Principle:TA Lib Ta lib python Streaming Data Buffering
| Knowledge Sources | |
|---|---|
| Domains | Real_Time_Processing, Technical_Analysis |
| Last Updated | 2026-02-09 22:00 GMT |
Overview
A data management pattern for accumulating price data in a rolling buffer to provide sufficient history for streaming (single-value) indicator computation.
Description
Streaming indicator functions compute only the latest indicator value from a buffer of historical data, rather than computing the full array output. This requires maintaining a buffer of accumulated price data that grows as new ticks/candles arrive.
The buffer must contain at least lookback + 1 data points for the indicator to return a meaningful value. If the buffer is shorter, the streaming function returns NaN.
Key characteristics of the streaming buffer pattern:
- Stateless computation: Each call recomputes from the full buffer (no internal state between calls)
- Full buffer required: The entire buffer is passed on each call, not just the latest tick
- Memory management: Users should trim old data to prevent unbounded memory growth
Usage
Apply this principle when building real-time trading systems that need the latest indicator value on each new price tick or candle.
Theoretical Basis
The streaming buffer pattern follows an append-and-compute model:
# Abstract streaming buffer pattern
buffer = [] # Accumulated price data
def on_new_tick(price):
buffer.append(price)
data = numpy.array(buffer, dtype=float)
latest_value = stream_indicator(data, parameters)
return latest_value
The stateless design means each call is independent — there is no hidden state or warm-up period between calls. The trade-off is that passing the full buffer on each call has O(n) overhead per tick.