fetch_data.py

#!/usr/bin/env python3
"""

╔══════════════════════════════════════════════════════════════════════╗

║  fetch_data.py — MT5 XAUUSDc M15 Data Fetcher                      ║

║  Fetches 1-year OHLCV + spread data from MetaTrader5, saves to CSV  ║

║  Run locally with MT5 terminal open and logged in.                  ║

╚══════════════════════════════════════════════════════════════════════╝

"""

import sys
import time
import numpy as np
import pandas as pd
from datetime import datetime, timedelta
from pathlib import Path

try:
    import MetaTrader5 as mt5
except ImportError:
    print("ERROR: MetaTrader5 package not installed. Run: pip install MetaTrader5")
    sys.exit(1)

# ══════════════════════════════════════════════════════════════════════
# CONFIGURATION
# ══════════════════════════════════════════════════════════════════════
SYMBOL        = "XAUUSDc"
TIMEFRAME     = mt5.TIMEFRAME_M15
TF_LABEL      = "M15"
LOOKBACK_DAYS = 365                      # 1 year
OUTPUT_DIR    = Path(__file__).parent
OUTPUT_FILE   = OUTPUT_DIR / f"{SYMBOL}_{TF_LABEL}_data.csv"

# ══════════════════════════════════════════════════════════════════════
# MT5 CONNECTION
# ══════════════════════════════════════════════════════════════════════
def init_mt5() -> None:
    """Initialize MT5 connection with retries."""
    for attempt in range(3):
        if mt5.initialize():
            info = mt5.terminal_info()
            print(f"✓ MT5 connected — Build {info.build}, Company: {info.company}")
            return
        print(f"  Attempt {attempt+1}/3 failed, retrying in 2s...")
        time.sleep(2)
    print(f"✗ MT5 initialization failed: {mt5.last_error()}")
    sys.exit(1)


def validate_symbol(symbol: str) -> dict:
    """Validate symbol exists and return its properties."""
    info = mt5.symbol_info(symbol)
    if info is None:
        # Try to find similar symbols
        symbols = mt5.symbols_get()
        gold_syms = [s.name for s in symbols if "XAU" in s.name or "GOLD" in s.name.upper()]
        print(f"✗ Symbol '{symbol}' not found.")
        if gold_syms:
            print(f"  Available gold symbols: {gold_syms}")
        else:
            print(f"  No gold symbols found. Check your broker.")
        sys.exit(1)

    if not info.visible:
        mt5.symbol_select(symbol, True)
        time.sleep(0.5)

    props = {
        "name":        info.name,
        "digits":      info.digits,
        "point":       info.point,
        "spread":      info.spread,
        "trade_mode":  info.trade_mode,
        "volume_min":  info.volume_min,
        "volume_max":  info.volume_max,
        "volume_step": info.volume_step,
    }
    print(f"✓ Symbol validated: {info.name}")
    print(f"  Digits: {info.digits} | Point: {info.point} | "
          f"Current Spread: {info.spread} | Min Lot: {info.volume_min}")
    return props


# ══════════════════════════════════════════════════════════════════════
# DATA FETCHING
# ══════════════════════════════════════════════════════════════════════
def fetch_ohlcv(symbol: str, timeframe: int, days: int) -> pd.DataFrame:
    """Fetch OHLCV bars from MT5. Chunked to avoid API limits."""
    utc_now = datetime.utcnow()
    date_from = utc_now - timedelta(days=days)

    print(f"\n→ Fetching {TF_LABEL} bars: {date_from.date()} to {utc_now.date()}...")

    # Fetch in chunks of 50000 (MT5 limit is ~100K per call)
    rates = mt5.copy_rates_range(symbol, timeframe, date_from, utc_now)

    if rates is None or len(rates) == 0:
        print(f"✗ No data returned: {mt5.last_error()}")
        sys.exit(1)

    df = pd.DataFrame(rates)
    df["time"] = pd.to_datetime(df["time"], unit="s", utc=True)
    df.rename(columns={"real_volume": "volume"}, inplace=True)

    # Ensure columns
    required = ["time", "open", "high", "low", "close", "tick_volume", "spread"]
    for col in required:
        if col not in df.columns:
            if col == "volume" and "tick_volume" in df.columns:
                df["volume"] = df["tick_volume"]
            elif col == "spread":
                df["spread"] = 0  # Will be filled from ticks
            else:
                print(f"✗ Missing column: {col}")
                sys.exit(1)

    print(f"✓ Fetched {len(df):,} bars")
    print(f"  Date range: {df['time'].iloc[0]} → {df['time'].iloc[-1]}")
    return df


def fetch_spread_from_ticks(symbol: str, days: int, bar_times: pd.Series) -> pd.Series:
    """Fetch tick data to compute average spread per bar. Falls back to bar spread."""
    print(f"\n→ Computing spread from tick data (sampling last 30 days)...")

    utc_now = datetime.utcnow()
    # Only fetch recent ticks for spread estimation (full year would be enormous)
    tick_start = utc_now - timedelta(days=min(days, 30))

    ticks = mt5.copy_ticks_range(symbol, tick_start, utc_now, mt5.COPY_TICKS_INFO)

    if ticks is None or len(ticks) == 0:
        print(f"  ⚠ No tick data available, using bar spread column")
        return None

    tick_df = pd.DataFrame(ticks)
    tick_df["time"] = pd.to_datetime(tick_df["time"], unit="s", utc=True)
    tick_df["spread_pts"] = (tick_df["ask"] - tick_df["bid"]) / mt5.symbol_info(symbol).point

    avg_spread = tick_df["spread_pts"].mean()
    median_spread = tick_df["spread_pts"].median()
    max_spread = tick_df["spread_pts"].quantile(0.99)

    print(f"✓ Processed {len(tick_df):,} ticks")
    print(f"  Avg spread: {avg_spread:.1f} pts | "
          f"Median: {median_spread:.1f} pts | "
          f"99th pctl: {max_spread:.1f} pts")

    return tick_df["spread_pts"]


# ══════════════════════════════════════════════════════════════════════
# DATA VALIDATION & CLEANING
# ══════════════════════════════════════════════════════════════════════
def validate_data(df: pd.DataFrame) -> pd.DataFrame:
    """Validate and clean OHLCV data."""
    print(f"\n→ Validating data quality...")
    issues = []

    # 1. Check for NaN
    nan_count = df[["open", "high", "low", "close"]].isnull().sum().sum()
    if nan_count > 0:
        issues.append(f"  ⚠ {nan_count} NaN values in OHLCV — forward-filling")
        df[["open", "high", "low", "close"]] = df[["open", "high", "low", "close"]].ffill()

    # 2. Check OHLC integrity
    bad_hl = (df["high"] < df["low"]).sum()
    if bad_hl > 0:
        issues.append(f"  ⚠ {bad_hl} bars where high < low — swapping")
        mask = df["high"] < df["low"]
        df.loc[mask, ["high", "low"]] = df.loc[mask, ["low", "high"]].values

    bad_range = ((df["open"] > df["high"]) | (df["open"] < df["low"]) |
                 (df["close"] > df["high"]) | (df["close"] < df["low"])).sum()
    if bad_range > 0:
        issues.append(f"  ⚠ {bad_range} bars where open/close outside high-low range — clamping")
        df["open"]  = df["open"].clip(lower=df["low"], upper=df["high"])
        df["close"] = df["close"].clip(lower=df["low"], upper=df["high"])

    # 3. Check for duplicates
    dups = df["time"].duplicated().sum()
    if dups > 0:
        issues.append(f"  ⚠ {dups} duplicate timestamps — keeping last")
        df = df.drop_duplicates(subset="time", keep="last")

    # 4. Check for large time gaps (> 2 days = likely holiday, OK; > 5 days = suspicious)
    time_diff = df["time"].diff()
    large_gaps = time_diff[time_diff > pd.Timedelta(days=5)]
    if len(large_gaps) > 0:
        for idx in large_gaps.index:
            gap = time_diff.loc[idx]
            issues.append(f"  ⚠ Large gap: {df['time'].iloc[idx-1]} → {df['time'].iloc[idx]} ({gap})")

    # 5. Ensure data is sorted
    df = df.sort_values("time").reset_index(drop=True)

    # 6. Filter weekends (Saturday/Sunday bars are artifacts)
    weekend_mask = df["time"].dt.dayofweek.isin([5, 6])
    weekend_count = weekend_mask.sum()
    if weekend_count > 0:
        issues.append(f"  ℹ Removed {weekend_count} weekend bars")
        df = df[~weekend_mask].reset_index(drop=True)

    if issues:
        for issue in issues:
            print(issue)
    else:
        print("  ✓ Data quality: PASS (no issues found)")

    # Summary stats
    print(f"\n  Final dataset: {len(df):,} bars")
    print(f"  Price range: {df['close'].min():.2f} — {df['close'].max():.2f}")
    print(f"  Avg spread: {df['spread'].mean():.1f} pts")
    print(f"  Date range: {df['time'].iloc[0].date()} → {df['time'].iloc[-1].date()}")

    return df


# ══════════════════════════════════════════════════════════════════════
# MAIN
# ══════════════════════════════════════════════════════════════════════
def main():
    print("=" * 68)
    print("  MT5 Data Fetcher — XAUUSDc M15 (1 Year)")
    print("=" * 68)

    # 1. Connect
    init_mt5()

    try:
        # 2. Validate symbol
        sym_props = validate_symbol(SYMBOL)

        # 3. Fetch OHLCV
        df = fetch_ohlcv(SYMBOL, TIMEFRAME, LOOKBACK_DAYS)

        # 4. Enhance spread data from ticks
        tick_spread = fetch_spread_from_ticks(SYMBOL, LOOKBACK_DAYS, df["time"])
        if tick_spread is not None:
            # Use tick-derived average as fallback for bars with 0 spread
            avg_tick_spread = tick_spread.median()
            zero_spread_mask = df["spread"] == 0
            if zero_spread_mask.sum() > 0:
                df.loc[zero_spread_mask, "spread"] = int(avg_tick_spread)
                print(f"  Filled {zero_spread_mask.sum()} zero-spread bars with median: {avg_tick_spread:.0f}")

        # 5. Validate
        df = validate_data(df)

        # 6. Add metadata columns useful for training
        df["hour"]      = df["time"].dt.hour
        df["dayofweek"] = df["time"].dt.dayofweek
        df["returns"]   = np.log(df["close"] / df["close"].shift(1))

        # 7. Save
        # Select and order columns for output
        output_cols = [
            "time", "open", "high", "low", "close",
            "tick_volume", "spread", "hour", "dayofweek", "returns"
        ]
        # Add volume if it exists separately
        if "volume" in df.columns and "volume" not in output_cols:
            output_cols.insert(5, "volume")

        df_out = df[[c for c in output_cols if c in df.columns]]
        df_out.to_csv(OUTPUT_FILE, index=False)

        print(f"\n{'=' * 68}")
        print(f"  ✓ SAVED: {OUTPUT_FILE}")
        print(f"  ✓ Rows: {len(df_out):,} | Columns: {len(df_out.columns)}")
        print(f"  ✓ File size: {OUTPUT_FILE.stat().st_size / 1024:.0f} KB")
        print(f"{'=' * 68}")

        # 8. Quick sanity print
        print(f"\nSample (first 3 rows):")
        print(df_out.head(3).to_string(index=False))
        print(f"\nSample (last 3 rows):")
        print(df_out.tail(3).to_string(index=False))

    finally:
        mt5.shutdown()
        print("\n✓ MT5 connection closed.")


if __name__ == "__main__":
    main()