What Is Tick by Tick Data?

Its Importance in Backtesting, Trading, and Strategy Development

By an Algo Trading Desk Analyst

Introduction : Tick by Tick Data in Algo Trading

In algorithmic trading, data granularity directly impacts profitability. While most traders rely on time-based candle data such as 1-minute or 5-minute charts, professional algo trading desks operate on tick by tick data. This data records every market event as it occurs, providing the most accurate representation of real trading conditions.

For systematic traders, backtesting on aggregated data often produces misleading results. Tick by tick data removes these distortions and forms the backbone of institutional-grade strategy development.

What Is Tick by Tick Data?

Tick by tick data captures each individual trade and price update in the market. It typically includes:

Traded price and quantity
Exact time stamp
Bid and ask prices
Bid–ask spread changes
Volume updates

Unlike OHLC candles, tick data is not averaged or compressed. Every price movement, no matter how small, is recorded. This makes it the most accurate and unbiased form of market data available to traders.

Importance of Tick by Tick Data in Backtesting

Backtesting accuracy depends on how closely simulated trades match real-world execution. Candle-based backtests assume ideal fills, which rarely occur in live markets.

Tick by tick data allows traders to:

Simulate realistic order fills
Account for bid–ask spreads
Model partial fills and missed trades
Measure true slippage and transaction costs

Without tick data, backtests usually overestimate profitability and underestimate risk, leading to fragile strategies that fail in live deployment.

Role in Live Algo Trading

In live algorithmic trading, decisions are often triggered by micro price movements, not candle closes. Tick data enables:

Precise entry and exit logic
Intrabar stop-loss execution
Real-time spread monitoring
Accurate volume and order flow analysis

For high-frequency trading, market making, and arbitrage strategies, tick data is indispensable. These strategies rely on speed, liquidity, and execution priority — all of which can only be analyzed at the tick level.

Importance in Options and Derivatives Trading

In options trading, especially delta-neutral and volatility-based strategies, tick data plays a critical role. Option prices can change rapidly due to small movements in the underlying or implied volatility.

Tick-level data helps in:

Accurate delta hedging
Gamma scalping execution
Spread and liquidity analysis
Avoiding hidden execution costs

Minute-level data often hides these dynamics, leading to incorrect risk assumptions.

Strategy Development and Risk Management

Tick by tick data improves strategy robustness by exposing market microstructure effects such as spread widening, liquidity gaps, and sudden volatility spikes. This helps eliminate false signals that appear profitable only on aggregated data.

From a risk management perspective, tick data enables:

Tick-based stop losses
Intrabar drawdown control
Realistic worst-case scenario modeling

Professional algo desks consider this level of detail essential before deploying capital.

Challenges of Using Tick Data

Despite its advantages, tick data comes with challenges:

High storage and computing requirements
Data cleaning and normalization issues
Increased backtesting complexity

However, for serious algo traders, these costs are justified by improved accuracy and reduced execution risk.

Conclusion : Tick by Tick Data in Algo Trading

Tick by tick data reflects the true structure of financial markets. Strategies built without it are exposed to hidden slippage, execution bias, and unrealistic expectations.

For an algo trading desk, the rule is simple:
If a strategy cannot survive tick-level testing, it should never trade live.

In professional algorithmic trading, precision begins with data — and tick data is the highest standard available.

Also Read : The Role of GPUs in High-Frequency Trading (HFT)

: www.nseindia.com

NSE-Specific Tick Data Examples
Example 1: NIFTY 50 Futures (Index Futures)
Time (IST) Price Quantity Bid Ask
09:15:00.235 24,112.50 75 24,112.45 24,112.55
09:15:00.418 24,112.55 150 24,112.50 24,112.60
09:15:00.602 24,112.40 225 24,112.35 24,112.45

Insight:
A candle chart would show a single price move, but tick data reveals spread behavior, liquidity absorption, and execution feasibility.

Example 2: NIFTY ATM Call Option (Options Tick Data)
Time (IST) LTP Bid Ask OI Change
10:01:12.118 182.50 182.30 182.65 +1,200
10:01:12.421 183.20 183.00 183.45 +800
10:01:12.905 181.90 181.60 182.10 -600

Insight:
Tick data exposes volatility shocks and spread expansion, critical for gamma scalping and intraday option strategies.

Example 3: NSE Order Book Imbalance
Level Bid Qty Bid Price Ask Price Ask Qty
1 12,500 24,110.80 24,111.00 3,200
2 9,800 24,110.60 24,111.20 4,100

Insight:
This imbalance suggests short-term upward pressure, invisible in candle-based data.