How to conduct a tick analysis? - briefly
First, gather tick‑by‑tick price data and split it into uniform time intervals. Then compute metrics such as average price, volatility, and autocorrelation for each interval to reveal micro‑structural patterns.
How to conduct a tick analysis? - in detail
Conducting a tick analysis requires a systematic approach that captures every market movement at the most granular level. Follow these steps to ensure completeness and accuracy.
First, acquire high‑frequency data. Use a reliable source that delivers tick‑by‑tick records, including timestamp, price, volume, and trade direction. Verify data integrity by checking for missing timestamps, duplicate entries, and outliers. Clean the dataset before proceeding.
Second, synchronize the data with the reference clock. Convert timestamps to a uniform time zone and format (e.g., Unix epoch milliseconds). Align the series with any auxiliary information such as order‑book snapshots or news events.
Third, segment the data into logical intervals. Common choices are:
- Per‑second buckets for ultra‑short‑term analysis.
- Per‑minute aggregates for intraday patterns.
- Custom windows defined by market events (e.g., earnings releases).
Within each interval compute the following metrics:
- Trade count: total number of executed ticks.
- Volume sum: cumulative traded shares or contracts.
- Price statistics: mean, median, high, low, and standard deviation.
- Direction imbalance: difference between buyer‑initiated and seller‑initiated trades.
- VWAP (Volume‑Weighted Average Price): Σ(price × volume) / Σ(volume).
Fourth, apply statistical techniques to detect anomalies. Use Z‑score thresholds to flag spikes in volume or price changes. Implement rolling windows to observe trends and calculate autocorrelation, which reveals persistence in tick flow.
Fifth, visualize the results. Plot time series of price, volume, and imbalance on a shared axis. Overlay moving averages or Bollinger bands to highlight deviations. Interactive charts enable drill‑down to individual ticks for forensic inspection.
Sixth, integrate findings into a trading model or risk‑management framework. Convert the derived indicators into signals (e.g., high imbalance + price surge = potential breakout). Back‑test the signals across multiple historical periods to assess robustness.
Finally, document the workflow. Record data sources, cleaning rules, parameter choices, and code versions. Store raw and processed files in a version‑controlled repository to facilitate reproducibility and auditability.