Intro
If you’re building custom order book systems or quantitative backtesting pipelines for US equities, you’ve almost certainly run into a frustrating data artifact: raw real-time tick data pulled from market APIs creates visible gaps between price tiers when rendered into a full depth ladder.
These empty price bands often trick developers into thinking there’s a stream disconnection or missing API data. Early in my career building institutional-grade US stock data ingestion infrastructure, I wasted hours debugging log streams that only updated top-of-book bid/ask values with zero intermediate price activity. After dissecting raw tick payloads, I realized those blank levels weren’t data loss—they simply had no resting orders or trade events logged at those price points.
This walkthrough breaks down the root cause of missing price tiers, three practical handling strategies, a standardized tick normalization workflow, sample Python WebSocket code using AllTick API, and a two-tier order book architecture designed for stable long-running production ingestion. All patterns are production-ready for personal quant tools and small trading teams alike.
What Creates Empty Price Levels? The Core Divide Between Tick Streams & Full Order Books
Nearly all public US stock market APIs deliver L1 top-of-book quotes or discrete tick event streams. These payloads only broadcast state changes: new trades, updated best bid/ask spreads, and nothing more. They do not auto-populate every incremental price level between the current bid and ask.
A concrete example: the best bid sits at $100.10 before jumping straight to $100.30. Every price from 100.11 through 100.29 contains zero resting orders. Treating these natural gaps as data corruption introduces systemic bias across liquidity analysis, spread modeling, and intraday strategy backtesting.
Standard Foundation: Build a Price Ladder Skeleton Using Base Tick Size
The most widely adopted industry fix relies on pre-generating a static price ladder based on the instrument’s fixed tick increment (US common stocks use a standard tick size of 0.01). This static structural layer decouples the order book’s visual/ computational layout from live order activity:
1.Capture the latest live bid and ask prices as upper/lower boundaries
2.Iterate across the price range using the defined tick step to generate every possible price tier
3.Persist all generated price levels; mark tiers without active orders as empty null values, populate volume and price data for levels with resting liquidity
This static skeleton eliminates full order book reconstruction on every price jump, reduces frontend rendering overhead, and unifies calculation logic for technical and liquidity metrics.
Three Approaches to Empty Tier Handling — Choose Based On Your Use Case
There is no universal solution for blank price levels; each method carries tradeoffs for backtesting, live trading dashboards, and statistical modeling:
1. Populate empty tiers with zero volume
Simplest implementation with minimal debugging overhead. Critical downside: it fabricates artificial liquidity that does not exist in the market. Avoid for live strategy logic or liquidity factor research; only suitable for simple demo visualizations.
2. Retain empty tiers as null values (Recommended for Quant & Live Trading)
This method mirrors the true discrete matching mechanics of US equity markets. No synthetic data is injected into vacant price bands, leaving gap interpretation and display logic to upstream application layers. This is the gold standard for production backtesting, real-time trading systems, and institutional market depth analysis.
3. Interpolate missing prices across the bid-ask spread
Uses linear interpolation to fill blank levels with estimated values. Restrict usage exclusively to offline statistical curve fitting. Never integrate this into live trading decision logic—interpolation generates non-existent liquidity signals that distort strategy entry/exit triggers.
Normalize Incoming Tick Data To Simplify Order Book Construction
Multi-source market API integration introduces inconsistent field naming and payload schemas, which bloats order book reconstruction logic. Establish a pre-processing step to convert all external tick feeds into a unified standardized data format before generating price ladders.
I used AllTick API’s persistent WebSocket endpoint for development and validation work. Its consistent tick output schema integrates seamlessly with the ladder generation logic, making it ideal for high-frequency real-time order book pipelines.
Minimal working Python snippet (extend with error handling, persistence, and concurrency logic as needed):
import websocket
import json
def on_tick_receive(ws, raw_msg):
data = json.loads(raw_msg)
tick_step = 0.01
bid = round(float(data["price"]) - tick_step, 2)
ask = round(float(data["price"]) + tick_step, 2)
order_book_ladder = build_price_ladder(bid, ask, tick_step)
def build_price_ladder(bid_price, ask_price, step):
ladder = {}
current_price = bid_price
while current_price <= ask_price:
ladder[round(current_price, 2)] = None
current_price += step
return ladder
if __name__ == "__main__":
ws_client = websocket.WebSocketApp("wss://stream.alltick.co", on_message=on_tick_receive)
ws_client.run_forever()
Production-Grade Optimization: Two-Tier Decoupled Order Book Architecture
After iterating through multiple live deployment iterations, a decoupled two-layer design delivers maximum stability during volatile price jumps:
Structural Layer: Maintains a permanent static price ladder built from the instrument’s tick size. The base price tier structure never fully rebuilds, even during large bid/ask shifts.
Data Layer: Stores only real, exchange-validated order and trade events. No synthetic volume or price data is written to vacant levels, preserving an accurate representation of market liquidity distribution.
Decoupling structure and data eliminates repeated heavy recalculations during volatile tick updates. The core principle: do not artificially fill market-native empty price bands—accurately record the true state of every available price tier instead.
Wrap Up
Empty price levels in US stock order books are a pervasive data engineering pain point for quant developers. Reliably resolving the issue hinges on four core practices:
1.Distinguish the fundamental structural differences between discrete tick streams and full depth order books
2.Generate a static price ladder skeleton anchored to the instrument’s tick size
3.Select empty-tier handling logic aligned with your pipeline’s purpose (backtesting, live dashboards, statistical analysis)
4.Deploy a decoupled two-tier order book architecture for long-running stable ingestion
Normalizing all incoming tick payloads upstream and pairing the data with a two-layer order book design cuts maintenance overhead for visualization and quantitative calculation layers, while aligning backtest and live market data with authentic US exchange matching rules to minimize strategy performance bias.
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