How does Bars act on ticks? - briefly
Bars aggregate incoming ticks into fixed‑duration intervals, updating the «open», «high», «low» and «close» values with each tick. The bar is emitted only after the interval ends, providing a concise summary of price activity.
How does Bars act on ticks? - in detail
Bars aggregate individual price updates, converting a rapid stream of ticks into fixed‑size data points suitable for analysis. Each tick carries a price, volume, and timestamp; the bar‑building algorithm monitors these attributes and determines when a new bar should be emitted.
The aggregation process follows defined thresholds:
- Tick count threshold – a bar closes after a predetermined number of ticks have been recorded. The count resets, and the next bar starts with the subsequent tick.
- Volume threshold – cumulative traded volume reaches a set limit, triggering bar finalization. This method balances activity across periods of varying trade intensity.
- Dollar value threshold – the product of price and volume accumulates to a target monetary amount, then the bar terminates. This approach reflects market impact more directly than pure tick counts.
- Time interval – a fixed duration (e.g., one minute) defines the bar’s lifespan, regardless of tick frequency. Time bars provide regular sampling but may contain sparse data during low‑liquidity intervals.
When a bar closes, the algorithm records four key values:
- Open – price of the first tick in the bar.
- High – maximum price observed among all ticks within the bar.
- Low – minimum price observed.
- Close – price of the last tick.
Additional fields may include total volume, number of ticks, and weighted average price. These summaries enable statistical calculations, charting, and algorithmic decision‑making while reducing noise inherent in raw tick streams.
The choice of threshold influences data resolution. Tick‑based bars retain a constant number of observations, preserving statistical properties across volatile periods. Volume‑ and dollar‑based bars adapt to market activity, producing denser information during high‑liquidity intervals and coarser granularity when activity wanes. Time bars guarantee uniform temporal spacing but can suffer from irregular sample quality.
Implementation typically involves a loop that processes each incoming tick, updates cumulative counters, compares them to the active thresholds, and, upon breach, outputs the completed bar and resets counters. Efficient handling requires low‑latency data structures to avoid bottlenecks in high‑frequency environments.
Overall, bar construction transforms disparate tick events into structured, comparable units, facilitating downstream analytics, risk assessment, and automated trading strategies.