Time Series, Tick Data, and an EMA Crossover Backtest

Summary

This tutorial explains time-series bars and tick data, then uses a simple EMA crossover strategy to connect market data with backtesting. It describes OHLCV bars and finer-grained snapshots, noting that smaller data intervals can represent intraperiod price movement more closely. It also outlines a platform setup and gives a Python example in which a fast and slow EMA crossover opens or closes long and short positions.

The article explains that backtests are a reference rather than a guarantee, since historical behavior may not persist. It discusses changing data granularity and using tick-like data to approximate live conditions, while acknowledging that exchange feeds differ in timeliness and detail. The worked strategy is presented as a learning template, not for live deployment, and the article does not provide a rigorous performance analysis or establish that the EMA rules are profitable.

Key ideas

• Time-series data can range from periodic OHLCV bars to finer-grained market snapshots.
• The example strategy uses fast and slow EMA crossovers to enter and exit long or short positions.
• Finer data granularity can make a backtest represent intraperiod price changes more closely.
• Backtests are historical references and do not guarantee future performance.
• The EMA example is intended for learning rather than live trading.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.