The confluence engine had nothing to work with. Daily moving averages were the only level source, and they sat 163 to 697 points from price, so every cluster had exactly one member and no alert could ever fire. Two new sources, chosen for having a real following — the engine is a bet that many participants watch the same price, which is what makes a level hold: - Prior day high/low/close, from the last *closed* daily bar so mid-session the levels do not silently switch to today's own developing range. Full daily weight rather than the 0.75 average discount: a traded high is structure, not a derived average. - Session VWAP, anchored to the 18:00 ET open like the daily bars. Institutional execution is benchmarked against it, and zero-volume overnight minutes are skipped rather than dividing by zero. Both are stamped 1d, so they get their own colours to stay distinguishable from the daily averages. Prior-day levels draw as price lines, which span the chart and label the axis instead of relying on bar-index interpolation. VWAP re-prices every minute while a daily average carries hundreds of points and changes once a session, so broadcasting the whole level set on the VWAP cadence would have pushed the entire history every minute. Levels now go out as a delta that clients merge by id. Adding the levels then exposed two defects that had been invisible while nothing could cluster: - Cluster identity was sha1(side + round(center / tolerance)), and tolerance derives from ATR, so it changed every bar. The same zone was continually issued a new id, never matched the cooldown table, and the cooldown did nothing. Identity is now the set of converging levels. - Alert suppression keyed on that identity, so a level drifting in or out of a group read as a new zone. It now suppresses by proximity: two zones within an ATR are the same zone, and the strongest is the one reported. Over six replayed sessions at threshold 28 that is 247 alerts, then 54, then 40; raising the cooldown to 4h — which only affects repeats of the same area, never a genuinely new zone — gives 17 total with a worst session of 9. calibrate_alerts.py now sweeps threshold and cooldown together in one pass, since the threshold turns out to be quantised and nearly useless as a control. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
141 lines
5.3 KiB
Python
141 lines
5.3 KiB
Python
import asyncio
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from fastapi import APIRouter, WebSocket, WebSocketDisconnect
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from app.api.deps import token_matches
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from app.bars.models import Timeframe
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from app.analysis.alerts import AlertEngine
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from app.analysis.confluence import cluster_levels
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from app.notify.ntfy import send_ntfy
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router = APIRouter()
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def level_enabled(level, enabled: dict) -> bool:
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kind = level.kind.value
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if kind == "ma":
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return level.period in enabled.get("ma", {}).get(level.tf.value, [])
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if kind == "manual":
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return enabled.get("manual", True)
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if kind == "trendline":
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return enabled.get("auto", False)
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if kind == "horizontal":
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return enabled.get("horizontal", True)
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if kind == "vwap":
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return enabled.get("vwap", True)
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return False
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def enabled_levels(runtime, prefs: dict | None):
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if not prefs or prefs.get("hidden_levels_score"):
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return runtime.levels
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enabled = prefs.get("enabled", {})
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return [level for level in runtime.levels if level_enabled(level, enabled)]
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def connection_clusters(runtime, prefs: dict | None):
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if runtime.price is None or runtime.stream.last_bar_t is None:
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return []
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return cluster_levels(
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enabled_levels(runtime, prefs), runtime.stream.last_bar_t, runtime.price, runtime.atr15
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)
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def snapshot(runtime, tf: Timeframe, prefs: dict | None = None) -> dict:
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return {
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"type": "snapshot",
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"tf": tf.value,
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"bars": [bar.to_dict() for bar in runtime.store.get(tf, 1000)],
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"levels": [level.to_dict() for level in runtime.levels],
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"clusters": [cluster.to_dict() for cluster in connection_clusters(runtime, prefs)],
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"price": runtime.store.get(Timeframe.M1, 1)[-1].c
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if runtime.store.get(Timeframe.M1, 1)
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else None,
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}
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@router.websocket("/ws")
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async def websocket_endpoint(websocket: WebSocket):
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# Browsers cannot set headers on a WebSocket handshake, so the token comes
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# in as a query parameter here. 1008 = policy violation.
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if not token_matches(websocket.app, websocket.query_params.get("token", "")):
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await websocket.close(code=1008, reason="Missing or invalid chart token")
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return
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await websocket.accept()
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runtime = websocket.app.state.runtime
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queue: asyncio.Queue = asyncio.Queue(maxsize=100)
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runtime.subscribers.add(queue)
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tf = Timeframe.M1
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prefs = None
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alert_engine = AlertEngine(
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runtime.settings.confluence_min_score, runtime.settings.alert_cooldown_seconds
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)
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await websocket.send_json(snapshot(runtime, tf, prefs))
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async def receive():
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nonlocal tf, prefs
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try:
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while True:
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message = await websocket.receive_json()
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if message.get("type") == "subscribe":
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tf = Timeframe(message.get("tf", "1m"))
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await websocket.send_json(snapshot(runtime, tf, prefs))
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elif message.get("type") == "prefs":
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prefs = message
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clusters = connection_clusters(runtime, prefs)
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await websocket.send_json(
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{
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"type": "clusters",
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"price": runtime.price,
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"clusters": [cluster.to_dict() for cluster in clusters],
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}
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)
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except WebSocketDisconnect:
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queue.put_nowait({"type": "disconnect"})
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receiver = asyncio.create_task(receive())
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try:
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while True:
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event = await queue.get()
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if event["type"] == "disconnect":
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break
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if event["type"] == "bar" and event["bar"].tf is tf:
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await websocket.send_json(
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{"type": "bar", "tf": tf.value, "bar": event["bar"].to_dict()}
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)
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elif event["type"] == "levels":
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await websocket.send_json(
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{"type": "levels", "changed": event["changed"], "removed": event["removed"]}
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)
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elif event["type"] == "clusters":
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clusters = connection_clusters(runtime, prefs)
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await websocket.send_json(
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{
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"type": "clusters",
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"price": runtime.price,
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"clusters": [cluster.to_dict() for cluster in clusters],
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}
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)
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alerts = (
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alert_engine.evaluate(
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clusters,
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runtime.price,
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runtime.atr15,
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runtime.stream.last_bar_t or 0,
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runtime.stream.symbol,
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)
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if event.get("evaluate_alerts")
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else []
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)
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for alert in alerts:
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await websocket.send_json(
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{"type": "alert", "cluster": alert.cluster.to_dict(), "message": alert.message}
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)
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await send_ntfy(
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runtime.settings.ntfy_server, runtime.settings.ntfy_topic, alert.message
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)
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except (WebSocketDisconnect, asyncio.CancelledError):
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pass
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finally:
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receiver.cancel()
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runtime.subscribers.discard(queue)
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