Which Historical Data Sources Provide Tick and Minute Level Accuracy

For any algorithmic trading workflow, historical data is not an optional input. It is the base layer on which every decision from designing a strategy to validating it. When traders test a system using smooth, approximate, or incomplete data, the backtest may look convincing, but the live results will almost certainly drift. Indian markets, in particular, demand precision because liquidity, depth, and volatility vary significantly across instruments and timeframes. This means even a small gap in data can change how entries and exits behave.

To avoid that gap, traders rely on two types of datasets: tick-level data, which captures every price change, and minute-level data, which builds structured OHLC bars suitable for most retail and mid-frequency systems. Understanding which source provides what level of accuracy helps you choose the right dataset for your strategy.

Why Data Accuracy Directly Impacts Strategy Reliability

When you replay a strategy on historical data, you want to simulate real market conditions as closely as possible. This is where accuracy matters. If highs and lows are not captured correctly, your stop losses will execute on paper but fail in reality. If timestamps are misaligned, your breakout entries will trigger earlier or later than they realistically could. And if tick sequences are missing, any model that depends on short-lived moves becomes untestable.

Data errors usually show up in three places: overestimated returns, underestimated drawdowns, and unrealistic fill assumptions. This is why serious traders place more importance on data quality than on indicators or coding syntax.

Tick Level Data Sources: When Every Price Change Matters

Tick data is the most granular dataset available. It captures each price update along with time, volume, and in some feeds, bid-ask depth. Strategies such as scalping, microstructure analysis, or fast mean-reversion models cannot be tested accurately without tick data.

GlobalDataFeeds (GDFL)
GDFL is one of the few retail-accessible sources in India that streams native tick feeds directly from the exchange. The historical archives carry millisecond timestamps, unaggregated price changes, and clean continuity across sessions. Traders building execution-sensitive systems often prefer GDFL because it preserves the true sequence of trades, not just the final candle.

TrueData
TrueData offers another reliable tick feed with multi-year archives. The datasets include full price movement history for equities, index futures, and options. Because the data comes with strict timestamp integrity, it is suitable for strategies that need to account for slippage, rapid reversals, or intraday volatility clusters.

Reconstructed Tick Feeds (Third Party)
Some vendors rebuild ticks from trade books when native ticks are not available. These are adequate for broad research or academic modelling but are not recommended for execution modelling. Reconstructed ticks miss depth and sometimes misrepresent order flow, which affects fill simulations.

Minute Level Data Sources: Practical for Most Retail Strategies

Minute-level data is widely used because it balances accuracy with efficiency. Most trend-following, breakout, swing, and multi-leg options strategies do not depend on micro-ticks. They depend on whether your one-minute high, low, open, and close are correct.

NSE Official Historical Data
NSE’s paid datasets provide one-minute bars directly from the exchange. The value here is consistency. The OHLC data is timestamped accurately and is reliable across several years. For positional or intraday breakout systems, this dataset is structurally sound.

GDFL Aggregated Minute Bars
Since GDFL builds minute candles from raw ticks, the highs, lows, and closes reflect actual intraday movements without approximation. This is particularly useful for systems that require precise high-low boundaries.

TrueData Minute Archives
TrueData also aggregates ticks into minute bars with correct sequencing. This works well for strategies where fill accuracy depends on the integrity of highs and lows rather than the full tick stream.

AlgoTest Option Data
AlgoTest provides minute-level options data built on clean underlying tick feeds. Options behave differently from equities because of time decay and fluctuating implied volatility. Minute-level options data allows traders to test spreads, straddles, and short-gamma systems more realistically than EOD data ever could.

Choosing Between Tick and Minute Data

Your choice depends entirely on what the strategy needs.
If your algorithm reacts to micro-moves lasting seconds, you need raw ticks. If it depends on candle patterns, volatility filters, or multi-leg positions held for minutes to days, one-minute data is usually enough.

What matters is consistency within the strategy. Mixing low-quality data for testing and high-quality data for execution leads to unpredictable behaviour.

What Makes a Data Source Reliable

Regardless of provider, accurate historical datasets must satisfy a few technical criteria:

Correct timestamps
If timestamps drift, your entries drift with them. The sequencing of events must be intact.

Accurate highs and lows
Synthetic or approximated candles can distort stops, targets, and breakout logic.

Proper corporate action adjustments
Splits, bonuses, and dividends must be applied consistently across the timeframe.

Accurate rollover adjustments for futures
Futures contracts should be back-adjusted to avoid artificial gaps.

No missing intervals
Gaps around volatile sessions can inflate performance metrics and reduce drawdown visibility.

When any of these elements break, backtests lose validity.

Conclusion

Selecting a data source is not a technical formality. It is a strategic decision that determines how trustworthy your strategy results are. Tick data is a requirement for high-frequency systems, while accurate minute data is sufficient for most retail and mid-frequency models. What matters is that the dataset reflects real exchange behaviour without smoothing or reconstruction that changes how trades would have played out.

A systematic trader’s edge begins with correct data. Everything else builds on top of it.

Where Stratzy Fits Into the Data Workflow

By reviewing Stratzy’s setups on https://stratzy.in/, traders get a clear understanding of what drives each strategy. This helps you convert insights into reliable algo trading logic on any platform.

Accurate historical data is the foundation of any reliable algorithmic trading workflow. Backtests, walk-forward tests, optimization runs, and Monte Carlo simulations are only as good as the data that feeds them. For Indian markets, this becomes even more critical because small variations in ticks, auction sessions, intraday gaps, or rollover adjustments can distort strategy behaviour.
Retail traders often rely on free or lightweight charting platforms that use approximations. These work for visual analysis, but not for execution-grade backtesting. To build deployable strategies, traders need historical datasets that mirror real exchange behaviour with high precision.

This blog outlines the data sources that support tick-level and minute-level accuracy, what makes them reliable, and how to choose the right format for systematic trading.

Why Data Accuracy Matters for Algo Trading

Algorithms depend on precise event sequencing. Any missing tick, rounded price, or inaccurate timestamp can exaggerate profitability or hide execution risks.
High frequency or intraday strategies are the most affected because even a one-second delay can shift entries, exits, and stop-loss behaviour.
Incorrect data also leads to misleading metrics:

• Sharpe ratio inflated due to smooth fills
• Profit factor improved because losing ticks were absent
• Drawdown understated due to incomplete intraday gaps
• Slippage ignored because quote depth was unavailable

Accurate data ensures your backtests reflect true market conditions rather than idealised charts.

Tick Level Data Sources

Tick data provides every price change as it occurred. It includes timestamp accuracy, trade volume, bid-ask quotes (if available), and full execution flow. It is mandatory for high frequency systems, scalping models, and short duration mean reversion strategies.

GlobalDataFeeds (GDFL)

GDFL provides one of the most precise commercial tick feeds for Indian markets.
Key attributes:
• Native exchange-sourced tick stream
• Millisecond timestamp granularity
• Long historical depth for NSE equity, futures, and options
• Zero aggregation, providing true tick sequences
GDFL is widely used for Amibroker, Python, and multi-timeframe testing where fills depend on exact price sequences.

TrueData

TrueData offers raw tick datasets suitable for intraday modelling.
Key attributes:
• Tick-by-tick trade information
• Reliable timestamp integrity
• Multi-year historical archives
• Separate feeds for equity, index futures, and derivatives
It is frequently used by traders running intraday trend-follow or fade models that depend on precision.

NSE Bhavcopy Tick Variants (Third-Party Reconstructed)

Some providers reconstruct tick series from trade books when raw tick data is unavailable.
These are useful for:
• Cost efficient large backtests
• Academic or large sample research
They are not recommended for execution modelling because reconstruction lacks true bid-ask depth.

One Minute and Multi Minute Data Sources

Minute-level data is accurate enough for most retail strategies such as breakout systems, swing models, multi-leg options strategies, and positional filters. Minute data reduces noise while maintaining realistic execution profiles.

NSE Official Historical Data (Paid)

NSE offers one minute historical datasets for indices and selected symbols.
Key attributes:
• Direct exchange-sourced format
• High timestamp reliability
• Clean OHLC structure
• Useful for multi-year testing
Suitable for strategies that do not depend on microstructure events.

GlobalDataFeeds (Aggregated Minute Data)

GDFL also offers internally aggregated minute bars created from raw tick data.
Benefit:
• One minute candles built from authentic tick feed
These bars reflect true market highs, lows, and fills, making them suitable for multi-timeframe systems.

TrueData Minute-Level Archives

Derived from native ticks, producing highly precise OHLC candles.
Useful for:
• Multi-year studies
• Cross-instrument comparisons
• Multi-leg options frameworks

AlgoTest Market Data for Options Backtesting

AlgoTest provides minute-level option data derived from clean tick streams.
Useful because:
• Options have high time decay and require accurate intraday candles
• Many retail platforms offer only end-of-day options data, which is insufficient
AlgoTest’s dataset aligns better with options execution logic.

Choosing Between Tick and Minute Data

Selection depends on strategy type:

• Scalping, fade trading, microstructure models → tick data
• Short duration breakouts and intraday swings → high quality minute data
• Multi-leg options, hedged positions, weekly expiry systems → minute data with correct IV modelling
• Positional or end-of-day strategies → minute data sufficient

Tick data is ideal but increases computational load. For traders without HFT objectives, minute-level data usually provides the right balance between accuracy and efficiency.

Factors That Define a Reliable Data Source

Before selecting any provider, evaluate the following:

Timestamp Integrity

Data must reflect the exact sequencing of events.
Incorrect timestamps distort entry and exit logic.

True High-Low Accuracy

Aggregated or synthetic candles may miss actual market highs or lows, leading to unrealistic stop-loss behaviour.

Corporate Action Adjustments

Split, bonus, rights, and dividend adjustments must be applied consistently.

Rollover Handling for Futures

Back-adjusted data avoids artificial gaps during contract transitions.

Quote Depth Availability

For microstructure-based strategies, real bid-ask depth is critical.

Data Continuity

No missing intervals, especially during volatile sessions.

Incomplete or misaligned datasets lead to flawed backtests regardless of strategy logic.

Conclusion

Reliable backtests depend entirely on the quality of historical data. Retail traders should choose data sources based on strategy type, required granularity, and execution sensitivity. Tick data is essential for short-duration systems, while minute-level data is sufficient for most multi-leg and positional strategies.

A strategy built on inaccurate or synthetic datasets may look profitable on charts but fail instantly in live markets. Data quality is a non-negotiable part of systematic trading.

Where Stratzy Fits in the Data Selection Process

Stratzy does not provide historical tick or minute datasets. Instead, it improves the step before data selection: identifying structured ideas worth testing. Traders can use Stratzy’s pre-built algos and structured insights as hypotheses, then pull accurate tick or minute-level data from the providers above to test those hypotheses.
This ensures that testing resources are spent on strategies with a logical foundation rather than random idea exploration. Stratzy enhances the research layer while the data providers ensure execution-level accuracy.