How to perform a tick analysis? - briefly
Gather high‑frequency price data, break it into individual ticks, then calculate statistical metrics such as mean, variance, and autocorrelation to reveal micro‑price patterns. Filter outliers and visualize the results to interpret tick‑level dynamics.
How to perform a tick analysis? - in detail
A tick analysis examines price movements at the most granular level, treating each individual trade or quote as a data point. The process requires precise data handling, systematic filtering, and rigorous statistical evaluation.
Begin by acquiring high‑frequency market data. Sources include exchange feeds, proprietary data vendors, or APIs that deliver tick‑by‑tick records. Ensure the dataset contains timestamps, trade price, trade size, and, if available, bid‑ask quotes. Verify time synchronization across all records to avoid misaligned sequences.
Clean the raw feed. Remove entries with missing fields, out‑of‑range timestamps, or obvious errors such as zero‑price trades. Apply a de‑duplication step for identical timestamps that may result from data aggregation. If the analysis involves multiple instruments, align them on a common time grid.
Select the analytical framework. Common approaches are:
- Descriptive statistics – compute mean, median, variance, and inter‑quartile range of price changes over defined intervals (e.g., per second, per minute).
- Microstructure metrics – calculate spread, depth, order‑flow imbalance, and trade‑direction autocorrelation.
- Event‑driven models – identify price jumps, clustering of trades, or liquidity shocks using thresholds or statistical tests (e.g., Hawkes processes).
- Volatility estimation – apply realized variance or bipower variation formulas to capture intraday volatility dynamics.
Implement the chosen metrics using a programming environment that supports large datasets (Python with pandas/numpy, R with data.table, or specialized platforms such as KDB+/q). Optimize memory usage by processing data in chunks or using columnar storage.
Interpretation follows the quantitative results. For example, a high autocorrelation of trade direction may indicate persistent buying pressure, while widening spreads signal deteriorating liquidity. Compare metrics across time periods to detect regime shifts, market stress, or the impact of news releases.
Validate findings through out‑of‑sample testing. Partition the data into training and validation sets, apply the same preprocessing pipeline, and assess whether patterns persist. Robustness checks include varying the sampling interval, adjusting filter thresholds, and benchmarking against alternative instruments.
Document the entire workflow: data provenance, cleaning rules, algorithmic steps, and parameter choices. Store scripts and logs in a version‑controlled repository to ensure reproducibility and facilitate future enhancements.