4c32e2b737
- load_price_snapshot picked the last snapshot by filename (lexicographic), selecting a stale 9-symbol collection over the full 50-symbol universe. Now picks the snapshot with the latest source.retrieved_at. - allocate_capital profit buckets now also require momentum > 0 (a falling-price name is not 'ทำกำไร'), while momentum/theme_signal stay Optional so the PIT backtest path (which doesn't provide them) still allocates. - Suggestion now allocates across all 50 SET50 names (B1: BGRIM,TTB; B2: BANPU; B3: ADVANC,SCB,LH). - Regression tests for both. Full suite 374 green.
180 lines
8.3 KiB
Python
180 lines
8.3 KiB
Python
"""Tests for the capital-allocation simulation engine."""
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from __future__ import annotations
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import unittest
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from app.simulation import Candidate, allocate_capital
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class AllocationTest(unittest.TestCase):
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def make_candidates(self):
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# dividend + high score
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return [
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Candidate("A", 10.0, 1.5, True, 5.0),
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Candidate("B", 20.0, 1.2, True, 3.0),
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Candidate("C", 5.0, 1.0, False, 0.0),
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Candidate("D", 2.0, 0.5, False, 0.0),
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Candidate("E", 50.0, 0.9, True, 8.0),
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]
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def test_allocates_three_buckets(self):
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res = allocate_capital(200_000, self.make_candidates())
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bucket_notional = res.bucket_notional
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self.assertIn(1, bucket_notional)
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self.assertIn(2, bucket_notional)
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self.assertIn(3, bucket_notional)
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# total invested + unallocated == capital
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self.assertAlmostEqual(res.invested + res.unallocated_cash, 200_000, places=2)
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# every order is a multiple of 100
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for o in res.orders:
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self.assertEqual(o.qty % 100, 0)
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self.assertGreaterEqual(o.qty, 100)
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def test_min_100_shares_respected(self):
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# tiny capital: bucket1 50% still must afford 100 shares
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res = allocate_capital(2_000, self.make_candidates())
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for o in res.orders:
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self.assertGreaterEqual(o.qty, 100)
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self.assertEqual(o.notional, o.qty * o.price)
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# never over-invest beyond capital
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self.assertLessEqual(res.invested, 2_000)
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def test_bucket3_excludes_already_bought(self):
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cands = [Candidate("A", 10.0, 1.5, True, 5.0),
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Candidate("B", 10.0, 0.1, True, 8.0)]
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res = allocate_capital(300_000, cands)
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# A (bought in bucket1) must NOT also appear in bucket3
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bucket3_syms = [o.symbol for o in res.orders if o.bucket == 3]
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bucket1_syms = [o.symbol for o in res.orders if o.bucket == 1]
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overlap = set(bucket3_syms) & set(bucket1_syms)
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self.assertEqual(overlap, set())
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def test_bucket3_ranks_by_yield_ignoring_score(self):
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# The spec: bucket3 = highest dividend yield, IGNORING score.
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# A low-score but high-yield name must rank above a high-score low-yield name.
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cands = [
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Candidate("HIGH_SCORE", 10.0, 8.0, True, 2.0), # score 8, yield 2%
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Candidate("HIGH_YIELD", 10.0, 0.1, True, 7.0), # score 0.1, yield 7%
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Candidate("MID", 10.0, 5.0, True, 3.0),
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]
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# Big capital so bucket1 consumes only the top score name, leaving
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# HIGH_YIELD (not HIGH_SCORE) to be the bucket3 top pick.
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res = allocate_capital(1_000_000, cands)
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b3 = [o.symbol for o in res.orders if o.bucket == 3]
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# HIGH_YIELD (7%) should be selected in bucket3 before HIGH_SCORE (2%)
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self.assertIn("HIGH_YIELD", b3)
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if "HIGH_SCORE" in b3:
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hi = b3.index("HIGH_SCORE")
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hy = b3.index("HIGH_YIELD")
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self.assertLess(hy, hi)
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def test_invalid_capital_raises(self):
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with self.assertRaises(Exception):
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allocate_capital(0, self.make_candidates())
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with self.assertRaises(Exception):
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allocate_capital(-100, self.make_candidates())
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def test_no_candidates_raises(self):
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with self.assertRaises(Exception):
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allocate_capital(100_000, [])
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def test_cash_fallback_when_nothing_fits(self):
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# only very high-priced names, tiny capital -> cannot buy 100 shares -> cash
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cands = [Candidate("X", 1000.0, 2.0, True, 3.0)]
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res = allocate_capital(50_000, cands) # 50% = 25k, can't buy 100*1000
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self.assertEqual(res.orders, [])
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self.assertAlmostEqual(res.unallocated_cash, 50_000, places=2)
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class MomentumSelectionTest(unittest.TestCase):
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"""Owner's "ทำกำไร" rule: bucket 1/2 rank by price-trend momentum and are
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gated on a POSITIVE theme signal — NOT by combined/EPS score."""
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def _cand(self, sym, price, combined, div, yield_, momentum, theme):
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return Candidate(sym, price, combined, div, yield_,
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momentum=momentum, theme_signal=theme)
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def test_bucket1_ranks_by_momentum_not_score(self):
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# A high-score but NEGATIVE-momentum dividend name must NOT win bucket 1
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# over a positive-momentum one (owner: "ทำกำไร" = price likely to rise).
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cands = [
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self._cand("SLOW", 10.0, 9.0, True, 2.0, -1.5, 0.5), # high score, falling price
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self._cand("FAST", 10.0, 0.5, True, 1.0, +2.0, 0.6), # low score, rising price
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]
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res = allocate_capital(1_000_000, cands)
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b1 = [o.symbol for o in res.orders if o.bucket == 1]
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self.assertEqual(b1, ["FAST"]) # FAST (momentum +2) beats SLOW (-1.5)
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def test_negative_theme_is_excluded_from_profit_bucket(self):
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# Even a high-momentum name is not eligible for bucket 1/2 when its
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# theme_signal is <= 0 (owner gate). It may still land in bucket 3 (yield).
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cands = [
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self._cand("NO_THEME", 10.0, 5.0, True, 8.0, +3.0, -0.2), # momentum up but theme negative
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self._cand("GOOD", 10.0, 5.0, True, 2.0, +1.0, 0.5),
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]
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res = allocate_capital(1_000_000, cands)
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b1 = [o.symbol for o in res.orders if o.bucket == 1]
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self.assertNotIn("NO_THEME", b1) # gated out of the profit bucket
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self.assertIn("GOOD", b1)
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# NO_THEME may still be picked by bucket 3 (highest yield, 8%)
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b3 = [o.symbol for o in res.orders if o.bucket == 3]
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self.assertIn("NO_THEME", b3)
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def test_bucket2_uses_non_dividend_momentum(self):
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cands = [
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self._cand("ND_UP", 10.0, 9.0, False, 0.0, +2.5, 0.7), # non-div, momentum up
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self._cand("ND_DN", 10.0, 9.0, False, 0.0, -2.5, 0.7), # non-div, momentum down
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]
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res = allocate_capital(1_000_000, cands)
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b2 = [o.symbol for o in res.orders if o.bucket == 2]
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self.assertEqual(b2, ["ND_UP"]) # rising price chosen over falling
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def test_negative_momentum_excluded_from_profit_buckets(self):
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# A rising-price name wins the profit bucket; a FALLING-price name —
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# even with a positive theme — must NOT be picked as "ทำกำไร".
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cands = [
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self._cand("UP", 10.0, 5.0, False, 0.0, +1.5, 0.6), # rising, non-div
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self._cand("DOWN", 10.0, 5.0, False, 0.0, -1.5, 0.6), # falling, same theme
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]
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res = allocate_capital(1_000_000, cands)
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b2 = [o.symbol for o in res.orders if o.bucket == 2]
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self.assertEqual(b2, ["UP"]) # DOWN (falling price) excluded
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self.assertNotIn("DOWN", b2)
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class PriceSnapshotTest(unittest.TestCase):
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"""Regression: load_price_snapshot must pick the snapshot retrieved MOST
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RECENTLY, not the last filename lexicographically (a partial 9-symbol file
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can sort after a full 50-symbol one and silently drop the universe)."""
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def test_load_picks_most_recently_retrieved(self):
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import json, tempfile
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from pathlib import Path
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from unittest import mock
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from app import simulation as sim
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def _snap(symbols, retrieved_at):
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return {
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"schema_version": 1,
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"source": {"retrieved_at": retrieved_at, "period_start": "2024-01-01", "period_end": "2026-08-29"},
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"series": {s: {"bars": [{"date": "2026-08-29", "adjusted_close": 10.0}]} for s in symbols},
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}
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with tempfile.TemporaryDirectory() as td:
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snap_dir = Path(td) / "snapshots"
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snap_dir.mkdir()
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# name that sorts LAST lexicographically, but is OLD (partial 9)
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old_partial = snap_dir / "prices-yahoo-chart-2024-01-01-2026-08-24-zzz.json"
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old_partial.write_text(json.dumps(_snap(["A", "B"], "2026-08-23T00:00:00+00:00")))
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# full 50-symbol, retrieved MORE recently — should win
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full = snap_dir / "prices-yahoo-chart-2023-08-28-2026-08-29-aaa.json"
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full.write_text(json.dumps(_snap([f"S{i}" for i in range(50)], "2026-08-30T00:00:00+00:00")))
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with mock.patch.object(sim, "_PRICES_DIR", Path(td)):
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series = sim.load_price_snapshot()
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self.assertEqual(len(series), 50) # the full universe, not the 9
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if __name__ == "__main__":
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unittest.main()
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