ohlcv-processinglisted

# 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