Initial implementation of Options Sidekick

Full-stack iOS options trading assistant: - Python FastAPI backend with SQLite, APScheduler (15-min position monitor), APNs push notifications, and yfinance market data integration - Signal engine: IV Rank (rolling HV proxy), SMA-50/200, swing-based support/resistance, earnings detection, signal strength scoring and noise-resistant SHA hash for change detection - Recommendation engine: covered call and cash-secured put strike/expiry selection across 0DTE, 1DTE, weekly, and monthly horizons - REST API: /devices, /portfolio, /recommendations, /positions, /signals, /alerts - iOS SwiftUI app (iOS 17+): dashboard, recommendations, trades, portfolio, and alerts tabs with push notification deep-linking - Unit + integration tests for signal engine and API layer Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-09 14:38:25 -04:00
commit b7d4e900cc
61 changed files with 4953 additions and 0 deletions
--- a/backend/tests/init.py
+++ b/backend/tests/init.py
--- a/backend/tests/conftest.py
+++ b/backend/tests/conftest.py
@@ -0,0 +1,4 @@
+"""Pytest configuration — sets asyncio mode."""
+import pytest
+
+pytest_plugins = ["pytest_asyncio"]
--- a/backend/tests/test_api.py
+++ b/backend/tests/test_api.py
@@ -0,0 +1,220 @@
+"""
+Integration tests for the FastAPI API layer.
+Uses TestClient with an in-memory SQLite DB — no live market data.
+"""
+
+from datetime import date, timedelta
+from unittest.mock import MagicMock, patch
+
+import pytest
+from fastapi.testclient import TestClient
+from sqlalchemy import create_engine
+from sqlalchemy.orm import sessionmaker
+
+from app.database import Base, get_db
+from app.main import app
+
+# ─── Test DB setup ─────────────────────────────────────────────────────────────
+
+TEST_DB_URL = "sqlite://"  # in-memory
+
+test_engine = create_engine(TEST_DB_URL, connect_args={"check_same_thread": False})
+TestSessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=test_engine)
+
+
+def override_get_db():
+    db = TestSessionLocal()
+    try:
+        yield db
+    finally:
+        db.close()
+
+
+@pytest.fixture(autouse=True)
+def setup_db():
+    Base.metadata.create_all(bind=test_engine)
+    yield
+    Base.metadata.drop_all(bind=test_engine)
+
+
+app.dependency_overrides[get_db] = override_get_db
+
+# Disable scheduler during tests
+with patch("app.main.start_scheduler"), patch("app.main.stop_scheduler"):
+    client = TestClient(app, raise_server_exceptions=True)
+
+FAKE_TOKEN = "abc123device0000000000000000000000000000000000000000000000000000"
+
+
+# ─── Device registration ───────────────────────────────────────────────────────
+
+def test_register_device():
+    resp = client.post("/api/v1/devices/register", json={"apns_token": FAKE_TOKEN, "device_name": "Test iPhone"})
+    assert resp.status_code == 200
+    data = resp.json()
+    assert data["apns_token"] == FAKE_TOKEN
+    assert "id" in data
+
+
+def test_register_device_idempotent():
+    client.post("/api/v1/devices/register", json={"apns_token": FAKE_TOKEN})
+    resp = client.post("/api/v1/devices/register", json={"apns_token": FAKE_TOKEN})
+    assert resp.status_code == 200
+
+
+# ─── Portfolio ─────────────────────────────────────────────────────────────────
+
+@pytest.fixture
+def registered_device():
+    client.post("/api/v1/devices/register", json={"apns_token": FAKE_TOKEN})
+    return FAKE_TOKEN
+
+
+def test_add_portfolio(registered_device):
+    resp = client.post(
+        "/api/v1/portfolio",
+        json=[{"ticker": "AAPL", "shares": 100}, {"ticker": "MSFT", "shares": 200}],
+        headers={"X-Device-Token": registered_device},
+    )
+    assert resp.status_code == 200
+    tickers = [p["ticker"] for p in resp.json()]
+    assert "AAPL" in tickers
+    assert "MSFT" in tickers
+
+
+def test_get_portfolio_empty(registered_device):
+    resp = client.get("/api/v1/portfolio", headers={"X-Device-Token": registered_device})
+    assert resp.status_code == 200
+    assert resp.json() == []
+
+
+def test_get_portfolio_after_add(registered_device):
+    client.post(
+        "/api/v1/portfolio",
+        json=[{"ticker": "NVDA", "shares": 50}],
+        headers={"X-Device-Token": registered_device},
+    )
+    resp = client.get("/api/v1/portfolio", headers={"X-Device-Token": registered_device})
+    assert resp.status_code == 200
+    assert resp.json()[0]["ticker"] == "NVDA"
+
+
+def test_delete_ticker(registered_device):
+    client.post(
+        "/api/v1/portfolio",
+        json=[{"ticker": "AMD", "shares": 100}],
+        headers={"X-Device-Token": registered_device},
+    )
+    resp = client.delete("/api/v1/portfolio/AMD", headers={"X-Device-Token": registered_device})
+    assert resp.status_code == 204
+    remaining = client.get("/api/v1/portfolio", headers={"X-Device-Token": registered_device}).json()
+    assert all(p["ticker"] != "AMD" for p in remaining)
+
+
+def test_portfolio_unregistered_device():
+    resp = client.get("/api/v1/portfolio", headers={"X-Device-Token": "nonexistent_token"})
+    assert resp.status_code == 404
+
+
+# ─── Option Positions ──────────────────────────────────────────────────────────
+
+def test_log_position(registered_device):
+    expiry = str(date.today() + timedelta(days=14))
+    resp = client.post(
+        "/api/v1/positions",
+        json={
+            "ticker": "AAPL",
+            "strategy": "covered_call",
+            "strike": 195.0,
+            "expiration": expiry,
+            "premium_received": 2.50,
+            "contracts": 1,
+        },
+        headers={"X-Device-Token": registered_device},
+    )
+    assert resp.status_code == 201
+    data = resp.json()
+    assert data["ticker"] == "AAPL"
+    assert data["status"] == "open"
+    assert data["id"] is not None
+
+
+def test_close_position(registered_device):
+    expiry = str(date.today() + timedelta(days=14))
+    pos = client.post(
+        "/api/v1/positions",
+        json={
+            "ticker": "AAPL",
+            "strategy": "covered_call",
+            "strike": 195.0,
+            "expiration": expiry,
+            "premium_received": 2.50,
+        },
+        headers={"X-Device-Token": registered_device},
+    ).json()
+
+    resp = client.patch(
+        f"/api/v1/positions/{pos['id']}",
+        json={"status": "closed", "close_reason": "bought_back"},
+        headers={"X-Device-Token": registered_device},
+    )
+    assert resp.status_code == 200
+    assert resp.json()["status"] == "closed"
+
+
+def test_get_open_positions_filter(registered_device):
+    expiry = str(date.today() + timedelta(days=14))
+    client.post(
+        "/api/v1/positions",
+        json={"ticker": "TSLA", "strategy": "cash_secured_put", "strike": 200.0, "expiration": expiry, "premium_received": 3.0},
+        headers={"X-Device-Token": registered_device},
+    )
+    resp = client.get("/api/v1/positions?status=open", headers={"X-Device-Token": registered_device})
+    assert resp.status_code == 200
+    assert len(resp.json()) >= 1
+    assert all(p["status"] == "open" for p in resp.json())
+
+
+# ─── Alerts ────────────────────────────────────────────────────────────────────
+
+def test_get_alerts_empty(registered_device):
+    resp = client.get("/api/v1/alerts", headers={"X-Device-Token": registered_device})
+    assert resp.status_code == 200
+    assert resp.json() == []
+
+
+def test_acknowledge_alert(registered_device):
+    from app.models.db_models import Alert
+    from datetime import datetime
+
+    db = TestSessionLocal()
+    device_id = client.post("/api/v1/devices/register", json={"apns_token": FAKE_TOKEN}).json()["id"]
+    alert = Alert(
+        device_id=device_id,
+        ticker="AAPL",
+        alert_type="close_early",
+        message="Test alert",
+        sent_at=datetime.utcnow(),
+        acknowledged=False,
+    )
+    db.add(alert)
+    db.commit()
+    db.refresh(alert)
+    alert_id = alert.id
+    db.close()
+
+    resp = client.patch(
+        f"/api/v1/alerts/{alert_id}/acknowledge",
+        headers={"X-Device-Token": FAKE_TOKEN},
+    )
+    assert resp.status_code == 200
+    assert resp.json()["acknowledged"] is True
+
+
+# ─── Health ────────────────────────────────────────────────────────────────────
+
+def test_health():
+    with patch("app.routers"), patch("app.services.position_monitor.last_run", None):
+        resp = client.get("/api/v1/health")
+    assert resp.status_code == 200
+    assert resp.json()["status"] == "ok"
--- a/backend/tests/test_signal_engine.py
+++ b/backend/tests/test_signal_engine.py
@@ -0,0 +1,267 @@
+"""Unit tests for signal_engine.py — all run without live market data."""
+
+import math
+from datetime import date, timedelta
+from unittest.mock import patch
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from app.services.signal_engine import (
+    compute_iv_rank,
+    compute_smas,
+    compute_swing_levels,
+    compute_trend,
+    compute_signal_strength,
+    compute_signal_hash,
+)
+
+
+# ─── Helpers ──────────────────────────────────────────────────────────────────
+
+def _make_price_df(closes: list[float]) -> pd.DataFrame:
+    """Create a minimal OHLCV DataFrame from a list of close prices."""
+    n = len(closes)
+    dates = pd.date_range(end="2024-01-31", periods=n, freq="B")
+    arr = np.array(closes)
+    df = pd.DataFrame(
+        {
+            "Open": arr * 0.999,
+            "High": arr * 1.005,
+            "Low": arr * 0.995,
+            "Close": arr,
+            "Volume": np.ones(n) * 1_000_000,
+        },
+        index=dates,
+    )
+    return df
+
+
+def _trending_prices(start: float, end: float, n: int = 252) -> list[float]:
+    return list(np.linspace(start, end, n))
+
+
+# ─── IV Rank ──────────────────────────────────────────────────────────────────
+
+def test_iv_rank_high_returns_above_50():
+    """When current volatility is near the 52-week high, IVR should be > 50."""
+    # Create prices that spike at the end (high recent volatility)
+    stable = [100.0] * 220
+    volatile = list(np.linspace(100, 130, 16)) + list(np.linspace(130, 85, 16))
+    closes = stable + volatile
+    df = _make_price_df(closes)
+    ivr = compute_iv_rank(df)
+    assert ivr > 50, f"Expected ivr > 50, got {ivr:.1f}"
+
+
+def test_iv_rank_low_returns_below_50():
+    """Flat price series → low historical volatility → IVR near 0."""
+    closes = [100.0] * 252
+    df = _make_price_df(closes)
+    ivr = compute_iv_rank(df)
+    assert ivr <= 50, f"Expected ivr <= 50, got {ivr:.1f}"
+
+
+def test_iv_rank_insufficient_data_returns_neutral():
+    """Too few data points should return the 50.0 neutral fallback."""
+    df = _make_price_df([100.0] * 20)
+    ivr = compute_iv_rank(df)
+    assert ivr == 50.0
+
+
+def test_iv_rank_clamped_to_0_100():
+    closes = [100.0] * 252
+    df = _make_price_df(closes)
+    ivr = compute_iv_rank(df)
+    assert 0.0 <= ivr <= 100.0
+
+
+# ─── SMAs ─────────────────────────────────────────────────────────────────────
+
+def test_sma_50_correct_value():
+    closes = list(range(1, 253))  # 1..252
+    df = _make_price_df(closes)
+    smas = compute_smas(df)
+    expected_sma50 = sum(range(203, 253)) / 50  # last 50 values: 203-252
+    assert abs(smas["sma_50"] - expected_sma50) < 0.01
+
+
+def test_sma_200_nan_when_insufficient_data():
+    df = _make_price_df([100.0] * 100)
+    smas = compute_smas(df)
+    assert math.isnan(smas["sma_200"])
+
+
+def test_sma_50_nan_when_insufficient_data():
+    df = _make_price_df([100.0] * 30)
+    smas = compute_smas(df)
+    assert math.isnan(smas["sma_50"])
+
+
+# ─── Trend ────────────────────────────────────────────────────────────────────
+
+def test_trend_uptrend():
+    assert compute_trend(110.0, 105.0, 100.0) == "uptrend"
+
+
+def test_trend_downtrend():
+    assert compute_trend(90.0, 95.0, 100.0) == "downtrend"
+
+
+def test_trend_sideways_mixed():
+    # price > sma50 but sma50 < sma200
+    assert compute_trend(106.0, 105.0, 107.0) == "sideways"
+
+
+def test_trend_sideways_with_nan_smas():
+    assert compute_trend(100.0, float("nan"), float("nan")) == "sideways"
+
+
+# ─── Support / Resistance ─────────────────────────────────────────────────────
+
+def test_swing_levels_detects_swing_high():
+    """A clear peak in the middle of the window should be detected as resistance."""
+    closes = [100.0] * 25
+    highs = [100.0] * 25
+    lows = [98.0] * 25
+    # Create a swing high at index 12
+    highs[12] = 108.0
+    closes[12] = 107.0
+
+    dates = pd.date_range(end="2024-01-31", periods=25, freq="B")
+    df = pd.DataFrame({"Open": closes, "High": highs, "Low": lows, "Close": closes, "Volume": [1e6] * 25}, index=dates)
+
+    result = compute_swing_levels(df, lookback=20)
+    assert result["nearest_resistance"] is not None
+    assert abs(result["nearest_resistance"] - 108.0) < 1.0
+
+
+def test_swing_levels_detects_swing_low():
+    """A clear trough should be detected as support."""
+    closes = [100.0] * 25
+    highs = [102.0] * 25
+    lows = [98.0] * 25
+    lows[12] = 90.0
+    closes[12] = 91.0
+
+    dates = pd.date_range(end="2024-01-31", periods=25, freq="B")
+    df = pd.DataFrame({"Open": closes, "High": highs, "Low": lows, "Close": closes, "Volume": [1e6] * 25}, index=dates)
+
+    result = compute_swing_levels(df, lookback=20)
+    assert result["nearest_support"] is not None
+    assert result["nearest_support"] < 100.0
+
+
+def test_swing_levels_flat_no_crash():
+    """Flat price should return None for support/resistance without crashing."""
+    df = _make_price_df([100.0] * 30)
+    result = compute_swing_levels(df, lookback=20)
+    # No assertion on values — just shouldn't raise
+    assert "nearest_support" in result
+    assert "nearest_resistance" in result
+
+
+# ─── Signal Strength ──────────────────────────────────────────────────────────
+
+def test_signal_strength_strong_covered_call():
+    strength = compute_signal_strength(
+        iv_rank=70.0,
+        trend="uptrend",
+        strategy="covered_call",
+        nearest_support=95.0,
+        nearest_resistance=110.0,
+        recommended_strike=107.0,
+        earnings_warning=False,
+    )
+    assert strength == "strong"
+
+
+def test_signal_strength_earnings_warning_downgrades():
+    strength_no_ew = compute_signal_strength(
+        iv_rank=70.0, trend="uptrend", strategy="covered_call",
+        nearest_support=95.0, nearest_resistance=110.0,
+        recommended_strike=107.0, earnings_warning=False,
+    )
+    strength_ew = compute_signal_strength(
+        iv_rank=70.0, trend="uptrend", strategy="covered_call",
+        nearest_support=95.0, nearest_resistance=110.0,
+        recommended_strike=107.0, earnings_warning=True,
+    )
+    assert strength_ew in ("weak", "moderate")
+    assert strength_no_ew == "strong"
+
+
+def test_signal_strength_low_ivr_gives_weak():
+    strength = compute_signal_strength(
+        iv_rank=10.0,
+        trend="downtrend",
+        strategy="covered_call",
+        nearest_support=None,
+        nearest_resistance=None,
+        recommended_strike=None,
+        earnings_warning=True,
+    )
+    assert strength == "weak"
+
+
+def test_signal_strength_csp_uptrend_bonus():
+    strength = compute_signal_strength(
+        iv_rank=55.0,
+        trend="uptrend",
+        strategy="cash_secured_put",
+        nearest_support=95.0,
+        nearest_resistance=110.0,
+        recommended_strike=97.0,
+        earnings_warning=False,
+    )
+    assert strength in ("strong", "moderate")
+
+
+# ─── Signal Hash ──────────────────────────────────────────────────────────────
+
+def test_signal_hash_stability():
+    """Same inputs must always produce the same hash."""
+    args = dict(
+        iv_rank=45.5,
+        sma_50=150.0,
+        sma_200=145.0,
+        nearest_support=148.0,
+        nearest_resistance=155.0,
+        recommended_strike=153.0,
+        recommended_expiration=date(2024, 3, 15),
+        earnings_warning=False,
+    )
+    h1 = compute_signal_hash(**args)
+    h2 = compute_signal_hash(**args)
+    assert h1 == h2
+    assert len(h1) == 16
+
+
+def test_signal_hash_changes_on_strike_change():
+    base = dict(
+        iv_rank=45.5, sma_50=150.0, sma_200=145.0,
+        nearest_support=148.0, nearest_resistance=155.0,
+        recommended_strike=153.0, recommended_expiration=date(2024, 3, 15),
+        earnings_warning=False,
+    )
+    h1 = compute_signal_hash(**base)
+    base["recommended_strike"] = 155.0
+    h2 = compute_signal_hash(**base)
+    assert h1 != h2
+
+
+def test_signal_hash_stable_on_noise():
+    """Tiny IV rank change (< 0.1) should produce same hash due to rounding."""
+    base = dict(
+        iv_rank=45.500,
+        sma_50=150.01, sma_200=145.009,
+        nearest_support=148.001, nearest_resistance=155.002,
+        recommended_strike=153.0, recommended_expiration=date(2024, 3, 15),
+        earnings_warning=False,
+    )
+    tweaked = dict(base)
+    tweaked["iv_rank"] = 45.503  # < 0.1 change → rounds to same 45.5
+    h1 = compute_signal_hash(**base)
+    h2 = compute_signal_hash(**tweaked)
+    assert h1 == h2