Chris Amow
45802c1a2f
Five defects found by exercising the running app rather than reading it: - Backspace inside the sidebar rename field deleted the trendline instead of a character. The window keydown handler never checked what was focused, so correcting a typo in a line's name destroyed the line. - playAlert() built a new AudioContext per alert and never closed it. Browsers cap a document at roughly six, after which alerts stop making any sound. One shared context now, with nodes released on end and a resume() for the autoplay policy. - Stored layer preferences were used verbatim, so any key added to defaultPrefs later would be missing for existing visitors. A missing enabled.ma is a crash rather than a cosmetic gap. They are now deep-merged onto the defaults, and unparseable state falls back instead of throwing. - The alert log keyed rows on a second-resolution timestamp, so two alerts in the same second collided. - Clusters embedded whole Level objects, including a moving average's entire point history — hundreds of entries reaching back years. Because clusters are re-sent on every closed 1m bar, this shipped the whole levels payload once a minute. Members are now compact summaries and the client joins on id; Cluster.to_dict() also stops round-tripping through asdict(), which was deep-copying those arrays before discarding them. /api/confluence drops from 61,838 to 1,245 bytes with five clusters live. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
69 lines
1.7 KiB
Python
69 lines
1.7 KiB
Python
from dataclasses import asdict, dataclass
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from enum import Enum
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from typing import Any
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from app.bars.models import Timeframe
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class LevelKind(str, Enum):
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MANUAL = "manual"
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MA = "ma"
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TRENDLINE = "trendline"
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HORIZONTAL = "horizontal"
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class Side(str, Enum):
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SUPPORT = "support"
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RESISTANCE = "resistance"
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@dataclass(slots=True)
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class Level:
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id: str
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kind: LevelKind
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tf: Timeframe
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side: Side
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weight: float
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score: float
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label: str
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anchor_t: int
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anchor_p: float
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slope: float
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points: list[tuple[int, float]] | None
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touches: int
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first_t: int
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last_t: int
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provisional: bool
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hidden: bool
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period: int | None = None
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color: str | None = None
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line_width: int | None = None
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number: int | None = None
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cutoff_t: int | None = None
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def price_at(self, t: int) -> float:
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return self.anchor_p + self.slope * (t - self.anchor_t)
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def to_dict(self) -> dict[str, Any]:
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value = asdict(self)
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value["kind"] = self.kind.value
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value["tf"] = self.tf.value
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value["side"] = self.side.value
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return value
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def summary(self) -> dict[str, Any]:
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"""Compact form for embedding inside a cluster.
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Clusters go out on every closed 1m bar, and a moving average carries its
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whole point history — hundreds of entries reaching back years. Embedding
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the full level duplicated the entire levels payload once a minute. The
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client already holds the full levels and joins on id.
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"""
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return {
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"id": self.id,
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"kind": self.kind.value,
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"tf": self.tf.value,
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"side": self.side.value,
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"weight": self.weight,
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"label": self.label,
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}
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