dennisthiessenandClaude Fable 5
247a92a89f
Review of the shadow book found seven ways the two books could leak into
each other; all are fixed here. The most serious silently invalidated the
comparison the shadow book exists to make.
- Shadow holdings no longer suppress the manual candidate list. The
open-trade exclusion filtered on any book, so shadow taking the
top-ranked names removed exactly those from the user's list and alerts,
confining the discretionary book to leftovers. Scoped to the manual
book. Closed-trade alerts and paper-book equity were leaking the same
way and are likewise scoped.
- Shadow sizing now matches _simulate_portfolio: min(1% risk, 20% notional
cap, available cash) from marked equity, plus the sub- dust guard.
Previously risk-only from realized equity, so a tight stop produced a
multiples-of-equity leveraged position the strategy would never take.
- Shadow only trades setups from the scan that just ran (<6h old) with one
setup per ticker. A failed or disabled scan step could otherwise open
positions from a prior session at stale prices.
- Gate-reset transitions are observed for both books, so a shadow stop-out
completes fail -> requalify instead of staying locked forever.
- Manual list/close endpoints default to the manual book and reject
hand-closing shadow trades; the performance endpoint is scoped to the
caller so 'your picks' is not every user's book.
- run_shadow_book is registered as a paused job so Admin can trigger it.
Also anchors three pre-existing paper-trade tests (and the new alpaca
window test) on the UTC date. They build fixtures from the local date but
the service stamps opened_at in UTC, so they failed only between 00:00 and
02:00 in a UTC+hh timezone -- latent on ba2df8b, exposed by the clock.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
150 lines
4.8 KiB
Python
150 lines
4.8 KiB
Python
"""Unit tests for app.scheduler module."""
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import pytest
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from app.scheduler import (
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_consume_backtest_options,
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_consume_backtest_target_model,
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_parse_frequency,
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_resume_tickers,
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_last_successful,
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configure_scheduler,
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queue_backtest_options,
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queue_backtest_target_model,
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scheduler,
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)
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def test_manual_backtest_target_model_is_one_shot():
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assert queue_backtest_target_model("structural_sr") == "structural_sr"
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assert _consume_backtest_target_model() == "structural_sr"
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assert _consume_backtest_target_model() == "production_gtl"
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def test_manual_backtest_target_model_rejects_removed_research_arms():
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with pytest.raises(ValueError, match="Unknown backtest target model"):
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queue_backtest_target_model("production_control")
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def test_manual_backtest_options_are_one_shot_and_default_back_to_weekly():
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assert queue_backtest_options("structural_sr", "daily") == (
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"structural_sr",
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"daily",
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)
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assert _consume_backtest_options() == ("structural_sr", "daily")
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assert _consume_backtest_options() == ("production_gtl", "weekly")
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class TestParseFrequency:
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def test_hourly(self):
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assert _parse_frequency("hourly") == {"hours": 1}
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def test_daily(self):
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assert _parse_frequency("daily") == {"hours": 24}
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def test_case_insensitive(self):
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assert _parse_frequency("Hourly") == {"hours": 1}
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assert _parse_frequency("DAILY") == {"hours": 24}
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def test_weekly_maps_to_one_week(self):
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assert _parse_frequency("weekly") == {"weeks": 1}
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def test_unknown_defaults_to_daily(self):
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assert _parse_frequency("monthly") == {"hours": 24}
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assert _parse_frequency("") == {"hours": 24}
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class TestResumeTickers:
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def test_no_previous_returns_full_list(self):
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symbols = ["AAPL", "GOOG", "MSFT"]
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_last_successful["test_job"] = None
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result = _resume_tickers(symbols, "test_job")
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assert result == ["AAPL", "GOOG", "MSFT"]
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def test_resume_after_first(self):
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symbols = ["AAPL", "GOOG", "MSFT"]
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_last_successful["test_job"] = "AAPL"
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result = _resume_tickers(symbols, "test_job")
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# Should start from GOOG, then wrap around
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assert result == ["GOOG", "MSFT", "AAPL"]
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def test_resume_after_middle(self):
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symbols = ["AAPL", "GOOG", "MSFT", "TSLA"]
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_last_successful["test_job"] = "GOOG"
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result = _resume_tickers(symbols, "test_job")
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assert result == ["MSFT", "TSLA", "AAPL", "GOOG"]
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def test_resume_after_last(self):
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symbols = ["AAPL", "GOOG", "MSFT"]
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_last_successful["test_job"] = "MSFT"
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result = _resume_tickers(symbols, "test_job")
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# All already processed, wraps to full list
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assert result == ["AAPL", "GOOG", "MSFT"]
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def test_unknown_last_returns_full_list(self):
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symbols = ["AAPL", "GOOG", "MSFT"]
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_last_successful["test_job"] = "NVDA"
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result = _resume_tickers(symbols, "test_job")
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assert result == ["AAPL", "GOOG", "MSFT"]
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def test_empty_list(self):
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_last_successful["test_job"] = "AAPL"
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result = _resume_tickers([], "test_job")
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assert result == []
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class TestConfigureScheduler:
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def test_configure_adds_all_jobs(self):
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# Remove any existing jobs first
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scheduler.remove_all_jobs()
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configure_scheduler()
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jobs = scheduler.get_jobs()
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job_ids = {j.id for j in jobs}
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assert job_ids == {
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"data_collector",
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"data_backfill",
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"benchmark_collector",
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"sentiment_collector",
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"fundamental_collector",
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"rr_scanner",
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"shadow_book",
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"ticker_universe_sync",
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"outcome_evaluator",
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"alerts",
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"market_regime",
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"regime_monitor",
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"event_study",
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"backtest",
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"daily_pipeline",
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"near_close_pipeline",
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"after_close_pipeline",
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"intraday_pipeline",
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}
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def test_configure_is_idempotent(self):
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scheduler.remove_all_jobs()
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configure_scheduler()
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configure_scheduler() # Should replace, not duplicate
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job_ids = [j.id for j in scheduler.get_jobs()]
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# Each ID should appear exactly once
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assert sorted(job_ids) == sorted([
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"after_close_pipeline",
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"alerts",
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"backtest",
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"benchmark_collector",
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"daily_pipeline",
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"intraday_pipeline",
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"data_collector",
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"data_backfill",
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"fundamental_collector",
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"market_regime",
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"near_close_pipeline",
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"regime_monitor",
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"event_study",
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"outcome_evaluator",
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"rr_scanner",
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"sentiment_collector",
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"shadow_book",
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"ticker_universe_sync",
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])
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