + "FEDFUNDS", # Fed Funds Rate + "INDPRO", # Industrial Production + "RSXFS", # Retail Sales + "HOUST", # Housing Starts + ] + + indicators_list = "', '".join(key_indicators) + date_filter = f"AND date <= '{cutoff_date}'" if cutoff_date else "" + + query = f""" + SELECT + series_code, + series_name, + date, + value, + diff_value, + pct_change, + prev_value + FROM fred_monthly_diff + WHERE series_code IN ('{indicators_list}') + {date_filter} + ORDER BY series_name, date DESC + LIMIT 100 + """ + + df = md_resource.execute_query(query, read_only=True) + if df.is_empty(): + return "" + return df.write_csv() + def _get_token_usage( economic_analysis: EconomicAnalysisResource, @@ -947,12 +1239,17 @@ def extract_economy_state_summary(analysis_content: str) -> Dict[str, Any]: description="Analyze current economic indicators and commodity data to determine the state of the economy", deps=[ dg.AssetKey(["fred_series_latest_aggregates"]), + dg.AssetKey(["fred_monthly_diff"]), dg.AssetKey(["leading_econ_return_indicator"]), dg.AssetKey(["us_sector_summary"]), + dg.AssetKey(["major_indicies_summary"]), dg.AssetKey(["energy_commodities_summary"]), dg.AssetKey(["input_commodities_summary"]), dg.AssetKey(["agriculture_commodities_summary"]), dg.AssetKey(["financial_conditions_index"]), + dg.AssetKey(["housing_inventory_latest_aggregates"]), + dg.AssetKey(["housing_mortgage_rates"]), + dg.AssetKey(["stg_treasury_yields"]), dg.AssetKey(["auto_promote_best_models_to_production"]), ], ) @@ -985,6 +1282,15 @@ def analyze_economy_state( context.log.info("Gathering Financial Conditions Index data...") fci_data = economic_analysis.get_financial_conditions_index(md) + context.log.info("Gathering housing market data...") + housing_data = economic_analysis.get_housing_data(md) + + context.log.info("Gathering yield curve data...") + yield_curve_data = economic_analysis.get_yield_curve_data(md) + + context.log.info("Gathering economic trends data...") + economic_trends = economic_analysis.get_economic_trends(md) + optimized_analyzer = None if economic_analysis.use_optimized_models: optimized_analyzer = economic_analysis.load_optimized_module( @@ -1006,12 +1312,15 @@ def analyze_economy_state( ) context.log.info( - f"Running economy state analysis with economic, commodity, and FCI data (personality: {config.personality})..." + f"Running economy state analysis with economic, commodity, FCI, housing, yield curve, and trends data (personality: {config.personality})..." ) analysis_result = analyzer_to_use( economic_data=economic_data, commodity_data=commodity_data, financial_conditions_index=fci_data, + housing_data=housing_data, + yield_curve_data=yield_curve_data, + economic_trends=economic_trends, personality=config.personality, ) @@ -1033,6 +1342,16 @@ def analyze_economy_state( "agriculture_commodities_summary", ], "financial_conditions_index_table": "financial_conditions_index", + "housing_data_tables": [ + "housing_inventory_latest_aggregates", + "housing_mortgage_rates", + ], + "yield_curve_table": "stg_treasury_yields", + "economic_trends_table": "fred_monthly_diff", + "market_data_tables": [ + "us_sector_summary", + "major_indicies_summary", + ], }, } diff --git a/macro_agents/tests/test_dspy_modules.py b/macro_agents/tests/test_dspy_modules.py index db59a48..dd22283 100644 --- a/macro_agents/tests/test_dspy_modules.py +++ b/macro_agents/tests/test_dspy_modules.py @@ -29,6 +29,10 @@ def test_signature_fields(self): annotations = EconomyStateAnalysisSignature.__annotations__ assert "economic_data" in annotations assert "commodity_data" in annotations + assert "financial_conditions_index" in annotations + assert "housing_data" in annotations + assert "yield_curve_data" in annotations + assert "economic_trends" in annotations assert "personality" in annotations assert "analysis" in annotations @@ -72,6 +76,9 @@ def test_module_forward_with_default_personality(self): economic_data="test economic data", commodity_data="test commodity data", financial_conditions_index="test financial conditions", + housing_data="No housing data available", + yield_curve_data="No yield curve data available", + economic_trends="No economic trends data available", personality="neutral", ) @@ -95,6 +102,9 @@ def test_module_forward_with_override_personality(self): economic_data="test economic data", commodity_data="test commodity data", financial_conditions_index="test financial conditions", + housing_data="No housing data available", + yield_curve_data="No yield curve data available", + economic_trends="No economic trends data available", personality="bullish", ) @@ -114,6 +124,41 @@ def test_module_forward_with_empty_data(self): assert result.analysis == "Analysis with empty data" module.analyze_state.assert_called_once() + # Verify it was called with default values for new parameters + call_args = module.analyze_state.call_args + assert call_args.kwargs["housing_data"] == "No housing data available" + assert call_args.kwargs["yield_curve_data"] == "No yield curve data available" + assert ( + call_args.kwargs["economic_trends"] == "No economic trends data available" + ) + + def test_module_forward_with_explicit_new_data(self): + """Test module forward method with new data parameters explicitly provided.""" + module = EconomyStateModule(personality="neutral") + + mock_result = Mock() + mock_result.analysis = "Test analysis output" + module.analyze_state = Mock(return_value=mock_result) + + result = module.forward( + economic_data="test economic data", + commodity_data="test commodity data", + financial_conditions_index="test financial conditions", + housing_data="test housing data", + yield_curve_data="test yield curve data", + economic_trends="test economic trends", + ) + + assert result.analysis == "Test analysis output" + module.analyze_state.assert_called_once_with( + economic_data="test economic data", + commodity_data="test commodity data", + financial_conditions_index="test financial conditions", + housing_data="test housing data", + yield_curve_data="test yield curve data", + economic_trends="test economic trends", + personality="neutral", + ) class TestInvestmentRecommendationsSignature: @@ -363,6 +408,9 @@ def test_personality_override_priority(self): economic_data="test", commodity_data="test", financial_conditions_index="test financial conditions", + housing_data="No housing data available", + yield_curve_data="No yield curve data available", + economic_trends="No economic trends data available", personality="bullish", ) @@ -386,6 +434,11 @@ def test_economy_state_module_with_mock_lm(self): assert result.analysis == "Comprehensive economic analysis" assert module.analyze_state.called + # Verify new parameters are included + call_args = module.analyze_state.call_args + assert "housing_data" in call_args.kwargs + assert "yield_curve_data" in call_args.kwargs + assert "economic_trends" in call_args.kwargs def test_investment_recommendations_module_with_mock_lm(self): """Test InvestmentRecommendationsModule with mocked LM.""" diff --git a/macro_agents/uv.lock b/macro_agents/uv.lock index 898f898..a747172 100644 --- a/macro_agents/uv.lock +++ b/macro_agents/uv.lock @@ -981,7 +981,7 @@ wheels = [ [[package]] name = "dbt-duckdb" -version = "1.9.4" +version = "1.10.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "dbt-adapters" }, @@ -989,9 +989,9 @@ dependencies = [ { name = "dbt-core" }, { name = "duckdb" }, ] -sdist = { url = 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--processes 4 test: - cd macro_agents && uv sync && source .venv/bin/activate && python -m pytest tests/ -v + cd macro_agents && uv sync --extra dev && uv run pytest tests/ -v dbt-manifest: cd dbt_project && dbt parse @@ -17,7 +17,7 @@ dbt-manifest: pre-pr: @echo "Running pre-PR checks..." @echo "1. Installing dependencies..." - cd macro_agents && uv sync && source .venv/bin/activate && pip install -e .[dev] pytest-cov pytest-xdist mypy bandit safety + cd macro_agents && uv sync --extra dev && uv pip install pytest-cov pytest-xdist mypy bandit safety @echo "2. Running ruff linting..." cd macro_agents && source .venv/bin/activate && ruff check macro_agents/ @echo "3. Running ruff formatting check..."