Add project scaffold with config and dependencies

tradingbot/data/loader.py

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+"""Lightweight CSV loading and data-quality helpers.
+
+Loads raw 5-minute candles and inspects the gaps between consecutive timestamps.
+"""
+import pandas as pd
+
+
+def load_ohlcv_csv(file_path, timestamp_col="timestamp"):
+    """Load an OHLCV CSV with a datetime index.
+
+    Parameters
+    ----------
+    file_path : str
+        Path to a CSV containing a timestamp column plus OHLCV columns.
+    timestamp_col : str
+        Name of the timestamp column to parse and use as the index.
+    """
+    data = pd.read_csv(file_path, index_col=timestamp_col, parse_dates=True)
+    data = data[~data.index.duplicated(keep="first")]
+    return data.sort_index()
+
+
+def summarize(data):
+    """Print basic information about a loaded OHLCV frame."""
+    print("Data Info:")
+    print(data.info())
+    print("\nFirst few rows:")
+    print(data.head())
+    print("\nLast few rows:")
+    print(data.tail())
+    print(f"\nTotal candles: {len(data)}")
+
+
+def analyze_time_gaps(data, expected_gap=pd.Timedelta(minutes=5),
+                      big_gap=pd.Timedelta(hours=1), verbose=True):
+    """Report gaps between consecutive timestamps.
+
+    Returns the Series of gaps larger than ``expected_gap``.
+    """
+    time_diffs = data.index.to_series().diff()
+    gaps = time_diffs[time_diffs > expected_gap]
+
+    if verbose:
+        print(f"Total gaps larger than {expected_gap}: {len(gaps)}")
+        print("\nGap statistics:")
+        print(gaps.describe())
+
+        big_gaps = gaps[gaps > big_gap]
+        print(f"\nNumber of gaps > {big_gap}: {len(big_gaps)}")
+        print(f"Big gaps (> {big_gap}):")
+        for gap_start, gap_size in big_gaps.items():
+            gap_end = gap_start + gap_size
+            print(f"Gap from {gap_start} to {gap_end} ({gap_size})")
+
+    return gaps