ohlcv-processing

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OHLCV Processing — Market Data Preparation

Clean, consistent OHLCV data is the foundation of every trading analysis. Garbage in, garbage out — a single anomalous candle can trigger false signals, corrupt indicator calculations, and produce misleading backtest results. This skill covers the full data preparation pipeline: validation, cleaning, resampling, normalization, and multi-source merging.

Why this matters: Crypto OHLCV data is messier than traditional markets. 24/7 trading means no official close, DEX aggregators disagree on prices, low-liquidity tokens produce impossible candles, and API outages create gaps. Every analysis workflow should start with this pipeline.

Quick Start

1. Install Dependencies

bash

uv pip install pandas numpy httpx

2. Standard OHLCV DataFrame Format

All processing functions expect this canonical format:

python

import pandas as pd

# Canonical OHLCV DataFrame
# - DatetimeIndex in UTC
# - Columns: open, high, low, close, volume (lowercase)
# - Sorted ascending by timestamp
# - No duplicate timestamps

df = pd.DataFrame({
    "open": [1.10, 1.12, 1.11],
    "high": [1.15, 1.14, 1.13],
    "low": [1.08, 1.10, 1.09],
    "close": [1.12, 1.11, 1.12],
    "volume": [50000, 48000, 52000],
}, index=pd.to_datetime([
    "2025-01-01 00:00:00",
    "2025-01-01 00:01:00",
    "2025-01-01 00:02:00",
], utc=True))
df.index.name = "timestamp"

3. Full Processing Pipeline

python

import pandas as pd
import numpy as np

def process_ohlcv(df: pd.DataFrame) -> pd.DataFrame:
    """Run complete OHLCV processing pipeline."""
    df = standardize_columns(df)
    df = validate_ohlcv(df)
    df = handle_gaps(df, method="ffill")
    df = detect_and_flag_anomalies(df)
    return df

Data Validation

Column Checks

python

REQUIRED_COLUMNS = {"open", "high", "low", "close", "volume"}

def standardize_columns(df: pd.DataFrame) -> pd.DataFrame:
    """Normalize column names to lowercase standard."""
    df.columns = df.columns.str.lower().str.strip()
    # Common renames
    rename_map = {"vol": "volume", "v": "volume", "o": "open",
                  "h": "high", "l": "low", "c": "close"}
    df = df.rename(columns=rename_map)
    missing = REQUIRED_COLUMNS - set(df.columns)
    if missing:
        raise ValueError(f"Missing columns: {missing}")
    return df[["open", "high", "low", "close", "volume"]]

Structural Validation

python

def validate_ohlcv(df: pd.DataFrame) -> pd.DataFrame:
    """Validate OHLCV structural integrity."""
    # Ensure DatetimeIndex in UTC
    if not isinstance(df.index, pd.DatetimeIndex):
        df.index = pd.to_datetime(df.index, utc=True)
    if df.index.tz is None:
        df.index = df.index.tz_localize("UTC")

    # Sort and deduplicate
    df = df.sort_index()
    dupes = df.index.duplicated(keep="last")
    if dupes.any():
        print(f"Warning: Removed {dupes.sum()} duplicate timestamps")
        df = df[~dupes]

    # Type enforcement
    for col in ["open", "high", "low", "close", "volume"]:
        df[col] = pd.to_numeric(df[col], errors="coerce")

    return df

Impossible Candle Detection

python

def find_impossible_candles(df: pd.DataFrame) -> pd.DataFrame:
    """Find candles that violate OHLC constraints."""
    issues = pd.DataFrame(index=df.index)
    issues["high_lt_low"] = df["high"] < df["low"]
    issues["high_lt_open"] = df["high"] < df["open"]
    issues["high_lt_close"] = df["high"] < df["close"]
    issues["low_gt_open"] = df["low"] > df["open"]
    issues["low_gt_close"] = df["low"] > df["close"]
    issues["negative_price"] = (df[["open", "high", "low", "close"]] < 0).any(axis=1)
    issues["negative_volume"] = df["volume"] < 0
    issues["any_issue"] = issues.any(axis=1)
    return issues[issues["any_issue"]]

Gap Handling

Crypto trades 24/7, but gaps still occur from API outages, low liquidity, or aggregator downtime.

Detect Gaps

python

def detect_gaps(df: pd.DataFrame, expected_freq: str = "1min") -> pd.Series:
    """Find missing timestamps based on expected frequency."""
    full_index = pd.date_range(
        start=df.index.min(), end=df.index.max(), freq=expected_freq, tz="UTC"
    )
    missing = full_index.difference(df.index)
    return missing

Fill Gaps

python

def handle_gaps(
    df: pd.DataFrame,
    freq: str = "1min",
    method: str = "ffill",
    max_gap: int = 5,
) -> pd.DataFrame:
    """Fill gaps in OHLCV data.

    Args:
        df: OHLCV DataFrame with DatetimeIndex.
        freq: Expected bar frequency.
        method: 'ffill' (forward fill) or 'interpolate'.
        max_gap: Maximum consecutive bars to fill. Larger gaps are left as NaN.
    """
    full_index = pd.date_range(
        start=df.index.min(), end=df.index.max(), freq=freq, tz="UTC"
    )
    df = df.reindex(full_index)
    df.index.name = "timestamp"

    # Mark which bars were filled
    df["is_filled"] = df["close"].isna()

    if method == "ffill":
        # Forward fill OHLC (flat candle), zero volume
        df[["open", "high", "low", "close"]] = (
            df[["open", "high", "low", "close"]].ffill(limit=max_gap)
        )
        df["volume"] = df["volume"].fillna(0)
    elif method == "interpolate":
        df[["open", "high", "low", "close"]] = (
            df[["open", "high", "low", "close"]].interpolate(
                method="time", limit=max_gap
            )
        )
        df["volume"] = df["volume"].fillna(0)

    return df

Anomaly Detection

See references/data_quality.md for the complete anomaly taxonomy.

Price Spike Detection

python

def detect_price_spikes(
    df: pd.DataFrame, window: int = 20, threshold: float = 3.0
) -> pd.Series:
    """Flag bars where return exceeds threshold * rolling std."""
    returns = df["close"].pct_change()
    rolling_std = returns.rolling(window, min_periods=5).std()
    spike = returns.abs() > (threshold * rolling_std)
    return spike.fillna(False)

Zero Volume Detection

python

def detect_zero_volume(df: pd.DataFrame, min_volume: float = 0) -> pd.Series:
    """Flag bars with zero or below-minimum volume."""
    return df["volume"] <= min_volume

Composite Anomaly Flagging

python

def flag_anomalies(df: pd.DataFrame) -> pd.DataFrame:
    """Add anomaly flag columns to DataFrame."""
    df["anomaly_spike"] = detect_price_spikes(df)
    df["anomaly_zero_vol"] = detect_zero_volume(df)
    impossible = find_impossible_candles(df)
    df["anomaly_impossible"] = False
    if not impossible.empty:
        df.loc[impossible.index, "anomaly_impossible"] = True
    df["anomaly_any"] = (
        df["anomaly_spike"] | df["anomaly_zero_vol"] | df["anomaly_impossible"]
    )
    return df

Resampling

See references/resampling_guide.md for detailed guidance.

Standard Resample

python

OHLCV_RESAMPLE_RULES = {
    "open": "first",
    "high": "max",
    "low": "min",
    "close": "last",
    "volume": "sum",
}

def resample_ohlcv(df: pd.DataFrame, target_freq: str) -> pd.DataFrame:
    """Resample OHLCV to a coarser timeframe.

    Args:
        df: OHLCV DataFrame (must be finer than target_freq).
        target_freq: Pandas frequency string ('5min', '15min', '1h', '4h', '1D').

    Returns:
        Resampled OHLCV DataFrame with no NaN rows.
    """
    ohlcv_cols = ["open", "high", "low", "close", "volume"]
    resampled = df[ohlcv_cols].resample(target_freq).agg(OHLCV_RESAMPLE_RULES)
    return resampled.dropna(subset=["close"])

Common Timeframe Ladder

python

TIMEFRAME_LADDER = ["1min", "5min", "15min", "1h", "4h", "1D"]

def resample_ladder(df: pd.DataFrame) -> dict[str, pd.DataFrame]:
    """Resample 1-minute data to all standard timeframes."""
    results = {"1min": df.copy()}
    for tf in TIMEFRAME_LADDER[1:]:
        results[tf] = resample_ohlcv(df, tf)
    return results

VWAP Calculation

python

def compute_vwap(df: pd.DataFrame) -> pd.Series:
    """Compute cumulative VWAP over the DataFrame."""
    typical_price = (df["high"] + df["low"] + df["close"]) / 3
    cum_vol = df["volume"].cumsum()
    cum_tp_vol = (typical_price * df["volume"]).cumsum()
    return cum_tp_vol / cum_vol

Normalization

python

def normalize_prices(
    df: pd.DataFrame, method: str = "returns"
) -> pd.DataFrame:
    """Normalize OHLCV price columns.

    Methods:
        'returns' — Percentage returns (close-to-close).
        'log_returns' — Log returns.
        'minmax' — Min-max scale to [0, 1].
        'zscore' — Z-score normalization.
    """
    price_cols = ["open", "high", "low", "close"]
    result = df.copy()

    if method == "returns":
        for col in price_cols:
            result[f"{col}_ret"] = result[col].pct_change()
    elif method == "log_returns":
        for col in price_cols:
            result[f"{col}_logret"] = np.log(result[col] / result[col].shift(1))
    elif method == "minmax":
        for col in price_cols:
            cmin, cmax = result[col].min(), result[col].max()
            result[f"{col}_norm"] = (result[col] - cmin) / (cmax - cmin)
    elif method == "zscore":
        for col in price_cols:
            result[f"{col}_z"] = (
                (result[col] - result[col].mean()) / result[col].std()
            )
    return result

Multi-Source Merging

When combining data from multiple sources (e.g., Birdeye + DexScreener), timestamps may not align and prices may differ due to different DEX aggregation.

python

def merge_ohlcv_sources(
    primary: pd.DataFrame,
    secondary: pd.DataFrame,
    tolerance: str = "30s",
) -> pd.DataFrame:
    """Merge two OHLCV sources, preferring the higher-volume source per bar.

    Args:
        primary: First OHLCV source.
        secondary: Second OHLCV source.
        tolerance: Maximum time difference for alignment.
    """
    merged = pd.merge_asof(
        primary.sort_index(),
        secondary.sort_index(),
        left_index=True, right_index=True,
        tolerance=pd.Timedelta(tolerance),
        suffixes=("_pri", "_sec"),
    )
    # Use higher-volume source per bar
    use_secondary = merged["volume_sec"] > merged["volume_pri"]
    for col in ["open", "high", "low", "close", "volume"]:
        merged[col] = np.where(
            use_secondary, merged[f"{col}_sec"], merged[f"{col}_pri"]
        )
    merged["source"] = np.where(use_secondary, "secondary", "primary")
    return merged[["open", "high", "low", "close", "volume", "source"]]

Timezone Handling

Standard: Always store and process in UTC. Convert only for display.

python

def ensure_utc(df: pd.DataFrame) -> pd.DataFrame:
    """Ensure DatetimeIndex is UTC."""
    if df.index.tz is None:
        df.index = df.index.tz_localize("UTC")
    elif str(df.index.tz) != "UTC":
        df.index = df.index.tz_convert("UTC")
    return df

Data Quality Report

python

def quality_report(df: pd.DataFrame) -> dict:
    """Generate a data quality summary."""
    total = len(df)
    return {
        "total_bars": total,
        "date_range": f"{df.index.min()} → {df.index.max()}",
        "missing_values": int(df[["open", "high", "low", "close"]].isna().sum().sum()),
        "zero_volume_bars": int((df["volume"] == 0).sum()),
        "impossible_candles": int((df["high"] < df["low"]).sum()),
        "duplicate_timestamps": int(df.index.duplicated().sum()),
        "negative_prices": int((df[["open", "high", "low", "close"]] < 0).any(axis=1).sum()),
        "completeness_pct": round((1 - df["close"].isna().mean()) * 100, 2),
    }

Files

References

references/data_quality.md — Anomaly types, detection methods, correction strategies, crypto-specific data issues
references/resampling_guide.md — Resample rules, timeframe use cases, partial bar handling, VWAP resampling, multi-timeframe alignment

Scripts

scripts/process_ohlcv.py — Full processing pipeline: validate, clean, resample, normalize with anomaly reporting (run with --demo for synthetic data)
scripts/merge_sources.py — Multi-source OHLCV merging with conflict resolution and discrepancy reporting (run with --demo)