harwinder
89030c96b6
- Drop USD/JPY from scan pairs (zero signals across all timeframes) - Disable divergence indicator (zero impact in 14-config lab test) - Parallelize evaluate.py with ProcessPoolExecutor (16 cores) - Add --pair CLI filter to evaluate.py for single-pair backtests - Add data freshness guard: stale cache auto-invalidated, primary TF stale data suppresses signals to prevent trading on old data - Add /cache command: shows fetch age and last bar per pair/TF - Add /clearcache command: wipes in-memory cache for fresh API fetch - Remove BB from WebSocket breach alerts (keep structural levels only) - Show live prices only for WS-monitored pairs (EUR/USD, XAU/USD) - Replace arrow emojis with text tags in indicator lines
317 lines
11 KiB
Python
Executable File
317 lines
11 KiB
Python
Executable File
"""
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ForexBot2 — Data Provider (Twelve Data SDK)
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==========================================================
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Fetches and validates OHLCV via the official `twelvedata` Python SDK.
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"""
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import logging
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import os
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import time
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from collections import deque
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from typing import Dict, Optional
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import pandas as pd
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from twelvedata import TDClient
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from config import PlatformConfig, Timeframe, USDJPY, PAIR_INFO
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logger = logging.getLogger(__name__)
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class DataIntegrityError(Exception):
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"""Raised when data fails validation checks."""
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pass
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class ForexDataProvider:
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"""Twelve Data SDK client + on-disk parquet cache."""
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TD_INTERVALS = {
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Timeframe.M1: "1min",
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Timeframe.M5: "5min",
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Timeframe.M15: "15min",
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Timeframe.M30: "30min",
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Timeframe.H1: "1h",
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Timeframe.H4: "4h",
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Timeframe.D1: "1day",
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Timeframe.W1: "1week",
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}
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BARS_PER_DAY = {
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Timeframe.M1: 1440, Timeframe.M5: 288, Timeframe.M15: 96,
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Timeframe.M30: 48, Timeframe.H1: 24, Timeframe.H4: 6,
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Timeframe.D1: 1, Timeframe.W1: 0.143,
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}
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CANDLE_SECONDS = {
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Timeframe.M1: 60, Timeframe.M5: 300, Timeframe.M15: 900,
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Timeframe.M30: 1800, Timeframe.H1: 3600, Timeframe.H4: 14400,
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Timeframe.D1: 86400, Timeframe.W1: 604800,
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}
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RATE_LIMIT = 55
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RATE_WINDOW = 60.0
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def __init__(self, config: PlatformConfig):
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self.config = config
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self._api_key = (
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os.environ.get("TWELVE_DATA_API_KEY")
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or config.twelve_data_api_key
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)
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self._cache: Dict[str, pd.DataFrame] = {}
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self._cache_timestamps: Dict[str, float] = {}
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self._request_times: deque = deque()
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self._td: Optional[TDClient] = (
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TDClient(apikey=self._api_key) if self._api_key else None
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)
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@staticmethod
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def _to_td_symbol(symbol: str) -> str:
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if "/" in symbol:
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return symbol
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clean = symbol.replace("=X", "")
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return f"{clean[:3]}/{clean[3:]}"
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# ── Public API ─────────────────────────────────────────────────
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def fetch_ohlcv(
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self,
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symbol: str = USDJPY,
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timeframe: Timeframe = Timeframe.H1,
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lookback_days: Optional[int] = None,
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) -> pd.DataFrame:
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"""Fetch OHLCV with in-memory caching for live mode."""
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if lookback_days is None:
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lookback_days = self.config.data_lookback_days
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cache_key = f"{symbol}_{timeframe.value}"
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now = time.time()
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cache_ttl = self.CANDLE_SECONDS.get(timeframe, self.config.refresh_interval)
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if (
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cache_key in self._cache_timestamps
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and (now - self._cache_timestamps[cache_key]) < cache_ttl
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and cache_key in self._cache
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):
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cached = self._cache.get(cache_key)
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if cached is not None and not cached.empty:
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if self._is_data_fresh(cached, symbol, timeframe):
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return cached
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logger.warning(f"{symbol} {timeframe.value}: cached data is stale, refetching")
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del self._cache[cache_key]
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del self._cache_timestamps[cache_key]
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outputsize = max(
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300,
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min(int(lookback_days * self.BARS_PER_DAY[timeframe]) + 20, 5000),
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)
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df = self._fetch_twelve_data(symbol, timeframe, outputsize)
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df = self._validate_and_clean(df, symbol, timeframe)
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if not self._is_data_fresh(df, symbol, timeframe):
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logger.warning(
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f"STALE DATA from API: {symbol} {timeframe.value} — "
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f"last bar {df.index[-1]} is too old. Signal quality may be degraded."
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)
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self._cache[cache_key] = df
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self._cache_timestamps[cache_key] = now
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return df
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def _is_data_fresh(self, df: pd.DataFrame, symbol: str, timeframe: Timeframe) -> bool:
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"""Check if the last candle is within acceptable age for this timeframe."""
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if df is None or df.empty:
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return False
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now = pd.Timestamp.now(tz="UTC")
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if now.weekday() >= 5:
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return True
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last_bar = df.index[-1]
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candle_seconds = self.CANDLE_SECONDS.get(timeframe, 3600)
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max_age_seconds = candle_seconds * 3
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age_seconds = (now - last_bar).total_seconds()
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if age_seconds > max_age_seconds:
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logger.warning(
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f"{symbol} {timeframe.value}: last bar {last_bar.strftime('%Y-%m-%d %H:%M')} "
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f"is {age_seconds / 60:.0f}m old (limit: {max_age_seconds / 60:.0f}m)"
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)
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return False
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return True
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def clear_cache(self) -> int:
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"""Wipe all cached data. Returns number of entries cleared."""
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count = len(self._cache)
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self._cache.clear()
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self._cache_timestamps.clear()
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logger.info(f"Cache cleared: {count} entries wiped")
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return count
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def get_latest_price(self, symbol: str = USDJPY) -> Optional[float]:
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try:
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df = self.fetch_ohlcv(symbol, Timeframe.M5, lookback_days=1)
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if df is not None and not df.empty:
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return float(df["Close"].iloc[-1])
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except Exception:
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pass
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return None
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def get_spread_estimate(self, symbol: str = USDJPY) -> float:
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try:
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df = self.fetch_ohlcv(symbol, Timeframe.M5, lookback_days=1)
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if df is not None and not df.empty:
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return float((df["High"] - df["Low"]).median())
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except Exception:
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pass
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info = PAIR_INFO.get(symbol, PAIR_INFO[USDJPY])
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return info["pip_value"]
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def get_pair_info(self, symbol: str = USDJPY) -> Dict:
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info = PAIR_INFO.get(symbol, PAIR_INFO[USDJPY])
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return {"symbol": symbol, **info}
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# ── Private ────────────────────────────────────────────────────
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def _throttle(self, credits: int = 1) -> None:
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"""Pro tier: 55 credits / minute. Block until budget allows."""
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while True:
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now = time.time()
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while self._request_times and now - self._request_times[0] >= self.RATE_WINDOW:
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self._request_times.popleft()
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available = self.RATE_LIMIT - len(self._request_times)
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if available >= credits:
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for _ in range(credits):
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self._request_times.append(time.time())
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return
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wait = (self._request_times[0] + self.RATE_WINDOW) - now + 0.2
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logger.info(
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f"Rate limit: {len(self._request_times)}/{self.RATE_LIMIT} credits used "
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f"— waiting {wait:.1f}s..."
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)
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time.sleep(wait)
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def _fetch_twelve_data(
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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_date: Optional[str] = None,
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end_date: Optional[str] = None,
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) -> pd.DataFrame:
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if self._td is None:
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raise DataIntegrityError(
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"No Twelve Data API key. Set TWELVE_DATA_API_KEY env var "
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"or config.twelve_data_api_key."
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)
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td_symbol = self._to_td_symbol(symbol)
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interval = self.TD_INTERVALS[timeframe]
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self._throttle(credits=1)
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kwargs = {
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"symbol": td_symbol,
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"interval": interval,
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"outputsize": min(outputsize, 5000),
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"timezone": "UTC",
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}
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if start_date:
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kwargs["start_date"] = start_date
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if end_date:
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kwargs["end_date"] = end_date
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try:
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df = self._td.time_series(**kwargs).as_pandas()
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except Exception as e:
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raise DataIntegrityError(
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f"Twelve Data fetch failed for {td_symbol}: {e}"
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) from e
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if df is None or df.empty:
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raise DataIntegrityError(f"Empty response from Twelve Data for {td_symbol}")
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# SDK returns lowercase columns; engine expects capitalised.
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df = df.rename(columns={
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"open": "Open", "high": "High", "low": "Low",
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"close": "Close", "volume": "Volume",
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})
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for col in ("Open", "High", "Low", "Close", "Volume"):
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if col in df.columns:
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df[col] = pd.to_numeric(df[col], errors="coerce")
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if "Volume" not in df.columns:
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df["Volume"] = 0.0
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# SDK returns newest-first; engine + indicators want oldest-first.
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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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logger.debug(f"Fetched {len(df)} bars for {td_symbol} @ {interval}")
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return df
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def _validate_and_clean(
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self, df: pd.DataFrame, symbol: str, timeframe: Timeframe
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) -> pd.DataFrame:
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if df is None or df.empty:
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raise DataIntegrityError(f"Empty dataset for {symbol}")
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original_len = len(df)
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df = df.dropna(subset=["Open", "High", "Low", "Close"], how="all")
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df = df.ffill(limit=3)
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df = df.dropna(subset=["Close"])
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now = pd.Timestamp.utcnow()
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future_mask = df.index > now + pd.Timedelta(minutes=5)
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if future_mask.any():
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logger.warning(f"{symbol}: Dropping {future_mask.sum()} future timestamps")
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df = df[~future_mask]
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invalid_ohlc = (
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(df["High"] < df["Low"]) |
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(df["High"] < df["Open"]) |
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(df["High"] < df["Close"]) |
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(df["Low"] > df["Open"]) |
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(df["Low"] > df["Close"])
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)
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if invalid_ohlc.any():
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logger.warning(f"{symbol}: Fixing {invalid_ohlc.sum()} OHLC-inconsistent bars")
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for idx in df[invalid_ohlc].index:
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vals = [df.loc[idx, "Open"], df.loc[idx, "Close"]]
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df.loc[idx, "High"] = max(vals + [df.loc[idx, "High"]])
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df.loc[idx, "Low"] = min(vals + [df.loc[idx, "Low"]])
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returns = df["Close"].pct_change()
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std_ret = returns.std()
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if std_ret > 0:
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outlier_mask = (returns - returns.mean()).abs() > 10 * std_ret
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if outlier_mask.any():
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logger.warning(f"{symbol}: Removing {outlier_mask.sum()} extreme outlier bars")
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df = df[~outlier_mask]
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min_required = max(
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self.config.technical.ema_trend,
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200,
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) + 10
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if len(df) < min_required:
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logger.warning(
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f"{symbol} {timeframe.value}: Only {len(df)} bars "
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f"(need {min_required}). Some indicators will be partial."
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)
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df = df.sort_index()
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df = df[~df.index.duplicated(keep="last")]
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cleaned = original_len - len(df)
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if cleaned > 0:
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logger.info(f"{symbol}: Cleaned {cleaned}/{original_len} bars")
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return df
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