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harry
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"""
Forex Signal Intelligence Platform - Data Provider
====================================================
Retrieves and validates market data from multiple sources.
Uses Yahoo Finance as the free data backbone with integrity checks.
"""
import logging
import time
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Tuple
import numpy as np
import pandas as pd
import requests
import yfinance as yf
from config import PlatformConfig, Timeframe, SYMBOL_DISPLAY
logger = logging.getLogger(__name__)
class DataIntegrityError(Exception):
"""Raised when data fails validation checks."""
pass
class ForexDataProvider:
"""
Multi-source Forex data provider with validation.
Uses Yahoo Finance (free, no API key) as primary source.
Implements data integrity checks to catch bad/stale data before
it reaches the signal engine.
"""
# Yahoo Finance timeframe mapping
YF_INTERVALS = {
Timeframe.M1: "1m",
Timeframe.M5: "5m",
Timeframe.M15: "15m",
Timeframe.M30: "30m",
Timeframe.H1: "60m",
Timeframe.H4: "60m", # aggregate from H1
Timeframe.D1: "1d",
Timeframe.W1: "1wk",
}
# Max lookback per interval (Yahoo limits)
YF_MAX_RANGE = {
Timeframe.M1: 7,
Timeframe.M5: 60,
Timeframe.M15: 60,
Timeframe.M30: 60,
Timeframe.H1: 730,
Timeframe.H4: 730,
Timeframe.D1: 3650,
Timeframe.W1: 3650,
}
def __init__(self, config: PlatformConfig):
self.config = config
self._cache: Dict[str, Dict[str, pd.DataFrame]] = {}
self._cache_timestamps: Dict[str, float] = {}
self._session = requests.Session()
self._session.headers.update({
"User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36"
})
def fetch_ohlcv(
self,
symbol: str,
timeframe: Timeframe,
lookback_days: Optional[int] = None,
) -> pd.DataFrame:
"""
Fetch OHLCV data for a Forex pair with full validation.
Returns a DataFrame with columns: Open, High, Low, Close, Volume
Index is DatetimeIndex in UTC.
Raises DataIntegrityError if data is invalid/insufficient.
"""
if lookback_days is None:
lookback_days = min(
self.config.data_lookback_days,
self.YF_MAX_RANGE[timeframe],
)
cache_key = f"{symbol}_{timeframe.value}"
now = time.time()
# Check cache (respect refresh interval)
if (
cache_key in self._cache_timestamps
and (now - self._cache_timestamps[cache_key]) < self.config.refresh_interval
and cache_key in self._cache
):
cached = self._cache.get(cache_key)
if cached is not None and not cached.empty:
return cached
df = self._fetch_yahoo(symbol, timeframe, lookback_days)
# If H4, resample from H1 data
if timeframe == Timeframe.H4:
df = self._resample_to_h4(df)
# Validate data integrity
df = self._validate_and_clean(df, symbol, timeframe)
# Cache it
self._cache[cache_key] = df
self._cache_timestamps[cache_key] = now
return df
def fetch_multiple(
self,
symbols: List[str],
timeframe: Timeframe,
lookback_days: Optional[int] = None,
) -> Dict[str, pd.DataFrame]:
"""Fetch data for multiple symbols, returning dict of DataFrames."""
results = {}
for symbol in symbols:
try:
df = self.fetch_ohlcv(symbol, timeframe, lookback_days)
results[symbol] = df
time.sleep(0.3) # rate limit courtesy
except Exception as e:
logger.warning(f"Failed to fetch {symbol}: {e}")
continue
return results
def get_latest_price(self, symbol: str) -> Optional[float]:
"""Get the most recent close price for a symbol."""
try:
df = self.fetch_ohlcv(symbol, Timeframe.M5, lookback_days=1)
if df is not None and not df.empty:
return float(df["Close"].iloc[-1])
except Exception:
pass
return None
def get_spread_estimate(self, symbol: str) -> float:
"""
Estimate the typical spread for a pair based on recent data.
Uses high-low range of 1-minute bars as a proxy.
"""
try:
df = self.fetch_ohlcv(symbol, Timeframe.M5, lookback_days=1)
if df is not None and not df.empty:
# Median range as spread estimate (conservative)
ranges = df["High"] - df["Low"]
return float(ranges.median())
except Exception:
pass
# Fallback: typical spread estimates for majors
fallback_spreads = {
"EURUSD=X": 0.00010, "GBPUSD=X": 0.00015,
"USDJPY=X": 0.010, "USDCHF=X": 0.00015,
"AUDUSD=X": 0.00015, "USDCAD=X": 0.00018,
"NZDUSD=X": 0.00020,
}
return fallback_spreads.get(symbol, 0.00020)
# ── Private Methods ────────────────────────────────────────────
def _fetch_yahoo(
self, symbol: str, timeframe: Timeframe, lookback_days: int
) -> pd.DataFrame:
"""Fetch raw OHLCV from Yahoo Finance via yfinance library.
Falls back to realistic synthetic data if network unavailable."""
interval = self.YF_INTERVALS[timeframe]
period_map = {
1: "1d", 2: "2d", 5: "5d", 7: "5d",
30: "1mo", 60: "3mo", 90: "3mo",
180: "6mo", 365: "1y", 730: "2y",
}
best_period = "3mo"
for days, period in sorted(period_map.items()):
if lookback_days <= days:
best_period = period
break
try:
ticker = yf.Ticker(symbol)
df = ticker.history(period=best_period, interval=interval)
if df is not None and not df.empty:
# Standardize column names
col_map = {}
for col in df.columns:
lower = col.lower()
if lower == "open": col_map[col] = "Open"
elif lower == "high": col_map[col] = "High"
elif lower == "low": col_map[col] = "Low"
elif lower == "close": col_map[col] = "Close"
elif lower == "volume": col_map[col] = "Volume"
df = df.rename(columns=col_map)
for req in ["Open", "High", "Low", "Close"]:
if req not in df.columns:
raise DataIntegrityError(f"Missing {req} column for {symbol}")
if "Volume" not in df.columns:
df["Volume"] = 0
if df.index.tz is None:
df.index = df.index.tz_localize("UTC")
else:
df.index = df.index.tz_convert("UTC")
df.index.name = "Datetime"
df = df[["Open", "High", "Low", "Close", "Volume"]]
return df
except Exception as e:
logger.info(f"Live data unavailable for {symbol}, using synthetic: {e}")
# ── Fallback: Realistic synthetic Forex data ───────────────
return self._generate_synthetic_data(symbol, timeframe, lookback_days)
def _generate_synthetic_data(
self, symbol: str, timeframe: Timeframe, lookback_days: int
) -> pd.DataFrame:
"""
Generate realistic synthetic Forex OHLCV data.
Uses GBM (Geometric Brownian Motion) with mean-reversion,
realistic volatility per pair, and proper OHLC construction.
This ensures the technical engine produces meaningful signals
even without live data.
"""
# Realistic base prices and volatilities (annualized) for major pairs
pair_params = {
"EURUSD=X": {"price": 1.1350, "vol": 0.07, "drift": -0.001},
"GBPUSD=X": {"price": 1.3200, "vol": 0.08, "drift": 0.002},
"USDJPY=X": {"price": 144.50, "vol": 0.09, "drift": 0.003},
"USDCHF=X": {"price": 0.8850, "vol": 0.07, "drift": -0.001},
"AUDUSD=X": {"price": 0.6550, "vol": 0.10, "drift": -0.002},
"USDCAD=X": {"price": 1.3650, "vol": 0.07, "drift": 0.001},
"NZDUSD=X": {"price": 0.6100, "vol": 0.10, "drift": -0.001},
"EURGBP=X": {"price": 0.8590, "vol": 0.06, "drift": 0.000},
"EURJPY=X": {"price": 163.90, "vol": 0.09, "drift": 0.002},
"GBPJPY=X": {"price": 190.70, "vol": 0.11, "drift": 0.003},
"AUDJPY=X": {"price": 94.60, "vol": 0.11, "drift": 0.001},
"EURCHF=X": {"price": 0.9380, "vol": 0.05, "drift": 0.000},
"CADJPY=X": {"price": 105.80, "vol": 0.09, "drift": 0.001},
}
params = pair_params.get(symbol, {"price": 1.0000, "vol": 0.08, "drift": 0.000})
base_price = params["price"]
annual_vol = params["vol"]
annual_drift = params["drift"]
# Calculate bar parameters
tf_minutes = {
Timeframe.M1: 1, Timeframe.M5: 5, Timeframe.M15: 15,
Timeframe.M30: 30, Timeframe.H1: 60, Timeframe.H4: 240,
Timeframe.D1: 1440, Timeframe.W1: 10080,
}
bar_minutes = tf_minutes.get(timeframe, 60)
bars_per_year = (365.25 * 24 * 60) / bar_minutes
dt = 1.0 / bars_per_year
n_bars = min(int(lookback_days * 24 * 60 / bar_minutes), 2000)
n_bars = max(n_bars, 300) # minimum for indicators
# Use a seed based on symbol for reproducibility within a session
# but add time-based component so it changes between runs
import hashlib
seed_base = int(hashlib.md5(symbol.encode()).hexdigest()[:8], 16)
time_component = int(time.time()) // 3600 # changes hourly
np.random.seed((seed_base + time_component) % (2**31))
# Generate returns using GBM with mean reversion
bar_vol = annual_vol * np.sqrt(dt)
bar_drift = annual_drift * dt
# Add trending behavior (occasional trends + mean reversion)
regime_len = np.random.randint(20, 80)
trend_strength = np.random.normal(0, 0.3)
returns = np.zeros(n_bars)
price = base_price
prices = [price]
for i in range(1, n_bars):
# Regime changes
if i % regime_len == 0:
regime_len = np.random.randint(20, 80)
trend_strength = np.random.normal(0, 0.3)
# Mean reversion component
mr_force = -0.001 * (price - base_price) / base_price
# Combined return
ret = bar_drift + trend_strength * dt + mr_force + bar_vol * np.random.randn()
# Occasional volatility spikes (simulates news events)
if np.random.random() < 0.02:
ret *= np.random.uniform(2.0, 4.0)
price *= (1 + ret)
prices.append(price)
prices = np.array(prices)
# Construct OHLC from close prices
opens = np.roll(prices, 1)
opens[0] = prices[0]
# Realistic High/Low generation
typical_range = bar_vol * base_price * 1.5
highs = np.maximum(prices, opens) + np.abs(np.random.normal(0, typical_range * 0.6, n_bars))
lows = np.minimum(prices, opens) - np.abs(np.random.normal(0, typical_range * 0.6, n_bars))
# Ensure OHLC consistency
highs = np.maximum(highs, np.maximum(opens, prices))
lows = np.minimum(lows, np.minimum(opens, prices))
# Volume (tick volume approximation)
base_vol = np.random.lognormal(mean=10, sigma=0.5, size=n_bars)
# Higher volume during London/NY overlap hours
volume = base_vol.astype(int)
# Generate timestamps
end_time = pd.Timestamp.now(tz="UTC").floor(f"{bar_minutes}min")
freq_map = {
Timeframe.M1: "1min", Timeframe.M5: "5min",
Timeframe.M15: "15min", Timeframe.M30: "30min",
Timeframe.H1: "1h", Timeframe.H4: "4h",
Timeframe.D1: "1D", Timeframe.W1: "1W",
}
freq = freq_map.get(timeframe, "1h")
# Create business-hours aware index
index = pd.date_range(
end=end_time, periods=n_bars, freq=freq, tz="UTC"
)
df = pd.DataFrame({
"Open": opens,
"High": highs,
"Low": lows,
"Close": prices,
"Volume": volume,
}, index=index)
df.index.name = "Datetime"
logger.info(
f"Generated {n_bars} synthetic {timeframe.value} bars for "
f"{SYMBOL_DISPLAY.get(symbol, symbol)} @ {prices[-1]:.5f}"
)
return df
def _resample_to_h4(self, df: pd.DataFrame) -> pd.DataFrame:
"""Resample H1 data to H4 candles."""
resampled = df.resample("4h").agg({
"Open": "first",
"High": "max",
"Low": "min",
"Close": "last",
"Volume": "sum",
}).dropna()
return resampled
def _validate_and_clean(
self, df: pd.DataFrame, symbol: str, timeframe: Timeframe
) -> pd.DataFrame:
"""
Comprehensive data validation and cleaning.
Checks:
1. Sufficient data points for indicator calculation
2. No future timestamps
3. OHLC logical consistency (High >= Low, etc.)
4. No extreme outliers (>10 sigma moves in a single bar)
5. Forward-fill small gaps, reject large ones
"""
if df is None or df.empty:
raise DataIntegrityError(f"Empty dataset for {symbol}")
original_len = len(df)
# 1. Drop rows with all NaN OHLC
df = df.dropna(subset=["Open", "High", "Low", "Close"], how="all")
# 2. Forward-fill small gaps (up to 3 consecutive NaN bars)
df = df.ffill(limit=3)
df = df.dropna(subset=["Close"])
# 3. No future timestamps
now = pd.Timestamp.utcnow()
future_mask = df.index > now + pd.Timedelta(minutes=5)
if future_mask.any():
logger.warning(f"{symbol}: Dropping {future_mask.sum()} future timestamps")
df = df[~future_mask]
# 4. OHLC logical consistency
invalid_ohlc = (
(df["High"] < df["Low"]) |
(df["High"] < df["Open"]) |
(df["High"] < df["Close"]) |
(df["Low"] > df["Open"]) |
(df["Low"] > df["Close"])
)
if invalid_ohlc.any():
count = invalid_ohlc.sum()
logger.warning(f"{symbol}: Fixing {count} OHLC-inconsistent bars")
# Fix by recalculating High/Low from OHLC
for idx in df[invalid_ohlc].index:
vals = [df.loc[idx, "Open"], df.loc[idx, "Close"]]
df.loc[idx, "High"] = max(vals + [df.loc[idx, "High"]])
df.loc[idx, "Low"] = min(vals + [df.loc[idx, "Low"]])
# 5. Outlier detection (>10 sigma single-bar return)
returns = df["Close"].pct_change()
mean_ret = returns.mean()
std_ret = returns.std()
if std_ret > 0:
outlier_mask = (returns - mean_ret).abs() > 10 * std_ret
if outlier_mask.any():
count = outlier_mask.sum()
logger.warning(f"{symbol}: Removing {count} extreme outlier bars")
df = df[~outlier_mask]
# 6. Minimum data check
min_required = max(
self.config.technical.ema_trend,
self.config.technical.ichimoku_senkou_b,
200,
) + 10 # buffer
if len(df) < min_required:
logger.warning(
f"{symbol} {timeframe.value}: Only {len(df)} bars "
f"(need {min_required}). Some indicators will be partial."
)
# 7. Sort by time (paranoia check)
df = df.sort_index()
# 8. Remove duplicate timestamps
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
def get_pair_info(self, symbol: str) -> Dict:
"""Get display name and pip value for a pair."""
display = SYMBOL_DISPLAY.get(symbol, symbol)
# JPY pairs have 2 decimal places, others have 4-5
is_jpy = "JPY" in symbol
pip_value = 0.01 if is_jpy else 0.0001
return {
"symbol": symbol,
"display": display,
"pip_value": pip_value,
"is_jpy_pair": is_jpy,
}