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1901 lines
68 KiB
Python

from __future__ import annotations
import asyncio
from datetime import datetime, timedelta
from types import SimpleNamespace
from zoneinfo import ZoneInfo
from libs.intraday.cache import DailyBarCache, IntradayCache, ReadOnlyDailyBarCache
from libs.intraday import screener as screener_module
from libs.intraday.domain import StrategyParams, UniverseParams
from libs.intraday.screener import (
fetch_daily_bars_bulk,
momentum_intraday_first_candidates,
momentum_pre_screen_candidates,
orb_pre_screen_candidates,
pre_screen_candidates,
resolve_universe,
)
def test_pre_screen_candidates_uses_opening_gap_not_same_day_high() -> None:
daily_bars = {
"AAA": [
{"date": "2026-01-02", "open": 100.0, "high": 102.0, "low": 99.0, "close": 100.0, "volume": 1_000},
{"date": "2026-01-05", "open": 100.0, "high": 130.0, "low": 95.0, "close": 96.0, "volume": 2_000},
],
"BBB": [
{"date": "2026-01-02", "open": 50.0, "high": 51.0, "low": 49.0, "close": 50.0, "volume": 1_000},
{"date": "2026-01-05", "open": 51.5, "high": 53.0, "low": 51.0, "close": 52.0, "volume": 2_000},
],
}
result = pre_screen_candidates(
daily_bars,
["2026-01-05"],
threshold=0.02,
max_per_day=30,
)
assert result == {"2026-01-05": ["BBB"]}
def test_pre_screen_candidates_can_use_precomputed_gap_enrichment() -> None:
daily_bars = {
"AAA": [
{"date": "2026-01-02", "open": 100.0, "high": 102.0, "low": 99.0, "close": 100.0, "volume": 1_000},
{"date": "2026-01-05", "open": 100.0, "high": 130.0, "low": 95.0, "close": 96.0, "volume": 2_000},
],
"BBB": [
{"date": "2026-01-02", "open": 50.0, "high": 51.0, "low": 49.0, "close": 50.0, "volume": 1_000},
{"date": "2026-01-05", "open": 51.5, "high": 53.0, "low": 51.0, "close": 52.0, "volume": 2_000},
],
}
enrichment = {
"AAA": {"2026-01-05": {"gap_pct": 0.0}},
"BBB": {"2026-01-05": {"gap_pct": 0.03}},
}
result = pre_screen_candidates(
daily_bars,
["2026-01-05"],
threshold=0.02,
max_per_day=30,
enrichment=enrichment,
)
assert result == {"2026-01-05": ["BBB"]}
def test_momentum_pre_screen_candidates_ranks_by_gap_then_prior_features() -> None:
daily_bars = {
"AAA": [
{"date": "2026-01-02", "open": 10.0, "high": 10.2, "low": 9.8, "close": 10.0, "volume": 1_000},
{"date": "2026-01-05", "open": 10.3, "high": 10.8, "low": 10.2, "close": 10.6, "volume": 2_000},
],
"BBB": [
{"date": "2026-01-02", "open": 10.0, "high": 10.1, "low": 9.9, "close": 10.0, "volume": 1_000},
{"date": "2026-01-05", "open": 10.3, "high": 10.5, "low": 10.1, "close": 10.2, "volume": 2_000},
],
"CCC": [
{"date": "2026-01-02", "open": 10.0, "high": 10.1, "low": 9.9, "close": 10.0, "volume": 1_000},
{"date": "2026-01-05", "open": 10.5, "high": 10.7, "low": 10.4, "close": 10.6, "volume": 2_000},
],
}
enrichment = {
"AAA": {
"2026-01-05": {
"gap_pct": 0.03,
"ret_5d": 0.10,
"entropy_20d": 0.50,
"avg_dollar_vol_30d": 50_000_000.0,
"atr_14": 1.5,
}
},
"BBB": {
"2026-01-05": {
"gap_pct": 0.03,
"ret_5d": 0.02,
"entropy_20d": 0.70,
"avg_dollar_vol_30d": 30_000_000.0,
"atr_14": 1.0,
}
},
"CCC": {
"2026-01-05": {
"gap_pct": 0.05,
"ret_5d": -0.01,
"entropy_20d": 0.80,
"avg_dollar_vol_30d": 10_000_000.0,
"atr_14": 0.8,
}
},
}
result = momentum_pre_screen_candidates(
daily_bars,
["2026-01-05"],
enrichment,
threshold=0.02,
max_per_day=3,
)
assert result == {"2026-01-05": ["CCC", "AAA", "BBB"]}
def test_momentum_pre_screen_candidates_can_require_event_and_attention() -> None:
daily_bars = {
"AAA": [
{"date": "2026-01-02", "open": 10.0, "high": 10.2, "low": 9.8, "close": 10.0, "volume": 1_000},
{"date": "2026-01-05", "open": 10.3, "high": 10.8, "low": 10.2, "close": 10.6, "volume": 2_000},
],
"BBB": [
{"date": "2026-01-02", "open": 10.0, "high": 10.1, "low": 9.9, "close": 10.0, "volume": 1_000},
{"date": "2026-01-05", "open": 10.4, "high": 10.6, "low": 10.3, "close": 10.5, "volume": 2_000},
],
}
enrichment = {
"AAA": {
"2026-01-05": {
"gap_pct": 0.03,
"ret_5d": 0.02,
"entropy_20d": 0.70,
"avg_dollar_vol_30d": 20_000_000.0,
"atr_14": 1.0,
"event_flag": True,
"event_score": 1.0,
"attention_wiki_spike_10d": 2.0,
"attention_article_count_3d": 5,
"attention_us_article_count_3d": 4,
"attention_resolver_confidence": 0.9,
}
},
"BBB": {
"2026-01-05": {
"gap_pct": 0.04,
"ret_5d": 0.04,
"entropy_20d": 0.50,
"avg_dollar_vol_30d": 25_000_000.0,
"atr_14": 1.2,
"event_flag": False,
"event_score": 0.0,
"attention_wiki_spike_10d": 0.2,
"attention_article_count_3d": 0,
"attention_us_article_count_3d": 0,
"attention_resolver_confidence": 0.2,
}
},
}
strategy = StrategyParams(
candidate_require_event_flag=True,
candidate_min_attention_wiki_spike_10d=1.0,
candidate_weight_event_score=1.0,
candidate_weight_attention_wiki=1.0,
candidate_weight_attention_news=1.0,
)
result = momentum_pre_screen_candidates(
daily_bars,
["2026-01-05"],
enrichment,
threshold=0.02,
max_per_day=5,
strategy=strategy,
)
assert result == {"2026-01-05": ["AAA"]}
def test_momentum_pre_screen_candidates_can_filter_event_types() -> None:
daily_bars = {
"AAA": [
{"date": "2026-01-02", "open": 10.0, "high": 10.2, "low": 9.8, "close": 10.0, "volume": 1_000},
{"date": "2026-01-05", "open": 10.3, "high": 10.8, "low": 10.2, "close": 10.6, "volume": 2_000},
],
"BBB": [
{"date": "2026-01-02", "open": 10.0, "high": 10.2, "low": 9.8, "close": 10.0, "volume": 1_000},
{"date": "2026-01-05", "open": 10.4, "high": 10.9, "low": 10.3, "close": 10.7, "volume": 2_000},
],
}
enrichment = {
"AAA": {
"2026-01-05": {
"gap_pct": 0.03,
"ret_5d": 0.03,
"entropy_20d": 0.60,
"avg_dollar_vol_30d": 20_000_000.0,
"atr_14": 1.0,
"event_flag": True,
"event_score": 1.0,
"event_types": ["earnings_release"],
}
},
"BBB": {
"2026-01-05": {
"gap_pct": 0.04,
"ret_5d": 0.04,
"entropy_20d": 0.50,
"avg_dollar_vol_30d": 25_000_000.0,
"atr_14": 1.2,
"event_flag": True,
"event_score": 1.0,
"event_types": ["management_change"],
}
},
}
strategy = StrategyParams(
candidate_require_event_flag=True,
candidate_allowed_event_types=["earnings_release"],
)
result = momentum_pre_screen_candidates(
daily_bars,
["2026-01-05"],
enrichment,
threshold=0.02,
max_per_day=5,
strategy=strategy,
)
assert result == {"2026-01-05": ["AAA"]}
def test_momentum_pre_screen_candidates_event_type_filter_does_not_block_non_event_names() -> None:
daily_bars = {
"AAA": [
{"date": "2026-01-02", "open": 10.0, "high": 10.2, "low": 9.8, "close": 10.0, "volume": 1_000},
{"date": "2026-01-05", "open": 10.3, "high": 10.8, "low": 10.2, "close": 10.6, "volume": 2_000},
],
"BBB": [
{"date": "2026-01-02", "open": 10.0, "high": 10.2, "low": 9.8, "close": 10.0, "volume": 1_000},
{"date": "2026-01-05", "open": 10.4, "high": 10.9, "low": 10.3, "close": 10.7, "volume": 2_000},
],
}
enrichment = {
"AAA": {
"2026-01-05": {
"gap_pct": 0.03,
"ret_5d": 0.03,
"entropy_20d": 0.60,
"avg_dollar_vol_30d": 20_000_000.0,
"atr_14": 1.0,
"event_flag": True,
"event_score": 1.0,
"event_types": ["management_change"],
}
},
"BBB": {
"2026-01-05": {
"gap_pct": 0.04,
"ret_5d": 0.04,
"entropy_20d": 0.50,
"avg_dollar_vol_30d": 25_000_000.0,
"atr_14": 1.2,
"event_flag": False,
"event_score": 0.0,
}
},
}
strategy = StrategyParams(
candidate_allowed_event_types=["earnings_release"],
)
result = momentum_pre_screen_candidates(
daily_bars,
["2026-01-05"],
enrichment,
threshold=0.02,
max_per_day=5,
strategy=strategy,
)
assert result == {"2026-01-05": ["BBB", "AAA"]}
def test_momentum_intraday_first_candidates_uses_entry_time_info_only() -> None:
strategy = StrategyParams(
candidate_source_mode="intraday_first",
entry_minutes_after_open=10,
confirmation_minutes_after_entry=5,
min_confirmation_return_pct=0.0,
min_morning_gain_pct=0.01,
min_entry_volume=50_000,
candidate_final_max_per_day=2,
candidate_require_event_flag=True,
)
all_intraday = {
"2026-01-05": {
"AAA": [
{"timestamp": "2026-01-05T14:30:00+00:00", "open": 10.0, "high": 10.2, "low": 9.9, "close": 10.1, "volume": 30_000},
{"timestamp": "2026-01-05T14:35:00+00:00", "open": 10.1, "high": 10.3, "low": 10.0, "close": 10.2, "volume": 30_000},
{"timestamp": "2026-01-05T14:40:00+00:00", "open": 10.2, "high": 10.5, "low": 10.1, "close": 10.4, "volume": 30_000},
{"timestamp": "2026-01-05T14:45:00+00:00", "open": 10.4, "high": 10.7, "low": 10.3, "close": 10.6, "volume": 30_000},
{"timestamp": "2026-01-05T14:50:00+00:00", "open": 10.6, "high": 10.8, "low": 10.5, "close": 10.7, "volume": 30_000},
],
"BBB": [
{"timestamp": "2026-01-05T14:30:00+00:00", "open": 20.0, "high": 20.1, "low": 19.9, "close": 20.0, "volume": 40_000},
{"timestamp": "2026-01-05T14:35:00+00:00", "open": 20.0, "high": 20.2, "low": 19.9, "close": 20.1, "volume": 40_000},
{"timestamp": "2026-01-05T14:40:00+00:00", "open": 20.1, "high": 20.7, "low": 20.0, "close": 20.5, "volume": 40_000},
{"timestamp": "2026-01-05T14:45:00+00:00", "open": 20.5, "high": 21.0, "low": 20.4, "close": 20.9, "volume": 40_000},
{"timestamp": "2026-01-05T14:50:00+00:00", "open": 20.9, "high": 21.3, "low": 20.8, "close": 21.1, "volume": 40_000},
],
}
}
daily_enrichment = {
"AAA": {"2026-01-05": {"event_flag": False, "gap_pct": 0.01, "avg_daily_vol_14d": 1_000_000.0}},
"BBB": {"2026-01-05": {"event_flag": True, "event_score": 1.0, "gap_pct": 0.01, "avg_daily_vol_14d": 1_000_000.0}},
}
result = momentum_intraday_first_candidates(
all_intraday,
["2026-01-05"],
strategy,
daily_enrichment=daily_enrichment,
max_per_day=2,
)
assert result == {"2026-01-05": ["BBB"]}
def test_momentum_intraday_first_candidates_can_filter_event_types() -> None:
strategy = StrategyParams(
candidate_source_mode="intraday_first",
entry_minutes_after_open=10,
confirmation_minutes_after_entry=5,
min_confirmation_return_pct=0.0,
min_morning_gain_pct=0.01,
min_entry_volume=50_000,
candidate_final_max_per_day=2,
candidate_require_event_flag=True,
candidate_allowed_event_types=["earnings_release"],
)
all_intraday = {
"2026-01-05": {
"AAA": [
{"timestamp": "2026-01-05T14:30:00+00:00", "open": 10.0, "high": 10.2, "low": 9.9, "close": 10.1, "volume": 30_000},
{"timestamp": "2026-01-05T14:35:00+00:00", "open": 10.1, "high": 10.3, "low": 10.0, "close": 10.2, "volume": 30_000},
{"timestamp": "2026-01-05T14:40:00+00:00", "open": 10.2, "high": 10.5, "low": 10.1, "close": 10.4, "volume": 30_000},
{"timestamp": "2026-01-05T14:45:00+00:00", "open": 10.4, "high": 10.7, "low": 10.3, "close": 10.6, "volume": 30_000},
{"timestamp": "2026-01-05T14:50:00+00:00", "open": 10.6, "high": 10.8, "low": 10.5, "close": 10.7, "volume": 30_000},
],
"BBB": [
{"timestamp": "2026-01-05T14:30:00+00:00", "open": 20.0, "high": 20.1, "low": 19.9, "close": 20.0, "volume": 40_000},
{"timestamp": "2026-01-05T14:35:00+00:00", "open": 20.0, "high": 20.2, "low": 19.9, "close": 20.1, "volume": 40_000},
{"timestamp": "2026-01-05T14:40:00+00:00", "open": 20.1, "high": 20.7, "low": 20.0, "close": 20.5, "volume": 40_000},
{"timestamp": "2026-01-05T14:45:00+00:00", "open": 20.5, "high": 21.0, "low": 20.4, "close": 20.9, "volume": 40_000},
{"timestamp": "2026-01-05T14:50:00+00:00", "open": 20.9, "high": 21.3, "low": 20.8, "close": 21.1, "volume": 40_000},
],
}
}
daily_enrichment = {
"AAA": {
"2026-01-05": {
"event_flag": True,
"event_score": 1.0,
"event_types": ["management_change"],
"gap_pct": 0.01,
"avg_daily_vol_14d": 1_000_000.0,
}
},
"BBB": {
"2026-01-05": {
"event_flag": True,
"event_score": 1.0,
"event_types": ["earnings_release"],
"gap_pct": 0.01,
"avg_daily_vol_14d": 1_000_000.0,
}
},
}
result = momentum_intraday_first_candidates(
all_intraday,
["2026-01-05"],
strategy,
daily_enrichment=daily_enrichment,
max_per_day=2,
)
assert result == {"2026-01-05": ["BBB"]}
def test_momentum_intraday_first_candidates_can_use_weighted_ranking() -> None:
strategy = StrategyParams(
candidate_source_mode="intraday_first",
candidate_intraday_rank_mode="weighted",
candidate_intraday_weight_gain=0.2,
candidate_intraday_weight_confirmation=0.5,
candidate_intraday_weight_volume_ratio=0.2,
candidate_intraday_weight_entry_dollar_volume=0.1,
entry_minutes_after_open=10,
confirmation_minutes_after_entry=5,
min_confirmation_return_pct=0.0,
min_morning_gain_pct=0.01,
candidate_final_max_per_day=1,
)
all_intraday = {
"2026-01-05": {
"AAA": [
{"timestamp": "2026-01-05T14:30:00+00:00", "open": 10.0, "high": 10.1, "low": 9.9, "close": 10.0, "volume": 80_000},
{"timestamp": "2026-01-05T14:35:00+00:00", "open": 10.0, "high": 10.2, "low": 10.0, "close": 10.1, "volume": 80_000},
{"timestamp": "2026-01-05T14:40:00+00:00", "open": 10.1, "high": 10.4, "low": 10.0, "close": 10.2, "volume": 80_000},
{"timestamp": "2026-01-05T14:45:00+00:00", "open": 10.2, "high": 10.8, "low": 10.2, "close": 10.7, "volume": 80_000},
{"timestamp": "2026-01-05T14:50:00+00:00", "open": 10.7, "high": 10.9, "low": 10.6, "close": 10.8, "volume": 80_000},
],
"BBB": [
{"timestamp": "2026-01-05T14:30:00+00:00", "open": 20.0, "high": 20.2, "low": 19.9, "close": 20.1, "volume": 70_000},
{"timestamp": "2026-01-05T14:35:00+00:00", "open": 20.1, "high": 20.4, "low": 20.0, "close": 20.3, "volume": 70_000},
{"timestamp": "2026-01-05T14:40:00+00:00", "open": 20.3, "high": 21.0, "low": 20.2, "close": 20.9, "volume": 70_000},
{"timestamp": "2026-01-05T14:45:00+00:00", "open": 20.9, "high": 21.0, "low": 20.7, "close": 20.95, "volume": 70_000},
{"timestamp": "2026-01-05T14:50:00+00:00", "open": 20.95, "high": 21.1, "low": 20.8, "close": 21.0, "volume": 70_000},
],
}
}
daily_enrichment = {
"AAA": {
"2026-01-05": {
"gap_pct": 0.01,
"avg_daily_vol_14d": 1_000_000.0,
}
},
"BBB": {
"2026-01-05": {
"gap_pct": 0.01,
"avg_daily_vol_14d": 1_000_000.0,
}
},
}
result = momentum_intraday_first_candidates(
all_intraday,
["2026-01-05"],
strategy,
daily_enrichment=daily_enrichment,
max_per_day=1,
)
assert result == {"2026-01-05": ["AAA"]}
def test_momentum_intraday_first_candidates_weighted_ranking_can_use_prior_dollar_volume() -> None:
strategy = StrategyParams(
candidate_source_mode="intraday_first",
candidate_intraday_rank_mode="weighted",
candidate_intraday_weight_gain=0.2,
candidate_intraday_weight_confirmation=0.4,
candidate_intraday_weight_volume_ratio=0.2,
candidate_intraday_weight_entry_dollar_volume=0.1,
candidate_intraday_weight_avg_dollar_vol_30d=0.5,
entry_minutes_after_open=10,
confirmation_minutes_after_entry=5,
min_confirmation_return_pct=0.0,
min_morning_gain_pct=0.005,
candidate_final_max_per_day=1,
)
all_intraday = {
"2026-01-05": {
"AAA": [
{"timestamp": "2026-01-05T14:30:00+00:00", "open": 10.0, "high": 10.1, "low": 9.9, "close": 10.0, "volume": 400_000},
{"timestamp": "2026-01-05T14:35:00+00:00", "open": 10.0, "high": 10.2, "low": 10.0, "close": 10.1, "volume": 400_000},
{"timestamp": "2026-01-05T14:40:00+00:00", "open": 10.1, "high": 10.2, "low": 10.0, "close": 10.1, "volume": 400_000},
{"timestamp": "2026-01-05T14:45:00+00:00", "open": 10.1, "high": 10.3, "low": 10.1, "close": 10.2, "volume": 400_000},
{"timestamp": "2026-01-05T14:50:00+00:00", "open": 10.2, "high": 10.4, "low": 10.1, "close": 10.3, "volume": 400_000},
],
"BBB": [
{"timestamp": "2026-01-05T14:30:00+00:00", "open": 20.0, "high": 20.1, "low": 19.9, "close": 20.0, "volume": 200_000},
{"timestamp": "2026-01-05T14:35:00+00:00", "open": 20.0, "high": 20.2, "low": 20.0, "close": 20.1, "volume": 200_000},
{"timestamp": "2026-01-05T14:40:00+00:00", "open": 20.1, "high": 20.2, "low": 20.0, "close": 20.1, "volume": 200_000},
{"timestamp": "2026-01-05T14:45:00+00:00", "open": 20.1, "high": 20.3, "low": 20.1, "close": 20.2, "volume": 200_000},
{"timestamp": "2026-01-05T14:50:00+00:00", "open": 20.2, "high": 20.4, "low": 20.1, "close": 20.3, "volume": 200_000},
],
}
}
daily_enrichment = {
"AAA": {
"2026-01-05": {
"gap_pct": 0.01,
"avg_daily_vol_14d": 5_000_000.0,
"avg_dollar_vol_30d": 200_000_000.0,
}
},
"BBB": {
"2026-01-05": {
"gap_pct": 0.01,
"avg_daily_vol_14d": 5_000_000.0,
"avg_dollar_vol_30d": 8_000_000_000.0,
}
},
}
result = momentum_intraday_first_candidates(
all_intraday,
["2026-01-05"],
strategy,
daily_enrichment=daily_enrichment,
max_per_day=1,
)
assert result == {"2026-01-05": ["BBB"]}
def test_momentum_intraday_first_candidates_weighted_ranking_can_use_sector_thrust() -> None:
strategy = StrategyParams(
candidate_source_mode="intraday_first",
candidate_intraday_rank_mode="weighted",
candidate_intraday_weight_gain=0.2,
candidate_intraday_weight_confirmation=0.2,
candidate_intraday_weight_entry_dollar_volume=0.1,
candidate_intraday_weight_sector_thrust=1.0,
entry_minutes_after_open=10,
confirmation_minutes_after_entry=5,
min_confirmation_return_pct=0.0,
min_morning_gain_pct=0.01,
candidate_final_max_per_day=1,
use_sector_thrust_sleeve=True,
sector_thrust_min_members=2,
sector_thrust_min_gain_pct=0.01,
sector_thrust_min_confirmation_return_pct=0.003,
sector_thrust_min_entry_dollar_volume=50_000_000.0,
sector_thrust_min_avg_dollar_vol_30d=500_000_000.0,
sector_thrust_min_sector_avg_confirmation_return_pct=0.003,
sector_thrust_min_sector_total_entry_dollar_volume=120_000_000.0,
)
all_intraday = {
"2026-01-05": {
"ALLY_A": [
{"timestamp": "2026-01-05T14:30:00+00:00", "open": 100.0, "high": 100.2, "low": 99.9, "close": 100.0, "volume": 180_000},
{"timestamp": "2026-01-05T14:35:00+00:00", "open": 100.0, "high": 100.8, "low": 99.9, "close": 100.6, "volume": 180_000},
{"timestamp": "2026-01-05T14:40:00+00:00", "open": 100.6, "high": 101.2, "low": 100.5, "close": 101.0, "volume": 180_000},
{"timestamp": "2026-01-05T14:45:00+00:00", "open": 101.0, "high": 101.7, "low": 100.9, "close": 101.5, "volume": 180_000},
{"timestamp": "2026-01-05T14:50:00+00:00", "open": 101.5, "high": 101.8, "low": 101.4, "close": 101.6, "volume": 180_000},
],
"ALLY_B": [
{"timestamp": "2026-01-05T14:30:00+00:00", "open": 80.0, "high": 80.1, "low": 79.9, "close": 80.0, "volume": 170_000},
{"timestamp": "2026-01-05T14:35:00+00:00", "open": 80.0, "high": 80.6, "low": 79.9, "close": 80.4, "volume": 170_000},
{"timestamp": "2026-01-05T14:40:00+00:00", "open": 80.4, "high": 80.9, "low": 80.3, "close": 80.8, "volume": 170_000},
{"timestamp": "2026-01-05T14:45:00+00:00", "open": 80.8, "high": 81.4, "low": 80.7, "close": 81.2, "volume": 170_000},
{"timestamp": "2026-01-05T14:50:00+00:00", "open": 81.2, "high": 81.5, "low": 81.1, "close": 81.3, "volume": 170_000},
],
"SOLO": [
{"timestamp": "2026-01-05T14:30:00+00:00", "open": 20.0, "high": 20.3, "low": 19.9, "close": 20.1, "volume": 300_000},
{"timestamp": "2026-01-05T14:35:00+00:00", "open": 20.1, "high": 20.8, "low": 20.0, "close": 20.6, "volume": 300_000},
{"timestamp": "2026-01-05T14:40:00+00:00", "open": 20.6, "high": 21.1, "low": 20.5, "close": 20.9, "volume": 300_000},
{"timestamp": "2026-01-05T14:45:00+00:00", "open": 20.9, "high": 21.3, "low": 20.8, "close": 21.1, "volume": 300_000},
{"timestamp": "2026-01-05T14:50:00+00:00", "open": 21.1, "high": 21.3, "low": 21.0, "close": 21.2, "volume": 300_000},
],
}
}
daily_enrichment = {
"ALLY_A": {"2026-01-05": {"gap_pct": 0.01, "avg_daily_vol_14d": 5_000_000.0, "avg_dollar_vol_30d": 900_000_000.0}},
"ALLY_B": {"2026-01-05": {"gap_pct": 0.01, "avg_daily_vol_14d": 5_000_000.0, "avg_dollar_vol_30d": 850_000_000.0}},
"SOLO": {"2026-01-05": {"gap_pct": 0.01, "avg_daily_vol_14d": 10_000_000.0, "avg_dollar_vol_30d": 1_200_000_000.0}},
}
result = momentum_intraday_first_candidates(
all_intraday,
["2026-01-05"],
strategy,
daily_enrichment=daily_enrichment,
ticker_sectors={
"ALLY_A": "Technology",
"ALLY_B": "Technology",
"SOLO": "Energy",
},
max_per_day=1,
)
assert result == {"2026-01-05": ["ALLY_A"]}
def test_momentum_intraday_first_candidates_can_use_liquid_continuation_rank_mode() -> None:
strategy = StrategyParams(
candidate_source_mode="intraday_first",
candidate_intraday_rank_mode="liquid_continuation",
entry_minutes_after_open=10,
confirmation_minutes_after_entry=5,
min_confirmation_return_pct=0.0,
min_morning_gain_pct=0.004,
candidate_final_max_per_day=1,
use_liquid_largecap_sleeve=True,
liquid_largecap_min_gain_pct=0.004,
liquid_largecap_max_gain_pct=0.03,
liquid_largecap_min_confirmation_return_pct=0.0005,
liquid_largecap_min_entry_dollar_volume=50_000_000.0,
liquid_largecap_min_avg_dollar_vol_30d=2_000_000_000.0,
liquid_largecap_max_entropy_20d=0.90,
use_moderate_gap_liquid_sleeve=True,
moderate_gap_liquid_min_gap_pct=0.002,
moderate_gap_liquid_max_gap_pct=0.04,
moderate_gap_liquid_min_gain_pct=0.005,
moderate_gap_liquid_max_gain_pct=0.04,
moderate_gap_liquid_min_confirmation_return_pct=0.001,
moderate_gap_liquid_min_entry_dollar_volume=25_000_000.0,
moderate_gap_liquid_min_avg_dollar_vol_30d=250_000_000.0,
moderate_gap_liquid_max_avg_dollar_vol_30d=4_000_000_000.0,
moderate_gap_liquid_min_volume_ratio_14d=0.02,
moderate_gap_liquid_max_entropy_20d=0.88,
)
all_intraday = {
"2026-01-05": {
"LIQ": [
{"timestamp": "2026-01-05T14:30:00+00:00", "open": 100.0, "high": 100.8, "low": 99.9, "close": 100.5, "volume": 220_000},
{"timestamp": "2026-01-05T14:35:00+00:00", "open": 100.5, "high": 101.2, "low": 100.4, "close": 101.0, "volume": 220_000},
{"timestamp": "2026-01-05T14:40:00+00:00", "open": 101.0, "high": 101.8, "low": 100.9, "close": 101.5, "volume": 220_000},
{"timestamp": "2026-01-05T14:45:00+00:00", "open": 101.5, "high": 102.2, "low": 101.4, "close": 102.0, "volume": 220_000},
{"timestamp": "2026-01-05T14:50:00+00:00", "open": 102.0, "high": 102.4, "low": 101.9, "close": 102.2, "volume": 220_000},
],
"HOT": [
{"timestamp": "2026-01-05T14:30:00+00:00", "open": 10.0, "high": 10.4, "low": 9.9, "close": 10.3, "volume": 120_000},
{"timestamp": "2026-01-05T14:35:00+00:00", "open": 10.3, "high": 10.7, "low": 10.2, "close": 10.6, "volume": 120_000},
{"timestamp": "2026-01-05T14:40:00+00:00", "open": 10.6, "high": 10.9, "low": 10.5, "close": 10.8, "volume": 120_000},
{"timestamp": "2026-01-05T14:45:00+00:00", "open": 10.8, "high": 11.0, "low": 10.7, "close": 10.9, "volume": 120_000},
{"timestamp": "2026-01-05T14:50:00+00:00", "open": 10.9, "high": 11.1, "low": 10.8, "close": 11.0, "volume": 120_000},
],
}
}
daily_enrichment = {
"LIQ": {
"2026-01-05": {
"gap_pct": 0.01,
"avg_daily_vol_14d": 5_000_000.0,
"avg_dollar_vol_30d": 3_000_000_000.0,
"entropy_20d": 0.70,
}
},
"HOT": {
"2026-01-05": {
"gap_pct": 0.02,
"avg_daily_vol_14d": 4_000_000.0,
"avg_dollar_vol_30d": 50_000_000.0,
"entropy_20d": 0.82,
}
},
}
result = momentum_intraday_first_candidates(
all_intraday,
["2026-01-05"],
strategy,
daily_enrichment=daily_enrichment,
max_per_day=2,
)
assert result == {"2026-01-05": ["LIQ"]}
def test_momentum_intraday_first_candidates_can_replace_tail_with_event_reserve() -> None:
strategy = StrategyParams(
candidate_source_mode="intraday_first",
candidate_intraday_rank_mode="weighted",
candidate_intraday_weight_gain=0.2,
candidate_intraday_weight_confirmation=0.5,
candidate_intraday_weight_volume_ratio=0.2,
candidate_intraday_weight_entry_dollar_volume=0.1,
candidate_intraday_event_reserve_slots=1,
candidate_intraday_event_reserve_min_score=1.0,
entry_minutes_after_open=10,
confirmation_minutes_after_entry=5,
min_confirmation_return_pct=0.0,
min_morning_gain_pct=0.01,
candidate_final_max_per_day=2,
)
all_intraday = {
"2026-01-05": {
"AAA": [
{"timestamp": "2026-01-05T14:30:00+00:00", "open": 10.0, "high": 10.1, "low": 9.9, "close": 10.0, "volume": 120_000},
{"timestamp": "2026-01-05T14:35:00+00:00", "open": 10.0, "high": 10.2, "low": 10.0, "close": 10.1, "volume": 120_000},
{"timestamp": "2026-01-05T14:40:00+00:00", "open": 10.1, "high": 10.6, "low": 10.0, "close": 10.5, "volume": 120_000},
{"timestamp": "2026-01-05T14:45:00+00:00", "open": 10.5, "high": 10.9, "low": 10.4, "close": 10.8, "volume": 120_000},
{"timestamp": "2026-01-05T14:50:00+00:00", "open": 10.8, "high": 11.0, "low": 10.7, "close": 10.9, "volume": 120_000},
],
"BBB": [
{"timestamp": "2026-01-05T14:30:00+00:00", "open": 20.0, "high": 20.2, "low": 19.9, "close": 20.0, "volume": 110_000},
{"timestamp": "2026-01-05T14:35:00+00:00", "open": 20.0, "high": 20.3, "low": 20.0, "close": 20.2, "volume": 110_000},
{"timestamp": "2026-01-05T14:40:00+00:00", "open": 20.2, "high": 20.7, "low": 20.1, "close": 20.6, "volume": 110_000},
{"timestamp": "2026-01-05T14:45:00+00:00", "open": 20.6, "high": 20.8, "low": 20.5, "close": 20.7, "volume": 110_000},
{"timestamp": "2026-01-05T14:50:00+00:00", "open": 20.7, "high": 20.9, "low": 20.6, "close": 20.8, "volume": 110_000},
],
"CAT": [
{"timestamp": "2026-01-05T14:30:00+00:00", "open": 30.0, "high": 30.1, "low": 29.9, "close": 30.0, "volume": 100_000},
{"timestamp": "2026-01-05T14:35:00+00:00", "open": 30.0, "high": 30.2, "low": 30.0, "close": 30.1, "volume": 100_000},
{"timestamp": "2026-01-05T14:40:00+00:00", "open": 30.1, "high": 30.5, "low": 30.0, "close": 30.4, "volume": 100_000},
{"timestamp": "2026-01-05T14:45:00+00:00", "open": 30.4, "high": 30.6, "low": 30.3, "close": 30.45, "volume": 100_000},
{"timestamp": "2026-01-05T14:50:00+00:00", "open": 30.45, "high": 30.7, "low": 30.4, "close": 30.5, "volume": 100_000},
],
}
}
daily_enrichment = {
"AAA": {"2026-01-05": {"gap_pct": 0.01, "avg_daily_vol_14d": 1_000_000.0}},
"BBB": {"2026-01-05": {"gap_pct": 0.01, "avg_daily_vol_14d": 1_000_000.0}},
"CAT": {
"2026-01-05": {
"gap_pct": 0.01,
"avg_daily_vol_14d": 1_000_000.0,
"event_flag": True,
"event_score": 1.5,
}
},
}
result = momentum_intraday_first_candidates(
all_intraday,
["2026-01-05"],
strategy,
daily_enrichment=daily_enrichment,
max_per_day=2,
)
assert result == {"2026-01-05": ["AAA", "CAT"]}
def test_momentum_intraday_first_candidates_can_reserve_moderate_liquid_followthrough() -> None:
strategy = StrategyParams(
candidate_source_mode="intraday_first",
candidate_intraday_rank_mode="weighted",
candidate_intraday_weight_gain=1.0,
entry_minutes_after_open=10,
confirmation_minutes_after_entry=5,
min_confirmation_return_pct=0.01,
min_morning_gain_pct=0.04,
candidate_final_max_per_day=2,
use_moderate_gap_liquid_sleeve=True,
moderate_gap_liquid_min_gap_pct=0.005,
moderate_gap_liquid_max_gap_pct=0.025,
moderate_gap_liquid_min_gain_pct=0.015,
moderate_gap_liquid_max_gain_pct=0.04,
moderate_gap_liquid_min_confirmation_return_pct=0.005,
moderate_gap_liquid_min_entry_dollar_volume=40_000_000.0,
moderate_gap_liquid_min_avg_dollar_vol_30d=250_000_000.0,
moderate_gap_liquid_max_avg_dollar_vol_30d=2_000_000_000.0,
moderate_gap_liquid_max_entropy_20d=0.86,
candidate_intraday_moderate_liquid_reserve_slots=1,
)
all_intraday = {
"2026-03-13": {
"AAA": [
{"timestamp": "2026-03-13T13:30:00+00:00", "open": 10.0, "high": 10.1, "low": 9.9, "close": 10.0, "volume": 80_000},
{"timestamp": "2026-03-13T13:35:00+00:00", "open": 10.0, "high": 10.5, "low": 10.0, "close": 10.4, "volume": 80_000},
{"timestamp": "2026-03-13T13:40:00+00:00", "open": 10.4, "high": 10.9, "low": 10.3, "close": 10.8, "volume": 80_000},
{"timestamp": "2026-03-13T13:45:00+00:00", "open": 10.8, "high": 11.1, "low": 10.7, "close": 11.0, "volume": 80_000},
{"timestamp": "2026-03-13T13:50:00+00:00", "open": 11.0, "high": 11.1, "low": 10.9, "close": 11.0, "volume": 80_000},
],
"BBB": [
{"timestamp": "2026-03-13T13:30:00+00:00", "open": 20.0, "high": 20.1, "low": 19.9, "close": 20.0, "volume": 80_000},
{"timestamp": "2026-03-13T13:35:00+00:00", "open": 20.0, "high": 20.7, "low": 20.0, "close": 20.5, "volume": 80_000},
{"timestamp": "2026-03-13T13:40:00+00:00", "open": 20.5, "high": 21.2, "low": 20.4, "close": 21.0, "volume": 80_000},
{"timestamp": "2026-03-13T13:45:00+00:00", "open": 21.0, "high": 21.3, "low": 20.9, "close": 21.2, "volume": 80_000},
{"timestamp": "2026-03-13T13:50:00+00:00", "open": 21.2, "high": 21.3, "low": 21.1, "close": 21.2, "volume": 80_000},
],
"TER": [
{"timestamp": "2026-03-13T13:30:00+00:00", "open": 100.0, "high": 100.2, "low": 99.8, "close": 100.0, "volume": 150_000},
{"timestamp": "2026-03-13T13:35:00+00:00", "open": 100.0, "high": 100.8, "low": 100.0, "close": 100.4, "volume": 150_000},
{"timestamp": "2026-03-13T13:40:00+00:00", "open": 100.4, "high": 101.4, "low": 100.3, "close": 101.2, "volume": 150_000},
{"timestamp": "2026-03-13T13:45:00+00:00", "open": 101.2, "high": 102.0, "low": 101.1, "close": 101.9, "volume": 150_000},
{"timestamp": "2026-03-13T13:50:00+00:00", "open": 101.9, "high": 102.1, "low": 101.8, "close": 102.0, "volume": 150_000},
],
}
}
daily_enrichment = {
"AAA": {"2026-03-13": {"gap_pct": 0.04, "avg_daily_vol_14d": 1_000_000.0, "avg_dollar_vol_30d": 100_000_000.0, "entropy_20d": 0.50}},
"BBB": {"2026-03-13": {"gap_pct": 0.04, "avg_daily_vol_14d": 1_000_000.0, "avg_dollar_vol_30d": 100_000_000.0, "entropy_20d": 0.50}},
"TER": {"2026-03-13": {"gap_pct": 0.012, "avg_daily_vol_14d": 8_000_000.0, "avg_dollar_vol_30d": 750_000_000.0, "entropy_20d": 0.79}},
}
result = momentum_intraday_first_candidates(
all_intraday,
["2026-03-13"],
strategy,
daily_enrichment=daily_enrichment,
max_per_day=2,
)
assert result == {"2026-03-13": ["AAA", "TER"]}
def test_momentum_intraday_moderate_liquid_reserve_can_be_sparse_only() -> None:
strategy = StrategyParams(
candidate_source_mode="intraday_first",
candidate_intraday_rank_mode="weighted",
candidate_intraday_weight_gain=1.0,
entry_minutes_after_open=10,
confirmation_minutes_after_entry=5,
min_confirmation_return_pct=0.0,
min_morning_gain_pct=0.01,
candidate_final_max_per_day=2,
top_n=2,
use_moderate_gap_liquid_sleeve=True,
moderate_gap_liquid_min_gap_pct=0.005,
moderate_gap_liquid_max_gap_pct=0.025,
moderate_gap_liquid_min_gain_pct=0.015,
moderate_gap_liquid_max_gain_pct=0.04,
moderate_gap_liquid_min_confirmation_return_pct=0.005,
moderate_gap_liquid_min_entry_dollar_volume=40_000_000.0,
moderate_gap_liquid_min_avg_dollar_vol_30d=250_000_000.0,
moderate_gap_liquid_max_avg_dollar_vol_30d=2_000_000_000.0,
moderate_gap_liquid_max_entropy_20d=0.86,
candidate_intraday_moderate_liquid_reserve_slots=1,
candidate_intraday_moderate_liquid_reserve_trigger_below=2,
)
bars = [
{"timestamp": "2026-03-13T13:30:00+00:00", "open": 100.0, "high": 100.2, "low": 99.8, "close": 100.0, "volume": 150_000},
{"timestamp": "2026-03-13T13:35:00+00:00", "open": 100.0, "high": 101.0, "low": 100.0, "close": 100.8, "volume": 150_000},
{"timestamp": "2026-03-13T13:40:00+00:00", "open": 100.8, "high": 102.0, "low": 100.7, "close": 101.7, "volume": 150_000},
{"timestamp": "2026-03-13T13:45:00+00:00", "open": 101.7, "high": 102.5, "low": 101.6, "close": 102.2, "volume": 150_000},
{"timestamp": "2026-03-13T13:50:00+00:00", "open": 102.2, "high": 102.4, "low": 102.0, "close": 102.3, "volume": 150_000},
]
all_intraday = {
"2026-03-13": {
"AAA": bars,
"BBB": [
{**bar, "open": bar["open"] * 2, "high": bar["high"] * 2, "low": bar["low"] * 2, "close": bar["close"] * 2}
for bar in bars
],
"TER": [
{**bar, "open": bar["open"] * 3, "high": bar["high"] * 3, "low": bar["low"] * 3, "close": bar["close"] * 3}
for bar in bars
],
}
}
daily_enrichment = {
"AAA": {"2026-03-13": {"gap_pct": 0.04, "avg_daily_vol_14d": 1_000_000.0, "avg_dollar_vol_30d": 100_000_000.0, "entropy_20d": 0.50}},
"BBB": {"2026-03-13": {"gap_pct": 0.04, "avg_daily_vol_14d": 1_000_000.0, "avg_dollar_vol_30d": 100_000_000.0, "entropy_20d": 0.50}},
"TER": {
"2026-03-13": {
"gap_pct": 0.012,
"avg_daily_vol_14d": 8_000_000.0,
"avg_dollar_vol_30d": 750_000_000.0,
"entropy_20d": 0.79,
"candidate_seed_moderate_liquid_overlay": True,
}
},
}
result = momentum_intraday_first_candidates(
all_intraday,
["2026-03-13"],
strategy,
daily_enrichment=daily_enrichment,
max_per_day=2,
)
assert set(result["2026-03-13"]) == {"AAA", "BBB"}
class _StubOracleClient:
def __init__(self, responses: dict[str, object], *, health_ok: bool = True) -> None:
self._responses = responses
self._health_ok = health_ok
self.calls: list[tuple[str, dict | None]] = []
async def get(self, path: str, params: dict | None = None) -> object:
self.calls.append((path, params))
response = self._responses[path]
if isinstance(response, Exception):
raise response
if callable(response):
return response(params)
return response
async def health_check_fast(self, timeout: float = 3.0) -> bool:
return self._health_ok
def test_orb_pre_screen_candidates_returns_full_ranked_universe_when_uncapped() -> None:
daily_bars = {
"AAA": [{"date": "2026-01-05", "open": 10.0}],
"BBB": [{"date": "2026-01-05", "open": 20.0}],
"CCC": [{"date": "2026-01-05", "open": 40.0}],
}
enrichment = {
"AAA": {"2026-01-05": {"atr_14": 4.0, "avg_dollar_vol_30d": 50_000_000.0}},
"BBB": {"2026-01-05": {"atr_14": 3.0, "avg_dollar_vol_30d": 50_000_000.0}},
"CCC": {"2026-01-05": {"atr_14": 1.0, "avg_dollar_vol_30d": 50_000_000.0}},
}
capped = orb_pre_screen_candidates(
daily_bars,
["2026-01-05"],
enrichment,
max_per_day=2,
)
uncapped = orb_pre_screen_candidates(
daily_bars,
["2026-01-05"],
enrichment,
max_per_day=None,
)
assert capped["2026-01-05"] == ["AAA", "BBB"]
assert uncapped["2026-01-05"] == ["AAA", "BBB", "CCC"]
def test_fetch_daily_bars_bulk_uses_bulk_endpoint_by_default() -> None:
client = _StubOracleClient(
{
"/api/v1/price/data": {
"bars": {
"AAA": [
{
"date": "2026-01-02",
"open": 10,
"high": 11,
"low": 9,
"close": 10.5,
"volume": 1000,
}
],
"BBB": [
{
"date": "2026-01-02",
"open": 20,
"high": 21,
"low": 19,
"close": 20.5,
"volume": 2000,
}
],
}
}
}
)
bars = asyncio.run(
fetch_daily_bars_bulk(
["AAA", "BBB"], "2026-01-02", "2026-01-02", client, concurrency=2
)
)
assert list(bars) == ["AAA", "BBB"]
assert len(client.calls) == 1
assert client.calls[0][0] == "/api/v1/price/data"
assert client.calls[0][1] == {
"tickers": "AAA,BBB",
"start_date": "2026-01-02",
"end_date": "2026-01-02",
}
def test_fetch_daily_bars_bulk_falls_back_to_single_ticker_on_chunk_failure() -> None:
client = _StubOracleClient(
{
"/api/v1/price/data": RuntimeError("bulk failed"),
"/api/v1/price/data/AAA": {
"ticker": "AAA",
"data": [
{
"date": "2026-01-02",
"open": 10,
"high": 11,
"low": 9,
"close": 10.5,
"volume": 1000,
}
],
},
"/api/v1/price/data/BBB": {
"ticker": "BBB",
"data": [
{
"date": "2026-01-02",
"open": 20,
"high": 21,
"low": 19,
"close": 20.5,
"volume": 2000,
}
],
},
}
)
bars = asyncio.run(
fetch_daily_bars_bulk(
["AAA", "BBB"], "2026-01-02", "2026-01-02", client, concurrency=2
)
)
assert list(bars) == ["AAA", "BBB"]
assert [call[0] for call in client.calls] == [
"/api/v1/price/data",
"/api/v1/price/data/AAA",
"/api/v1/price/data/BBB",
]
def test_fetch_daily_bars_bulk_repairs_suspiciously_short_bulk_responses() -> None:
short_rows = [
{
"date": f"2026-04-{6 + i:02d}",
"open": 100 + i,
"high": 101 + i,
"low": 99 + i,
"close": 100.5 + i,
"volume": 1000 + i,
}
for i in range(11)
]
full_row_dates = [
(datetime(2026, 1, 20) + timedelta(days=i * 3)).date().isoformat()
for i in range(24)
] + ["2026-04-20"]
full_rows = [
{
"date": day,
"open": 100 + i,
"high": 101 + i,
"low": 99 + i,
"close": 100.5 + i,
"volume": 1000 + i,
}
for i, day in enumerate(full_row_dates)
]
client = _StubOracleClient(
{
"/api/v1/price/data": {
"bars": {
"AAA": short_rows,
}
},
"/api/v1/price/data/AAA": {
"ticker": "AAA",
"data": full_rows,
},
}
)
bars = asyncio.run(
fetch_daily_bars_bulk(
["AAA"],
"2026-01-20",
"2026-04-20",
client,
concurrency=1,
)
)
assert len(bars["AAA"]) == 25
assert [call[0] for call in client.calls] == [
"/api/v1/price/data",
"/api/v1/price/data/AAA",
]
def test_fetch_daily_bars_bulk_hits_daily_cache_on_repeat_range(tmp_path) -> None:
cache = DailyBarCache(str(tmp_path))
client = _StubOracleClient(
{
"/api/v1/price/data": {
"bars": {
"AAA": [
{
"date": "2026-01-02",
"open": 10,
"high": 11,
"low": 9,
"close": 10.5,
"volume": 1000,
}
],
"BBB": [
{
"date": "2026-01-02",
"open": 20,
"high": 21,
"low": 19,
"close": 20.5,
"volume": 2000,
}
],
}
}
}
)
first = asyncio.run(
fetch_daily_bars_bulk(
["AAA", "BBB"],
"2026-01-01",
"2026-01-10",
client,
cache=cache,
concurrency=2,
)
)
assert list(first) == ["AAA", "BBB"]
assert len(client.calls) == 1
client.calls.clear()
second = asyncio.run(
fetch_daily_bars_bulk(
["AAA", "BBB"],
"2026-01-01",
"2026-01-10",
client,
cache=cache,
concurrency=2,
)
)
assert second == first
assert client.calls == []
def test_fetch_daily_bars_bulk_strict_snapshot_does_not_repair_misses(tmp_path) -> None:
base_cache = DailyBarCache(str(tmp_path))
cache = ReadOnlyDailyBarCache(base_cache)
base_cache.put(
"AAA",
"2026-01-01",
"2026-01-10",
[
{
"date": "2026-01-02",
"open": 10,
"high": 11,
"low": 9,
"close": 10.5,
"volume": 1000,
}
],
)
client = _StubOracleClient(
{
"/api/v1/price/data": {
"bars": {
"BBB": [
{
"date": "2026-01-02",
"open": 20,
"high": 21,
"low": 19,
"close": 20.5,
"volume": 2000,
}
],
}
}
}
)
bars = asyncio.run(
fetch_daily_bars_bulk(
["AAA", "BBB"],
"2026-01-01",
"2026-01-10",
client,
cache=cache,
concurrency=2,
)
)
assert list(bars) == ["AAA"]
assert client.calls == []
assert base_cache.get("BBB", "2026-01-01", "2026-01-10") is None
def test_fetch_daily_bars_bulk_drops_stale_tail_cache_when_oracle_has_no_tail(tmp_path) -> None:
cache = DailyBarCache(str(tmp_path))
cached_rows = [
{
"date": "2026-01-20",
"open": 100.0,
"high": 101.0,
"low": 99.0,
"close": 100.5,
"volume": 1000.0,
},
{
"date": "2026-02-01",
"open": 101.0,
"high": 102.0,
"low": 100.0,
"close": 101.5,
"volume": 1100.0,
},
]
cache.put("AAA", "2026-01-20", "2026-02-01", cached_rows)
client = _StubOracleClient({"/api/v1/price/data": {"bars": {"AAA": []}}})
bars = asyncio.run(
fetch_daily_bars_bulk(
["AAA"],
"2026-01-20",
"2026-04-20",
client,
cache=cache,
concurrency=1,
)
)
assert bars == {}
assert client.calls == [
(
"/api/v1/price/data",
{
"tickers": "AAA",
"start_date": "2026-02-02",
"end_date": "2026-04-20",
},
)
]
def test_fetch_intraday_bulk_recovers_from_bulk_chunk_failure_by_splitting() -> None:
from libs.intraday.screener import fetch_intraday_bulk
def intraday_response(params: dict | None) -> dict:
tickers = (params or {}).get("tickers", "")
if "," in tickers:
raise RuntimeError("chunk failed")
ticker = tickers
return {
"bars": {
ticker: [
{
"timestamp": f"2026-01-02T14:{30 + i:02d}:00+00:00",
"open": 10.0,
"high": 11.0,
"low": 9.5,
"close": 10.5,
"volume": 1000.0,
"vwap": 10.4,
}
for i in range(10)
]
}
}
client = _StubOracleClient({"/api/v1/alpaca/intraday": intraday_response})
bars = asyncio.run(
fetch_intraday_bulk(
{"2026-01-02": ["AAA", "BBB"]},
client,
cache=None,
concurrency=2,
)
)
assert list(bars["2026-01-02"]) == ["AAA", "BBB"]
assert [call[1]["tickers"] for call in client.calls] == ["AAA,BBB", "AAA", "BBB"]
def test_fetch_intraday_bulk_negative_caches_sparse_responses(tmp_path) -> None:
from libs.intraday.cache import IntradayCache
from libs.intraday.screener import fetch_intraday_bulk
def intraday_response(params: dict | None) -> dict:
tickers = (params or {}).get("tickers", "")
result = {"bars": {}}
for ticker in tickers.split(","):
if ticker == "AAA":
result["bars"][ticker] = [
{
"timestamp": "2026-01-02T14:30:00+00:00",
"open": 10.0,
"high": 11.0,
"low": 9.5,
"close": 10.5,
"volume": 1000.0,
"vwap": 10.4,
}
]
else:
result["bars"][ticker] = [
{
"timestamp": f"2026-01-02T14:{30 + i:02d}:00+00:00",
"open": 20.0,
"high": 21.0,
"low": 19.5,
"close": 20.5,
"volume": 1000.0,
"vwap": 20.4,
}
for i in range(10)
]
return result
client = _StubOracleClient({"/api/v1/alpaca/intraday": intraday_response})
cache = IntradayCache(str(tmp_path))
first = asyncio.run(
fetch_intraday_bulk(
{"2026-01-02": ["AAA", "BBB"]},
client,
cache=cache,
concurrency=2,
)
)
assert list(first["2026-01-02"]) == ["BBB"]
assert cache.has("AAA", "2026-01-02") is True
assert cache.get("AAA", "2026-01-02") == []
client.calls.clear()
second = asyncio.run(
fetch_intraday_bulk(
{"2026-01-02": ["AAA", "BBB"]},
client,
cache=cache,
concurrency=2,
)
)
assert list(second["2026-01-02"]) == ["BBB"]
assert client.calls == []
def test_fetch_intraday_bulk_uses_today_endpoint_without_caching_live_bars(tmp_path, monkeypatch) -> None:
from libs.intraday.cache import IntradayCache
from libs.intraday.screener import fetch_intraday_bulk
today = "2026-05-01"
class MarketHoursDateTime(datetime):
@classmethod
def now(cls, tz=None):
value = datetime(2026, 5, 1, 10, 0, tzinfo=ZoneInfo("America/New_York"))
return value.astimezone(tz) if tz else value
monkeypatch.setattr(screener_module, "datetime", MarketHoursDateTime)
def intraday_today_response(params: dict | None) -> dict:
ticker = (params or {}).get("tickers", "")
return {
"bars": {
ticker: [
{
"timestamp": (
datetime.fromisoformat(f"{today}T13:30:00+00:00")
+ timedelta(minutes=5 * i)
).isoformat(),
"open": 10.0 + i * 0.1,
"high": 10.2 + i * 0.1,
"low": 9.9 + i * 0.1,
"close": 10.1 + i * 0.1,
"volume": 1000.0,
"vwap": 10.05 + i * 0.1,
}
for i in range(78)
]
}
}
client = _StubOracleClient({"/api/v1/alpaca/intraday/today": intraday_today_response})
cache = IntradayCache(str(tmp_path))
bars = asyncio.run(
fetch_intraday_bulk(
{today: ["AAA"]},
client,
cache=cache,
concurrency=1,
)
)
assert list(bars[today]) == ["AAA"]
assert client.calls == [
(
"/api/v1/alpaca/intraday/today",
{"tickers": "AAA", "interval": "5m"},
)
]
assert cache.has("AAA", today) is False
def test_fetch_intraday_bulk_uses_historical_today_after_close_and_caches(tmp_path, monkeypatch) -> None:
from libs.intraday.cache import IntradayCache
from libs.intraday.screener import fetch_intraday_bulk
today = "2026-05-01"
class AfterCloseDateTime(datetime):
@classmethod
def now(cls, tz=None):
value = datetime(2026, 5, 1, 21, 15, tzinfo=ZoneInfo("America/New_York"))
return value.astimezone(tz) if tz else value
monkeypatch.setattr(screener_module, "datetime", AfterCloseDateTime)
def historical_response(params: dict | None) -> dict:
ticker = (params or {}).get("tickers", "")
return {
"bars": {
ticker: [
{
"timestamp": (
datetime.fromisoformat(f"{today}T13:30:00+00:00")
+ timedelta(minutes=5 * i)
).isoformat(),
"open": 10.0 + i * 0.1,
"high": 10.2 + i * 0.1,
"low": 9.9 + i * 0.1,
"close": 10.1 + i * 0.1,
"volume": 1000.0,
"vwap": 10.05 + i * 0.1,
}
for i in range(78)
]
}
}
client = _StubOracleClient({"/api/v1/alpaca/intraday": historical_response})
cache = IntradayCache(str(tmp_path))
bars = asyncio.run(
fetch_intraday_bulk(
{today: ["AAA"]},
client,
cache=cache,
concurrency=1,
)
)
assert list(bars[today]) == ["AAA"]
assert client.calls == [
(
"/api/v1/alpaca/intraday",
{"tickers": "AAA", "interval": "5m", "start_date": today, "end_date": today},
)
]
assert cache.has("AAA", today) is True
def test_fetch_intraday_bulk_ignores_existing_today_cache_after_close(tmp_path, monkeypatch) -> None:
from libs.intraday.cache import IntradayCache
from libs.intraday.screener import fetch_intraday_bulk
today = "2026-05-01"
class AfterCloseDateTime(datetime):
@classmethod
def now(cls, tz=None):
value = datetime(2026, 5, 1, 21, 15, tzinfo=ZoneInfo("America/New_York"))
return value.astimezone(tz) if tz else value
monkeypatch.setattr(screener_module, "datetime", AfterCloseDateTime)
cache = IntradayCache(str(tmp_path))
cache.put(
"AAA",
today,
[
{
"timestamp": (
datetime.fromisoformat(f"{today}T13:30:00+00:00")
+ timedelta(minutes=5 * i)
).isoformat(),
"open": 10.0,
"high": 10.1,
"low": 9.9,
"close": 10.0,
"volume": 1000.0,
"vwap": 10.0,
}
for i in range(10)
],
)
def historical_response(params: dict | None) -> dict:
ticker = (params or {}).get("tickers", "")
return {
"bars": {
ticker: [
{
"timestamp": (
datetime.fromisoformat(f"{today}T13:30:00+00:00")
+ timedelta(minutes=5 * i)
).isoformat(),
"open": 20.0,
"high": 20.2,
"low": 19.9,
"close": 20.1,
"volume": 2000.0,
"vwap": 20.05,
}
for i in range(78)
]
}
}
client = _StubOracleClient({"/api/v1/alpaca/intraday": historical_response})
bars = asyncio.run(
fetch_intraday_bulk(
{today: ["AAA"]},
client,
cache=cache,
concurrency=1,
)
)
assert client.calls == [
(
"/api/v1/alpaca/intraday",
{"tickers": "AAA", "interval": "5m", "start_date": today, "end_date": today},
)
]
assert bars[today]["AAA"][0]["open"] == 20.0
def test_fetch_intraday_bulk_rejects_truncated_today_after_close(tmp_path, monkeypatch) -> None:
from libs.intraday.cache import IntradayCache
from libs.intraday.screener import fetch_intraday_bulk
today = "2026-05-01"
class AfterCloseDateTime(datetime):
@classmethod
def now(cls, tz=None):
value = datetime(2026, 5, 1, 21, 15, tzinfo=ZoneInfo("America/New_York"))
return value.astimezone(tz) if tz else value
monkeypatch.setattr(screener_module, "datetime", AfterCloseDateTime)
def truncated_response(params: dict | None) -> dict:
ticker = (params or {}).get("tickers", "")
return {
"bars": {
ticker: [
{
"timestamp": (
datetime.fromisoformat(f"{today}T13:30:00+00:00")
+ timedelta(minutes=5 * i)
).isoformat(),
"open": 10.0,
"high": 10.2,
"low": 9.9,
"close": 10.1,
"volume": 1000.0,
"vwap": 10.05,
}
for i in range(20)
]
}
}
client = _StubOracleClient(
{
"/api/v1/alpaca/intraday": truncated_response,
"/api/v1/alpaca/intraday/today": truncated_response,
}
)
cache = IntradayCache(str(tmp_path))
bars = asyncio.run(
fetch_intraday_bulk(
{today: ["AAA"]},
client,
cache=cache,
concurrency=1,
)
)
assert "AAA" not in bars.get(today, {})
assert cache.has("AAA", today) is False
assert client.calls == [
(
"/api/v1/alpaca/intraday",
{"tickers": "AAA", "interval": "5m", "start_date": today, "end_date": today},
),
(
"/api/v1/alpaca/intraday/today",
{"tickers": "AAA", "interval": "5m"},
),
]
def test_fetch_daily_bars_bulk_rebuilds_from_intraday_cache_when_oracle_unavailable(tmp_path) -> None:
daily_cache = DailyBarCache(str(tmp_path / "daily"))
intraday_cache = IntradayCache(str(tmp_path / "intraday"))
intraday_cache.put(
"AAA",
"2026-01-02",
[
{
"timestamp": "2026-01-02T14:30:00+00:00",
"open": 10.0,
"high": 10.5,
"low": 9.9,
"close": 10.4,
"volume": 100.0,
"vwap": 10.2,
},
{
"timestamp": "2026-01-02T14:35:00+00:00",
"open": 10.4,
"high": 11.0,
"low": 10.2,
"close": 10.8,
"volume": 150.0,
"vwap": 10.7,
},
]
* 5,
)
intraday_cache.put(
"AAA",
"2026-01-03",
[
{
"timestamp": "2026-01-03T14:30:00+00:00",
"open": 11.0,
"high": 11.2,
"low": 10.8,
"close": 11.1,
"volume": 120.0,
"vwap": 11.0,
},
{
"timestamp": "2026-01-03T14:35:00+00:00",
"open": 11.1,
"high": 11.4,
"low": 11.0,
"close": 11.3,
"volume": 180.0,
"vwap": 11.2,
},
]
* 5,
)
client = _StubOracleClient({"/api/v1/price/data": RuntimeError("oracle down")})
bars = asyncio.run(
fetch_daily_bars_bulk(
["AAA"],
"2026-01-02",
"2026-01-03",
client,
cache=daily_cache,
intraday_cache_fallback=intraday_cache,
concurrency=1,
)
)
assert list(bars) == ["AAA"]
assert bars["AAA"] == [
{
"date": "2026-01-02",
"open": 10.0,
"high": 11.0,
"low": 9.9,
"close": 10.8,
"volume": 1250.0,
},
{
"date": "2026-01-03",
"open": 11.0,
"high": 11.4,
"low": 10.8,
"close": 11.3,
"volume": 1500.0,
},
]
assert daily_cache.get("AAA", "2026-01-02", "2026-01-03") == bars["AAA"]
def test_fetch_daily_bars_bulk_prefers_intraday_fallback_without_oracle_calls(tmp_path) -> None:
intraday_cache = IntradayCache(str(tmp_path / "intraday"))
intraday_cache.put(
"AAA",
"2026-01-02",
[
{
"timestamp": f"2026-01-02T14:{30 + i:02d}:00+00:00",
"open": 10.0,
"high": 10.0 + i * 0.1,
"low": 9.8,
"close": 10.0 + i * 0.1,
"volume": 100.0 + i,
"vwap": 10.0 + i * 0.05,
}
for i in range(10)
],
)
client = _StubOracleClient({"/api/v1/price/data": RuntimeError("should not call")})
bars = asyncio.run(
fetch_daily_bars_bulk(
["AAA"],
"2026-01-02",
"2026-01-02",
client,
intraday_cache_fallback=intraday_cache,
prefer_intraday_fallback=True,
concurrency=1,
)
)
assert list(bars) == ["AAA"]
assert client.calls == []
def test_fetch_daily_bars_bulk_repairs_sparse_intraday_fallback_before_using_it(tmp_path) -> None:
intraday_cache = IntradayCache(str(tmp_path / "intraday"))
for day in [f"2026-04-{6 + i:02d}" for i in range(11)]:
intraday_cache.put(
"AAA",
day,
[
{
"timestamp": f"{day}T14:{30 + i:02d}:00+00:00",
"open": 10.0,
"high": 10.2,
"low": 9.8,
"close": 10.1,
"volume": 100.0,
"vwap": 10.0,
}
for i in range(10)
],
)
daily_cache = DailyBarCache(str(tmp_path / "daily"))
row_dates = [
(datetime(2026, 1, 20) + timedelta(days=i * 3)).date().isoformat()
for i in range(24)
] + ["2026-04-20"]
full_rows = [
{
"date": day,
"open": 100 + i,
"high": 101 + i,
"low": 99 + i,
"close": 100.5 + i,
"volume": 1000 + i,
}
for i, day in enumerate(row_dates)
]
client = _StubOracleClient(
{
"/api/v1/price/data": {
"bars": {
"AAA": full_rows,
}
}
}
)
bars = asyncio.run(
fetch_daily_bars_bulk(
["AAA"],
"2026-01-20",
"2026-04-20",
client,
cache=daily_cache,
intraday_cache_fallback=intraday_cache,
prefer_intraday_fallback=True,
concurrency=1,
)
)
assert len(bars["AAA"]) == len(full_rows)
assert [call[0] for call in client.calls] == ["/api/v1/price/data"]
assert daily_cache.get("AAA", "2026-01-20", "2026-04-20") == [
{
"date": row["date"],
"open": float(row["open"]),
"high": float(row["high"]),
"low": float(row["low"]),
"close": float(row["close"]),
"volume": float(row["volume"]),
}
for row in full_rows
]
def test_fetch_daily_bars_bulk_skips_oracle_misses_when_health_check_fails(tmp_path) -> None:
intraday_cache = IntradayCache(str(tmp_path / "intraday"))
intraday_cache.put(
"AAA",
"2026-01-02",
[
{
"timestamp": f"2026-01-02T14:{30 + i:02d}:00+00:00",
"open": 10.0,
"high": 10.2,
"low": 9.8,
"close": 10.1,
"volume": 100.0,
"vwap": 10.0,
}
for i in range(10)
],
)
client = _StubOracleClient({"/api/v1/price/data": RuntimeError("should not call")}, health_ok=False)
bars = asyncio.run(
fetch_daily_bars_bulk(
["AAA", "BBB"],
"2026-01-02",
"2026-01-02",
client,
intraday_cache_fallback=intraday_cache,
prefer_intraday_fallback=True,
skip_oracle_when_unhealthy=True,
concurrency=1,
)
)
assert list(bars) == ["AAA"]
assert client.calls == []
def test_fetch_intraday_bulk_skips_uncached_pairs_when_oracle_health_fails(tmp_path) -> None:
from libs.intraday.screener import fetch_intraday_bulk
cache = IntradayCache(str(tmp_path))
cache.put(
"AAA",
"2026-01-02",
[
{
"timestamp": f"2026-01-02T14:{30 + i:02d}:00+00:00",
"open": 10.0,
"high": 10.5,
"low": 9.8,
"close": 10.3,
"volume": 1000.0,
"vwap": 10.2,
}
for i in range(10)
],
)
client = _StubOracleClient({"/api/v1/alpaca/intraday": RuntimeError("should not call")}, health_ok=False)
bars = asyncio.run(
fetch_intraday_bulk(
{"2026-01-02": ["AAA", "BBB"]},
client,
cache=cache,
skip_oracle_when_unhealthy=True,
concurrency=1,
)
)
assert list(bars["2026-01-02"]) == ["AAA"]
assert client.calls == []
def test_resolve_universe_screener_uses_snapshot_fallback(tmp_path, monkeypatch) -> None:
monkeypatch.setattr(
screener_module,
"get_settings",
lambda: SimpleNamespace(data_root=str(tmp_path)),
)
params = UniverseParams(
source="screener",
market_cap_min=100_000_000.0,
avg_volume_min=200_000,
min_price=2.0,
)
async def first_search(self, **kwargs):
return [
SimpleNamespace(symbol="TSLA"),
SimpleNamespace(symbol="NVDA"),
SimpleNamespace(symbol="TSLA"),
]
async def failing_search(self, **kwargs):
raise RuntimeError("screener down")
monkeypatch.setattr(screener_module.ScreenerService, "search_all_stocks", first_search)
first = asyncio.run(resolve_universe(params, client=object()))
assert first == ["NVDA", "TSLA"]
monkeypatch.setattr(screener_module.ScreenerService, "search_all_stocks", failing_search)
second = asyncio.run(resolve_universe(params, client=object()))
assert second == ["NVDA", "TSLA"]
def test_resolve_universe_broad_uses_named_yaml_snapshot() -> None:
params = UniverseParams(source="broad")
symbols = asyncio.run(resolve_universe(params, client=object()))
assert "AAPL" in symbols
assert "TSLA" in symbols
assert len(symbols) > 3000