Oanda demo daemon: auto-execution on practice account (XAU/USD)

New independent trading daemon (oanda_daemon.py) that places orders on
an Oanda v20 practice account instead of sending Telegram alerts.
Oanda-only market data (oanda_provider.py, mid candles + real bid/ask
spread) through the unchanged signal pipeline; order execution and
trade management via oanda_executor.py (raw requests, no new deps).

Book parity with the backtester: no broker-side TP (exits = SL/TRAIL/
TIMEOUT only), bar-close-only trail ratchet with each closed bar
processed exactly once, 5-bar per-TF entry cooldown, structural SL
from the setup. Accepted divergence: one position total (skipped
signals logged for book reconciliation).

Safety/ops: fail-fast boot on rejected credentials (transient outages
continue), market orders never retried (double-fill risk), entry lock
against aligned-boundary races, restart recovery + adoption via
oanda_state.json, hourly heartbeat, asyncio exception handler, every
failure path logged. Prices formatted at instrument precision.

Config: env OANDA_* -> config.json "oanda" section (validated in
app_config). Tests: Module 15 (30 tests), suite 240 green.

Claude-Session: https://claude.ai/code/session_01VUnCqjWBuvKRSjLwHeB9BJ

.gitignore

@@ -3,6 +3,9 @@
 .env.local
 config.json
 
+# Oanda daemon state
+oanda_state.json
+
 # Editor / tooling
 .claude/
 .vscode/

app_config.py

@@ -85,7 +85,11 @@ _VALID_LOG_LEVELS = ("DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL")
 # AUTOSCAN_CONFIRMATIONS entries in telegram_bot.py).
 _SCANNABLE_TFS = ("M5", "M15", "H1", "H4")
 
-_KNOWN_TOP = {"telegram", "data_providers", "scanning", "pairs", "log_level"}
+_KNOWN_TOP = {"telegram", "data_providers", "scanning", "pairs", "log_level", "oanda"}
+_KNOWN_OANDA = {
+    "account_id", "api_token", "base_url", "lot_size",
+    "quality_gate", "max_hold_bars",
+}
 _KNOWN_TELEGRAM = {"token", "chat_id", "channel_id", "alert_min_quality"}
 _KNOWN_SCANNING = {"pairs", "timeframes"}
 _KNOWN_PAIR_KEYS = {"blocked_hours_utc"}
@@ -107,6 +111,7 @@ class AppConfig:
     scan_timeframes: List[Timeframe] = field(default_factory=lambda: [
         Timeframe.M5, Timeframe.M15, Timeframe.H1, Timeframe.H4])
     pair_overrides: Dict[str, Dict[str, Any]] = field(default_factory=dict)
+    oanda_settings: Dict[str, Any] = field(default_factory=dict)
     log_level: str = "INFO"
 
 
@@ -212,6 +217,24 @@ def load_app_config(path: str = CONFIG_PATH) -> AppConfig:
                     "be a list of integers in 0-23")
     app.pair_overrides = {s: dict(o) for s, o in pair_section.items()}
 
+    oanda = raw.get("oanda", {})
+    _check_keys(oanda, _KNOWN_OANDA, '"oanda"')
+    if "lot_size" in oanda:
+        ls = oanda["lot_size"]
+        if not isinstance(ls, int) or isinstance(ls, bool) or ls < 1:
+            raise AppConfigError(
+                "config.json: oanda.lot_size must be a positive integer")
+    if "quality_gate" in oanda and oanda["quality_gate"] not in _VALID_QUALITIES:
+        raise AppConfigError(
+            f'config.json: oanda.quality_gate "{oanda["quality_gate"]}" '
+            f"not in {_VALID_QUALITIES}")
+    if "max_hold_bars" in oanda:
+        mhb = oanda["max_hold_bars"]
+        if not isinstance(mhb, int) or isinstance(mhb, bool) or mhb < 1:
+            raise AppConfigError(
+                "config.json: oanda.max_hold_bars must be a positive integer")
+    app.oanda_settings = dict(oanda)
+
     level = raw.get("log_level", app.log_level)
     if level not in _VALID_LOG_LEVELS:
         raise AppConfigError(
@@ -271,6 +294,26 @@ def provider_api_key(app: Optional[AppConfig] = None) -> str:
     return str(block.get("api_key", ""))
 
 
+# ── Oanda resolution helpers (env → config.json → default) ───────────
+
+def oanda_account_id(app: Optional[AppConfig] = None) -> str:
+    app = app or get_app_config()
+    return os.environ.get("OANDA_ACCOUNT_ID") or str(
+        app.oanda_settings.get("account_id", ""))
+
+
+def oanda_api_token(app: Optional[AppConfig] = None) -> str:
+    app = app or get_app_config()
+    return os.environ.get("OANDA_API_TOKEN") or str(
+        app.oanda_settings.get("api_token", ""))
+
+
+def oanda_base_url(app: Optional[AppConfig] = None) -> str:
+    app = app or get_app_config()
+    return os.environ.get("OANDA_BASE_URL") or str(
+        app.oanda_settings.get("base_url", "https://api-fxpractice.oanda.com"))
+
+
 # ── PlatformConfig overlay ────────────────────────────────────────────
 
 def apply_pair_overrides(

config.json.example

@@ -20,5 +20,13 @@
       "blocked_hours_utc": [0, 1, 2, 10, 11, 13, 16, 21]
     }
   },
+  "oanda": {
+    "account_id": "REPLACE_WITH_ACCOUNT_ID",
+    "api_token": "REPLACE_WITH_API_TOKEN",
+    "base_url": "https://api-fxpractice.oanda.com",
+    "lot_size": 1,
+    "quality_gate": "C",
+    "max_hold_bars": 300
+  },
   "log_level": "INFO"
 }

oanda_daemon.py

@@ -0,0 +1,775 @@
+"""
+ForexBot2 — Oanda Automated Trading Daemon
+============================================
+Autonomous trading process for XAU/USD on an Oanda practice account.
+Runs candle-boundary scan loops (M5/M15/H1/H4), executes trades via
+the Oanda v20 API, and manages trailing stops at bar close — matching
+the backtested bar-close ratchet model.
+
+Book-parity notes (matching backtest/gpu_backtest.py):
+- No broker-side take-profit: in the trailing model TP1/TP2 are
+  advisory; the only exits are SL (broker), TRAIL (daemon ratchet),
+  and TIMEOUT (max_hold_bars). A hard TP would cap winners the trail
+  lets run and diverge the live book from the backtest.
+- Entry cooldown is 5 bars per timeframe (the backtester's only
+  sequential rule) — not a fingerprint block.
+- The trail ratchets at bar CLOSE only, and each closed bar is
+  processed exactly once (last_bar_ts guard), so weekends and feed
+  lag can't double-count bars or inflate the hold counter.
+
+Divergence from the backtest book (accepted for demo safety): one
+position total. While a trade is open, other signals are logged and
+skipped, so skipped entries can be compared against the book later.
+
+Usage:  python oanda_daemon.py
+Config: env vars OANDA_ACCOUNT_ID, OANDA_API_TOKEN, OANDA_BASE_URL
+        or config.json "oanda" section (env wins).
+"""
+
+import asyncio
+import json
+import logging
+import os
+import signal
+import sys
+import threading
+from concurrent.futures import ThreadPoolExecutor
+from datetime import datetime, timezone
+from logging.handlers import TimedRotatingFileHandler
+from typing import Dict, Optional
+
+import pandas as pd
+
+from app_config import (
+    get_app_config, oanda_account_id, oanda_api_token, oanda_base_url,
+)
+from config import PAIR_INFO, PlatformConfig, Timeframe, XAUUSD
+from signal_aggregator import SignalAggregator, SignalReport, SignalStrength
+from pair_signals import PAIR_PRODUCERS
+from oanda_provider import OandaDataProvider
+from oanda_executor import OandaExecutor, OandaAPIError
+
+logger = logging.getLogger(__name__)
+
+# ── Constants ──────────────────────────────────────────────────────────
+
+SYMBOL = XAUUSD
+INSTRUMENT = "XAU_USD"
+
+SCAN_CONFIRMATIONS = {
+    Timeframe.M5:  [Timeframe.M15, Timeframe.H1],
+    Timeframe.M15: [Timeframe.M5, Timeframe.H1],
+    Timeframe.H1:  [Timeframe.M15, Timeframe.H4],
+    Timeframe.H4:  [Timeframe.H1, Timeframe.D1],
+}
+
+TF_INTERVAL_MINUTES = {
+    Timeframe.M5: 5, Timeframe.M15: 15, Timeframe.M30: 30,
+    Timeframe.H1: 60, Timeframe.H4: 240, Timeframe.D1: 1440,
+}
+
+SCAN_TIMEFRAMES = [Timeframe.M5, Timeframe.M15, Timeframe.H1, Timeframe.H4]
+
+QUALITY_RANK = {"A+": 4, "A": 3, "B": 2, "C": 1, "D": 0, "F": -1}
+
+# Matches the backtester's --cooldown-bars default (the only sequential
+# rule in the replay): no new entry within 5 bars of the previous entry
+# on the same timeframe.
+COOLDOWN_BARS = 5
+
+# How often the heartbeat confirms the process is alive and the account
+# is still reachable — independent of the candle-boundary loops, so a
+# wedged scan or mid-run credential expiry still surfaces in the log.
+HEARTBEAT_SECONDS = 3600
+
+STATE_FILE = os.path.join(
+    os.path.dirname(os.path.abspath(__file__)), "oanda_state.json")
+
+# ── Config resolution (env → config.json → default) ──────────────────
+
+def _oanda_setting_int(env_key: str, json_key: str, default: int) -> int:
+    env = os.environ.get(env_key)
+    if env:
+        return int(env)
+    return int(get_app_config().oanda_settings.get(json_key, default))
+
+
+def _oanda_setting_str(env_key: str, json_key: str, default: str) -> str:
+    return (os.environ.get(env_key)
+            or str(get_app_config().oanda_settings.get(json_key, default)))
+
+# ── Logging setup ──────────────────────────────────────────────────────
+
+def _setup_logging(level: str = "INFO") -> None:
+    log_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "logs")
+    os.makedirs(log_dir, exist_ok=True)
+    log_file = os.path.join(log_dir, "daemon.log")
+
+    fmt = logging.Formatter(
+        "%(asctime)s [%(levelname)s] %(name)s: %(message)s",
+        datefmt="%Y-%m-%d %H:%M:%S")
+
+    file_handler = TimedRotatingFileHandler(
+        log_file, when="h", interval=3, backupCount=240, utc=True)
+    file_handler.setFormatter(fmt)
+
+    console_handler = logging.StreamHandler(sys.stdout)
+    console_handler.setFormatter(fmt)
+
+    logging.basicConfig(
+        level=getattr(logging, level.upper(), logging.INFO),
+        handlers=[file_handler, console_handler],
+        force=True)
+
+# ── Boundary timing (same as telegram_bot.py) ─────────────────────────
+
+def _seconds_until_boundary(interval_minutes: int, buffer: int = 10) -> float:
+    now = datetime.now(timezone.utc)
+    total_minutes = now.hour * 60 + now.minute
+    elapsed_minutes = total_minutes % interval_minutes
+    elapsed_seconds = elapsed_minutes * 60 + now.second + now.microsecond / 1e6
+    interval_seconds = interval_minutes * 60
+    remaining = interval_seconds - elapsed_seconds + buffer
+    if remaining <= buffer:
+        remaining += interval_seconds
+    return remaining
+
+# ── Trade state persistence ───────────────────────────────────────────
+
+def _load_state() -> Dict:
+    if os.path.exists(STATE_FILE):
+        try:
+            with open(STATE_FILE, encoding="utf-8") as fh:
+                return json.load(fh)
+        except (OSError, json.JSONDecodeError):
+            logger.warning("Could not load state file, starting fresh")
+    return {"trades": {}, "last_entries": {}}
+
+
+def _save_state(state: Dict) -> None:
+    try:
+        tmp = STATE_FILE + ".tmp"
+        with open(tmp, "w", encoding="utf-8") as fh:
+            json.dump(state, fh, indent=2)
+        os.replace(tmp, STATE_FILE)
+    except OSError:
+        logger.exception("Failed to save state file")
+
+# ── Trading Daemon ─────────────────────────────────────────────────────
+
+class TradingDaemon:
+
+    def __init__(self):
+        account_id = oanda_account_id()
+        api_token = oanda_api_token()
+        base_url = oanda_base_url()
+        lot_size = _oanda_setting_int("OANDA_LOT_SIZE", "lot_size", 1)
+        self._quality_gate = _oanda_setting_str(
+            "OANDA_QUALITY_GATE", "quality_gate", "C")
+        self._max_hold_bars = _oanda_setting_int(
+            "OANDA_MAX_HOLD_BARS", "max_hold_bars", 300)
+        self._price_precision = PAIR_INFO[SYMBOL]["price_precision"]
+
+        if not account_id or not api_token:
+            msg = (
+                "Missing Oanda credentials. Set OANDA_ACCOUNT_ID and "
+                "OANDA_API_TOKEN env vars or config.json oanda section.")
+            logger.critical(msg)
+            raise SystemExit(msg)
+
+        self._provider = OandaDataProvider(
+            PlatformConfig(), api_token, base_url)
+        self._executor = OandaExecutor(
+            account_id, api_token, base_url, lot_size,
+            price_precision=self._price_precision)
+        self._thread_pool = ThreadPoolExecutor(max_workers=4)
+
+        self._state = _load_state()
+        self._state.setdefault("trades", {})
+        self._state.setdefault("last_entries", {})
+        self._state_lock = threading.Lock()
+        # Serializes the position-check + order-placement critical section
+        # across concurrent TF loops (all four fire at aligned boundaries).
+        self._entry_lock = threading.Lock()
+        self._running = True
+
+    # ── Main entry point ───────────────────────────────────────────
+
+    async def run(self) -> None:
+        loop = asyncio.get_running_loop()
+        loop.set_exception_handler(self._asyncio_exception_handler)
+        for sig in (signal.SIGINT, signal.SIGTERM):
+            loop.add_signal_handler(sig, self._request_shutdown)
+
+        logger.info("=== Oanda Trading Daemon starting ===")
+        logger.info(
+            "Quality gate: %s | Max hold: %d bars | Cooldown: %d bars | "
+            "Timeframes: %s",
+            self._quality_gate, self._max_hold_bars, COOLDOWN_BARS,
+            "/".join(tf.name for tf in SCAN_TIMEFRAMES))
+
+        # Credentials first: with a bad token every later call fails, so
+        # refuse to start before touching positions or spawning loops.
+        self._verify_account_access()
+
+        self._discover_existing_positions()
+
+        tasks = []
+        for tf in SCAN_TIMEFRAMES:
+            interval = TF_INTERVAL_MINUTES[tf]
+            tasks.append(asyncio.create_task(
+                self._candle_loop(tf, interval)))
+        tasks.append(asyncio.create_task(self._heartbeat_loop()))
+
+        logger.info("Candle-boundary loops started")
+        try:
+            await asyncio.gather(*tasks)
+        except asyncio.CancelledError:
+            pass
+        finally:
+            self._shutdown()
+
+    def _verify_account_access(self) -> None:
+        """Boot-time credential check. A 4xx here means the token/account
+        id is wrong — that never fixes itself, so refuse to start rather
+        than run a daemon that can't place or manage trades. A connection
+        failure or 5xx is a transient outage: log it and continue, since
+        broker-side stops already protect any open position and the
+        hourly heartbeat will keep reporting account health."""
+        try:
+            self._executor.get_account_summary()
+        except OandaAPIError as e:
+            if 400 <= e.status_code < 500:
+                logger.critical(
+                    "Oanda rejected the account credentials (HTTP %d): "
+                    "%s — refusing to start", e.status_code, e)
+                raise SystemExit(
+                    f"Oanda rejected the account credentials "
+                    f"(HTTP {e.status_code}): {e}")
+            logger.error(
+                "Failed to fetch account summary at boot (HTTP %d): %s",
+                e.status_code, e)
+
+    def _asyncio_exception_handler(self, loop, context) -> None:
+        exc = context.get("exception")
+        if exc is not None:
+            logger.critical(
+                "Uncaught asyncio exception: %s",
+                context.get("message", ""), exc_info=exc)
+        else:
+            logger.critical(
+                "Uncaught asyncio error: %s", context.get("message", ""))
+
+    def _request_shutdown(self) -> None:
+        logger.info("Shutdown requested")
+        self._running = False
+        for task in asyncio.all_tasks():
+            task.cancel()
+
+    def _shutdown(self) -> None:
+        with self._state_lock:
+            _save_state(self._state)
+        self._thread_pool.shutdown(wait=False)
+        logger.info("=== Oanda Trading Daemon stopped ===")
+
+    # ── Position discovery (restart recovery) ──────────────────────
+
+    def _discover_existing_positions(self) -> None:
+        try:
+            open_trades = self._executor.get_open_trades(INSTRUMENT)
+        except OandaAPIError:
+            logger.exception("Failed to query open trades at boot")
+            return
+
+        saved = self._state.get("trades", {})
+        live_ids = {t["id"] for t in open_trades}
+
+        stale = [tid for tid in saved if tid not in live_ids]
+        for tid in stale:
+            logger.info(
+                "Stale trade %s in state file (closed on Oanda), removing", tid)
+            del saved[tid]
+
+        for trade in open_trades:
+            tid = trade["id"]
+            if tid in saved:
+                logger.info(
+                    "Recovered trade %s from state: %s %s @ %.2f, SL %.2f",
+                    tid, saved[tid].get("direction"), INSTRUMENT,
+                    saved[tid].get("entry"), saved[tid].get("current_sl"))
+                continue
+
+            direction = "BUY" if float(trade.get("currentUnits", 0)) > 0 else "SELL"
+            entry = float(trade.get("price", 0))
+            sl_order = trade.get("stopLossOrder", {})
+            sl = float(sl_order.get("price", entry))
+            risk = abs(entry - sl) if sl else 1.0
+
+            saved[tid] = {
+                "tf": "H1",
+                "direction": direction,
+                "entry": entry,
+                "sl_initial": sl,
+                "current_sl": sl,
+                "risk": risk,
+                "peak": entry,
+                "trail_armed": False,
+                "trail_arm_r": 0.6,
+                "trail_dist_r": 0.75,
+                "bars_held": 0,
+                "last_bar_ts": None,
+                "signal_bar_ts": None,
+            }
+            logger.info(
+                "Adopted unknown trade %s: %s @ %.2f, SL %.2f, risk %.2f "
+                "(conservative defaults)",
+                tid, direction, entry, sl, risk)
+
+        self._state["trades"] = saved
+        _save_state(self._state)
+        logger.info(
+            "Position discovery: %d active trade(s)", len(saved))
+
+    # ── Candle-boundary loop ───────────────────────────────────────
+
+    async def _candle_loop(self, tf: Timeframe, interval_minutes: int) -> None:
+        label = tf.value
+        try:
+            while self._running:
+                wait = _seconds_until_boundary(interval_minutes, buffer=10)
+                logger.info("%s loop: next scan in %.0fs", label, wait)
+                await asyncio.sleep(wait)
+                if not self._running:
+                    break
+                logger.info("%s loop: firing scan", label)
+                try:
+                    await self._scan_and_manage(tf)
+                except Exception:
+                    logger.exception("%s loop: cycle failed", label)
+        except asyncio.CancelledError:
+            pass
+        finally:
+            logger.info("%s loop stopped", label)
+
+    async def _scan_and_manage(self, tf: Timeframe) -> None:
+        loop = asyncio.get_running_loop()
+
+        # Trade management FIRST: the SL ratchet must not wait on (or be
+        # starved by) the signal scan — news fetches can take seconds and
+        # a failed scan must never cost an open trade its trail update.
+        try:
+            await loop.run_in_executor(
+                self._thread_pool, self._manage_open_trades, tf)
+        except Exception:
+            logger.exception("%s: trade management failed", tf.value)
+
+        report = await loop.run_in_executor(
+            self._thread_pool, self._run_signal, tf)
+
+        if not self._should_execute(report, tf):
+            return
+
+        await loop.run_in_executor(
+            self._thread_pool, self._execute_trade, report, tf)
+
+    def _manage_open_trades(self, tf: Timeframe) -> None:
+        self._check_closed_trades()
+        self._manage_trailing(tf)
+
+    # ── Heartbeat ─────────────────────────────────────────────────
+
+    def _heartbeat_once(self) -> None:
+        with self._state_lock:
+            n = len(self._state.get("trades", {}))
+        try:
+            self._executor.get_account_summary()
+        except OandaAPIError as e:
+            logger.error("HEARTBEAT: account summary failed: %s", e)
+        # Logged unconditionally — even on a failed summary call the
+        # heartbeat line itself must reach the log, so a wedged process
+        # doesn't look identical to a healthy but silent one.
+        logger.info("HEARTBEAT: alive | tracked trades: %d", n)
+
+    async def _heartbeat_loop(self) -> None:
+        loop = asyncio.get_running_loop()
+        try:
+            while self._running:
+                await asyncio.sleep(HEARTBEAT_SECONDS)
+                if not self._running:
+                    break
+                try:
+                    await loop.run_in_executor(
+                        self._thread_pool, self._heartbeat_once)
+                except Exception:
+                    logger.exception("heartbeat loop: cycle failed")
+        except asyncio.CancelledError:
+            pass
+        finally:
+            logger.info("heartbeat loop stopped")
+
+    # ── Signal pipeline ────────────────────────────────────────────
+
+    def _run_signal(self, tf: Timeframe) -> SignalReport:
+        conf_tfs = SCAN_CONFIRMATIONS.get(
+            tf, [Timeframe.M15, Timeframe.H4])
+        factory = PAIR_PRODUCERS.get(SYMBOL)
+        if factory is not None:
+            config = factory(tf, conf_tfs)
+        else:
+            config = PlatformConfig()
+            config.primary_timeframe = tf
+            config.confirmation_timeframes = conf_tfs
+
+        aggregator = SignalAggregator(config)
+        aggregator.data_provider = self._provider
+        report = aggregator.analyze_pair(symbol=SYMBOL)
+
+        logger.info(
+            "%s scan: signal=%s score=%.1f conf=%.0f quality=%s "
+            "regime=%s vol=%s price=%.2f spread=%.4f",
+            tf.value,
+            report.final_signal.name, report.final_score,
+            report.final_confidence, report.signal_quality,
+            report.market_regime, report.volatility_regime,
+            report.current_price,
+            self._provider.get_spread(SYMBOL))
+
+        # Stale-data/suppression warnings must be visible at WARNING;
+        # routine session-filter notices go to DEBUG — the M5 loop fires
+        # 288x/day and would drown the log at WARNING otherwise.
+        for w in report.warnings:
+            if "STALE" in w.upper() or "FAIL" in w.upper():
+                logger.warning("%s scan: %s", tf.value, w)
+            else:
+                logger.debug("%s scan: %s", tf.value, w)
+
+        return report
+
+    # ── Execution decision ─────────────────────────────────────────
+
+    def _should_execute(self, report: SignalReport, tf: Timeframe) -> bool:
+        if report.final_signal == SignalStrength.NEUTRAL:
+            return False
+
+        if report.trade_setup is None:
+            return False
+
+        quality_rank = QUALITY_RANK.get(report.signal_quality, -1)
+        gate_rank = QUALITY_RANK.get(self._quality_gate, 1)
+        if quality_rank < gate_rank:
+            return False
+
+        if self._in_cooldown(tf):
+            logger.info(
+                "%s: actionable signal (%s %s) but inside %d-bar entry "
+                "cooldown, skipping",
+                tf.value, report.trade_setup.direction,
+                report.signal_quality, COOLDOWN_BARS)
+            return False
+
+        with self._state_lock:
+            if self._state.get("trades"):
+                logger.info(
+                    "%s: actionable signal (%s %s) but position already "
+                    "open, skipping",
+                    tf.value, report.trade_setup.direction,
+                    report.signal_quality)
+                return False
+
+        return True
+
+    def _in_cooldown(self, tf: Timeframe) -> bool:
+        """Backtest parity: no entry within COOLDOWN_BARS bars of the
+        previous entry on the same timeframe (wall-clock approximation)."""
+        with self._state_lock:
+            last = self._state.get("last_entries", {}).get(tf.value)
+        if not last:
+            return False
+        try:
+            last_ts = datetime.fromisoformat(last)
+        except ValueError:
+            return False
+        interval_s = TF_INTERVAL_MINUTES[tf] * 60
+        elapsed = (datetime.now(timezone.utc) - last_ts).total_seconds()
+        return elapsed < COOLDOWN_BARS * interval_s
+
+    # ── Trade execution ────────────────────────────────────────────
+
+    def _execute_trade(self, report: SignalReport, tf: Timeframe) -> None:
+        ts = report.trade_setup
+        if ts is None:
+            return
+
+        # All four TF loops can fire at an aligned boundary (e.g. :00) and
+        # pass _should_execute before any of them has recorded a fill —
+        # re-check under the entry lock so only one order goes out.
+        with self._entry_lock:
+            with self._state_lock:
+                if self._state.get("trades"):
+                    logger.info(
+                        "%s: position opened by a concurrent scan, skipping",
+                        tf.value)
+                    return
+
+            # NO take-profit on the order: the backtested trailing model
+            # exits via SL/TRAIL/TIMEOUT only — a hard TP would cap
+            # winners and diverge the live book from the backtest.
+            try:
+                result = self._executor.place_market_order(
+                    instrument=INSTRUMENT,
+                    direction=ts.direction,
+                    stop_loss=ts.stop_loss,
+                )
+            except OandaAPIError:
+                logger.exception(
+                    "Failed to place %s order for %s %s",
+                    ts.direction, SYMBOL, tf.value)
+                return
+
+            fill_tx = result.get("orderFillTransaction")
+            if not fill_tx:
+                logger.warning(
+                    "Order not filled for %s %s", SYMBOL, tf.value)
+                return
+
+            trade_opened = fill_tx.get("tradeOpened", {})
+            trade_id = trade_opened.get("tradeID")
+            if not trade_id:
+                logger.warning("No trade ID in fill response")
+                return
+
+            fill_price = float(fill_tx.get("price", ts.entry_price))
+            risk = abs(fill_price - ts.stop_loss)
+
+            trail_arm = ts.trail_arm_r if ts.trail_arm_r is not None else 0.6
+            trail_dist = ts.trail_dist_r if ts.trail_dist_r is not None else 0.75
+
+            now_iso = datetime.now(timezone.utc).isoformat()
+            trade_state = {
+                "tf": tf.value,
+                "direction": ts.direction,
+                "entry": fill_price,
+                "sl_initial": ts.stop_loss,
+                "current_sl": ts.stop_loss,
+                "tp1": ts.take_profit_1,
+                "tp2": ts.take_profit_2,
+                "risk": risk,
+                "peak": fill_price,
+                "trail_armed": False,
+                "trail_arm_r": trail_arm,
+                "trail_dist_r": trail_dist,
+                "bars_held": 0,
+                # The signal bar is already closed; the first bar this
+                # trade lives through is the one that just started. Guard
+                # so a lagging feed can't trail on the signal bar itself.
+                "signal_bar_ts": report.timestamp,
+                "last_bar_ts": None,
+                "strategy": ts.strategy,
+                "quality": report.signal_quality,
+                "rr": ts.risk_reward,
+                "opened_at": now_iso,
+            }
+
+            with self._state_lock:
+                self._state["trades"][trade_id] = trade_state
+                self._state["last_entries"][tf.value] = now_iso
+                _save_state(self._state)
+
+        logger.info(
+            "TRADE OPENED: %s %s @ %.2f | SL %.2f | TP1 %.2f (advisory) | "
+            "risk $%.2f | RR %.2f | quality %s | strategy %s | TF %s | "
+            "trail arm %.2fR dist %.2fR",
+            ts.direction, SYMBOL, fill_price, ts.stop_loss,
+            ts.take_profit_1,
+            risk, ts.risk_reward, report.signal_quality,
+            ts.strategy, tf.value, trail_arm, trail_dist)
+
+    # ── Trailing stop management ───────────────────────────────────
+
+    def _manage_trailing(self, tf: Timeframe) -> None:
+        with self._state_lock:
+            trades = dict(self._state.get("trades", {}))
+
+        for trade_id, info in trades.items():
+            if info.get("tf") != tf.value:
+                continue
+
+            try:
+                df = self._provider.fetch_ohlcv(SYMBOL, tf)
+            except Exception:
+                logger.exception(
+                    "Failed to fetch candle data for trail update")
+                continue
+
+            if df.empty:
+                logger.warning(
+                    "%s: no candle data for trail update of trade %s",
+                    tf.value, trade_id)
+                continue
+
+            bar_ts = df.index[-1].isoformat()
+
+            # Process each closed bar exactly once. Weekends and feed lag
+            # re-serve the same last bar; without this guard bars_held
+            # inflates (an M5 trade would "time out" over a weekend) and
+            # the peak gets double-counted.
+            if bar_ts == info.get("last_bar_ts"):
+                continue
+            sig_ts = info.get("signal_bar_ts")
+            if sig_ts:
+                try:
+                    if df.index[-1] <= pd.Timestamp(sig_ts):
+                        continue
+                except (ValueError, TypeError):
+                    pass
+            info["last_bar_ts"] = bar_ts
+
+            info["bars_held"] = info.get("bars_held", 0) + 1
+
+            if info["bars_held"] >= self._max_hold_bars:
+                logger.info(
+                    "TIMEOUT: trade %s held %d bars, closing",
+                    trade_id, info["bars_held"])
+                try:
+                    self._executor.close_trade(trade_id)
+                except OandaAPIError:
+                    # Keep tracking — the trade is still live on the
+                    # broker; we retry the close at the next bar.
+                    logger.exception(
+                        "Failed to close timed-out trade %s — will retry",
+                        trade_id)
+                    with self._state_lock:
+                        self._state["trades"][trade_id] = info
+                        _save_state(self._state)
+                    continue
+                with self._state_lock:
+                    self._state["trades"].pop(trade_id, None)
+                    _save_state(self._state)
+                continue
+
+            last_bar = df.iloc[-1]
+            bar_high = float(last_bar["High"])
+            bar_low = float(last_bar["Low"])
+
+            new_sl = self._compute_trail(info, bar_high, bar_low)
+
+            if new_sl is not None:
+                # Round to instrument precision and track exactly what the
+                # broker holds, so tracked vs broker SL can never drift.
+                new_sl = round(new_sl, self._price_precision)
+                if new_sl != info["current_sl"]:
+                    try:
+                        self._executor.modify_trade_sl(trade_id, new_sl)
+                        info["current_sl"] = new_sl
+                    except OandaAPIError:
+                        logger.exception(
+                            "Failed to update trailing SL for trade %s",
+                            trade_id)
+
+            with self._state_lock:
+                self._state["trades"][trade_id] = info
+                _save_state(self._state)
+
+    def _compute_trail(
+        self, info: Dict, bar_high: float, bar_low: float
+    ) -> Optional[float]:
+        buy = info["direction"] == "BUY"
+        risk = info["risk"]
+        if risk <= 0:
+            return None
+        arm = info.get("trail_arm_r", 0.6)
+        dist = info.get("trail_dist_r", 0.75)
+        if arm <= 0:
+            return None
+
+        fav = bar_high if buy else bar_low
+        if buy:
+            info["peak"] = max(info.get("peak", info["entry"]), fav)
+        else:
+            info["peak"] = min(info.get("peak", info["entry"]), fav)
+
+        exc = ((info["peak"] - info["entry"]) if buy
+               else (info["entry"] - info["peak"])) / risk
+
+        if exc >= arm:
+            if buy:
+                lvl = info["peak"] - dist * risk
+                cur_sl = info.get("current_sl", info["sl_initial"])
+                if lvl > cur_sl:
+                    info["trail_armed"] = True
+                    return lvl
+            else:
+                lvl = info["peak"] + dist * risk
+                cur_sl = info.get("current_sl", info["sl_initial"])
+                if lvl < cur_sl:
+                    info["trail_armed"] = True
+                    return lvl
+
+        return None
+
+    # ── Closed-trade detection ─────────────────────────────────────
+
+    def _check_closed_trades(self) -> None:
+        with self._state_lock:
+            tracked = set(self._state.get("trades", {}).keys())
+
+        if not tracked:
+            return
+
+        try:
+            open_trades = self._executor.get_open_trades(INSTRUMENT)
+        except OandaAPIError:
+            logger.exception("Failed to query open trades")
+            return
+
+        live_ids = {t["id"] for t in open_trades}
+        closed = tracked - live_ids
+
+        for tid in closed:
+            with self._state_lock:
+                info = self._state["trades"].pop(tid, {})
+            entry = info.get("entry", 0)
+            direction = info.get("direction", "?")
+            risk = info.get("risk", 1)
+            sl = info.get("current_sl", 0)
+            was_trailed = info.get("trail_armed", False)
+
+            logger.info(
+                "TRADE CLOSED BY BROKER: %s %s %s | entry %.2f | "
+                "SL was %.2f | risk $%.2f | trailed=%s | bars_held=%d",
+                tid, direction, SYMBOL, entry, sl, risk,
+                was_trailed, info.get("bars_held", 0))
+
+        if closed:
+            with self._state_lock:
+                _save_state(self._state)
+
+# ── Uncaught exception handlers ───────────────────────────────────────
+
+def _excepthook(exc_type, exc_value, exc_tb):
+    if issubclass(exc_type, KeyboardInterrupt):
+        sys.__excepthook__(exc_type, exc_value, exc_tb)
+        return
+    logger.critical(
+        "Uncaught exception", exc_info=(exc_type, exc_value, exc_tb))
+
+def _threading_excepthook(args):
+    logger.critical(
+        "Uncaught exception in thread %s",
+        args.thread.name if args.thread else "?",
+        exc_info=(args.exc_type, args.exc_value, args.exc_traceback))
+
+# ── Entry point ────────────────────────────────────────────────────────
+
+def main() -> None:
+    _setup_logging(
+        os.environ.get("OANDA_LOG_LEVEL") or get_app_config().log_level)
+    sys.excepthook = _excepthook
+    threading.excepthook = _threading_excepthook
+
+    daemon = TradingDaemon()
+    asyncio.run(daemon.run())
+
+
+if __name__ == "__main__":
+    main()

oanda_executor.py

@@ -0,0 +1,212 @@
+"""
+ForexBot2 — Oanda v20 Order Executor
+======================================
+Handles all Oanda account operations: market orders, trade queries,
+SL/TP modification, and trade closure.  Uses raw ``requests`` against
+the Oanda v20 REST API — no extra dependencies.
+"""
+
+import logging
+import time
+from typing import Dict, List, Optional
+
+import requests
+
+logger = logging.getLogger(__name__)
+
+
+class OandaAPIError(Exception):
+    """Raised when an Oanda API call fails."""
+
+    def __init__(self, message: str, status_code: int = 0, body: str = ""):
+        super().__init__(message)
+        self.status_code = status_code
+        self.body = body
+
+
+class OandaExecutor:
+    """Oanda v20 order execution and trade management."""
+
+    MAX_RETRIES = 1
+    RETRY_DELAY = 2.0
+
+    def __init__(
+        self,
+        account_id: str,
+        api_token: str,
+        base_url: str = "https://api-fxpractice.oanda.com",
+        lot_size: int = 1,
+        price_precision: int = 2,
+    ):
+        self._account_id = account_id
+        self._base_url = base_url.rstrip("/")
+        self._lot_size = lot_size
+        self._price_precision = price_precision
+        self._session = requests.Session()
+        self._session.headers.update({
+            "Authorization": f"Bearer {api_token}",
+            "Accept-Datetime-Format": "RFC3339",
+            "Content-Type": "application/json",
+        })
+
+    # ── Order Placement ────────────────────────────────────────────
+
+    def place_market_order(
+        self,
+        instrument: str,
+        direction: str,
+        stop_loss: float,
+        take_profit: Optional[float] = None,
+        units: Optional[int] = None,
+    ) -> Dict:
+        if units is None:
+            units = self._lot_size
+        signed_units = str(units) if direction.upper() == "BUY" else str(-units)
+
+        order_body = {
+            "order": {
+                "type": "MARKET",
+                "instrument": instrument,
+                "units": signed_units,
+                "timeInForce": "FOK",
+                "stopLossOnFill": {
+                    "price": self._fmt_price(stop_loss),
+                },
+            }
+        }
+        if take_profit is not None:
+            order_body["order"]["takeProfitOnFill"] = {
+                "price": self._fmt_price(take_profit),
+            }
+
+        url = f"{self._base_url}/v3/accounts/{self._account_id}/orders"
+        # Never retry order placement: a timeout after the server already
+        # filled would open a SECOND position on retry. Fail loudly instead;
+        # the next candle boundary re-evaluates.
+        resp = self._request_with_retry("POST", url, json=order_body, retries=0)
+        data = self._parse_json(resp)
+
+        fill_tx = data.get("orderFillTransaction")
+        if fill_tx:
+            trade_id = fill_tx.get("tradeOpened", {}).get("tradeID", "unknown")
+            fill_price = float(fill_tx.get("price", 0))
+            logger.info(
+                "ORDER FILLED: %s %s %s units @ %.2f — trade %s, SL %.2f%s",
+                direction.upper(), instrument, signed_units, fill_price,
+                trade_id, stop_loss,
+                f", TP {take_profit:.2f}" if take_profit else "")
+        else:
+            cancel_tx = data.get("orderCancelTransaction", {})
+            reason = cancel_tx.get("reason", "unknown")
+            logger.warning(
+                "ORDER NOT FILLED: %s %s — reason: %s",
+                direction.upper(), instrument, reason)
+
+        return data
+
+    # ── Trade Queries ──────────────────────────────────────────────
+
+    def get_open_trades(self, instrument: Optional[str] = None) -> List[Dict]:
+        url = f"{self._base_url}/v3/accounts/{self._account_id}/openTrades"
+        resp = self._request_with_retry("GET", url)
+        trades = self._parse_json(resp).get("trades", [])
+        if instrument:
+            trades = [t for t in trades if t.get("instrument") == instrument]
+        return trades
+
+    def get_trade(self, trade_id: str) -> Dict:
+        url = (f"{self._base_url}/v3/accounts/{self._account_id}"
+               f"/trades/{trade_id}")
+        resp = self._request_with_retry("GET", url)
+        return self._parse_json(resp).get("trade", {})
+
+    def get_account_summary(self) -> Dict:
+        url = f"{self._base_url}/v3/accounts/{self._account_id}/summary"
+        resp = self._request_with_retry("GET", url)
+        account = self._parse_json(resp).get("account", {})
+        logger.info(
+            "Account: balance=%.2f, NAV=%.2f, unrealizedPL=%.2f, openTrades=%s",
+            float(account.get("balance", 0)),
+            float(account.get("NAV", 0)),
+            float(account.get("unrealizedPL", 0)),
+            account.get("openTradeCount", 0))
+        return account
+
+    # ── Trade Modification ─────────────────────────────────────────
+
+    def modify_trade_sl(self, trade_id: str, new_sl: float) -> Dict:
+        url = (f"{self._base_url}/v3/accounts/{self._account_id}"
+               f"/trades/{trade_id}/orders")
+        body = {"stopLoss": {"price": self._fmt_price(new_sl)}}
+        resp = self._request_with_retry("PUT", url, json=body)
+        logger.info(
+            "TRAIL UPDATE: trade %s — new SL %.2f", trade_id, new_sl)
+        return self._parse_json(resp)
+
+    def modify_trade_tp(self, trade_id: str, new_tp: float) -> Dict:
+        url = (f"{self._base_url}/v3/accounts/{self._account_id}"
+               f"/trades/{trade_id}/orders")
+        body = {"takeProfit": {"price": self._fmt_price(new_tp)}}
+        resp = self._request_with_retry("PUT", url, json=body)
+        logger.info("TP UPDATE: trade %s — new TP %.2f", trade_id, new_tp)
+        return self._parse_json(resp)
+
+    def close_trade(self, trade_id: str) -> Dict:
+        url = (f"{self._base_url}/v3/accounts/{self._account_id}"
+               f"/trades/{trade_id}/close")
+        resp = self._request_with_retry("PUT", url, json={"units": "ALL"})
+        data = self._parse_json(resp)
+        close_tx = data.get("orderFillTransaction", {})
+        close_price = float(close_tx.get("price", 0))
+        pl = float(close_tx.get("pl", 0))
+        logger.info(
+            "TRADE CLOSED: trade %s @ %.2f — PL %.2f",
+            trade_id, close_price, pl)
+        return data
+
+    # ── Internals ──────────────────────────────────────────────────
+
+    @staticmethod
+    def _parse_json(resp: requests.Response) -> Dict:
+        try:
+            return resp.json()
+        except ValueError as e:
+            raise OandaAPIError(
+                f"Oanda returned a non-JSON response (HTTP {resp.status_code})",
+                resp.status_code, resp.text[:300]) from e
+
+    def _fmt_price(self, price: float) -> str:
+        # Oanda rejects prices with more decimals than the instrument's
+        # displayPrecision (XAU_USD: sub-cent precision is invalid).
+        return f"{price:.{self._price_precision}f}"
+
+    def _request_with_retry(
+        self, method: str, url: str, retries: Optional[int] = None, **kwargs
+    ) -> requests.Response:
+        if retries is None:
+            retries = self.MAX_RETRIES
+        last_err = None
+        for attempt in range(1 + retries):
+            try:
+                resp = self._session.request(method, url, timeout=15, **kwargs)
+                if resp.status_code >= 500:
+                    raise OandaAPIError(
+                        f"Oanda server error {resp.status_code}",
+                        resp.status_code, resp.text)
+                if resp.status_code >= 400:
+                    raise OandaAPIError(
+                        f"Oanda API error {resp.status_code}: {resp.text}",
+                        resp.status_code, resp.text)
+                return resp
+            except (requests.ConnectionError, requests.Timeout,
+                    OandaAPIError) as e:
+                last_err = e
+                if isinstance(e, OandaAPIError) and 400 <= e.status_code < 500:
+                    raise
+                if attempt < retries:
+                    logger.warning(
+                        "Oanda request failed (attempt %d/%d): %s — retrying in %.0fs",
+                        attempt + 1, 1 + retries, e,
+                        self.RETRY_DELAY)
+                    time.sleep(self.RETRY_DELAY)
+        raise OandaAPIError(f"Oanda request failed after retries: {last_err}")

oanda_provider.py

@@ -0,0 +1,361 @@
+"""
+ForexBot2 — Oanda v20 Data Provider
+====================================
+Drop-in replacement for ForexDataProvider that fetches OHLCV candles
+from the Oanda v20 REST API.  Returns the same DataFrame format
+(Open/High/Low/Close/Volume, UTC DatetimeIndex, oldest-first, closed
+bars only) so the signal pipeline (engine + aggregator) runs unchanged.
+
+Additionally exposes real bid/ask spread from Oanda's MBA candles.
+"""
+
+import logging
+import threading
+import time
+from typing import Dict, Optional
+
+import pandas as pd
+import requests
+
+from config import PAIR_INFO, PlatformConfig, Timeframe, XAUUSD
+
+logger = logging.getLogger(__name__)
+
+
+class OandaDataError(Exception):
+    """Raised when an Oanda data fetch fails or returns invalid data."""
+
+
+def _is_forex_weekend(now=None) -> bool:
+    if now is None:
+        now = pd.Timestamp.now(tz="UTC")
+    wd = now.weekday()
+    hour = now.hour
+    if wd == 4 and hour >= 22:
+        return True
+    if wd == 5:
+        return True
+    if wd == 6 and hour < 22:
+        return True
+    return False
+
+
+class OandaDataProvider:
+    """Oanda v20 REST candle provider with in-memory caching."""
+
+    OANDA_GRANULARITY = {
+        Timeframe.M1: "M1",
+        Timeframe.M5: "M5",
+        Timeframe.M15: "M15",
+        Timeframe.M30: "M30",
+        Timeframe.H1: "H1",
+        Timeframe.H4: "H4",
+        Timeframe.D1: "D",
+        Timeframe.W1: "W",
+    }
+
+    OANDA_INSTRUMENT = {
+        "XAU/USD": "XAU_USD",
+    }
+
+    BARS_PER_DAY = {
+        Timeframe.M1: 1440, Timeframe.M5: 288, Timeframe.M15: 96,
+        Timeframe.M30: 48, Timeframe.H1: 24, Timeframe.H4: 6,
+        Timeframe.D1: 1, Timeframe.W1: 0.143,
+    }
+
+    CANDLE_SECONDS = {
+        Timeframe.M1: 60, Timeframe.M5: 300, Timeframe.M15: 900,
+        Timeframe.M30: 1800, Timeframe.H1: 3600, Timeframe.H4: 14400,
+        Timeframe.D1: 86400, Timeframe.W1: 604800,
+    }
+
+    FRESH_FETCH_RETRY_SECONDS = 3.0
+
+    def __init__(self, config: PlatformConfig, api_token: str, base_url: str):
+        self.config = config
+        self._base_url = base_url.rstrip("/")
+        self._session = requests.Session()
+        self._session.headers.update({
+            "Authorization": f"Bearer {api_token}",
+            "Accept-Datetime-Format": "RFC3339",
+            "Content-Type": "application/json",
+        })
+        self._cache: Dict[str, pd.DataFrame] = {}
+        self._cache_timestamps: Dict[str, float] = {}
+        self._cache_lock = threading.Lock()
+        self._spread_cache: Dict[str, float] = {}
+
+    # ── Public API ─────────────────────────────────────────────────
+
+    def fetch_ohlcv(
+        self,
+        symbol: str = XAUUSD,
+        timeframe: Timeframe = Timeframe.H1,
+        lookback_days: Optional[int] = None,
+    ) -> pd.DataFrame:
+        if lookback_days is None:
+            lookback_days = self.config.data_lookback_days
+
+        cache_key = f"{symbol}_{timeframe.value}"
+        now = time.time()
+
+        cache_ttl = self.CANDLE_SECONDS.get(timeframe, 3600)
+        with self._cache_lock:
+            cached = self._cache.get(cache_key)
+            cached_at = self._cache_timestamps.get(cache_key)
+        if (
+            cached is not None
+            and cached_at is not None
+            and (now - cached_at) < cache_ttl
+            and not cached.empty
+        ):
+            if (self._is_data_fresh(cached, symbol, timeframe)
+                    and self._has_latest_closed_bar(cached, timeframe)):
+                return cached
+            logger.warning(
+                "%s %s: cached data is stale, refetching",
+                symbol, timeframe.value)
+            with self._cache_lock:
+                self._cache.pop(cache_key, None)
+                self._cache_timestamps.pop(cache_key, None)
+
+        count = max(300, min(int(lookback_days * self.BARS_PER_DAY[timeframe]) + 20, 5000))
+        df = self._fetch_oanda_candles(symbol, timeframe, count)
+        df = self._validate_and_clean(df, symbol, timeframe)
+
+        if not df.empty and not self._has_latest_closed_bar(df, timeframe):
+            time.sleep(self.FRESH_FETCH_RETRY_SECONDS)
+            try:
+                retry = self._fetch_oanda_candles(symbol, timeframe, count)
+                retry = self._validate_and_clean(retry, symbol, timeframe)
+                if retry is not None and not retry.empty:
+                    df = retry
+            except OandaDataError as e:
+                logger.warning(
+                    "%s %s: boundary retry refetch failed (%s) — keeping "
+                    "original data", symbol, timeframe.value, e)
+            if not self._has_latest_closed_bar(df, timeframe):
+                logger.warning(
+                    "%s %s: feed lagging — just-closed bar still missing "
+                    "after retry; this scan may run one bar behind",
+                    symbol, timeframe.value)
+
+        if not self._is_data_fresh(df, symbol, timeframe):
+            logger.warning(
+                "STALE DATA from Oanda: %s %s — last bar %s is too old. "
+                "Signal quality may be degraded.",
+                symbol, timeframe.value,
+                df.index[-1] if not df.empty else "N/A")
+
+        with self._cache_lock:
+            self._cache[cache_key] = df
+            self._cache_timestamps[cache_key] = now
+        return df
+
+    def get_pair_info(self, symbol: str = XAUUSD) -> Dict:
+        info = PAIR_INFO.get(symbol, PAIR_INFO[XAUUSD])
+        return {"symbol": symbol, **info}
+
+    def get_spread(self, symbol: str = XAUUSD) -> float:
+        return self._spread_cache.get(
+            symbol, PAIR_INFO.get(symbol, PAIR_INFO[XAUUSD])["default_spread"])
+
+    def clear_cache(self) -> int:
+        with self._cache_lock:
+            count = len(self._cache)
+            self._cache.clear()
+            self._cache_timestamps.clear()
+        logger.info("Cache cleared: %d entries wiped", count)
+        return count
+
+    # ── Private ────────────────────────────────────────────────────
+
+    def _to_oanda_instrument(self, symbol: str) -> str:
+        if symbol in self.OANDA_INSTRUMENT:
+            return self.OANDA_INSTRUMENT[symbol]
+        return symbol.replace("/", "_")
+
+    def _fetch_oanda_candles(
+        self, symbol: str, timeframe: Timeframe, count: int
+    ) -> pd.DataFrame:
+        instrument = self._to_oanda_instrument(symbol)
+        granularity = self.OANDA_GRANULARITY.get(timeframe)
+        if granularity is None:
+            raise OandaDataError(
+                f"Unsupported timeframe {timeframe} for Oanda")
+
+        url = f"{self._base_url}/v3/instruments/{instrument}/candles"
+        params = {
+            "granularity": granularity,
+            "count": min(count, 5000),
+            "price": "MBA",
+            "dailyAlignment": 0,
+            "alignmentTimezone": "Etc/UTC",
+        }
+
+        try:
+            resp = self._session.get(url, params=params, timeout=30)
+        except requests.RequestException as e:
+            raise OandaDataError(
+                f"Oanda candle fetch failed for {instrument}: {e}") from e
+
+        if resp.status_code != 200:
+            raise OandaDataError(
+                f"Oanda candle fetch failed for {instrument}: "
+                f"HTTP {resp.status_code}: {resp.text[:300]}")
+
+        try:
+            data = resp.json()
+        except ValueError as e:
+            raise OandaDataError(
+                f"Oanda candle response for {instrument} is not valid "
+                f"JSON: {e}") from e
+        candles = data.get("candles", [])
+        if not candles:
+            raise OandaDataError(
+                f"Empty candle response from Oanda for {instrument}")
+
+        rows = []
+        last_bid_close = None
+        last_ask_close = None
+        for c in candles:
+            if not c.get("complete", False):
+                continue
+            mid = c.get("mid", {})
+            bid = c.get("bid", {})
+            ask = c.get("ask", {})
+            rows.append({
+                "Datetime": pd.Timestamp(c["time"]).tz_convert("UTC"),
+                "Open": float(mid.get("o", 0)),
+                "High": float(mid.get("h", 0)),
+                "Low": float(mid.get("l", 0)),
+                "Close": float(mid.get("c", 0)),
+                "Volume": int(c.get("volume", 0)),
+            })
+            if bid and ask:
+                last_bid_close = float(bid.get("c", 0))
+                last_ask_close = float(ask.get("c", 0))
+
+        if not rows:
+            raise OandaDataError(
+                f"No complete candles from Oanda for {instrument}")
+
+        if last_bid_close and last_ask_close:
+            self._spread_cache[symbol] = round(
+                last_ask_close - last_bid_close, 4)
+
+        df = pd.DataFrame(rows)
+        df = df.set_index("Datetime")
+        df = df.sort_index()
+        if df.index.tz is None:
+            df.index = df.index.tz_localize("UTC")
+        df.index.name = "Datetime"
+
+        for col in ("Open", "High", "Low", "Close"):
+            df[col] = pd.to_numeric(df[col], errors="coerce")
+        df["Volume"] = pd.to_numeric(df["Volume"], errors="coerce").fillna(0).astype(float)
+
+        logger.debug(
+            "Fetched %d bars for %s @ %s from Oanda",
+            len(df), instrument, granularity)
+        return df
+
+    def _has_latest_closed_bar(self, df: pd.DataFrame, timeframe: Timeframe) -> bool:
+        if df.empty:
+            return False
+        now = pd.Timestamp.now(tz="UTC")
+        if _is_forex_weekend(now):
+            return True
+        candle_seconds = self.CANDLE_SECONDS.get(timeframe, 3600)
+        dur = pd.Timedelta(seconds=candle_seconds)
+        expected_label = now.floor(dur) - dur
+        return df.index[-1] >= expected_label
+
+    def _is_data_fresh(
+        self, df: pd.DataFrame, symbol: str, timeframe: Timeframe
+    ) -> bool:
+        if df is None or df.empty:
+            return False
+        now = pd.Timestamp.now(tz="UTC")
+        if _is_forex_weekend(now):
+            return True
+        last_bar = df.index[-1]
+        candle_seconds = self.CANDLE_SECONDS.get(timeframe, 3600)
+        max_age_seconds = candle_seconds * 3
+        age_seconds = (now - last_bar).total_seconds()
+        if age_seconds > max_age_seconds:
+            logger.warning(
+                "%s %s: last bar %s is %dm old (limit: %dm)",
+                symbol, timeframe.value,
+                last_bar.strftime("%Y-%m-%d %H:%M"),
+                age_seconds / 60, max_age_seconds / 60)
+            return False
+        return True
+
+    def _validate_and_clean(
+        self, df: pd.DataFrame, symbol: str, timeframe: Timeframe
+    ) -> pd.DataFrame:
+        if df is None or df.empty:
+            raise OandaDataError(f"Empty dataset for {symbol}")
+
+        original_len = len(df)
+
+        df = df.dropna(subset=["Open", "High", "Low", "Close"], how="all")
+        df = df.ffill(limit=3)
+        df = df.dropna(subset=["Close"])
+
+        df = df.sort_index()
+        df = df[~df.index.duplicated(keep="last")]
+
+        now = pd.Timestamp.utcnow()
+        future_mask = df.index > now + pd.Timedelta(minutes=5)
+        if future_mask.any():
+            logger.warning(
+                "%s: Dropping %d future timestamps", symbol, future_mask.sum())
+            df = df[~future_mask]
+
+        invalid_ohlc = (
+            (df["High"] < df["Low"])
+            | (df["High"] < df["Open"])
+            | (df["High"] < df["Close"])
+            | (df["Low"] > df["Open"])
+            | (df["Low"] > df["Close"])
+        )
+        if invalid_ohlc.any():
+            logger.warning(
+                "%s: Fixing %d OHLC-inconsistent bars",
+                symbol, invalid_ohlc.sum())
+            for idx in df[invalid_ohlc].index:
+                vals = [df.loc[idx, "Open"], df.loc[idx, "Close"]]
+                df.loc[idx, "High"] = max(vals + [df.loc[idx, "High"]])
+                df.loc[idx, "Low"] = min(vals + [df.loc[idx, "Low"]])
+
+        returns = df["Close"].pct_change()
+        std_ret = returns.std()
+        if std_ret > 0:
+            dev = returns - returns.mean()
+            outlier_mask = dev.abs() > 10 * std_ret
+            recovery = (
+                outlier_mask
+                & outlier_mask.shift(1, fill_value=False)
+                & (dev * dev.shift(1) < 0)
+            )
+            outlier_mask = outlier_mask & ~recovery
+            if outlier_mask.any():
+                logger.warning(
+                    "%s: Removing %d extreme outlier bars",
+                    symbol, outlier_mask.sum())
+                df = df[~outlier_mask]
+
+        min_required = max(self.config.technical.ema_trend, 200) + 10
+        if len(df) < min_required:
+            logger.warning(
+                "%s %s: Only %d bars (need %d). Some indicators will be partial.",
+                symbol, timeframe.value, len(df), min_required)
+
+        cleaned = original_len - len(df)
+        if cleaned > 0:
+            logger.info("%s: Cleaned %d/%d bars", symbol, cleaned, original_len)
+
+        return df

test_all.py

@@ -8,6 +8,7 @@ Tests every function in the codebase without requiring live API connections.
 import sys
 import os
 import time
+import threading
 import traceback
 from datetime import datetime, timedelta, timezone
 from unittest.mock import patch, MagicMock
@@ -3121,6 +3122,847 @@ def _():
         tb._autoscan_chats.clear()
 
 
+# ══════════════════════════════════════════════════════════════════════
+#  MODULE 15: Oanda provider / executor / daemon
+# ══════════════════════════════════════════════════════════════════════
+
+print("\n═══ Module 15: Oanda provider / executor / daemon ═══")
+
+import oanda_provider
+import oanda_executor
+import oanda_daemon
+
+
+@test("oanda_provider: _is_forex_weekend matches data_provider's")
+def _():
+    from data_provider import _is_forex_weekend as dp_weekend
+    from oanda_provider import _is_forex_weekend as oa_weekend
+    import pandas as pd
+    for ts_str in [
+        "2026-07-17 12:00",   # Friday noon
+        "2026-07-17 22:30",   # Friday after close
+        "2026-07-18 15:00",   # Saturday
+        "2026-07-19 12:00",   # Sunday before open
+        "2026-07-19 22:30",   # Sunday after open
+        "2026-07-20 08:00",   # Monday
+    ]:
+        ts = pd.Timestamp(ts_str, tz="UTC")
+        assert dp_weekend(ts) == oa_weekend(ts), \
+            f"Weekend check mismatch at {ts_str}"
+
+
+@test("oanda_provider: OandaDataProvider parses Oanda candle JSON into correct DataFrame")
+def _():
+    sample_candles = [
+        {
+            "complete": True,
+            "time": "2026-07-20T10:00:00.000000000Z",
+            "mid": {"o": "2400.00", "h": "2405.00", "l": "2398.00", "c": "2403.00"},
+            "bid": {"o": "2399.85", "h": "2404.85", "l": "2397.85", "c": "2402.85"},
+            "ask": {"o": "2400.15", "h": "2405.15", "l": "2398.15", "c": "2403.15"},
+            "volume": 1234,
+        },
+        {
+            "complete": True,
+            "time": "2026-07-20T11:00:00.000000000Z",
+            "mid": {"o": "2403.00", "h": "2408.00", "l": "2401.00", "c": "2406.00"},
+            "bid": {"o": "2402.85", "h": "2407.85", "l": "2400.85", "c": "2405.85"},
+            "ask": {"o": "2403.15", "h": "2408.15", "l": "2401.15", "c": "2406.15"},
+            "volume": 5678,
+        },
+        {
+            "complete": False,
+            "time": "2026-07-20T12:00:00.000000000Z",
+            "mid": {"o": "2406.00", "h": "2406.50", "l": "2405.50", "c": "2406.20"},
+            "bid": {"o": "2405.85", "h": "2406.35", "l": "2405.35", "c": "2406.05"},
+            "ask": {"o": "2406.15", "h": "2406.65", "l": "2405.65", "c": "2406.35"},
+            "volume": 100,
+        },
+    ]
+
+    mock_resp = type("MockResp", (), {
+        "status_code": 200,
+        "json": lambda self: {"candles": sample_candles, "instrument": "XAU_USD"},
+        "raise_for_status": lambda self: None,
+    })()
+
+    provider2 = oanda_provider.OandaDataProvider.__new__(
+        oanda_provider.OandaDataProvider)
+    provider2.config = PlatformConfig()
+    provider2._base_url = "https://test"
+    provider2._cache = {}
+    provider2._cache_timestamps = {}
+    provider2._cache_lock = threading.Lock()
+    provider2._spread_cache = {}
+    provider2._session = type("S", (), {
+        "get": lambda self, *a, **k: mock_resp,
+        "headers": {},
+    })()
+
+    df = provider2._fetch_oanda_candles("XAU/USD", Timeframe.H1, 300)
+
+    assert len(df) == 2, f"Expected 2 complete bars, got {len(df)}"
+    assert list(df.columns) == ["Open", "High", "Low", "Close", "Volume"], \
+        f"Wrong columns: {list(df.columns)}"
+    assert df.index.name == "Datetime"
+    assert df.index.tz is not None, "Index must be tz-aware"
+    assert df.iloc[0]["Open"] == 2400.0
+    assert df.iloc[1]["Close"] == 2406.0
+    assert df.iloc[0]["Volume"] == 1234
+
+    spread = provider2._spread_cache.get("XAU/USD")
+    assert spread is not None, "Spread should be cached"
+    assert abs(spread - 0.30) < 0.01, f"Expected spread ~0.30, got {spread}"
+
+
+@test("oanda_provider: instrument name mapping")
+def _():
+    provider = oanda_provider.OandaDataProvider.__new__(
+        oanda_provider.OandaDataProvider)
+    assert provider._to_oanda_instrument("XAU/USD") == "XAU_USD"
+    assert provider._to_oanda_instrument("EUR/USD") == "EUR_USD"
+
+
+@test("oanda_provider: granularity mapping covers all scan timeframes")
+def _():
+    for tf in [Timeframe.M5, Timeframe.M15, Timeframe.H1, Timeframe.H4]:
+        assert tf in oanda_provider.OandaDataProvider.OANDA_GRANULARITY, \
+            f"{tf} missing from OANDA_GRANULARITY"
+
+
+@test("oanda_provider: get_spread returns default when no cache")
+def _():
+    provider = oanda_provider.OandaDataProvider.__new__(
+        oanda_provider.OandaDataProvider)
+    provider._spread_cache = {}
+    spread = provider.get_spread("XAU/USD")
+    assert spread == 0.30, f"Expected default spread 0.30, got {spread}"
+
+
+@test("oanda_executor: market order body construction")
+def _():
+    executor = oanda_executor.OandaExecutor.__new__(
+        oanda_executor.OandaExecutor)
+    executor._account_id = "test-account"
+    executor._base_url = "https://test"
+    executor._lot_size = 1
+    executor._price_precision = 2
+    executor._session = None
+
+    captured = {}
+
+    def mock_request(self, method, url, **kwargs):
+        captured["method"] = method
+        captured["url"] = url
+        captured["json"] = kwargs.get("json")
+        return type("R", (), {
+            "status_code": 201,
+            "json": lambda self: {
+                "orderFillTransaction": {
+                    "price": "2400.50",
+                    "tradeOpened": {"tradeID": "123"},
+                }
+            },
+        })()
+
+    executor._session = type("S", (), {
+        "request": mock_request,
+        "headers": {},
+    })()
+
+    result = executor.place_market_order(
+        instrument="XAU_USD",
+        direction="BUY",
+        stop_loss=2392.50,
+        take_profit=2420.00,
+    )
+
+    body = captured["json"]
+    order = body["order"]
+    assert order["type"] == "MARKET"
+    assert order["instrument"] == "XAU_USD"
+    assert order["units"] == "1"
+    # XAU price precision is 2 — Oanda rejects excess decimals.
+    assert order["stopLossOnFill"]["price"] == "2392.50"
+    assert order["takeProfitOnFill"]["price"] == "2420.00"
+
+    fill = result.get("orderFillTransaction")
+    assert fill is not None
+    assert fill["tradeOpened"]["tradeID"] == "123"
+
+
+@test("oanda_executor: SELL order uses negative units")
+def _():
+    executor = oanda_executor.OandaExecutor.__new__(
+        oanda_executor.OandaExecutor)
+    executor._account_id = "test-account"
+    executor._base_url = "https://test"
+    executor._lot_size = 1
+    executor._price_precision = 2
+
+    captured = {}
+
+    def mock_request(self, method, url, **kwargs):
+        captured["json"] = kwargs.get("json")
+        return type("R", (), {
+            "status_code": 201,
+            "json": lambda self: {
+                "orderFillTransaction": {
+                    "price": "2400.50",
+                    "tradeOpened": {"tradeID": "456"},
+                }
+            },
+        })()
+
+    executor._session = type("S", (), {
+        "request": mock_request,
+        "headers": {},
+    })()
+
+    executor.place_market_order(
+        instrument="XAU_USD", direction="SELL", stop_loss=2410.00)
+
+    order = captured["json"]["order"]
+    assert order["units"] == "-1", f"SELL should be -1, got {order['units']}"
+    assert "takeProfitOnFill" not in order, "No TP should be set when None"
+
+
+@test("oanda_executor: 4xx error raises immediately (no retry)")
+def _():
+    executor = oanda_executor.OandaExecutor.__new__(
+        oanda_executor.OandaExecutor)
+    executor._account_id = "test"
+    executor._base_url = "https://test"
+    executor._lot_size = 1
+    call_count = {"n": 0}
+
+    def mock_request(self, method, url, **kwargs):
+        call_count["n"] += 1
+        return type("R", (), {
+            "status_code": 400,
+            "text": "INVALID_ORDER",
+        })()
+
+    executor._session = type("S", (), {
+        "request": mock_request,
+        "headers": {},
+    })()
+
+    try:
+        executor._request_with_retry("POST", "https://test/orders")
+        assert False, "Should have raised OandaAPIError"
+    except oanda_executor.OandaAPIError as e:
+        assert e.status_code == 400
+    assert call_count["n"] == 1, "4xx should not retry"
+
+
+@test("oanda_daemon: trailing stop ratchet matches backtester logic")
+def _():
+    daemon = oanda_daemon.TradingDaemon.__new__(oanda_daemon.TradingDaemon)
+
+    info_buy = {
+        "direction": "BUY",
+        "entry": 2400.0,
+        "sl_initial": 2384.0,
+        "current_sl": 2384.0,
+        "risk": 16.0,
+        "peak": 2400.0,
+        "trail_armed": False,
+        "trail_arm_r": 0.6,
+        "trail_dist_r": 0.75,
+    }
+
+    # Bar 1: price moves up but not enough to arm (< 0.6R = $9.60)
+    new_sl = daemon._compute_trail(info_buy, bar_high=2408.0, bar_low=2399.0)
+    assert new_sl is None, "Should not arm yet (excursion 0.5R)"
+    assert info_buy["peak"] == 2408.0
+
+    # Bar 2: excursion reaches 0.625R = $10 — arms, trail = 2408 - 0.75*16 = 2396
+    new_sl = daemon._compute_trail(info_buy, bar_high=2410.0, bar_low=2403.0)
+    assert new_sl is not None, "Should arm at 0.625R"
+    assert abs(new_sl - 2398.0) < 0.01, f"Expected trail ~2398, got {new_sl}"
+    assert info_buy["trail_armed"] is True
+
+    # Bar 3: price goes higher, trail ratchets up
+    info_buy["current_sl"] = new_sl
+    new_sl2 = daemon._compute_trail(info_buy, bar_high=2420.0, bar_low=2410.0)
+    assert new_sl2 is not None
+    assert new_sl2 > new_sl, "Trail must ratchet up"
+    expected = 2420.0 - 0.75 * 16.0  # 2408.0
+    assert abs(new_sl2 - expected) < 0.01, f"Expected {expected}, got {new_sl2}"
+
+    # Bar 4: price pulls back — trail must NOT ratchet down
+    info_buy["current_sl"] = new_sl2
+    new_sl3 = daemon._compute_trail(info_buy, bar_high=2412.0, bar_low=2405.0)
+    assert new_sl3 is None, "Trail must not ratchet backwards"
+
+
+@test("oanda_daemon: trailing stop for SELL direction")
+def _():
+    daemon = oanda_daemon.TradingDaemon.__new__(oanda_daemon.TradingDaemon)
+
+    info_sell = {
+        "direction": "SELL",
+        "entry": 2400.0,
+        "sl_initial": 2416.0,
+        "current_sl": 2416.0,
+        "risk": 16.0,
+        "peak": 2400.0,
+        "trail_armed": False,
+        "trail_arm_r": 0.6,
+        "trail_dist_r": 0.75,
+    }
+
+    # Not enough excursion
+    new_sl = daemon._compute_trail(info_sell, bar_high=2401.0, bar_low=2393.0)
+    assert new_sl is None, "Should not arm yet"
+    assert info_sell["peak"] == 2393.0
+
+    # Arms: peak at 2390, excursion = 10/16 = 0.625R
+    new_sl = daemon._compute_trail(info_sell, bar_high=2395.0, bar_low=2390.0)
+    assert new_sl is not None
+    expected = 2390.0 + 0.75 * 16.0  # 2402.0
+    assert abs(new_sl - expected) < 0.01, f"Expected {expected}, got {new_sl}"
+    assert info_sell["trail_armed"] is True
+
+    # Ratchets down
+    info_sell["current_sl"] = new_sl
+    new_sl2 = daemon._compute_trail(info_sell, bar_high=2392.0, bar_low=2380.0)
+    assert new_sl2 is not None
+    assert new_sl2 < new_sl, "SELL trail must ratchet down"
+
+
+@test("oanda_daemon: _should_execute respects quality gate")
+def _():
+    daemon = oanda_daemon.TradingDaemon.__new__(oanda_daemon.TradingDaemon)
+    daemon._quality_gate = "B"
+    daemon._state = {"trades": {}, "last_entries": {}}
+    daemon._state_lock = threading.Lock()
+
+    from signal_aggregator import TradeSetup
+    setup = TradeSetup(
+        pair="XAU/USD", display_name="XAU/USD", direction="BUY",
+        entry_price=2400.0, stop_loss=2384.0,
+        take_profit_1=2416.0, take_profit_2=2432.0,
+        risk_reward=1.0, risk_pips=160.0, reward_pips=160.0,
+        pip_value=0.10)
+
+    report_c = SignalReport(
+        symbol="XAU/USD", display_name="XAU/USD", timestamp="2026-07-20")
+    report_c.final_signal = SignalStrength.BUY
+    report_c.signal_quality = "C"
+    report_c.trade_setup = setup
+
+    assert not daemon._should_execute(report_c, Timeframe.H1), \
+        "C should be blocked by gate=B"
+
+    report_a = SignalReport(
+        symbol="XAU/USD", display_name="XAU/USD", timestamp="2026-07-20")
+    report_a.final_signal = SignalStrength.BUY
+    report_a.signal_quality = "A"
+    report_a.trade_setup = setup
+
+    assert daemon._should_execute(report_a, Timeframe.H1), \
+        "A should pass gate=B"
+
+
+@test("oanda_daemon: _should_execute blocks when position exists")
+def _():
+    daemon = oanda_daemon.TradingDaemon.__new__(oanda_daemon.TradingDaemon)
+    daemon._quality_gate = "C"
+    daemon._state = {"trades": {"12345": {"direction": "BUY"}},
+                     "last_entries": {}}
+    daemon._state_lock = threading.Lock()
+
+    from signal_aggregator import TradeSetup
+    setup = TradeSetup(
+        pair="XAU/USD", display_name="XAU/USD", direction="BUY",
+        entry_price=2400.0, stop_loss=2384.0,
+        take_profit_1=2416.0, take_profit_2=2432.0,
+        risk_reward=1.0, risk_pips=160.0, reward_pips=160.0,
+        pip_value=0.10)
+
+    report = SignalReport(
+        symbol="XAU/USD", display_name="XAU/USD", timestamp="2026-07-20")
+    report.final_signal = SignalStrength.BUY
+    report.signal_quality = "A"
+    report.trade_setup = setup
+
+    assert not daemon._should_execute(report, Timeframe.H1), \
+        "Should block when a position already exists"
+
+
+@test("oanda_daemon: NEUTRAL signal is never executed")
+def _():
+    daemon = oanda_daemon.TradingDaemon.__new__(oanda_daemon.TradingDaemon)
+    daemon._quality_gate = "C"
+    daemon._state = {"trades": {}, "last_entries": {}}
+    daemon._state_lock = threading.Lock()
+
+    report = SignalReport(
+        symbol="XAU/USD", display_name="XAU/USD", timestamp="2026-07-20")
+    report.final_signal = SignalStrength.NEUTRAL
+    report.signal_quality = "F"
+    report.trade_setup = None
+
+    assert not daemon._should_execute(report, Timeframe.H1)
+
+
+def _make_daemon_report(quality="A", direction="BUY"):
+    from signal_aggregator import TradeSetup
+    setup = TradeSetup(
+        pair="XAU/USD", display_name="XAU/USD", direction=direction,
+        entry_price=2400.0, stop_loss=2384.0,
+        take_profit_1=2416.0, take_profit_2=2432.0,
+        risk_reward=1.0, risk_pips=160.0, reward_pips=160.0,
+        pip_value=0.10, trail_arm_r=0.6, trail_dist_r=0.85)
+    report = SignalReport(
+        symbol="XAU/USD", display_name="XAU/USD",
+        timestamp="2026-07-20 10:00:00+00:00")
+    report.final_signal = (SignalStrength.BUY if direction == "BUY"
+                           else SignalStrength.SELL)
+    report.signal_quality = quality
+    report.trade_setup = setup
+    return report
+
+
+@test("oanda_daemon: 5-bar entry cooldown blocks re-entry (backtest parity)")
+def _():
+    daemon = oanda_daemon.TradingDaemon.__new__(oanda_daemon.TradingDaemon)
+    daemon._quality_gate = "C"
+    daemon._state = {"trades": {}, "last_entries": {}}
+    daemon._state_lock = threading.Lock()
+
+    report = _make_daemon_report()
+
+    # Entry 2 minutes ago on M5 → inside 5×5min cooldown → blocked
+    recent = (datetime.now(timezone.utc) - timedelta(minutes=2)).isoformat()
+    daemon._state["last_entries"]["5min"] = recent
+    assert not daemon._should_execute(report, Timeframe.M5), \
+        "Entry 2min ago must block M5 (cooldown 25min)"
+
+    # Entry 30 minutes ago on M5 → past 5×5min cooldown → allowed
+    old = (datetime.now(timezone.utc) - timedelta(minutes=30)).isoformat()
+    daemon._state["last_entries"]["5min"] = old
+    assert daemon._should_execute(report, Timeframe.M5), \
+        "Entry 30min ago must clear the 25min M5 cooldown"
+
+    # M5 cooldown must not block H1
+    daemon._state["last_entries"] = {"5min": recent}
+    assert daemon._should_execute(report, Timeframe.H1), \
+        "Cooldown is per-timeframe"
+
+
+@test("oanda_daemon: orders carry NO take-profit (trailing book parity)")
+def _():
+    import tempfile
+    daemon = oanda_daemon.TradingDaemon.__new__(oanda_daemon.TradingDaemon)
+    daemon._state = {"trades": {}, "last_entries": {}}
+    daemon._state_lock = threading.Lock()
+    daemon._entry_lock = threading.Lock()
+
+    captured = {}
+
+    class MockExecutor:
+        def place_market_order(self, instrument, direction, stop_loss,
+                               take_profit=None, units=None):
+            captured["take_profit"] = take_profit
+            captured["stop_loss"] = stop_loss
+            return {
+                "orderFillTransaction": {
+                    "price": "2400.60",
+                    "tradeOpened": {"tradeID": "777"},
+                }
+            }
+
+    daemon._executor = MockExecutor()
+
+    with tempfile.NamedTemporaryFile(suffix=".json", delete=False) as f:
+        tmp_path = f.name
+    orig_state_file = oanda_daemon.STATE_FILE
+    try:
+        oanda_daemon.STATE_FILE = tmp_path
+        daemon._execute_trade(_make_daemon_report(), Timeframe.M15)
+    finally:
+        oanda_daemon.STATE_FILE = orig_state_file
+        os.unlink(tmp_path)
+
+    assert captured.get("take_profit") is None, \
+        "Backtested trailing model has no hard TP — order must not carry one"
+    assert captured["stop_loss"] == 2384.0
+    trade = daemon._state["trades"]["777"]
+    assert trade["entry"] == 2400.60, "Risk must be computed from actual fill"
+    assert abs(trade["risk"] - 16.60) < 0.01
+    assert trade["trail_arm_r"] == 0.6 and trade["trail_dist_r"] == 0.85, \
+        "Trail params must come from the setup stamp (per-TF factory values)"
+    assert daemon._state["last_entries"]["15min"], \
+        "Entry must be recorded for the cooldown"
+
+
+@test("oanda_daemon: entry-lock recheck prevents double entry from concurrent scans")
+def _():
+    import tempfile
+    daemon = oanda_daemon.TradingDaemon.__new__(oanda_daemon.TradingDaemon)
+    # A concurrent TF loop already opened a trade after our _should_execute
+    daemon._state = {"trades": {"111": {"direction": "BUY"}},
+                     "last_entries": {}}
+    daemon._state_lock = threading.Lock()
+    daemon._entry_lock = threading.Lock()
+
+    placed = {"n": 0}
+
+    class MockExecutor:
+        def place_market_order(self, *a, **k):
+            placed["n"] += 1
+            return {}
+
+    daemon._executor = MockExecutor()
+
+    with tempfile.NamedTemporaryFile(suffix=".json", delete=False) as f:
+        tmp_path = f.name
+    orig_state_file = oanda_daemon.STATE_FILE
+    try:
+        oanda_daemon.STATE_FILE = tmp_path
+        daemon._execute_trade(_make_daemon_report(), Timeframe.M5)
+    finally:
+        oanda_daemon.STATE_FILE = orig_state_file
+        os.unlink(tmp_path)
+
+    assert placed["n"] == 0, \
+        "Recheck under the entry lock must skip when a position exists"
+
+
+@test("oanda_daemon: same closed bar is never processed twice")
+def _():
+    import tempfile
+    daemon = oanda_daemon.TradingDaemon.__new__(oanda_daemon.TradingDaemon)
+    daemon._max_hold_bars = 300
+    daemon._price_precision = 2
+    daemon._state_lock = threading.Lock()
+
+    bar_time = pd.Timestamp("2026-07-20 10:00:00", tz="UTC")
+    df = pd.DataFrame(
+        {"Open": [2400.0], "High": [2405.0], "Low": [2398.0],
+         "Close": [2403.0], "Volume": [100.0]},
+        index=pd.DatetimeIndex([bar_time], name="Datetime"))
+
+    class MockProvider:
+        def fetch_ohlcv(self, symbol, tf):
+            return df
+
+    daemon._provider = MockProvider()
+    daemon._executor = None  # trail must not arm on this bar
+
+    daemon._state = {
+        "trades": {
+            "555": {
+                "tf": "5min", "direction": "BUY", "entry": 2400.0,
+                "sl_initial": 2384.0, "current_sl": 2384.0, "risk": 16.0,
+                "peak": 2400.0, "trail_armed": False,
+                "trail_arm_r": 0.6, "trail_dist_r": 0.85,
+                "bars_held": 0, "last_bar_ts": None, "signal_bar_ts": None,
+            }
+        },
+        "last_entries": {},
+    }
+
+    with tempfile.NamedTemporaryFile(suffix=".json", delete=False) as f:
+        tmp_path = f.name
+    orig_state_file = oanda_daemon.STATE_FILE
+    try:
+        oanda_daemon.STATE_FILE = tmp_path
+        daemon._manage_trailing(Timeframe.M5)
+        daemon._manage_trailing(Timeframe.M5)   # same bar re-served (weekend/lag)
+        daemon._manage_trailing(Timeframe.M5)
+    finally:
+        oanda_daemon.STATE_FILE = orig_state_file
+        os.unlink(tmp_path)
+
+    assert daemon._state["trades"]["555"]["bars_held"] == 1, \
+        "A re-served bar must not inflate bars_held (weekend timeout bug)"
+
+
+@test("oanda_daemon: failed timeout-close keeps the trade tracked")
+def _():
+    import tempfile
+    daemon = oanda_daemon.TradingDaemon.__new__(oanda_daemon.TradingDaemon)
+    daemon._max_hold_bars = 1
+    daemon._price_precision = 2
+    daemon._state_lock = threading.Lock()
+
+    bar_time = pd.Timestamp("2026-07-20 10:00:00", tz="UTC")
+    df = pd.DataFrame(
+        {"Open": [2400.0], "High": [2405.0], "Low": [2398.0],
+         "Close": [2403.0], "Volume": [100.0]},
+        index=pd.DatetimeIndex([bar_time], name="Datetime"))
+
+    class MockProvider:
+        def fetch_ohlcv(self, symbol, tf):
+            return df
+
+    class MockExecutor:
+        def close_trade(self, trade_id):
+            raise oanda_executor.OandaAPIError("market halted", 503)
+
+    daemon._provider = MockProvider()
+    daemon._executor = MockExecutor()
+
+    daemon._state = {
+        "trades": {
+            "666": {
+                "tf": "5min", "direction": "BUY", "entry": 2400.0,
+                "sl_initial": 2384.0, "current_sl": 2384.0, "risk": 16.0,
+                "peak": 2400.0, "trail_armed": False,
+                "trail_arm_r": 0.6, "trail_dist_r": 0.85,
+                "bars_held": 0, "last_bar_ts": None, "signal_bar_ts": None,
+            }
+        },
+        "last_entries": {},
+    }
+
+    with tempfile.NamedTemporaryFile(suffix=".json", delete=False) as f:
+        tmp_path = f.name
+    orig_state_file = oanda_daemon.STATE_FILE
+    try:
+        oanda_daemon.STATE_FILE = tmp_path
+        daemon._manage_trailing(Timeframe.M5)
+    finally:
+        oanda_daemon.STATE_FILE = orig_state_file
+        os.unlink(tmp_path)
+
+    assert "666" in daemon._state["trades"], \
+        "A failed broker close must keep the trade tracked for retry"
+
+
+@test("app_config: invalid oanda section values raise AppConfigError")
+def _():
+    import tempfile
+    for bad in (
+        {"oanda": {"quality_gate": "Z"}},
+        {"oanda": {"lot_size": 0}},
+        {"oanda": {"max_hold_bars": -5}},
+        {"oanda": {"unknown_key": 1}},
+    ):
+        with tempfile.NamedTemporaryFile(
+                mode="w", suffix=".json", delete=False) as f:
+            json.dump(bad, f)
+            tmp_path = f.name
+        try:
+            app_config.load_app_config(tmp_path)
+            assert False, f"Should have raised for {bad}"
+        except app_config.AppConfigError:
+            pass
+        finally:
+            os.unlink(tmp_path)
+
+
+@test("oanda_daemon: boundary timing matches telegram_bot")
+def _():
+    from telegram_bot import _seconds_until_boundary as tb_boundary
+    from oanda_daemon import _seconds_until_boundary as od_boundary
+    for interval in (5, 15, 60, 240):
+        tb_val = tb_boundary(interval, buffer=10)
+        od_val = od_boundary(interval, buffer=10)
+        assert abs(tb_val - od_val) < 0.01, \
+            f"Boundary timing mismatch for {interval}min: {tb_val} vs {od_val}"
+
+
+@test("oanda_daemon: state file round-trip")
+def _():
+    import tempfile
+    state = {
+        "trades": {
+            "999": {
+                "tf": "M15",
+                "direction": "BUY",
+                "entry": 2400.0,
+                "sl_initial": 2384.0,
+                "current_sl": 2392.0,
+                "risk": 16.0,
+                "peak": 2412.0,
+                "trail_armed": True,
+                "trail_arm_r": 0.6,
+                "trail_dist_r": 0.75,
+                "bars_held": 25,
+            }
+        }
+    }
+    with tempfile.NamedTemporaryFile(
+            mode="w", suffix=".json", delete=False) as f:
+        tmp_path = f.name
+
+    orig_state_file = oanda_daemon.STATE_FILE
+    try:
+        oanda_daemon.STATE_FILE = tmp_path
+        oanda_daemon._save_state(state)
+        loaded = oanda_daemon._load_state()
+        assert loaded["trades"]["999"]["entry"] == 2400.0
+        assert loaded["trades"]["999"]["trail_armed"] is True
+        assert loaded["trades"]["999"]["bars_held"] == 25
+    finally:
+        oanda_daemon.STATE_FILE = orig_state_file
+        os.unlink(tmp_path)
+
+
+@test("app_config: oanda in _KNOWN_TOP (no unknown-key error)")
+def _():
+    assert "oanda" in app_config._KNOWN_TOP, \
+        "'oanda' must be in _KNOWN_TOP to avoid config.json validation errors"
+
+
+@test("app_config: oanda resolver helpers return defaults without config")
+def _():
+    url = app_config.oanda_base_url()
+    assert "oanda.com" in url, f"Expected Oanda URL, got {url}"
+
+
+@test("oanda_provider: HTTP error surfaces status code and body")
+def _():
+    mock_resp = type("MockResp", (), {
+        "status_code": 401,
+        "text": '{"errorMessage":"Insufficient authorization to perform request"}',
+    })()
+
+    provider = oanda_provider.OandaDataProvider.__new__(
+        oanda_provider.OandaDataProvider)
+    provider._base_url = "https://test"
+    provider._spread_cache = {}
+    provider._session = type("S", (), {
+        "get": lambda self, *a, **k: mock_resp,
+        "headers": {},
+    })()
+
+    try:
+        provider._fetch_oanda_candles("XAU/USD", Timeframe.H1, 300)
+        assert False, "Should have raised OandaDataError"
+    except oanda_provider.OandaDataError as e:
+        msg = str(e)
+        assert "401" in msg, f"Status code missing from error: {msg}"
+        assert "Insufficient authorization" in msg, \
+            f"Body detail missing from error: {msg}"
+
+
+@test("oanda_provider: boundary-retry failure keeps original data")
+def _():
+    provider = oanda_provider.OandaDataProvider.__new__(
+        oanda_provider.OandaDataProvider)
+    provider.config = PlatformConfig()
+    provider._cache = {}
+    provider._cache_timestamps = {}
+    provider._cache_lock = threading.Lock()
+    provider._spread_cache = {}
+    provider.FRESH_FETCH_RETRY_SECONDS = 0  # instance override — no sleep
+
+    call_count = {"n": 0}
+    idx = pd.date_range("2026-07-01", periods=250, freq="5min", tz="UTC")
+    original_df = pd.DataFrame({
+        "Open": 2400.0, "High": 2401.0, "Low": 2399.0,
+        "Close": 2400.5, "Volume": 100.0,
+    }, index=idx)
+
+    def _fetch_oanda_candles(symbol, timeframe, count):
+        call_count["n"] += 1
+        if call_count["n"] == 1:
+            return original_df
+        raise oanda_provider.OandaDataError("boom")
+
+    def _validate_and_clean(df, symbol, timeframe):
+        return df
+
+    def _has_latest_closed_bar(df, timeframe):
+        return False   # forces the retry path every call
+
+    def _is_data_fresh(df, symbol, timeframe):
+        return True
+
+    # Plain functions assigned to the instance bypass the descriptor
+    # protocol — they run unbound, so they take exactly the params
+    # fetch_ohlcv passes (no self).
+    provider._fetch_oanda_candles = _fetch_oanda_candles
+    provider._validate_and_clean = _validate_and_clean
+    provider._has_latest_closed_bar = _has_latest_closed_bar
+    provider._is_data_fresh = _is_data_fresh
+
+    result = provider.fetch_ohlcv("XAU/USD", Timeframe.M5)
+
+    assert call_count["n"] == 2, \
+        f"Expected original fetch + one retry attempt, got {call_count['n']} calls"
+    assert len(result) == 250, \
+        f"Retry failure must keep the original 250-bar df, got {len(result)}"
+
+
+@test("oanda_executor: non-JSON response raises OandaAPIError with status")
+def _():
+    def _bad_json(self):
+        raise ValueError("Expecting value: line 1 column 1 (char 0)")
+
+    mock_resp = type("MockResp", (), {
+        "status_code": 200,
+        "text": "<html>gateway error</html>",
+        "json": _bad_json,
+    })()
+
+    executor = oanda_executor.OandaExecutor.__new__(
+        oanda_executor.OandaExecutor)
+    executor._account_id = "test-account"
+    executor._base_url = "https://test"
+    executor._session = type("S", (), {
+        "request": lambda self, method, url, **kwargs: mock_resp,
+        "headers": {},
+    })()
+
+    try:
+        executor.get_open_trades()
+        assert False, "Should have raised OandaAPIError"
+    except oanda_executor.OandaAPIError as e:
+        assert e.status_code == 200, f"Expected status 200, got {e.status_code}"
+        assert "non-JSON" in str(e), f"Expected non-JSON in message: {e}"
+
+
+@test("oanda_daemon: boot fails fast on rejected credentials (4xx)")
+def _():
+    daemon = oanda_daemon.TradingDaemon.__new__(oanda_daemon.TradingDaemon)
+
+    class MockExecutor401:
+        def get_account_summary(self):
+            raise oanda_executor.OandaAPIError("unauthorized", 401)
+
+    daemon._executor = MockExecutor401()
+    try:
+        daemon._verify_account_access()
+        assert False, "A 4xx credential rejection must raise SystemExit"
+    except SystemExit:
+        pass
+
+    class MockExecutorConnFail:
+        def get_account_summary(self):
+            raise oanda_executor.OandaAPIError("conn refused", 0)
+
+    daemon._executor = MockExecutorConnFail()
+    daemon._verify_account_access()   # transient — must NOT raise
+
+
+@test("oanda_daemon: heartbeat never raises and logs on account failure")
+def _():
+    daemon = oanda_daemon.TradingDaemon.__new__(oanda_daemon.TradingDaemon)
+    daemon._state = {"trades": {}, "last_entries": {}}
+    daemon._state_lock = threading.Lock()
+
+    class MockExecutorDown:
+        def get_account_summary(self):
+            raise oanda_executor.OandaAPIError("down", 503)
+
+    daemon._executor = MockExecutorDown()
+    daemon._heartbeat_once()   # must not raise
+
+    class MockExecutorUp:
+        def get_account_summary(self):
+            return {"balance": "1000"}
+
+    daemon._executor = MockExecutorUp()
+    daemon._heartbeat_once()   # must not raise
+
+
 # ══════════════════════════════════════════════════════════════════════
 #  SUMMARY
 # ══════════════════════════════════════════════════════════════════════