How to use BARS for ticks? - briefly
Define tick‑related actions, create clear behavioral anchors for each performance level, and assign numeric scores to those anchors; then evaluate each tick against this scale to obtain consistent ratings. Use the resulting scores for analysis, reporting, and decision‑making.
How to use BARS for ticks? - in detail
BARS provide a framework for converting raw tick streams into aggregated time‑based or volume‑based bars. The conversion process consists of three essential stages: data acquisition, bar definition, and output handling.
First, capture tick information from the market feed. Each tick must contain at least a timestamp, price, and size. Store the incoming records in a buffer that preserves chronological order, as any out‑of‑sequence entry will distort bar boundaries.
Second, select the bar type that matches the analysis goal. Common options include:
- Time bars – fixed intervals (e.g., 1 minute). Close a bar when the elapsed time reaches the interval limit, then start a new bar with the next tick.
- Volume bars – fixed cumulative volume (e.g., 10 000 contracts). Accumulate tick sizes until the threshold is met, then emit a bar.
- Dollar bars – fixed monetary turnover (e.g., $1 million). Sum price × size for each tick; close the bar when the sum exceeds the target.
- Tick bars – fixed count of ticks (e.g., 500). Increment a counter for each tick; emit a bar after the specified count.
For each bar, compute the open, high, low, close (OHLC) prices and the aggregated volume. The open price is the first tick’s price within the bar, the close price is the last tick’s price, while high and low are the extreme values observed. Record the bar’s start and end timestamps to preserve temporal context.
Third, write the completed bars to the desired destination. Options include:
- Appending to a CSV file with columns for timestamp, OHLC, and volume.
- Publishing to a message queue (e.g., Kafka) for downstream consumption.
- Inserting into a time‑series database for rapid retrieval.
When implementing the workflow, ensure thread‑safe handling of the tick buffer if multiple threads feed data simultaneously. Validate each tick’s fields before inclusion to avoid corrupt bars. Periodically flush any partially filled bar at market close to retain the final incomplete interval.
By following these steps, practitioners can reliably transform high‑frequency tick streams into structured bar data suitable for statistical analysis, algorithmic trading, or back‑testing.