6183159974
- Install Prometheus + Grafana via kube-prometheus-stack (ClusterIP only, no public ingress) - Deploy Tailscale Operator for secure VPN access to internal services - Add CNPG/PostgreSQL monitoring dashboards - Add vLLM inference monitoring dashboards (tokens, latency, GPU) - Add Cilium networking dashboards (policy, traffic, drops) - Update infra-controllers staging kustomization to include all controllers - Add monitoring-configs Flux sync for dashboard deployment - Update README with monitoring architecture and access instructions - Remove broken stale monitoring files (Azure Key Vault refs, wrong domains) Access: kubectl port-forward or Tailscale VPN (replace auth key before deploy)
305 lines
12 KiB
Python
305 lines
12 KiB
Python
#!/usr/bin/env python3
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"""
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ORB (Opening Range Breakout) Monitor for Futures
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=================================================
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Monitors session opens across global markets and detects ORB signals.
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Sessions monitored (ET / UTC-4):
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• Asia (Tokyo) — 19:00 ET (23:00 UTC)
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• London — 03:00 ET (07:00 UTC)
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• New York — 09:30 ET (13:30 UTC)
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Strategy: After the session opens, the opening range high/low is captured
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over `range_minutes`. If price later breaks above/below that range, an
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ORB signal is logged.
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⚠️ EDUCATIONAL PURPOSE ONLY — Not financial advice.
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Uses yfinance (delayed data). NOT suitable for live trading.
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"""
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from __future__ import annotations
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import sys
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import logging
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from datetime import datetime, timedelta, timezone
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import yaml
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import pandas as pd
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import yfinance as yf
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# ─── Constants ────────────────────────────────────────────────────────────────
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# Yahoo Finance futures tickers
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SYMBOLS = {
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"ES": {"ticker": "ES=F", "name": "S&P 500 E-mini", "multiplier": 0.25},
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"NQ": {"ticker": "NQ=F", "name": "Nasdaq 100 E-mini", "multiplier": 0.25},
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"YM": {"ticker": "YM=F", "name": "Dow E-mini", "multiplier": 0.05},
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"CL": {"ticker": "CL=F", "name": "Crude Oil WTI", "multiplier": 0.01},
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"GC": {"ticker": "GC=F", "name": "Gold", "multiplier": 0.10},
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}
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# Session open times in UTC (no DST ambiguity)
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SESSIONS = {
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"asia": {"name": "Asia (Tokyo)", "open_utc": 23, "offset_min": 0},
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"london": {"name": "London", "open_utc": 7, "offset_min": 0},
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"ny": {"name": "New York", "open_utc": 13, "offset_min": 30},
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}
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UTC = timezone.utc
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logger = logging.getLogger("ORB")
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# ─── Helpers ──────────────────────────────────────────────────────────────────
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def load_config(path: str = "config.yaml") -> dict:
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"""Load YAML configuration."""
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try:
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with open(path) as fh:
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return yaml.safe_load(fh)
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except FileNotFoundError:
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logger.warning("config.yaml not found — using defaults")
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return {}
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def session_utc_start(date: datetime, session: dict) -> datetime:
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"""Return UTC datetime when this session opens on the given UTC date."""
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return datetime(date.year, date.month, date.day,
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session["open_utc"], session["offset_min"],
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tzinfo=UTC)
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def fetch_data(ticker: str, days: int = 5) -> pd.DataFrame:
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"""Fetch intraday futures data from Yahoo Finance (1-min bars)."""
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end = datetime.now(UTC)
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start = end - timedelta(days=days)
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try:
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df = yf.download(ticker, start=start, end=end,
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interval="1m", progress=False, auto_adjust=True)
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if df.empty:
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logger.warning(f"No data returned for {ticker}")
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return df
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# Flatten MultiIndex columns (yf sometimes returns ('Close', ticker), etc.)
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if isinstance(df.columns, pd.MultiIndex):
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df.columns = [col[0] for col in df.columns]
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return df
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except Exception as e:
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logger.error(f"Failed to fetch {ticker}: {e}")
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return pd.DataFrame()
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def find_session_bars(df: pd.DataFrame, session_start: datetime,
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range_minutes: int) -> pd.DataFrame | None:
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"""Extract the opening-range bars for a session, if data exists."""
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# Allow ±2 min tolerance for session start
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tolerance = timedelta(minutes=2)
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end_bound = session_start + timedelta(minutes=range_minutes) + tolerance
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mask = (df.index >= session_start - tolerance) & \
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(df.index < end_bound)
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range_bars = df.loc[mask]
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return range_bars if len(range_bars) >= 5 else None # Need meaningful data
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def analyze_orb(symbol_key: str, symbol_info: dict, df: pd.DataFrame,
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session_key: str, session_info: dict,
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cfg_orb: dict, date: datetime) -> list[dict]:
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"""Check for ORB signals in the data for a given session date."""
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range_min = cfg_orb.get("range_minutes", 30)
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min_range = cfg_orb.get("min_range_ticks", 4)
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max_range = cfg_orb.get("max_range_ticks", 100)
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multiplier = symbol_info["multiplier"]
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signals: list[dict] = []
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session_start = session_utc_start(date, session_info)
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# Try both start date and day before (in case of overnight sessions)
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for offset in [0, -1]:
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check_date = date + timedelta(days=offset)
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try_start = datetime(check_date.year, check_date.month, check_date.day,
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session_info["open_utc"], session_info["offset_min"],
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tzinfo=UTC)
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range_bars = find_session_bars(df, try_start, range_min)
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if range_bars is None:
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continue
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# Opening range high/low — force scalar extraction
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range_high = range_bars["High"].max().item()
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range_low = range_bars["Low"].min().item()
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range_size = range_high - range_low
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range_ticks = range_size / multiplier
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if range_ticks < min_range or range_ticks > max_range:
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continue # Skip — range too small or too large
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# Look for breakout in remaining data after range period
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# Skip NaN rows and only use real data
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remaining = df.loc[range_bars.index[-1]:].dropna(subset=["Close"])
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if remaining.empty:
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continue
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# Bullish breakout: price closes above range high
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bullish_bars = remaining[remaining["Close"] > range_high]
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if not bullish_bars.empty:
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breakout_time = bullish_bars.index[0]
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breakout_price = float(bullish_bars.loc[breakout_time, "Close"])
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signals.append({
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"symbol": symbol_key,
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"name": symbol_info["name"],
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"session": session_info["name"],
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"direction": "LONG",
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"range_high": round(range_high, 2),
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"range_low": round(range_low, 2),
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"range_size": round(range_size, 2),
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"range_ticks": round(range_ticks, 1),
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"breakout_time": breakout_time,
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"breakout_price": round(breakout_price, 2),
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})
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# Bearish breakout: price closes below range low
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bearish_bars = remaining[remaining["Close"] < range_low]
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if not bearish_bars.empty:
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breakout_time = bearish_bars.index[0]
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breakout_price = float(bearish_bars.loc[breakout_time, "Close"])
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signals.append({
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"symbol": symbol_key,
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"name": symbol_info["name"],
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"session": session_info["name"],
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"direction": "SHORT",
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"range_high": round(range_high, 2),
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"range_low": round(range_low, 2),
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"range_size": round(range_size, 2),
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"range_ticks": round(range_ticks, 1),
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"breakout_time": breakout_time,
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"breakout_price": round(breakout_price, 2),
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})
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return signals
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# ─── Main ─────────────────────────────────────────────────────────────────────
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def run(date_str: str | None = None, days: int = 5,
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config_path: str = "config.yaml") -> list[dict]:
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"""
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Analyze ORB patterns for all sessions and symbols.
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Args:
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date_str: Optional date in YYYY-MM-DD format. If None, uses today.
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days: How many days of history to fetch.
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config_path: Path to config.yaml.
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Returns:
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List of signal dicts sorted by breakout_time.
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"""
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cfg = load_config(config_path)
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cfg_orb = cfg.get("orb", {})
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target_date = datetime.strptime(date_str, "%Y-%m-%d").replace(tzinfo=UTC) if date_str else datetime.now(UTC)
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# Expand search window to cover all sessions around the target date
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search_dates = [target_date + timedelta(days=d) for d in range(-1, days)]
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all_signals: list[dict] = []
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symbol_items = cfg.get("symbols", SYMBOLS) or SYMBOLS
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for sym_key, sym_info in symbol_items.items():
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ticker = sym_info.get("ticker", f"{sym_key}=F")
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print(f" Fetching {ticker} ({sym_info.get('name', sym_key)}) …", flush=True)
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df = fetch_data(ticker, days=days)
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if df.empty:
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continue
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# Reconcile multiplier from config vs hardcoded
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sym_info.setdefault("multiplier", SYMBOLS.get(sym_key, {}).get("multiplier", 0.25))
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for sd in search_dates:
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for sess_key, sess_info in SESSIONS.items():
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signals = analyze_orb(
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sym_key, sym_info, df,
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sess_key, sess_info, cfg_orb, sd
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)
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all_signals.extend(signals)
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# Sort by breakout time
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all_signals.sort(key=lambda s: s["breakout_time"])
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return all_signals
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def format_report(signals: list[dict]) -> str:
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"""Pretty-print ORB signals for Telegram / terminal."""
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if not signals:
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return (
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"📊 **ORB Scan Complete — No Signals Found**\n\n"
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"No opening range breakouts detected in the scanned period.\n"
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"The market may be quiet, or the data may be too delayed.\n\n"
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"*Run again closer to session opens for best results.*"
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)
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lines = [
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f"📊 **ORB Signals Found** ({len(signals)} signals)",
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f"_Scanned: {datetime.now(UTC).strftime('%Y-%m-%d %H:%M UTC')}_",
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"─" * 40,
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]
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for s in signals:
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direction = "🟢 LONG" if s["direction"] == "LONG" else "🔴 SHORT"
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lines.append(
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f"\n**{s['symbol']}** ({s['name']}) — {s['session']}\n"
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f"{direction}\n"
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f" Range: {s['range_low']} – {s['range_high']} "
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f"({s['range_size']} pts / {s['range_ticks']} ticks)\n"
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f" Breakout: {s['breakout_price']} at "
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f"`{s['breakout_time'].strftime('%H:%M UTC')}`"
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)
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lines.append("\n" + "─" * 40)
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lines.append(
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"⚠️ _Educational analysis only. Uses delayed data._\n"
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"_Not financial advice. Verify with live data before trading._"
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)
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return "\n".join(lines)
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# ─── CLI ──────────────────────────────────────────────────────────────────────
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def main():
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logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
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import argparse
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parser = argparse.ArgumentParser(description="ORB Futures Monitor")
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parser.add_argument("--date", type=str, default=None,
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help="Target date YYYY-MM-DD (default: today)")
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parser.add_argument("--days", type=int, default=5,
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help="Days of history to scan (default: 5)")
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parser.add_argument("--config", type=str, default="config.yaml",
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help="Path to config file")
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parser.add_argument("--json", action="store_true",
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help="Output as JSON instead of formatted text")
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args = parser.parse_args()
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print("\n🔍 ORB Monitor — Scanning futures data …\n", flush=True)
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signals = run(date_str=args.date, days=args.days, config_path=args.config)
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if args.json:
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import json
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print(json.dumps(signals, indent=2, default=str))
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else:
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report = format_report(signals)
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print(report)
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# Also save to log
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log_path = "orb_signals.log"
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with open(log_path, "a") as fh:
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fh.write(f"\n{'='*50}\n")
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fh.write(f"Scan: {datetime.now(UTC).isoformat()}\n")
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fh.write(report + "\n")
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print(f"\n✅ Done. {len(signals)} signals detected.\n", flush=True)
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if __name__ == "__main__":
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main()
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