Harwinder
505 lines
18 KiB
Python
505 lines
18 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 Twelve Data API.
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Raises DataUnavailableError when the API is unreachable or credits are exhausted.
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No synthetic/simulated data fallback.
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"""
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import logging
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import os
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import threading
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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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import requests
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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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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 (no key, rate limit, API error)."""
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pass
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# ── Module-level shared rate limiter ───────────────────────────────
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# All ForexDataProvider instances share these so concurrent scans
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# don't each think they have a full 8-credit budget.
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_rate_lock = threading.Lock()
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_rate_request_times: deque = deque()
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_rate_daily_credits: int = 0
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_rate_daily_reset: float = 0.0
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class ForexDataProvider:
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"""
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Forex data provider backed by Twelve Data API.
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Twelve Data has native 4h candles, eliminating the H1→H4 resampling
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hack needed for Yahoo Finance. Free tier: 800 credits/day, 8 req/min.
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"""
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TD_BASE_URL = "https://api.twelvedata.com"
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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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# Approximate bars per day for each timeframe (used to size outputsize)
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BARS_PER_DAY = {
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Timeframe.M1: 1440,
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Timeframe.M5: 288,
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Timeframe.M15: 96,
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Timeframe.M30: 48,
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Timeframe.H1: 24,
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Timeframe.H4: 6,
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Timeframe.D1: 1,
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Timeframe.W1: 0.143,
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}
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# Free plan: 8 credits/minute, 800 credits/day
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RATE_LIMIT = 8
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RATE_WINDOW = 60.0
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DAILY_LIMIT = 800
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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._session = requests.Session()
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self._session.headers.update({
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"User-Agent": "Mozilla/5.0 (compatible; ForexIntel/1.0)",
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})
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if self._api_key:
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self._session.headers["Authorization"] = f"apikey {self._api_key}"
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@staticmethod
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def _to_td_symbol(symbol: str) -> str:
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"""Convert internal symbol (EURUSD=X) to Twelve Data format (EUR/USD)."""
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clean = symbol.replace("=X", "")
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return f"{clean[:3]}/{clean[3:]}"
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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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if (
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cache_key in self._cache_timestamps
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and (now - self._cache_timestamps[cache_key]) < self.config.refresh_interval
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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_twelve_data(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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Twelve Data accepts comma-separated symbols, so 7 pairs × 3 timeframes
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= 3 requests instead of 21 — well within the free-tier 8 req/min limit.
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Results are stored in the local cache; subsequent fetch_ohlcv calls
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for the same symbol/TF will be served from cache.
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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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for tf in timeframes:
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try:
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self._batch_fetch_timeframe(symbols, tf, lookback_days)
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time.sleep(1.0) # brief pause between TF requests
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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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# Commodity-specific pip values take priority, then JPY pairs, then standard forex
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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 _throttle(self, credits: int = 1) -> None:
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"""
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Block until `credits` per-minute API credits are available.
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Raises DataUnavailableError if the daily limit is exhausted.
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Uses module-level state so all ForexDataProvider instances (including
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those running in parallel threads) share the same credit accounting.
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Twelve Data free tier: 8 credits/minute, 800 credits/day.
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"""
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global _rate_daily_credits, _rate_daily_reset
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with _rate_lock:
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now = time.time()
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# Reset daily counter after 24 hours
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if _rate_daily_reset == 0.0:
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_rate_daily_reset = now + 86400
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if now >= _rate_daily_reset:
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_rate_daily_credits = 0
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_rate_daily_reset = now + 86400
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# Daily limit check — raise immediately, no fallback
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if _rate_daily_credits + credits > self.DAILY_LIMIT:
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raise DataUnavailableError(
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f"Twelve Data daily API limit reached "
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f"({_rate_daily_credits}/{self.DAILY_LIMIT} credits). "
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"Resets in 24 hours."
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)
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if _rate_daily_credits + credits > self.DAILY_LIMIT * 0.8:
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logger.warning(
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f"Approaching daily API limit: "
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f"{_rate_daily_credits}/{self.DAILY_LIMIT} credits used."
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)
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# Per-minute rate limit — wait inside the lock so other threads
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# queue up rather than over-counting available credits.
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while True:
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now = time.time()
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while _rate_request_times and now - _rate_request_times[0] >= self.RATE_WINDOW:
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_rate_request_times.popleft()
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available = self.RATE_LIMIT - len(_rate_request_times)
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if available >= credits:
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ts = time.time()
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for _ in range(credits):
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_rate_request_times.append(ts)
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_rate_daily_credits += credits
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return
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wait = (_rate_request_times[0] + self.RATE_WINDOW) - now + 0.2
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logger.info(
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f"Rate limit: {len(_rate_request_times)}/{self.RATE_LIMIT} credits used "
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f"— waiting {wait:.1f}s for next window…"
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)
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# Release lock while sleeping so other threads can check state
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_rate_lock.release()
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time.sleep(wait)
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_rate_lock.acquire()
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def _batch_fetch_timeframe(
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self, symbols: List[str], timeframe: Timeframe, lookback_days: int
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) -> None:
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"""
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Single API call fetching all symbols for one timeframe.
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Twelve Data returns a dict keyed by symbol when multiple are requested.
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Parsed DataFrames are validated and stored in the cache.
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"""
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if not self._api_key:
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return
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td_symbols = [self._to_td_symbol(s) for s in symbols]
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interval = self.TD_INTERVALS[timeframe]
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outputsize = min(
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int(lookback_days * self.BARS_PER_DAY[timeframe]) + 20, 5000
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)
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outputsize = max(outputsize, 300)
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# Each symbol in a batch request costs 1 credit — raises DataUnavailableError
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# if daily limit reached; per-minute waits are handled inside _throttle.
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self._throttle(credits=len(symbols))
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resp = self._session.get(
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f"{self.TD_BASE_URL}/time_series",
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params={
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"symbol": ",".join(td_symbols),
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"interval": interval,
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"outputsize": outputsize,
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"timezone": "UTC",
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"format": "json",
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},
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timeout=30,
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)
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resp.raise_for_status()
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data = resp.json()
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# If the whole batch request returned a top-level error, raise it
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if isinstance(data, dict) and data.get("status") == "error":
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raise DataIntegrityError(
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f"Batch API error ({timeframe.value}): {data.get('message', 'unknown')}"
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)
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# Batch response: {"EUR/USD": {...}, "GBP/USD": {...}, ...}
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now = time.time()
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for symbol, td_sym in zip(symbols, td_symbols):
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try:
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sym_data = data.get(td_sym, {})
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if sym_data.get("status") == "error":
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logger.warning(
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f"Batch fetch error for {td_sym}: {sym_data.get('message')}"
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)
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continue
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values = sym_data.get("values")
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if not values:
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logger.warning(
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f"No data returned by Twelve Data for {td_sym} "
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f"— symbol may require a paid plan."
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)
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continue
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records = [
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{
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"Datetime": pd.Timestamp(v["datetime"], tz="UTC"),
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"Open": float(v["open"]) if v.get("open") is not None else None,
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"High": float(v["high"]) if v.get("high") is not None else None,
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"Low": float(v["low"]) if v.get("low") is not None else None,
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"Close": float(v["close"]) if v.get("close") is not None else None,
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"Volume": float(v["volume"]) if v.get("volume") is not None else 0.0,
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}
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for v in reversed(values)
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if v.get("datetime")
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]
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df = pd.DataFrame(records).set_index("Datetime")
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df.index.name = "Datetime"
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df = self._validate_and_clean(df, symbol, timeframe)
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cache_key = f"{symbol}_{timeframe.value}"
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self._cache[cache_key] = df
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self._cache_timestamps[cache_key] = now
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logger.info(f"[live] {td_sym} {interval}: {len(df)} bars, last close={df['Close'].iloc[-1]:.5f}")
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except Exception as e:
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logger.warning(f"Failed to parse batch data for {td_sym}: {e}")
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def _fetch_twelve_data(
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self, symbol: str, timeframe: Timeframe, lookback_days: int
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) -> pd.DataFrame:
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"""
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Fetch OHLCV from Twelve Data REST API.
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Raises DataUnavailableError when data cannot be retrieved.
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"""
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if not self._api_key:
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raise DataUnavailableError(
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"No Twelve Data API key — set the TWELVE_DATA_API_KEY environment variable."
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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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outputsize = min(
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int(lookback_days * self.BARS_PER_DAY[timeframe]) + 20,
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5000,
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)
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outputsize = max(outputsize, 300)
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# Raises DataUnavailableError on daily limit; blocks on per-minute limit.
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self._throttle(credits=1)
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try:
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resp = self._session.get(
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f"{self.TD_BASE_URL}/time_series",
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params={
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"symbol": td_symbol,
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"interval": interval,
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"outputsize": outputsize,
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"timezone": "UTC",
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"format": "json",
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},
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timeout=15,
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)
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resp.raise_for_status()
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data = resp.json()
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if data.get("status") == "error":
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raise DataUnavailableError(
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f"Twelve Data API error for {td_symbol}: {data.get('message', 'unknown')}"
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)
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values = data.get("values")
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if not values:
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raise DataUnavailableError(
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f"No data returned for {td_symbol} — symbol may require a paid plan."
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)
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records = [
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{
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"Datetime": pd.Timestamp(v["datetime"], tz="UTC"),
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"Open": float(v["open"]) if v.get("open") is not None else None,
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"High": float(v["high"]) if v.get("high") is not None else None,
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"Low": float(v["low"]) if v.get("low") is not None else None,
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"Close": float(v["close"]) if v.get("close") is not None else None,
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"Volume": float(v["volume"]) if v.get("volume") is not None else 0.0,
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}
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for v in reversed(values)
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if v.get("datetime")
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]
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df = pd.DataFrame(records).set_index("Datetime")
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df.index.name = "Datetime"
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logger.info(f"[live] {td_symbol} {interval}: {len(df)} bars, last close={df['Close'].iloc[-1]:.5f}")
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return df
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except (DataUnavailableError, DataIntegrityError):
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raise
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except Exception as e:
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raise DataUnavailableError(
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f"Failed to fetch {td_symbol}: {e}"
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) from 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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Steps: drop all-NaN rows → ffill small gaps → remove future timestamps
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→ fix OHLC inconsistencies → remove >10-sigma outliers → sort & deduplicate.
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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.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.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.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")]
|
||
|
||
cleaned = original_len - len(df)
|
||
if cleaned > 0:
|
||
logger.info(f"{symbol}: Cleaned {cleaned}/{original_len} bars")
|
||
|
||
return df
|