chart/app/analysis/confluence.py

74 lines
2.3 KiB
Python

from dataclasses import asdict, dataclass
from hashlib import sha1
from typing import Any
from app.analysis.levels import Level, Side
@dataclass(slots=True)
class Cluster:
id: str
side: Side
low: float
high: float
center: float
score: float
members: list[Level]
distance: float
def to_dict(self) -> dict[str, Any]:
value = asdict(self)
value["side"] = self.side.value
value["members"] = [member.to_dict() for member in self.members]
return value
def cluster_levels(
levels: list[Level], current_t: int, current_price: float, atr15: float
) -> list[Cluster]:
tolerance = 0.4 * atr15
if tolerance <= 0:
return []
groups: list[list[tuple[float, Level]]] = []
positioned = [(level.price_at(current_t), level) for level in levels if not level.hidden]
for positional_side in (Side.SUPPORT, Side.RESISTANCE):
side_levels = sorted(
(
item
for item in positioned
if (Side.RESISTANCE if item[0] >= current_price else Side.SUPPORT)
is positional_side
),
key=lambda item: item[0],
)
side_groups: list[list[tuple[float, Level]]] = []
for item in side_levels:
if not side_groups or item[0] - side_groups[-1][-1][0] > tolerance:
side_groups.append([item])
else:
side_groups[-1].append(item)
groups.extend(side_groups)
clusters: list[Cluster] = []
for group in groups:
score = sum(level.weight for _, level in group)
if len(group) < 2 and score < 8:
continue
low, high = group[0][0], group[-1][0]
center = (low + high) / 2
side = Side.RESISTANCE if center >= current_price else Side.SUPPORT
identity_bucket = round(center / tolerance)
identity = sha1(f"{side.value}:{identity_bucket}".encode()).hexdigest()[:12]
clusters.append(
Cluster(
id=f"cl_{identity}",
side=side,
low=low,
high=high,
center=center,
score=score,
members=[level for _, level in group],
distance=center - current_price,
)
)
return sorted(clusters, key=lambda cluster: abs(cluster.distance))