How can I obtain a tick analysis? - briefly
Request the tick analysis via the data provider’s API or export tool, selecting a subscription that includes high‑frequency data. Import the resulting file (commonly CSV) into analysis software such as Python or MATLAB for further processing.
How can I obtain a tick analysis? - in detail
To obtain a tick analysis, follow a systematic workflow that covers data acquisition, preparation, processing, and interpretation.
First, identify a reliable source of tick‑by‑tick market data. Preferred options include direct feeds from exchanges, data‑vendor APIs (for example, «AlphaVantage», «Polygon.io»), or broker‑provided streams. Verify that the feed delivers timestamps, price, volume, and trade direction for each individual tick.
Second, store the incoming stream in a time‑ordered database optimized for high‑frequency inserts. Common choices are column‑oriented stores (ClickHouse) or key‑value systems (Redis) with appropriate retention policies. Ensure that the storage schema preserves the original granularity and includes fields for bid/ask quotes if required.
Third, clean the raw feed. Typical steps are:
- Remove duplicate timestamps.
- Filter out erroneous price spikes exceeding a predefined multiple of the median absolute deviation.
- Align trades with corresponding quote updates, if a quote‑driven analysis is needed.
Fourth, apply analytical techniques. Depending on the objective, select from the following methods:
- Descriptive statistics – compute mean, variance, and skewness of price changes per tick.
- Microstructure metrics – calculate realized volatility, bid‑ask spread, and order‑flow imbalance.
- Event‑driven models – implement Hawkes processes or Markov chains to model self‑exciting trade arrivals.
- Machine‑learning pipelines – feed engineered features (e.g., tick‑size, inter‑trade duration) into classification or regression models for predictive purposes.
Fifth, visualize results using high‑resolution plots. Candlestick charts with overlaid tick‑level markers, heatmaps of trade intensity, and cumulative sum diagrams help reveal patterns that are invisible at coarser timeframes.
Finally, document the entire pipeline, including data sources, cleaning rules, algorithmic parameters, and version‑controlled code. This documentation supports reproducibility and facilitates future refinements.