Pandas for Crypto Financial Data Analysis

Before you code any algorithmic trading python strategy, you need to be fluent in Pandas. It is the Swiss Army knife of quantitative research. This guide covers the most important patterns for working with financial time series.

Resampling OHLCV Data

df_hourly = df_1min.resample("1H").agg({\n    "open": "first",\n    "high": "max",\n    "low": "min",\n    "close": "last",\n    "volume": "sum"\n})

Rolling Statistics

Rolling windows are the engine of technical indicators. A simple 20-period SMA: df["sma20"] = df["close"].rolling(20).mean(). Bollinger Bands add two standard deviations: df["upper"] = df["sma20"] + 2 * df["close"].rolling(20).std().

Merging Multiple Data Sources

Align funding rate data with price data using pd.merge_asof for time-aware merging. This is essential when building crypto futures algo trading strategies that incorporate funding as a signal or cost model.