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https://ghfast.top/https://github.com/aeroxw/tick-stock-panel.git
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shy3130
657d4d9948
- tickflow SDK 0.1.25: intraday.universe 标的池单请求拉全市场当日分钟 - 新能力位 Cap.INTRADAY_UNIVERSE (TickFlow Expert 专有) + 探测/别名/schema v6 - 分钟刷新两阶段: 冷启动与缺口修复走 intraday_batch 全天突发(分块容错), 稳态走 universe 增量 - 覆盖看门狗: 落后>3min / 无数据 / 连续空轮自动升级全量自愈 - 刷新间隔钳制 [3,300]s 默认 6s; 监控页全量分钟开关与状态入口 - CONTRIBUTING: 分钟 K 北京时间墙钟契约 (naive, 入口强制归一)
91 lines
3.6 KiB
Python
91 lines
3.6 KiB
Python
"""fetch_intraday_full_market_burst 单块容错契约。
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一个块失败不得拖垮整轮: 成功块必须照常返回供落盘; 失败块单独重试一次;
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失败块过多 (系统性故障) 时跳过重试。全部用假 client, 不发真实网络请求。
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"""
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from types import SimpleNamespace
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from unittest.mock import patch
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import polars as pl
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from app.services import kline_sync
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from app.tickflow.capabilities import Cap, CapabilityLimits, CapabilitySet
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def _capset(batch: int = 2) -> CapabilitySet:
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return CapabilitySet({Cap.INTRADAY_BATCH: CapabilityLimits(rpm=60, batch=batch)})
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def _frame() -> pl.DataFrame:
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# _normalize_minute 的最小输入: 毫秒 timestamp → 北京墙钟 datetime。
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# 时间必须落在交易时段 (时区契约守卫会拒绝非交易小时的脏数据)
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from datetime import datetime
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from zoneinfo import ZoneInfo
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ts = int(datetime(2026, 8, 28, 9, 31, tzinfo=ZoneInfo("Asia/Shanghai")).timestamp() * 1000)
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return pl.DataFrame({
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"timestamp": [ts],
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"open": [1.0], "high": [1.0], "low": [1.0], "close": [1.0],
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"volume": [100.0], "amount": [100.0],
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})
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class _FakeKlines:
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"""intraday_batch 假实现: fail_once 首次失败重试成功, fail_always 恒失败。"""
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def __init__(self, fail_once: set[str], fail_always: set[str]) -> None:
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self.fail_once = set(fail_once)
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self.fail_always = set(fail_always)
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self.calls: list[list[str]] = []
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def intraday_batch(self, chunk, **kwargs):
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self.calls.append(list(chunk))
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syms = set(chunk)
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if syms & self.fail_always:
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raise RuntimeError("permanent failure")
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if syms & self.fail_once:
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self.fail_once -= syms
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raise RuntimeError("transient failure")
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return {s: _frame() for s in chunk}
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def _run(symbols: list[str], fake: _FakeKlines, batch: int = 2):
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client = SimpleNamespace(klines=fake)
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with patch.object(kline_sync, "get_client", return_value=client):
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return kline_sync.fetch_intraday_full_market_burst(symbols, _capset(batch))
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def test_transient_chunk_failure_retried_and_all_symbols_returned():
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symbols = [f"S{i}" for i in range(6)] # 3 chunks (batch=2)
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fake = _FakeKlines(fail_once={"S0", "S1"}, fail_always=set())
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df, requests = _run(symbols, fake)
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# 失败块 (S0,S1) 重试后成功 → 6 只全在, 请求数 = 3 块 + 1 次重试
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assert set(df["symbol"].to_list()) == set(symbols)
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assert requests == 4
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def test_permanent_chunk_failure_skipped_without_losing_other_chunks():
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symbols = [f"S{i}" for i in range(6)]
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fake = _FakeKlines(fail_once=set(), fail_always={"S4", "S5"})
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df, requests = _run(symbols, fake)
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# 失败块重试仍失败 → 只跳过该块, 其余 4 只必须返回 (旧实现会整轮丢弃)
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assert set(df["symbol"].to_list()) == {"S0", "S1", "S2", "S3"}
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assert requests == 4
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def test_all_chunks_succeed_requests_equals_chunk_count():
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symbols = [f"S{i}" for i in range(6)]
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fake = _FakeKlines(fail_once=set(), fail_always=set())
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df, requests = _run(symbols, fake)
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assert set(df["symbol"].to_list()) == set(symbols)
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assert requests == 3
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def test_systemic_failure_skips_retry_to_avoid_pressuring_overloaded_server():
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symbols = [f"S{i}" for i in range(12)] # 6 chunks (batch=2)
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# 5 个块恒失败 (>4) → 系统性故障, 不再重试
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fake = _FakeKlines(fail_once=set(), fail_always={s for s in symbols if s not in ("S0", "S1")})
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df, requests = _run(symbols, fake)
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assert set(df["symbol"].to_list()) == {"S0", "S1"}
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assert requests == 6 # 无重试
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assert len(fake.calls) == 6
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