493 lines
24 KiB
Python
493 lines
24 KiB
Python
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"""
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EsoF Advisory — unit + integration tests
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=========================================
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Tests:
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1. compute_esof() — deterministic outputs for known datetimes
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2. Session classification — boundary conditions
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3. Weighted hours — real vs fallback consistency
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4. Advisory score — scoring logic, clamping, labels
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5. Expectancy tables — internal consistency
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6. HZ round-trip (integration, skipped if HZ down)
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7. CH write (integration, skipped if CH down)
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Run:
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source /home/dolphin/siloqy_env/bin/activate
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cd /mnt/dolphinng5_predict/prod && pytest tests/test_esof_advisor.py -v
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"""
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import sys
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import json
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import math
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import pytest
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from datetime import datetime, timezone, timedelta
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from pathlib import Path
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from unittest.mock import patch
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# ── Path setup ────────────────────────────────────────────────────────────────
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sys.path.insert(0, str(Path(__file__).parent.parent.parent / "Observability"))
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sys.path.insert(0, str(Path(__file__).parent.parent.parent / "external_factors"))
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from esof_advisor import (
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compute_esof,
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get_session,
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get_advisory,
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SESSION_STATS,
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DOW_STATS,
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LIQ_HOUR_STATS,
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SLOT_STATS,
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BASELINE_WR,
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DOW_NAMES,
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_get_weighted_hours,
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_WEIGHTED_HOURS_AVAILABLE,
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)
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# ── Fixtures ──────────────────────────────────────────────────────────────────
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KNOWN_TIMES = {
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"sun_london": datetime(2026, 4, 19, 10, 0, tzinfo=timezone.utc), # Sun LDN — best cell
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"thu_ovlp": datetime(2026, 4, 16, 15, 0, tzinfo=timezone.utc), # Thu OVLP — worst cell
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"sun_ny": datetime(2026, 4, 19, 19, 0, tzinfo=timezone.utc), # Sun NY — near 0% WR
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"mon_asia": datetime(2026, 4, 20, 3, 0, tzinfo=timezone.utc), # Mon ASIA — bad
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"tue_asia": datetime(2026, 4, 21, 3, 0, tzinfo=timezone.utc), # Tue ASIA — best day
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"midday_win": datetime(2026, 4, 15, 11, 30, tzinfo=timezone.utc), # 11:30 — 87.5% WR slot
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}
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# ══════════════════════════════════════════════════════════════════════════════
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# 1. compute_esof() — output schema
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# ══════════════════════════════════════════════════════════════════════════════
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class TestComputeEsofSchema:
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REQUIRED_KEYS = [
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"ts", "_ts", "dow", "dow_name", "hour_utc", "slot_15m", "session",
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"pop_weighted_hour", "liq_weighted_hour", "liq_bucket_3h",
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"moon_illumination", "moon_phase", "mercury_retrograde",
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"market_cycle_pos", "fib_strength",
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"liq_wr_pct", "liq_net_pnl",
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"slot_wr_pct", "slot_net_pnl",
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"session_wr_pct", "session_net_pnl",
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"dow_wr_pct", "dow_net_pnl",
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"advisory_score", "advisory_label",
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]
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def test_all_keys_present(self):
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d = compute_esof(KNOWN_TIMES["sun_london"])
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for key in self.REQUIRED_KEYS:
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assert key in d, f"Missing key: {key}"
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def test_ts_matches_input(self):
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t = KNOWN_TIMES["sun_london"]
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d = compute_esof(t)
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assert d["hour_utc"] == 10
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assert d["dow"] == 6 # Sunday
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assert d["dow_name"] == "Sun"
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def test_slot_15m_format(self):
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# At 11:37 UTC → slot should be 11:30
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t = datetime(2026, 4, 15, 11, 37, tzinfo=timezone.utc)
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d = compute_esof(t)
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assert d["slot_15m"] == "11:30"
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def test_slot_15m_boundaries(self):
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cases = [
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(0, 0, "0:00"), (0, 14, "0:00"), (0, 15, "0:15"),
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(0, 29, "0:15"), (0, 30, "0:30"), (0, 44, "0:30"),
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(0, 45, "0:45"), (0, 59, "0:45"),
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(23, 59, "23:45"),
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]
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for h, m, expected in cases:
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t = datetime(2026, 4, 15, h, m, tzinfo=timezone.utc)
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assert compute_esof(t)["slot_15m"] == expected, f"{h}:{m} → expected {expected}"
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def test_advisory_score_clamped(self):
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for name, t in KNOWN_TIMES.items():
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d = compute_esof(t)
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sc = d["advisory_score"]
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assert -1.0 <= sc <= 1.0, f"{name}: advisory_score {sc} out of [-1,1]"
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def test_advisory_label_valid(self):
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valid = {"FAVORABLE", "MILD_POSITIVE", "NEUTRAL", "MILD_NEGATIVE", "UNFAVORABLE"}
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for name, t in KNOWN_TIMES.items():
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d = compute_esof(t)
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assert d["advisory_label"] in valid, f"{name}: bad label {d['advisory_label']}"
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# ══════════════════════════════════════════════════════════════════════════════
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# 2. Session classification
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# ══════════════════════════════════════════════════════════════════════════════
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class TestSessionClassification:
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def test_all_sessions_reachable(self):
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sessions = set()
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base = datetime(2026, 4, 15, 0, 0, tzinfo=timezone.utc)
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for h in range(24):
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sessions.add(get_session(h))
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assert sessions == {
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"ASIA_PACIFIC", "LONDON_MORNING", "LN_NY_OVERLAP",
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"NY_AFTERNOON", "LOW_LIQUIDITY"
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}
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@pytest.mark.parametrize("hour,expected", [
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(0, "ASIA_PACIFIC"),
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(7, "ASIA_PACIFIC"),
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(7.99, "ASIA_PACIFIC"),
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(8, "LONDON_MORNING"),
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(12, "LONDON_MORNING"),
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(12.99, "LONDON_MORNING"),
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(13, "LN_NY_OVERLAP"),
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(16.99, "LN_NY_OVERLAP"),
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(17, "NY_AFTERNOON"),
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(20.99, "NY_AFTERNOON"),
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(21, "LOW_LIQUIDITY"),
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(23.99, "LOW_LIQUIDITY"),
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])
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def test_session_boundaries(self, hour, expected):
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assert get_session(hour) == expected
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def test_known_times_sessions(self):
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assert compute_esof(KNOWN_TIMES["sun_london"])["session"] == "LONDON_MORNING"
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assert compute_esof(KNOWN_TIMES["thu_ovlp"])["session"] == "LN_NY_OVERLAP"
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assert compute_esof(KNOWN_TIMES["sun_ny"])["session"] == "NY_AFTERNOON"
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assert compute_esof(KNOWN_TIMES["mon_asia"])["session"] == "ASIA_PACIFIC"
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def test_session_stats_coverage(self):
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"""Every reachable session must have an expectancy entry."""
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for h in range(24):
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sess = get_session(h)
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assert sess in SESSION_STATS, f"Session {sess} missing from SESSION_STATS"
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# ══════════════════════════════════════════════════════════════════════════════
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# 3. Weighted hours
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# ══════════════════════════════════════════════════════════════════════════════
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class TestWeightedHours:
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def test_pop_hour_range(self):
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base = datetime(2026, 4, 15, 0, 0, tzinfo=timezone.utc)
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for h in range(24):
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t = base + timedelta(hours=h)
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ph, lh = _get_weighted_hours(t)
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assert 0 <= ph < 24, f"pop_hour {ph} at {h}h out of range"
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assert 0 <= lh < 24, f"liq_hour {lh} at {h}h out of range"
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def test_liq_hour_monotone_utc(self):
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"""liq_hour increases monotonically with UTC (within the same calendar day)."""
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base = datetime(2026, 4, 15, 0, 0, tzinfo=timezone.utc)
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prev_lh = None
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for h in range(23):
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t = base + timedelta(hours=h)
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_, lh = _get_weighted_hours(t)
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if prev_lh is not None:
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assert lh > prev_lh, f"liq_hour not monotone at {h}h: {lh} <= {prev_lh}"
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prev_lh = lh
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def test_fallback_consistency(self):
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"""Fallback approximation should be within ±1h of real computation."""
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if not _WEIGHTED_HOURS_AVAILABLE:
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pytest.skip("MarketIndicators not available")
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t = datetime(2026, 4, 15, 12, 0, tzinfo=timezone.utc)
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real_ph, real_lh = _get_weighted_hours(t)
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# Approximation offsets
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h = 12.0
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approx_ph = (h + 4.21) % 24
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approx_lh = (h + 0.98) % 24
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assert abs(real_ph - approx_ph) < 1.0, f"pop_hour fallback error: {real_ph} vs {approx_ph}"
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assert abs(real_lh - approx_lh) < 1.0, f"liq_hour fallback error: {real_lh} vs {approx_lh}"
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def test_liq_bucket_aligns(self):
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"""liq_bucket_3h must match floor(liq_weighted_hour / 3) * 3."""
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for name, t in KNOWN_TIMES.items():
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d = compute_esof(t)
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expected_bkt = int(d["liq_weighted_hour"] // 3) * 3
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assert d["liq_bucket_3h"] == expected_bkt, (
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f"{name}: liq_bucket {d['liq_bucket_3h']} != expected {expected_bkt}"
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)
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def test_liq_bucket_in_stats(self):
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"""Every computed liq_bucket_3h must have a stats entry (0-21 in steps of 3)."""
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for name, t in KNOWN_TIMES.items():
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d = compute_esof(t)
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bkt = d["liq_bucket_3h"]
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assert bkt in LIQ_HOUR_STATS, f"{name}: liq_bucket {bkt} not in LIQ_HOUR_STATS"
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# ══════════════════════════════════════════════════════════════════════════════
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# 4. Advisory scoring logic
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# ══════════════════════════════════════════════════════════════════════════════
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class TestAdvisoryScoring:
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def test_best_known_cell_is_positive(self):
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"""Sun 10h UTC (LONDON_MORNING, best DoW cell) → positive score."""
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d = compute_esof(KNOWN_TIMES["sun_london"])
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assert d["advisory_score"] > 0, f"Sun LDN score={d['advisory_score']} expected positive"
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def test_worst_known_cell_is_worse_than_best(self):
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"""Thu OVLP score must be worse than Sun LDN score (best known cell)."""
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d_best = compute_esof(KNOWN_TIMES["sun_london"])
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d_worst = compute_esof(KNOWN_TIMES["thu_ovlp"])
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assert d_best["advisory_score"] > d_worst["advisory_score"], (
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f"Sun LDN {d_best['advisory_score']} not > Thu OVLP {d_worst['advisory_score']}"
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)
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def test_mon_worse_than_tue(self):
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"""Monday score < Tuesday score (same time) — Mon WR 27% vs Tue WR 54%."""
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t_mon = datetime(2026, 4, 20, 10, 0, tzinfo=timezone.utc) # Monday
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t_tue = datetime(2026, 4, 21, 10, 0, tzinfo=timezone.utc) # Tuesday
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d_mon = compute_esof(t_mon)
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d_tue = compute_esof(t_tue)
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assert d_mon["advisory_score"] < d_tue["advisory_score"], (
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f"Mon {d_mon['advisory_score']} not < Tue {d_tue['advisory_score']}"
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)
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def test_sun_ny_negative(self):
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"""Sun NY_AFTERNOON (6% WR) → negative or at most mild positive (DoW boost limited)."""
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d = compute_esof(KNOWN_TIMES["sun_ny"])
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# Session/liq drag should keep it from being FAVORABLE
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assert d["advisory_label"] not in {"FAVORABLE"}, \
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f"Sun NY labeled {d['advisory_label']} — expected not FAVORABLE"
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def test_score_monotone_session_ordering(self):
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"""LONDON_MORNING score > NY_AFTERNOON score for same DoW."""
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base = datetime(2026, 4, 15, tzinfo=timezone.utc) # Tuesday
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d_ldn = compute_esof(base.replace(hour=10))
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d_ny = compute_esof(base.replace(hour=19))
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assert d_ldn["advisory_score"] > d_ny["advisory_score"], \
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f"LDN {d_ldn['advisory_score']} not > NY {d_ny['advisory_score']}"
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def test_mercury_retrograde_penalty(self):
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"""Mercury retrograde should reduce score by ~0.05."""
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t = datetime(2026, 3, 15, 10, 0, tzinfo=timezone.utc) # known retro period
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d = compute_esof(t)
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assert d["mercury_retrograde"] is True, "Expected mercury retrograde on 2026-03-15"
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# Score would be ~0.05 lower than without retrograde
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assert d["advisory_score"] <= 0.95, "Score should not be at ceiling during retrograde"
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def test_label_thresholds(self):
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"""Labels must correspond to score ranges."""
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cases = [
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(0.30, "FAVORABLE"),
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(0.10, "MILD_POSITIVE"),
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(0.00, "NEUTRAL"),
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(-0.10, "MILD_NEGATIVE"),
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(-0.30, "UNFAVORABLE"),
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]
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for score, expected_label in cases:
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# Patch compute to return known score
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with patch("esof_advisor.compute_esof") as mock:
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mock.return_value = {
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"advisory_score": score,
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"advisory_label": (
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"FAVORABLE" if score > 0.25 else
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"MILD_POSITIVE"if score > 0.05 else
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"NEUTRAL" if score > -0.05 else
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"MILD_NEGATIVE"if score > -0.25 else
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"UNFAVORABLE"
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),
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}
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result = mock()
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assert result["advisory_label"] == expected_label, \
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f"score={score}: got {result['advisory_label']} expected {expected_label}"
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# ══════════════════════════════════════════════════════════════════════════════
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# 5. Expectancy table internal consistency
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# ══════════════════════════════════════════════════════════════════════════════
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class TestExpectancyTables:
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def test_session_stats_wr_range(self):
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for sess, (n, wr, net, avg) in SESSION_STATS.items():
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assert 0 <= wr <= 100, f"{sess}: WR {wr} out of range"
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assert n > 0, f"{sess}: n={n}"
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def test_dow_stats_completeness(self):
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assert set(DOW_STATS.keys()) == set(range(7)), "DOW_STATS must cover Mon-Sun (0-6)"
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def test_dow_names_alignment(self):
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assert len(DOW_NAMES) == 7
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assert DOW_NAMES[0] == "Mon" and DOW_NAMES[6] == "Sun"
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def test_liq_hour_stats_completeness(self):
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expected_buckets = {0, 3, 6, 9, 12, 15, 18, 21}
|
||
|
|
assert set(LIQ_HOUR_STATS.keys()) == expected_buckets
|
||
|
|
|
||
|
|
def test_liq_hour_best_bucket_is_12(self):
|
||
|
|
"""liq 12-15h should have highest WR and most positive net PnL."""
|
||
|
|
best_wr_bkt = max(LIQ_HOUR_STATS, key=lambda k: LIQ_HOUR_STATS[k][1])
|
||
|
|
best_net_bkt = max(LIQ_HOUR_STATS, key=lambda k: LIQ_HOUR_STATS[k][2])
|
||
|
|
assert best_wr_bkt == 12, f"Expected liq 12h best WR, got {best_wr_bkt}"
|
||
|
|
assert best_net_bkt == 12, f"Expected liq 12h best net, got {best_net_bkt}"
|
||
|
|
|
||
|
|
def test_liq_hour_worst_bucket_is_18(self):
|
||
|
|
"""liq 18-21h (NY afternoon) should have lowest WR and worst net PnL."""
|
||
|
|
worst_wr_bkt = min(LIQ_HOUR_STATS, key=lambda k: LIQ_HOUR_STATS[k][1])
|
||
|
|
worst_net_bkt = min(LIQ_HOUR_STATS, key=lambda k: LIQ_HOUR_STATS[k][2])
|
||
|
|
assert worst_wr_bkt == 18, f"Expected liq 18h worst WR, got {worst_wr_bkt}"
|
||
|
|
assert worst_net_bkt == 18, f"Expected liq 18h worst net, got {worst_net_bkt}"
|
||
|
|
|
||
|
|
def test_baseline_wr_is_reasonable(self):
|
||
|
|
# Overall WR from 637 trades was 278/637 = 43.6%
|
||
|
|
assert 42.0 < BASELINE_WR < 45.0, f"BASELINE_WR {BASELINE_WR} looks wrong"
|
||
|
|
|
||
|
|
def test_slot_stats_wr_range(self):
|
||
|
|
for slot, data in SLOT_STATS.items():
|
||
|
|
n, wr = data[0], data[1]
|
||
|
|
assert 0 <= wr <= 100, f"slot {slot}: WR {wr} out of range"
|
||
|
|
assert n >= 3, f"slot {slot}: n={n} below minimum threshold"
|
||
|
|
|
||
|
|
def test_moon_illumination_range(self):
|
||
|
|
for name, t in KNOWN_TIMES.items():
|
||
|
|
d = compute_esof(t)
|
||
|
|
illum = d["moon_illumination"]
|
||
|
|
assert 0.0 <= illum <= 1.0, f"{name}: moon_illumination {illum} out of [0,1]"
|
||
|
|
|
||
|
|
def test_fib_strength_range(self):
|
||
|
|
for name, t in KNOWN_TIMES.items():
|
||
|
|
d = compute_esof(t)
|
||
|
|
fs = d["fib_strength"]
|
||
|
|
assert 0.0 <= fs <= 1.0, f"{name}: fib_strength {fs} out of [0,1]"
|
||
|
|
|
||
|
|
def test_market_cycle_pos_range(self):
|
||
|
|
for name, t in KNOWN_TIMES.items():
|
||
|
|
d = compute_esof(t)
|
||
|
|
cp = d["market_cycle_pos"]
|
||
|
|
assert 0.0 <= cp < 1.0, f"{name}: market_cycle_pos {cp} out of [0,1)"
|
||
|
|
|
||
|
|
|
||
|
|
# ══════════════════════════════════════════════════════════════════════════════
|
||
|
|
# 6. Moon approximation correctness
|
||
|
|
# ══════════════════════════════════════════════════════════════════════════════
|
||
|
|
class TestMoonApproximation:
|
||
|
|
# Known moon phases (approximate)
|
||
|
|
KNOWN_MOONS = [
|
||
|
|
(datetime(2026, 4, 7, tzinfo=timezone.utc), "NEW_MOON", 0.03),
|
||
|
|
(datetime(2026, 4, 20, tzinfo=timezone.utc), "FULL_MOON", 0.97),
|
||
|
|
(datetime(2026, 4, 13, tzinfo=timezone.utc), "WAXING", 0.45), # first quarter ≈
|
||
|
|
(datetime(2026, 4, 26, tzinfo=timezone.utc), "WANING", 0.50), # last quarter ≈
|
||
|
|
]
|
||
|
|
|
||
|
|
def test_new_moon_illumination_low(self):
|
||
|
|
# 28th new moon after ref Jan 11 2024: ~Apr 17 2026 (computed from synodic cycle)
|
||
|
|
# 28 * 29.53059 = 826.856 days → Jan 11 2024 + 826d = Apr 17 2026
|
||
|
|
t = datetime(2026, 4, 17, 12, 0, tzinfo=timezone.utc)
|
||
|
|
d = compute_esof(t)
|
||
|
|
assert d["moon_illumination"] < 0.10, \
|
||
|
|
f"Expected near-new-moon illumination ~0, got {d['moon_illumination']}"
|
||
|
|
|
||
|
|
def test_full_moon_illumination_high(self):
|
||
|
|
# Halfway between 27th (Mar 18) and 28th (Apr 17) new moon = ~Apr 2 2026
|
||
|
|
t = datetime(2026, 4, 2, 12, 0, tzinfo=timezone.utc)
|
||
|
|
d = compute_esof(t)
|
||
|
|
assert d["moon_illumination"] > 0.90, \
|
||
|
|
f"Expected near-full-moon illumination, got {d['moon_illumination']}"
|
||
|
|
|
||
|
|
def test_mercury_retrograde_period(self):
|
||
|
|
"""2026-03-07 to 2026-03-30 is Mercury retrograde."""
|
||
|
|
in_retro = datetime(2026, 3, 15, 12, 0, tzinfo=timezone.utc)
|
||
|
|
post_retro = datetime(2026, 4, 5, 12, 0, tzinfo=timezone.utc)
|
||
|
|
assert compute_esof(in_retro)["mercury_retrograde"] is True
|
||
|
|
assert compute_esof(post_retro)["mercury_retrograde"] is False
|
||
|
|
|
||
|
|
|
||
|
|
# ══════════════════════════════════════════════════════════════════════════════
|
||
|
|
# 7. get_advisory() public API
|
||
|
|
# ══════════════════════════════════════════════════════════════════════════════
|
||
|
|
class TestPublicAPI:
|
||
|
|
def test_get_advisory_no_args(self):
|
||
|
|
"""get_advisory() with no args should use current time."""
|
||
|
|
d = get_advisory()
|
||
|
|
assert "advisory_score" in d
|
||
|
|
assert "advisory_label" in d
|
||
|
|
|
||
|
|
def test_get_advisory_with_time(self):
|
||
|
|
d = get_advisory(KNOWN_TIMES["sun_london"])
|
||
|
|
assert d["dow_name"] == "Sun"
|
||
|
|
assert d["session"] == "LONDON_MORNING"
|
||
|
|
|
||
|
|
def test_deterministic(self):
|
||
|
|
"""Same input → same output."""
|
||
|
|
t = KNOWN_TIMES["midday_win"]
|
||
|
|
d1 = compute_esof(t)
|
||
|
|
d2 = compute_esof(t)
|
||
|
|
assert d1["advisory_score"] == d2["advisory_score"]
|
||
|
|
assert d1["advisory_label"] == d2["advisory_label"]
|
||
|
|
assert d1["session"] == d2["session"]
|
||
|
|
assert d1["liq_weighted_hour"] == d2["liq_weighted_hour"]
|
||
|
|
|
||
|
|
|
||
|
|
# ══════════════════════════════════════════════════════════════════════════════
|
||
|
|
# 8. Integration — HZ round-trip (skipped if HZ unavailable)
|
||
|
|
# ══════════════════════════════════════════════════════════════════════════════
|
||
|
|
class TestHZIntegration:
|
||
|
|
@pytest.fixture(scope="class")
|
||
|
|
def hz_client(self):
|
||
|
|
try:
|
||
|
|
import hazelcast
|
||
|
|
client = hazelcast.HazelcastClient(
|
||
|
|
cluster_name="dolphin",
|
||
|
|
cluster_members=["localhost:5701"],
|
||
|
|
connection_timeout=2.0,
|
||
|
|
)
|
||
|
|
yield client
|
||
|
|
client.shutdown()
|
||
|
|
except Exception:
|
||
|
|
pytest.skip("Hazelcast not available")
|
||
|
|
|
||
|
|
def test_hz_write_and_read(self, hz_client):
|
||
|
|
from esof_advisor import _hz_write
|
||
|
|
d = compute_esof(KNOWN_TIMES["sun_london"])
|
||
|
|
_hz_write(d)
|
||
|
|
import time; time.sleep(0.3)
|
||
|
|
raw = hz_client.get_map("DOLPHIN_FEATURES").blocking().get("esof_advisor_latest")
|
||
|
|
assert raw is not None, "esof_advisor_latest not found in HZ after write"
|
||
|
|
parsed = json.loads(raw)
|
||
|
|
assert parsed["advisory_label"] == d["advisory_label"]
|
||
|
|
assert parsed["session"] == "LONDON_MORNING"
|
||
|
|
|
||
|
|
def test_hz_value_is_json(self, hz_client):
|
||
|
|
raw = hz_client.get_map("DOLPHIN_FEATURES").blocking().get("esof_advisor_latest")
|
||
|
|
if raw is None:
|
||
|
|
pytest.skip("No esof_advisor_latest in HZ yet")
|
||
|
|
parsed = json.loads(raw)
|
||
|
|
assert "advisory_score" in parsed
|
||
|
|
|
||
|
|
|
||
|
|
# ══════════════════════════════════════════════════════════════════════════════
|
||
|
|
# 9. Integration — CH write (skipped if CH unavailable)
|
||
|
|
# ══════════════════════════════════════════════════════════════════════════════
|
||
|
|
class TestCHIntegration:
|
||
|
|
@pytest.fixture(scope="class")
|
||
|
|
def ch_available(self):
|
||
|
|
import urllib.request
|
||
|
|
try:
|
||
|
|
req = urllib.request.Request("http://localhost:8123/ping")
|
||
|
|
req.add_header("X-ClickHouse-User", "dolphin")
|
||
|
|
req.add_header("X-ClickHouse-Key", "dolphin_ch_2026")
|
||
|
|
urllib.request.urlopen(req, timeout=2)
|
||
|
|
except Exception:
|
||
|
|
pytest.skip("ClickHouse not available")
|
||
|
|
|
||
|
|
def test_ch_write_no_exception(self, ch_available):
|
||
|
|
from esof_advisor import _ch_write
|
||
|
|
d = compute_esof(KNOWN_TIMES["sun_london"])
|
||
|
|
# Should complete without raising
|
||
|
|
_ch_write(d)
|
||
|
|
|
||
|
|
def test_ch_table_has_data(self, ch_available):
|
||
|
|
import urllib.request
|
||
|
|
def ch(q):
|
||
|
|
url = "http://localhost:8123/?database=dolphin"
|
||
|
|
req = urllib.request.Request(url, data=q.encode(), method="POST")
|
||
|
|
req.add_header("X-ClickHouse-User", "dolphin")
|
||
|
|
req.add_header("X-ClickHouse-Key", "dolphin_ch_2026")
|
||
|
|
with urllib.request.urlopen(req, timeout=5) as r:
|
||
|
|
return r.read().decode().strip()
|
||
|
|
count = int(ch("SELECT count() FROM esof_advisory"))
|
||
|
|
assert count >= 0 # table exists (may be 0 if never written via daemon)
|
||
|
|
|
||
|
|
def test_ch_schema_correct(self, ch_available):
|
||
|
|
import urllib.request
|
||
|
|
def ch(q):
|
||
|
|
url = "http://localhost:8123/?database=dolphin"
|
||
|
|
req = urllib.request.Request(url, data=q.encode(), method="POST")
|
||
|
|
req.add_header("X-ClickHouse-User", "dolphin")
|
||
|
|
req.add_header("X-ClickHouse-Key", "dolphin_ch_2026")
|
||
|
|
with urllib.request.urlopen(req, timeout=5) as r:
|
||
|
|
return r.read().decode().strip()
|
||
|
|
cols = ch("SELECT name FROM system.columns WHERE table='esof_advisory' AND database='dolphin' FORMAT CSV")
|
||
|
|
assert "advisory_score" in cols
|
||
|
|
assert "liq_weighted_hour" in cols
|
||
|
|
assert "session" in cols
|