Increase test coverage

quantflow_tests/test_ai.py

@@ -10,6 +10,7 @@
 import pytest
 from mcp.server.fastmcp import FastMCP
 
+from quantflow.ai import server as ai_server
 from quantflow.ai.tools import charts, crypto, fred, stocks, vault
 from quantflow.ai.tools.base import McpTool
 from quantflow.data.vault import Vault
@@ -459,3 +460,28 @@ async def test_ascii_chart_empty(
     with patch("quantflow.ai.tools.base.FMP", return_value=mock_fmp):
         result = await charts_server.call_tool("ascii_chart", {"symbol": "FAKE"})
     assert "No price data" in text(result)
+
+
+def test_create_server_registers_all_tools() -> None:
+    fake_tool = MagicMock()
+    with (
+        patch("quantflow.ai.server.McpTool", return_value=fake_tool),
+        patch("quantflow.ai.server.vault.register") as vault_register,
+        patch("quantflow.ai.server.crypto.register") as crypto_register,
+        patch("quantflow.ai.server.stocks.register") as stocks_register,
+        patch("quantflow.ai.server.fred.register") as fred_register,
+        patch("quantflow.ai.server.charts.register") as charts_register,
+    ):
+        mcp = ai_server.create_server()
+    vault_register.assert_called_once_with(mcp, fake_tool)
+    crypto_register.assert_called_once_with(mcp, fake_tool)
+    stocks_register.assert_called_once_with(mcp, fake_tool)
+    fred_register.assert_called_once_with(mcp, fake_tool)
+    charts_register.assert_called_once_with(mcp, fake_tool)
+
+
+def test_main_runs_server() -> None:
+    mock_server = MagicMock()
+    with patch("quantflow.ai.server.create_server", return_value=mock_server):
+        ai_server.main()
+    mock_server.run.assert_called_once_with()

quantflow_tests/test_data.py

@@ -1,4 +1,6 @@
+from datetime import date
 from typing import AsyncIterator
+from unittest.mock import AsyncMock
 
 import pytest
 
@@ -59,3 +61,38 @@ async def __test_fiscal_data() -> None:
         assert df is not None
         assert df.shape[0] > 0
         assert df.shape[1] == 2
+
+
+async def test_fiscal_securities_builds_previous_month_filter() -> None:
+    fd = FiscalData()
+    fd.get_all = AsyncMock(return_value=[{"a": 1}])  # type: ignore[method-assign]
+    df = await fd.securities(record_date=date(2024, 3, 15))
+    fd.get_all.assert_awaited_once_with(
+        "/v1/debt/mspd/mspd_table_3_market",
+        {"filter": "record_date:eq:2024-02-29"},
+    )
+    assert len(df) == 1
+
+
+async def test_fiscal_get_all_multi_page() -> None:
+    fd = FiscalData()
+    fd.get = AsyncMock(  # type: ignore[method-assign]
+        side_effect=[
+            {
+                "data": [{"id": 1}],
+                "links": {"next": "/v1/debt/mspd/mspd_table_3_market?page=2"},
+            },
+            {"data": [{"id": 2}], "links": {"next": None}},
+        ]
+    )
+    data = await fd.get_all("/v1/debt/mspd/mspd_table_3_market", {"a": "b"})
+    assert data == [{"id": 1}, {"id": 2}]
+    assert fd.get.await_count == 2
+
+
+async def test_fiscal_get_all_single_page_without_links() -> None:
+    fd = FiscalData()
+    fd.get = AsyncMock(return_value={"data": [{"id": 7}]})  # type: ignore[method-assign]
+    data = await fd.get_all("/v1/debt/mspd/mspd_table_3_market", {"a": "b"})
+    assert data == [{"id": 7}]
+    fd.get.assert_awaited_once()

quantflow_tests/test_divfm.py

@@ -10,8 +10,12 @@
 try:
     import torch
 
-    from quantflow.options.divfm.network import DIVFMNetwork
-    from quantflow.options.divfm.trainer import DayData, DIVFMTrainer
+    from quantflow.options.divfm.network import (
+        DIVFMNetwork,
+    )
+    from quantflow.options.divfm.network import _extract_subnet as extract_subnet_torch
+    from quantflow.options.divfm.network import _make_subnet as make_subnet_torch
+    from quantflow.options.divfm.trainer import DayData, DIVFMTrainer, _day_loss
 
     has_torch = True
 except ImportError:
@@ -191,6 +195,12 @@ def test_network_default_construction() -> None:
     assert net.extra_features == 0
 
 
+@pytest.mark.skipif(not has_torch, reason="torch not installed")
+def test_network_minimum_factors_validation() -> None:
+    with pytest.raises(ValueError, match="at least 3"):
+        DIVFMNetwork(num_factors=2)
+
+
 @pytest.mark.skipif(not has_torch, reason="torch not installed")
 def test_network_forward_shape() -> None:
     net = DIVFMNetwork(num_factors=NUM_FACTORS, hidden_size=HIDDEN_SIZE)
@@ -202,6 +212,28 @@ def test_network_forward_shape() -> None:
     assert (out[:, 0] == 1.0).all()  # f_1 = 1
 
 
+@pytest.mark.skipif(not has_torch, reason="torch not installed")
+def test_make_subnet_layout() -> None:
+    subnet = make_subnet_torch(2, 4, 2, 3)
+    modules = list(subnet.children())
+    assert isinstance(modules[0], torch.nn.Linear)
+    assert isinstance(modules[1], torch.nn.Sigmoid)
+    assert isinstance(modules[2], torch.nn.BatchNorm1d)
+    assert isinstance(modules[-1], torch.nn.BatchNorm1d)
+    assert modules[-1].affine is False
+
+
+@pytest.mark.skipif(not has_torch, reason="torch not installed")
+def test_extract_subnet_output_structure() -> None:
+    subnet = make_subnet_torch(2, 4, 1, 1)
+    subnet.eval()
+    extracted = extract_subnet_torch(subnet)
+    assert isinstance(extracted, SubnetWeights)
+    assert len(extracted.layers) == 2
+    assert extracted.layers[0].apply_activation is True
+    assert extracted.layers[-1].apply_activation is False
+
+
 @pytest.mark.skipif(not has_torch, reason="torch not installed")
 def test_to_weights_forward_matches_network() -> None:
     net = DIVFMNetwork(num_factors=NUM_FACTORS, hidden_size=HIDDEN_SIZE)
@@ -220,6 +252,14 @@ def test_to_weights_forward_matches_network() -> None:
     np.testing.assert_allclose(torch_out, numpy_out, atol=1e-5)
 
 
+@pytest.mark.skipif(not has_torch, reason="torch not installed")
+def test_to_weights_without_joint_subnet() -> None:
+    net = DIVFMNetwork(num_factors=3, hidden_size=HIDDEN_SIZE)
+    weights = net.to_weights()
+    assert weights.subnet_joint is None
+    assert weights.num_factors == 3
+
+
 # ---------------------------------------------------------------------------
 # DIVFMTrainer tests (requires torch)
 # ---------------------------------------------------------------------------
@@ -252,6 +292,14 @@ def test_trainer_construction() -> None:
     assert trainer.network is net
 
 
+@pytest.mark.skipif(not has_torch, reason="torch not installed")
+def test_day_loss_non_negative() -> None:
+    net = DIVFMNetwork(num_factors=NUM_FACTORS, hidden_size=HIDDEN_SIZE)
+    day = _make_days(num_days=1)[0]
+    loss = _day_loss(net, day, ridge=1e-6)
+    assert float(loss.detach().item()) >= 0.0
+
+
 @pytest.mark.skipif(not has_torch, reason="torch not installed")
 def test_trainer_step_returns_loss() -> None:
     net = DIVFMNetwork(num_factors=NUM_FACTORS, hidden_size=HIDDEN_SIZE)
@@ -272,6 +320,13 @@ def test_trainer_evaluate() -> None:
     assert val_loss >= 0.0
 
 
+@pytest.mark.skipif(not has_torch, reason="torch not installed")
+def test_trainer_evaluate_empty_days() -> None:
+    net = DIVFMNetwork(num_factors=NUM_FACTORS, hidden_size=HIDDEN_SIZE)
+    trainer = DIVFMTrainer(net)
+    assert trainer.evaluate([]) == 0.0
+
+
 @pytest.mark.skipif(not has_torch, reason="torch not installed")
 def test_trainer_fit_loss_decreases() -> None:
     """Loss should decrease over training steps on a structured IV surface.
@@ -296,6 +351,16 @@ def test_trainer_fit_loss_decreases() -> None:
     assert np.mean(losses[-10:]) < np.mean(losses[:10])
 
 
+@pytest.mark.skipif(not has_torch, reason="torch not installed")
+def test_trainer_fit_with_validation_days() -> None:
+    torch.manual_seed(1)
+    net = DIVFMNetwork(num_factors=NUM_FACTORS, hidden_size=HIDDEN_SIZE)
+    trainer = DIVFMTrainer(net, lr=1e-2, batch_days=4)
+    days = _make_days(num_days=8)
+    losses = trainer.fit(days, num_steps=5, val_days=days[:2], log_every=2)
+    assert len(losses) == 5
+
+
 @pytest.mark.skipif(not has_torch, reason="torch not installed")
 def test_trainer_to_weights_produces_pricer() -> None:
     net = DIVFMNetwork(num_factors=NUM_FACTORS, hidden_size=HIDDEN_SIZE)

quantflow_tests/test_fmp_unit.py

@@ -0,0 +1,75 @@
+from __future__ import annotations
+
+from datetime import date
+from unittest.mock import AsyncMock
+
+import pandas as pd
+
+from quantflow.data.fmp import FMP, nice_sector_performance, summary_sector_performance
+
+
+def test_freq_crate_and_join_and_params() -> None:
+    assert FMP.freq.crate(None) == FMP.freq.daily
+    assert FMP.freq.crate("1hour") == FMP.freq.one_hour
+    assert FMP.freq.crate("bad") == FMP.freq.daily
+
+    fmp = FMP(key="k")
+    assert fmp.join("AAPL", "MSFT") == "AAPL,MSFT"
+    assert fmp.params({"a": 1}) == {"params": {"a": 1, "apikey": "k"}}
+
+
+async def test_prices_daily_and_intraday_paths() -> None:
+    fmp = FMP(key="k")
+    fmp.get_path = AsyncMock(return_value=[{"date": "2024-01-01", "close": 100}])  # type: ignore[method-assign]
+    df = await fmp.prices("AAPL", frequency=None, convert_to_date=True)
+    assert isinstance(df, pd.DataFrame)
+    assert str(df["date"].dtype).startswith("datetime64")
+    fmp.get_path.assert_awaited_with(
+        "historical-price-eod/full",
+        params={"symbol": "AAPL"},
+    )
+
+    fmp.get_path = AsyncMock(return_value={"historical": [{"date": "2024-01-01"}]})  # type: ignore[method-assign]
+    await fmp.prices("AAPL", frequency="1hour")
+    fmp.get_path.assert_awaited_with(
+        "historical-chart/1hour",
+        params={"frequency": "1hour", "symbol": "AAPL"},
+    )
+
+
+async def test_sector_performance_summary_and_timeseries() -> None:
+    fmp = FMP(key="k")
+    fmp.get_path = AsyncMock(  # type: ignore[method-assign]
+        side_effect=[
+            [{"sector": "Tech", "changesPercentage": "1.23%"}],
+            [
+                {"date": "2024-01-01", "technologyChangesPercentage": 1.0},
+                {"date": "2024-01-02", "technologyChangesPercentage": 2.0},
+            ],
+        ]
+    )
+    snapshot = await fmp.sector_performance()
+    assert isinstance(snapshot, dict)
+    assert str(snapshot["Tech"]) == "1.23"
+
+    summary = await fmp.sector_performance(from_date=date(2024, 1, 1), summary=True)
+    assert isinstance(summary, dict)
+    assert str(summary["Technology"]) == "3.02"
+
+
+def test_sector_helpers() -> None:
+    nice = dict(
+        nice_sector_performance(
+            {"date": "2024-01-01", "consumerStaplesChangesPercentage": 1.5}
+        )
+    )
+    assert nice["date"] == date(2024, 1, 1)
+    assert nice["Consumer Staples"] == 1.5
+
+    summary = summary_sector_performance(
+        [
+            {"date": date(2024, 1, 1), "Tech": 1.0},
+            {"date": date(2024, 1, 2), "Tech": 2.0},
+        ]
+    )
+    assert str(summary["Tech"]) == "3.02"

quantflow_tests/test_heston_calibration.py

@@ -0,0 +1,95 @@
+from __future__ import annotations
+
+import numpy as np
+
+from quantflow.options.calibration import (
+    DoubleHestonCalibration,
+    DoubleHestonJCalibration,
+    HestonCalibration,
+    HestonJCalibration,
+)
+from quantflow.options.pricer import OptionPricer
+from quantflow.sp.heston import DoubleHeston, DoubleHestonJ, Heston, HestonJ
+from quantflow.utils.distributions import DoubleExponential
+
+
+def test_heston_calibration_get_set_and_penalize(vol_surface) -> None:
+    cal: HestonCalibration = HestonCalibration(
+        pricer=OptionPricer(
+            model=Heston.create(vol=0.4, kappa=2.0, sigma=0.6, rho=-0.4)
+        ),
+        vol_surface=vol_surface,
+    )
+    params = cal.get_params()
+    assert len(params) == 5
+    updated = np.array([0.1, 0.1, 1.0, 1.0, -0.2], dtype=float)
+    cal.set_params(updated)
+    assert np.allclose(cal.get_params(), updated)
+    assert cal.penalize() >= 0.0
+    bounds = cal.get_bounds()
+    assert len(bounds.lb) == 5
+    assert len(bounds.ub) == 5
+
+
+def test_hestonj_calibration_get_set_and_bounds(vol_surface) -> None:
+    model = HestonJ.create(
+        DoubleExponential,
+        vol=0.3,
+        kappa=1.5,
+        sigma=0.4,
+        rho=-0.3,
+        jump_fraction=0.2,
+        jump_asymmetry=0.1,
+    )
+    cal: HestonJCalibration = HestonJCalibration(
+        pricer=OptionPricer(model=model), vol_surface=vol_surface
+    )
+    params = cal.get_params()
+    cal.set_params(params)
+    assert np.allclose(cal.get_params(), params)
+    bounds = cal.get_bounds()
+    assert len(bounds.lb) == len(bounds.ub) == len(params)
+
+
+def test_double_heston_calibration_param_logic(vol_surface) -> None:
+    model = DoubleHeston(
+        heston1=Heston.create(vol=0.3, kappa=2.0, sigma=0.4, rho=-0.2),
+        heston2=Heston.create(vol=0.25, kappa=1.0, sigma=0.3, rho=-0.4),
+    )
+    cal: DoubleHestonCalibration = DoubleHestonCalibration(
+        pricer=OptionPricer(model=model), vol_surface=vol_surface
+    )
+    params = cal.get_params()
+    assert len(params) == 10
+    cal.set_params(params)
+    assert (
+        cal.model.heston1.variance_process.kappa
+        >= cal.model.heston2.variance_process.kappa
+    )
+    assert cal.penalize() >= 0.0
+    assert len(cal.feller_residuals()) == 2
+    assert cal.maturity_split() > 0.0
+
+
+def test_double_hestonj_calibration_get_set_and_bounds(vol_surface) -> None:
+    model = DoubleHestonJ(
+        heston1=HestonJ.create(
+            DoubleExponential,
+            vol=0.3,
+            kappa=2.5,
+            sigma=0.4,
+            rho=-0.2,
+            jump_fraction=0.2,
+            jump_asymmetry=0.1,
+        ),
+        heston2=Heston.create(vol=0.25, kappa=1.2, sigma=0.3, rho=-0.4),
+    )
+    cal: DoubleHestonJCalibration = DoubleHestonJCalibration(
+        pricer=OptionPricer(model=model),
+        vol_surface=vol_surface,
+    )
+    params = cal.get_params()
+    cal.set_params(params)
+    assert np.allclose(cal.get_params(), params)
+    bounds = cal.get_bounds()
+    assert len(bounds.lb) == len(bounds.ub) == len(params)