Harwinder
395 lines
14 KiB
Python
395 lines
14 KiB
Python
"""
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Forex Signal Intelligence Platform - Data Provider
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====================================================
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Retrieves and validates market data from Yahoo Finance API.
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Raises DataUnavailableError when the API is unreachable.
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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 datetime import datetime, timedelta, timezone
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from typing import Dict, List, Optional
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import pandas as pd
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try:
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import yfinance as yf
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_YF_AVAILABLE = True
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except ImportError:
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yf = None
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_YF_AVAILABLE = False
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from config import PlatformConfig, Timeframe, SYMBOL_DISPLAY, PIP_VALUES
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logger = logging.getLogger(__name__)
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# Fallbacks for specific symbols to get accurate Forex/Commodity data from yfinance.
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_YF_FALLBACK_MAP = {
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"XAUUSD=X": "GC=F",
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"XAGUSD=X": "SI=F",
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}
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_YF_INTERVALS = {
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Timeframe.M1: "1m",
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Timeframe.M5: "5m",
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Timeframe.M15: "15m",
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Timeframe.M30: "30m",
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Timeframe.H1: "1h",
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Timeframe.H4: "1h", # Resampled in code
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Timeframe.D1: "1d",
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Timeframe.W1: "1wk",
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}
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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 DataUnavailableError(Exception):
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"""Raised when live market data cannot be fetched (API error)."""
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pass
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class ForexDataProvider:
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"""
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Forex data provider backed by yfinance API.
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"""
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def __init__(self, config: PlatformConfig):
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self.config = config
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self._cache: Dict[str, pd.DataFrame] = {}
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self._cache_timestamps: Dict[str, float] = {}
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def _cache_ttl(self, timeframe: Timeframe) -> int:
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"""Cache TTL for a given timeframe — falls back to refresh_interval."""
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return self.config.cache_ttl_per_tf.get(timeframe, self.config.refresh_interval)
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def fetch_ohlcv(
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self,
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symbol: str,
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timeframe: Timeframe,
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lookback_days: Optional[int] = None,
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) -> pd.DataFrame:
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"""
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Fetch OHLCV data for a Forex pair with full validation.
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Returns a DataFrame with columns: Open, High, Low, Close, Volume
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Index is a timezone-aware DatetimeIndex in UTC.
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"""
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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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ttl = self._cache_ttl(timeframe)
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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]) < 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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return cached
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df = self._fetch_yfinance(symbol, timeframe, lookback_days)
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df = self._validate_and_clean(df, symbol, timeframe)
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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 prefetch_batch(
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self,
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symbols: List[str],
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timeframes: List[Timeframe],
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lookback_days: Optional[int] = None,
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) -> None:
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"""
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Batch-fetch all symbols for each timeframe in a single API call per TF.
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"""
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if lookback_days is None:
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lookback_days = self.config.data_lookback_days
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now = time.time()
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for tf in timeframes:
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ttl = self._cache_ttl(tf)
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cached = all(
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f"{s}_{tf.value}" in self._cache_timestamps
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and (now - self._cache_timestamps[f"{s}_{tf.value}"]) < ttl
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and not self._cache.get(f"{s}_{tf.value}", pd.DataFrame()).empty
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for s in symbols
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)
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if cached:
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logger.debug(f"Skipping prefetch for {tf.value} — all {len(symbols)} symbols cached")
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continue
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try:
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self._batch_fetch_timeframe(symbols, tf, lookback_days)
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except Exception as e:
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logger.warning(f"Batch prefetch failed for {tf.value}: {e}")
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def fetch_multiple(
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self,
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symbols: List[str],
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timeframe: Timeframe,
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lookback_days: Optional[int] = None,
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) -> Dict[str, pd.DataFrame]:
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"""Fetch data for multiple symbols, returning dict of DataFrames."""
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results = {}
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for symbol in symbols:
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try:
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df = self.fetch_ohlcv(symbol, timeframe, lookback_days)
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results[symbol] = df
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except Exception as e:
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logger.warning(f"Failed to fetch {symbol}: {e}")
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return results
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def get_latest_price(self, symbol: str) -> Optional[float]:
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"""Return the most recent close price for a symbol."""
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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) -> float:
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"""Estimate typical spread from recent M5 high-low ranges."""
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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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fallback_spreads = {
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"EURUSD=X": 0.00010, "GBPUSD=X": 0.00015,
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"USDJPY=X": 0.010, "USDCHF=X": 0.00015,
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"AUDUSD=X": 0.00015, "USDCAD=X": 0.00018,
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"NZDUSD=X": 0.00020,
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}
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return fallback_spreads.get(symbol, 0.00020)
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def get_pair_info(self, symbol: str) -> Dict:
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"""Return display name and pip value for a pair."""
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display = SYMBOL_DISPLAY.get(symbol, symbol)
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is_jpy = "JPY" in symbol
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pip_value = PIP_VALUES.get(symbol, 0.01 if is_jpy else 0.0001)
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return {
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"symbol": symbol,
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"display": display,
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"pip_value": pip_value,
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"is_jpy_pair": is_jpy,
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}
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# ── Private Methods ────────────────────────────────────────────
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def _fetch_yfinance(self, symbol: str, timeframe: Timeframe, lookback_days: int) -> pd.DataFrame:
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if not _YF_AVAILABLE:
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raise DataUnavailableError("yfinance is not installed.")
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yf_symbol = _YF_FALLBACK_MAP.get(symbol, symbol)
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# Determine actual interval and resampling needs
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actual_timeframe = timeframe
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resample_needed = False
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if timeframe == Timeframe.H4:
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actual_timeframe = Timeframe.H1
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resample_needed = True
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yf_interval = _YF_INTERVALS.get(actual_timeframe)
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if yf_interval is None:
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raise DataUnavailableError(f"Unsupported timeframe {timeframe.value} for yfinance")
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# Period logic based on Yahoo limitations
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if yf_interval in ("1m", "2m", "5m"):
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period = "7d" if lookback_days < 7 else "60d"
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elif yf_interval in ("15m", "30m", "1h", "60m"):
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period = "60d"
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elif yf_interval in ("1d", "1wk"):
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period = "2y"
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else:
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period = f"{lookback_days}d"
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try:
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df = yf.download(
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yf_symbol, interval=yf_interval, period=period,
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progress=False, auto_adjust=False, threads=False,
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)
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if df is None or df.empty:
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raise DataUnavailableError(f"No data returned for {symbol}")
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if isinstance(df.columns, pd.MultiIndex):
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df.columns = df.columns.get_level_values(0)
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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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else:
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df.index = df.index.tz_convert("UTC")
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df.index.name = "Datetime"
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df = df[["Open", "High", "Low", "Close", "Volume"]].copy()
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if resample_needed and timeframe == Timeframe.H4:
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# Resample 1h data into 4h blocks
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df = df.resample('4h').agg({
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'Open': 'first',
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'High': 'max',
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'Low': 'min',
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'Close': 'last',
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'Volume': 'sum'
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}).dropna()
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logger.info(f"[yf live] {yf_symbol} {timeframe.value}: {len(df)} bars, last close={df['Close'].iloc[-1]:.5f}")
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return df
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except Exception as e:
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raise DataUnavailableError(f"yfinance fetch failed for {symbol} {timeframe.value}: {e}")
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def _batch_fetch_timeframe(self, symbols: List[str], timeframe: Timeframe, lookback_days: int) -> None:
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if not _YF_AVAILABLE:
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return
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actual_timeframe = timeframe
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resample_needed = False
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if timeframe == Timeframe.H4:
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actual_timeframe = Timeframe.H1
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resample_needed = True
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yf_interval = _YF_INTERVALS.get(actual_timeframe)
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if not yf_interval:
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return
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yf_symbols = [_YF_FALLBACK_MAP.get(s, s) for s in symbols]
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if yf_interval in ("1m", "2m", "5m"):
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period = "7d" if lookback_days < 7 else "60d"
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elif yf_interval in ("15m", "30m", "1h", "60m"):
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period = "60d"
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elif yf_interval in ("1d", "1wk"):
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period = "2y"
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else:
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period = f"{lookback_days}d"
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try:
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df_multi = yf.download(
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" ".join(yf_symbols), interval=yf_interval, period=period,
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progress=False, auto_adjust=False, threads=True, group_by="ticker"
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)
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if df_multi is None or df_multi.empty:
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logger.warning(f"Batch fetch empty for {timeframe.value}")
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return
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if df_multi.index.tz is None:
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df_multi.index = df_multi.index.tz_localize("UTC")
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else:
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df_multi.index = df_multi.index.tz_convert("UTC")
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df_multi.index.name = "Datetime"
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now = time.time()
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for symbol, yf_sym in zip(symbols, yf_symbols):
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try:
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if len(yf_symbols) == 1:
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df_sym = df_multi.copy()
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if isinstance(df_sym.columns, pd.MultiIndex):
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df_sym.columns = df_sym.columns.get_level_values(0)
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else:
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if yf_sym in df_multi.columns.get_level_values(0):
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df_sym = df_multi[yf_sym].copy()
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else:
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# Might be single level columns if download only returned 1 valid ticker
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if yf_sym == yf_symbols[0] and not isinstance(df_multi.columns, pd.MultiIndex):
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df_sym = df_multi.copy()
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else:
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continue
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# Ignore if column names are not Open/High/Low/Close
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if 'Close' not in df_sym.columns:
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continue
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df_sym = df_sym[["Open", "High", "Low", "Close", "Volume"]].copy()
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if resample_needed and timeframe == Timeframe.H4:
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df_sym = df_sym.resample('4h').agg({
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'Open': 'first',
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'High': 'max',
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'Low': 'min',
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'Close': 'last',
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'Volume': 'sum'
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}).dropna()
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df_sym = self._validate_and_clean(df_sym, symbol, timeframe)
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cache_key = f"{symbol}_{timeframe.value}"
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self._cache[cache_key] = df_sym
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self._cache_timestamps[cache_key] = now
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logger.info(f"[yf batch] {yf_sym} {timeframe.value}: {len(df_sym)} bars, last close={df_sym['Close'].iloc[-1]:.5f}")
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except Exception as e:
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logger.warning(f"Failed to process batch data for {symbol}: {e}")
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except Exception as e:
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logger.warning(f"Batch fetch failed for {timeframe.value}: {e}")
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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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"""
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Validate and clean OHLCV data.
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"""
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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.debug(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.debug(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.debug(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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self.config.technical.ichimoku_senkou_b,
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200,
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) + 10
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if len(df) < min_required:
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logger.debug(
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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.debug(f"{symbol}: Cleaned {cleaned}/{original_len} bars")
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return df
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