chart/app/analysis/levels.py
Chris Amow 2efcec6a76 Price trendlines across bars, add one-shot alerts, collapse layers, drop 1h MAs
The trendline bug: a line continued past its second anchor at a different
slope. Two conventions were fighting, and both were wrong.

Lightweight Charts spaces bars evenly however much time separates them — a
weekend is forty-nine hours and one bar wide. The renderer extended the line by
interpolating between bar indices, which looked straight but disagreed with the
server, since price_at() advances per second. Measured on real bars that reached
147 points: the chart drew a level the alerts did not believe in. Making the
renderer match price_at() fixed the disagreement and made the visible kick worse,
because now the line really did climb an hour's worth of slope across a one-bar
maintenance break.

Neither convention is what a person means by drawing a line. A trendline advances
per bar, so both sides now evaluate in bar space: a new bar_space module the
runtime uses to position sloped levels, mirrored by indexAt() in the chart. The
line is straight on screen and the alert fires where it is drawn.

Also, from testing against the live chart:

- A plain click with the trendline tool armed did nothing and left the tool
  armed, so the next click began a new line — which is how the slope change was
  first noticed. Click-click and press-drag-release are both supported now, with
  the rubber band following the cursor between clicks.
- Hand-placed levels are armed, fire once, then disarm themselves, and can be
  re-armed from the sidebar. Verified end to end: created armed, tripped within
  thirty seconds, disarmed, re-armed.
- Layers is collapsible.
- The 1h moving averages are gone; only the daily set remains.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-10 04:15:05 -05:00

77 lines
2.2 KiB
Python

from dataclasses import asdict, dataclass
from enum import Enum
from typing import Any
from app.bars.models import Timeframe
class LevelKind(str, Enum):
MANUAL = "manual"
MA = "ma"
VWAP = "vwap"
TRENDLINE = "trendline"
HORIZONTAL = "horizontal"
class Side(str, Enum):
SUPPORT = "support"
RESISTANCE = "resistance"
@dataclass(slots=True)
class Level:
id: str
kind: LevelKind
tf: Timeframe
side: Side
weight: float
score: float
label: str
anchor_t: int
anchor_p: float
slope: float
points: list[tuple[int, float]] | None
touches: int
first_t: int
last_t: int
provisional: bool
hidden: bool
period: int | None = None
color: str | None = None
line_width: int | None = None
number: int | None = None
cutoff_t: int | None = None
# Hand-placed levels fire once and disarm themselves; everything derived
# (averages, prior-day, VWAP) is permanently armed.
armed: bool = True
# Sloped lines are evaluated across bars, not seconds (see bar_space). The
# runtime fills this in where the bar series is available; price_at() is the
# fallback for levels that are already flat or have no series to measure.
current_p: float | None = None
def price_at(self, t: int) -> float:
return self.anchor_p + self.slope * (t - self.anchor_t)
def to_dict(self) -> dict[str, Any]:
value = asdict(self)
value["kind"] = self.kind.value
value["tf"] = self.tf.value
value["side"] = self.side.value
return value
def summary(self) -> dict[str, Any]:
"""Compact form for embedding inside a cluster.
Clusters go out on every closed 1m bar, and a moving average carries its
whole point history — hundreds of entries reaching back years. Embedding
the full level duplicated the entire levels payload once a minute. The
client already holds the full levels and joins on id.
"""
return {
"id": self.id,
"kind": self.kind.value,
"tf": self.tf.value,
"side": self.side.value,
"weight": self.weight,
"label": self.label,
}