Harwinder
43cc4bdd42
Adds FinnhubDataProvider as a drop-in replacement for ForexDataProvider. Rate limit or API errors raise DataUnavailableError immediately — the Telegram bot shows a clean per-TF error card rather than fake prices. - data_provider_finnhub.py: Finnhub client, OANDA symbol mapping, 4h resampled from 60-min candles, shared module-level rate limiter (60 calls/min free tier), full OHLCV validation copied from original - signal_aggregator.py: import FinnhubDataProvider alias ForexDataProvider - config.py: add finnhub_api_key field (FINNHUB_API_KEY env var overrides) - telegram_bot.py: remove synthetic-data warning and _SUPPORTED_PAIRS; unsupported pairs now show the engine's "data unavailable" card - requirements.txt: add finnhub-python>=1.0 Set FINNHUB_API_KEY env var before running. main branch is untouched. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
387 lines
13 KiB
Python
387 lines
13 KiB
Python
"""
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Forex Signal Intelligence Platform - Finnhub Data Provider
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===========================================================
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Drop-in replacement for ForexDataProvider using Finnhub instead of Twelve Data.
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Raises DataUnavailableError on rate limit or API errors — no synthetic fallback.
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Setup:
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set FINNHUB_API_KEY=your_key_here (Windows CMD)
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export FINNHUB_API_KEY=your_key (Linux/Mac)
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Free tier limits: 60 API calls/minute.
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Symbol broker: OANDA (e.g. OANDA:EUR_USD).
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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 typing import Dict, List, Optional
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import pandas as pd
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import finnhub
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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 rate limiter shared across all provider instances and threads.
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_rate_lock = threading.Lock()
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_rate_request_times: deque = deque()
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RATE_LIMIT = 60 # Finnhub free tier: 60 calls/minute
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RATE_WINDOW = 60.0
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class FinnhubDataProvider:
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"""
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Forex data provider backed by the Finnhub API (OANDA feed).
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Free tier: 60 calls/minute, no hard daily cap.
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When the rate limit or any API error occurs, DataUnavailableError is raised
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immediately — there is no synthetic/simulated data fallback.
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Supported resolutions: 1, 5, 15, 30, 60, D, W.
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H4 bars are synthesised client-side by resampling 60-minute candles.
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"""
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# Finnhub resolution strings (H4 uses 60-min candles, then resampled)
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FH_RESOLUTIONS = {
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Timeframe.M1: "1",
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Timeframe.M5: "5",
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Timeframe.M15: "15",
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Timeframe.M30: "30",
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Timeframe.H1: "60",
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Timeframe.H4: "60",
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Timeframe.D1: "D",
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Timeframe.W1: "W",
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}
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def __init__(self, config: PlatformConfig):
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self.config = config
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api_key = (
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os.environ.get("FINNHUB_API_KEY")
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or getattr(config, "finnhub_api_key", "")
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)
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if not api_key:
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raise DataUnavailableError(
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"No Finnhub API key — set the FINNHUB_API_KEY environment variable."
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)
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self._client = finnhub.Client(api_key=api_key)
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self._cache: Dict[str, pd.DataFrame] = {}
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self._cache_timestamps: Dict[str, float] = {}
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@staticmethod
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def _to_finnhub_symbol(symbol: str) -> str:
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"""Convert EURUSD=X → OANDA:EUR_USD."""
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clean = symbol.replace("=X", "")
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return f"OANDA:{clean[:3]}_{clean[3:]}"
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# ── Public interface (matches ForexDataProvider) ───────────────────
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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_finnhub(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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Sequentially prefetch symbols × timeframes.
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Finnhub has no batch endpoint, so we fetch one symbol at a time.
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The module-level rate limiter ensures we never exceed 60 calls/minute
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even when multiple threads call this concurrently.
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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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for symbol in symbols:
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try:
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self.fetch_ohlcv(symbol, tf, lookback_days)
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except DataUnavailableError as e:
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logger.warning(f"Prefetch skipped {symbol} {tf.value}: {e}")
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except Exception as e:
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logger.warning(f"Prefetch failed {symbol} {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: Dict[str, pd.DataFrame] = {}
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for symbol in symbols:
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try:
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results[symbol] = self.fetch_ohlcv(symbol, timeframe, lookback_days)
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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 _throttle(self) -> None:
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"""
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Block until a per-minute API call slot is available.
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Uses module-level state so all FinnhubDataProvider instances
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(including those in parallel threads) share the same budget.
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"""
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with _rate_lock:
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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] >= RATE_WINDOW:
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_rate_request_times.popleft()
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if len(_rate_request_times) < RATE_LIMIT:
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_rate_request_times.append(time.time())
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return
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wait = (_rate_request_times[0] + RATE_WINDOW) - now + 0.2
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logger.info(
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f"Finnhub rate limit: {len(_rate_request_times)}/{RATE_LIMIT} "
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f"calls used — waiting {wait:.1f}s…"
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)
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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 _fetch_finnhub(
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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 Finnhub REST API via the official Python client.
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Raises DataUnavailableError on any API failure or rate limit.
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"""
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fh_symbol = self._to_finnhub_symbol(symbol)
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resolution = self.FH_RESOLUTIONS[timeframe]
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to_ts = int(time.time())
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# For H4 resampling we need the full 1h candle history
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from_ts = to_ts - (lookback_days * 86400)
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self._throttle()
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try:
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data = self._client.forex_candles(fh_symbol, resolution, from_ts, to_ts)
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except finnhub.FinnhubAPIException as e:
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if e.status_code == 429:
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raise DataUnavailableError(
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"Finnhub API rate limit reached — too many requests. "
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"Free tier allows 60 calls/minute. Try again shortly."
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) from e
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raise DataUnavailableError(
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f"Finnhub API error for {fh_symbol} ({e.status_code}): {e}"
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) from e
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except Exception as e:
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raise DataUnavailableError(
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f"Failed to fetch {fh_symbol} from Finnhub: {e}"
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) from e
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status = data.get("s") if data else None
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if status == "no_data":
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raise DataUnavailableError(
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f"Finnhub returned no data for {fh_symbol} — "
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"symbol may not be supported on your plan, or the market is closed."
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)
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if status != "ok":
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raise DataUnavailableError(
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f"Finnhub returned unexpected status '{status}' for {fh_symbol}."
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)
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timestamps = data.get("t", [])
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if not timestamps:
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raise DataUnavailableError(
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f"Finnhub response for {fh_symbol} contained no candles."
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)
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records = [
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{
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"Datetime": pd.Timestamp(ts, unit="s", tz="UTC"),
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"Open": float(o),
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"High": float(h),
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"Low": float(l),
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"Close": float(c),
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"Volume": float(v),
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}
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for ts, o, h, l, c, v in zip(
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data["t"], data["o"], data["h"], data["l"], data["c"], data["v"]
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)
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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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if timeframe == Timeframe.H4:
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df = self._resample_to_4h(df)
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logger.debug(f"Fetched {len(df)} bars for {fh_symbol} @ {resolution}")
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return df
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@staticmethod
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def _resample_to_4h(df: pd.DataFrame) -> pd.DataFrame:
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"""Resample 1-hour OHLCV candles into 4-hour bars."""
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resampled = 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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})
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return resampled.dropna(subset=["Close"])
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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.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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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")]
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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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