d2e679cd20
2026-09-07 signal-log inspection follow-up — three fixes that do not depend on sample size.
- providers/base.py: the signal-side 10σ outlier filter is now a bad-print filter. A >10σ
close-to-close bar is dropped only when the NEXT close snaps back ≥80% of the way
(OUTLIER_SIGMA / OUTLIER_REVERT_FRACTION); persisting moves and the newest bar are kept.
The unconditional rule deleted ~36 real M5 news bars/yr in the backtester (whole-frame σ)
and a different ~24/yr live (fetch-window σ) — e.g. the 2026-09-04 12:30 UTC NFP bar
vanished from M5+M15 and the 12:35 scan was a byte-identical repeat of 12:30. 3-yr Oanda
drops are now M5 2 / M15 1 / H1 0.
- providers/oanda.py: transient 401/5xx/network faults retry along RETRY_DELAYS_S=(2,5,10)s
under RETRY_BUDGET_S=45s. 11 boundary scans in two days had lost their primary frame at
xx:00:22–:26 UTC; one 2 s retry could not outlast the top-of-hour blip.
- signal_aggregator/_log_signal: a scan whose primary fetch failed logs scan_kind='failed'
(price 0, no signal); analyze_signal_log.parity_report skips it like 'blocked'.
- test_all.py: 466 tests (+6): retry ladder walks all delays, 5xx blip recovers on the third
attempt, retry budget stops the ladder, persisting/newest/partially-retraced 10σ bars kept,
failed-scan row kind, parity skip.
- CLAUDE.md: cleaning rule, retry ladder, failed scan kind, dated current-state note.
Book impact at $0.65 (rule 1 still passes everywhere, both halves positive):
12mo M5 557/+112.21/PF1.56 → 560/+108.81/1.54 M15 397/+73.93/1.53 → 398/+74.61/1.53 H1 identical
3yr M5 857/+161.3/1.53 → 870/+153.6/1.50 M15 1086/+148.7/1.37 → 1092/+151.9/1.38
H1 255/+70.5/1.98 → 253/+70.0/1.98 H4 113/+22.7/1.63 → 114/+21.7/1.59
Only trades on the previously-deleted event bars move; the pinned M5/M15 CSVs are no longer
byte-reproducible and should be regenerated at the next re-pin.
Claude-Session: https://claude.ai/code/session_01NDnBxJ2y8GRUM6ZNFJs1hZ
258 lines
9.9 KiB
Python
258 lines
9.9 KiB
Python
"""
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ForexBot2 — Oanda v20 data provider
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====================================
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OHLCV candles from the Oanda v20 REST API. All shared behavior (cache,
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validation, boundary guarantees, range pagination) lives in
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`BaseDataProvider`; this module only maps symbols/granularities, parses
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Oanda's candle JSON, and exposes the real bid/ask spread from MBA candles.
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"""
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import logging
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import time
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from datetime import datetime
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from typing import Dict, Optional
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import pandas as pd
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import requests
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from app_config import oanda_api_token, oanda_base_url
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from config import PAIR_INFO, PlatformConfig, Timeframe, XAUUSD
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from providers.base import BaseDataProvider, DataIntegrityError
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logger = logging.getLogger(__name__)
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class OandaDataError(DataIntegrityError):
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"""Raised when an Oanda data fetch fails or returns invalid data."""
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class OandaDataProvider(BaseDataProvider):
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"""Oanda v20 REST candle provider."""
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name = "oanda"
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DATA_ERROR = OandaDataError
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# Transient-fault retry ladder (see _fetch_oanda_candles): sleeps
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# between attempts, plus a wall-clock cap on the whole sequence.
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RETRY_DELAYS_S = (2.0, 5.0, 10.0)
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RETRY_BUDGET_S = 45.0
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RETRY_DELAY_S = RETRY_DELAYS_S[0] # first delay; kept for callers/tests
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OANDA_GRANULARITY = {
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Timeframe.M1: "M1",
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Timeframe.M5: "M5",
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Timeframe.M15: "M15",
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Timeframe.M30: "M30",
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Timeframe.H1: "H1",
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Timeframe.H4: "H4",
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Timeframe.D1: "D",
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Timeframe.W1: "W",
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}
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OANDA_INSTRUMENT = {
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"XAU/USD": "XAU_USD",
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}
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def __init__(
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self,
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config: PlatformConfig,
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api_token: Optional[str] = None,
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base_url: Optional[str] = None,
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):
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super().__init__(config)
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# Credentials default to the config.json `oanda` block / env vars
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# so the provider registry can build this class with config only.
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if api_token is None:
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api_token = oanda_api_token()
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if base_url is None:
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base_url = oanda_base_url()
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self._base_url = base_url.rstrip("/")
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self._session = requests.Session()
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self._session.headers.update({
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"Authorization": f"Bearer {api_token}",
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"Accept-Datetime-Format": "RFC3339",
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"Content-Type": "application/json",
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})
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self._spread_cache: Dict[str, float] = {}
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# ── Public extras ──────────────────────────────────────────────
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def get_spread(self, symbol: str = XAUUSD) -> float:
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return self._spread_cache.get(
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symbol, PAIR_INFO.get(symbol, PAIR_INFO[XAUUSD])["default_spread"])
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# ── Raw fetch ──────────────────────────────────────────────────
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def _fetch_raw(
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self,
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symbol: str,
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timeframe: Timeframe,
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outputsize: int,
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start: Optional[datetime] = None,
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end: Optional[datetime] = None,
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) -> pd.DataFrame:
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if start is not None or end is not None:
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return self._fetch_oanda_candles(
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symbol, timeframe, outputsize, start, end)
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return self._fetch_oanda_candles(symbol, timeframe, outputsize)
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def _to_oanda_instrument(self, symbol: str) -> str:
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try:
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return self.OANDA_INSTRUMENT[symbol]
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except KeyError:
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raise OandaDataError(
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f"Unsupported symbol {symbol!r} for Oanda") from None
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def _fetch_oanda_candles(
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self,
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symbol: str,
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timeframe: Timeframe,
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count: int,
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start: Optional[datetime] = None,
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end: Optional[datetime] = None,
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) -> pd.DataFrame:
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instrument = self._to_oanda_instrument(symbol)
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granularity = self.OANDA_GRANULARITY.get(timeframe)
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if granularity is None:
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raise OandaDataError(
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f"Unsupported timeframe {timeframe} for Oanda")
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url = f"{self._base_url}/v3/instruments/{instrument}/candles"
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params = {
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"granularity": granularity,
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"price": "MBA",
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"dailyAlignment": 0,
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"alignmentTimezone": "Etc/UTC",
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}
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# v20 rejects `count` combined with both `from` and `to`; a date
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# window replaces it (used by fetch_range pagination).
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if start is not None and end is not None:
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params["from"] = pd.Timestamp(start).isoformat()
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params["to"] = pd.Timestamp(end).isoformat()
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else:
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params["count"] = min(count, self.MAX_BARS_PER_REQUEST)
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# Retry ladder for transient faults: Oanda's sporadic spurious
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# 401s (~15-20/day on the practice API — the same token succeeds
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# on adjacent requests), 5xx and network errors. Other 4xx (bad
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# request, 429) are never retried; a real credential problem
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# fails every attempt and raises as before. The 2026-09-07
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# signal-log audit found 11 boundary scans in two days that lost
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# their primary frame at xx:00:22-:26 UTC: a single 2 s retry
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# could not outlast a 10-15 s provider blip at the top of the
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# hour, and a scan without its primary frame logs a price-0
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# NEUTRAL row. RETRY_BUDGET_S bounds the whole sequence so a scan
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# (three fetches) still finishes well inside an M5 bar.
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last_err: Optional[OandaDataError] = None
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resp = None
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attempts = 1 + len(self.RETRY_DELAYS_S)
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started = time.monotonic()
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for attempt in range(attempts):
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if attempt:
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delay = self.RETRY_DELAYS_S[attempt - 1]
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if time.monotonic() - started + delay > self.RETRY_BUDGET_S:
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logger.warning(
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f"Oanda fetch for {instrument}: retry budget "
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f"({self.RETRY_BUDGET_S:.0f}s) exhausted after "
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f"{attempt} attempt(s) — giving up")
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break
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time.sleep(delay)
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try:
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resp = self._session.get(url, params=params, timeout=30)
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except requests.RequestException as e:
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last_err = OandaDataError(
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f"Oanda candle fetch failed for {instrument}: {e}")
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last_err.__cause__ = e
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resp = None
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logger.warning(
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f"Oanda fetch for {instrument} attempt "
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f"{attempt + 1}/{attempts} failed: {e}")
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continue
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if resp.status_code == 401 or resp.status_code >= 500:
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last_err = OandaDataError(
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f"Oanda candle fetch failed for {instrument}: "
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f"HTTP {resp.status_code}: {resp.text[:300]}")
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logger.warning(
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f"Oanda fetch for {instrument} attempt "
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f"{attempt + 1}/{attempts} got HTTP {resp.status_code}")
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resp = None
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continue
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break
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if resp is None:
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raise last_err
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if resp.status_code != 200:
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raise OandaDataError(
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f"Oanda candle fetch failed for {instrument}: "
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f"HTTP {resp.status_code}: {resp.text[:300]}")
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try:
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data = resp.json()
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except ValueError as e:
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raise OandaDataError(
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f"Oanda candle response for {instrument} is not valid "
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f"JSON: {e}") from e
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candles = data.get("candles", [])
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if not candles:
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raise OandaDataError(
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f"Empty candle response from Oanda for {instrument}")
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rows = []
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last_bid_close = None
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last_ask_close = None
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skipped_malformed = 0
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for c in candles:
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if not c.get("complete", False):
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continue
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mid = c.get("mid", {})
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bid = c.get("bid", {})
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ask = c.get("ask", {})
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# A candle missing any OHLC field must be SKIPPED, never
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# defaulted to 0.0: a zero price flows through cleaning as a
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# "legit" bar and poisons ATR/Keltner/SL math — and a
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# mid-frame zero Close makes pct_change() emit inf, which
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# NaNs the std and disarms the 10σ outlier filter for the
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# whole frame (2026-08-04 audit, I45).
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if any(mid.get(k) is None for k in ("o", "h", "l", "c")):
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skipped_malformed += 1
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continue
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rows.append({
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"Datetime": pd.Timestamp(c["time"]).tz_convert("UTC"),
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"Open": float(mid["o"]),
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"High": float(mid["h"]),
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"Low": float(mid["l"]),
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"Close": float(mid["c"]),
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"Volume": int(c.get("volume", 0)),
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})
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if bid and ask:
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last_bid_close = float(bid.get("c", 0))
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last_ask_close = float(ask.get("c", 0))
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if skipped_malformed:
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logger.warning(
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"Oanda returned %d malformed candle(s) for %s (missing "
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"OHLC fields) — skipped", skipped_malformed, instrument)
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if not rows:
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raise OandaDataError(
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f"No complete candles from Oanda for {instrument}")
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if last_bid_close and last_ask_close:
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self._spread_cache[symbol] = round(
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last_ask_close - last_bid_close, 4)
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df = pd.DataFrame(rows)
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df = df.set_index("Datetime")
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df = df.sort_index()
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if df.index.tz is None:
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df.index = df.index.tz_localize("UTC")
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df.index.name = "Datetime"
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for col in ("Open", "High", "Low", "Close"):
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df[col] = pd.to_numeric(df[col], errors="coerce")
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df["Volume"] = pd.to_numeric(df["Volume"], errors="coerce").fillna(0).astype(float)
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logger.debug(
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"Fetched %d bars for %s @ %s from Oanda",
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len(df), instrument, granularity)
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return df
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