Chris Amow
32e25b84aa
Alerts get a number, assigned server-side and shown in both the push and the Events list, so a notification on a phone can be matched to a row on a screen when several fire together. It could not come from the browser: that counter restarts on reload and differs between tabs. It is persisted next to the cooldown state, because numbering restarting after a deploy would collide with a phone's existing notification history — which changed that file from a list to an object, with the loader still reading the old shape. Pushes now carry a timestamp in the configured zone rather than the server's. ALERT_TIMEZONE defaults to America/Chicago; containers run UTC, and a push reading 02:14 to someone seeing 21:14 costs a translation every time. The browser already formats its own times locally and is unchanged. Confluence zones are one line each, ordered by price rather than by proximity, so the list reads top to bottom the way the chart does and all of them fit on screen — sixteen zones in 394px, about 25px each, where each previously took a four-line block. Ordering is a display concern only: the server still returns them nearest-first, which is what the alert path wants. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
94 lines
3.8 KiB
Python
94 lines
3.8 KiB
Python
from pathlib import Path
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from pydantic_settings import BaseSettings, SettingsConfigDict
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from app.bars.models import Timeframe
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TIMEFRAME_WEIGHT = {
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Timeframe.M1: 1,
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Timeframe.M2: 1,
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Timeframe.M5: 1,
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Timeframe.M15: 2,
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Timeframe.M30: 3,
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Timeframe.H1: 4,
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Timeframe.D1: 16,
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}
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MA_WEIGHT_FACTOR = 0.75
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class Settings(BaseSettings):
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model_config = SettingsConfigDict(env_file=".env", extra="ignore")
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live_source: str = "yahoo"
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seed_source: str = "yahoo"
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yahoo_symbol: str = "ES=F"
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yahoo_poll_seconds: float = 20
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seed_1h_range: str = "730d"
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seed_30m_range: str = "60d"
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seed_1m_range: str = "8d"
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timeframes: str = "1m,5m,15m,30m,1h,1d"
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base_timeframes: str = "1m,30m,1d"
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max_bars_per_tf: int = 5000
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ma_sets__1d: str = "sma10,sma20,sma50,sma100,sma200"
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daily_anchor_et: str = "18:00"
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manual_lines_path: Path = Path("./data/manual_lines.json")
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# Schwab. Empty until the app's keys are issued; nothing reads them while
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# live_source is yahoo. The token lives under data/ so it lands on the
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# Coolify persistent volume — a rebuild would otherwise log you out, and
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# re-authenticating is an interactive browser flow.
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schwab_api_key: str = ""
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schwab_app_secret: str = ""
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schwab_callback_url: str = "https://chart.amow.com/api/qt"
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schwab_token_path: Path = Path("./data/.schwab_token.json")
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schwab_symbol: str = "/ES"
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# Seconds between forming-bar emissions built from LEVEL_ONE_FUTURES ticks.
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# Set negative to drop the Level 1 subscription and take closed minute bars
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# only. Measured in regular hours, /ES supplies a price-changing trade far
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# faster than this, so the value is the update rate: at 1.0 the throttle was
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# the limiter and the chart felt sluggish.
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schwab_tick_seconds: float = 0.25
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confluence_min_score: float = 28
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# Four hours, chosen from the sweep in scripts/calibrate_alerts.py. Suppression is
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# per price zone, so an unrelated zone still alerts immediately; this only
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# governs how often the *same* area repeats itself.
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alert_cooldown_seconds: int = 14400
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# On the persistent volume in production: suppression has to outlive a
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# deploy or every push re-fires every zone that currently qualifies.
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alert_state_path: Path = Path("./data/alert_state.json")
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ntfy_topic: str = ""
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ntfy_server: str = "https://ntfy.sh"
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chart_auth_token: str = ""
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chart_password: str = ""
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# Zone used for times a person reads rather than a machine: the timestamp in
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# an alert push. The container runs UTC; this is where you are.
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alert_timezone: str = "America/Chicago"
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replay_file: Path | None = None
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@property
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def live_symbol(self) -> str:
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"""What the live source calls the instrument.
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Yahoo wants ES=F, Schwab wants /ES. Seeding always uses the Yahoo
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symbol, because Yahoo is always the source of history.
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"""
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return self.schwab_symbol if self.live_source == "schwab" else self.yahoo_symbol
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@property
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def enabled_timeframes(self) -> list[Timeframe]:
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return [Timeframe(value.strip()) for value in self.timeframes.split(",") if value.strip()]
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@property
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def ma_sets(self) -> dict[Timeframe, list[tuple[str, int]]]:
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configured = {Timeframe.D1: self.ma_sets__1d}
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result: dict[Timeframe, list[tuple[str, int]]] = {}
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for tf, value in configured.items():
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definitions = []
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for item in filter(None, (part.strip().lower() for part in value.split(","))):
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kind = "sma" if item.startswith("sma") else "ema" if item.startswith("ema") else ""
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if not kind or not item[len(kind) :].isdigit():
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raise ValueError(f"Invalid MA definition: {item}")
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definitions.append((kind, int(item[len(kind) :])))
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result[tf] = definitions
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return result
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