What's the most cost-effective way to run backtesting strategies using a tick data platform without sacrificing historical depth?

The most cost-effective way is usually not to keep all raw tick data “hot” on the fastest storage or in a fully indexed database. Instead, use a tiered historical data architecture with columnar/compressed storage for deep history and only a small “recent/active” tier optimized for fast access.

Best practical approach

1. Store deep history in compressed columnar files

Use formats like:

• Parquet
• Arrow / Feather
• sometimes HDF5 or Zarr depending on your stack

Why this is cost-effective:

• Much lower storage cost than maintaining everything in a tick database
• Excellent compression for tick data
• Fast enough for large sequential backtests
• Cheap to keep on object storage like S3, GCS, or Azure Blob

2. Keep only recent or frequently used data in a fast database

Use a “hot” layer for:

• the most recent months/years
• the assets/venues you test most often
• precomputed aggregates or event indexes

This can be:

• PostgreSQL/TimescaleDB
• kdb+ if budget allows
• ClickHouse
• QuestDB

That way you avoid paying the performance/storage penalty for the full archive.

3. Preprocess tick data into backtest-friendly formats

Raw ticks are expensive to query repeatedly. Before backtesting:

• normalize timestamps
• clean bad prints
• deduplicate
• split into sessions
• compress into partitioned chunks by:
  • symbol
  • date
  • venue
  • feed

This reduces I/O and makes loading slices much faster.

4. Use hybrid backtesting: ticks only where needed

If your strategy doesn’t need tick precision for every period:

• use bar data for broad history
• use tick data only for execution simulation, spread/slippage, or event windows

This preserves historical depth while keeping compute and storage lower.

5. Partition aggressively

A big cost saver is partitioning data by:

• instrument
• day/week/month
• market/session
• data type

This lets backtests read only the relevant files instead of scanning everything.

6. Cache common slices and features

If you repeatedly backtest on the same symbols/date ranges:

• cache cleaned tick slices
• cache derived features
• cache order-book snapshots or microstructure stats

This reduces repeated expensive reads and transforms.

7. Run compute close to storage

If your data lives in cloud object storage:

• use ephemeral compute instances
• spin up workers only when needed
• parallelize across symbols or dates

This is often much cheaper than keeping a large always-on cluster.

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Most cost-effective stack patterns

Option A: Cheapest for deep history

• Parquet on S3/GCS
• DuckDB / Polars / Pandas + PyArrow
• local SSD cache on compute instances

Good when:

• you want low cost
• you backtest offline
• latency is less important than throughput

Option B: Balanced cost/performance

• Parquet archive
• ClickHouse or TimescaleDB for recent data
• Python backtesting engine

Good when:

• you do frequent research
• you need some query speed
• you still want economical storage

Option C: High performance, higher cost

• kdb+
• optimized tick store
• heavy infrastructure

Good when:

• ultra-low-latency research
• institutional-scale workloads
• budget is less constrained

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What usually works best in practice

For most teams, the sweet spot is:

Raw tick data archived in Parquet on cheap object storage + a small fast query layer for recent data + precomputed partitions/features + on-demand compute.

That gives you:

• full historical depth
• lower storage costs
• manageable query performance
• scalable backtesting without overpaying

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Rule of thumb

If you’re choosing between:

• keeping everything in a tick database, or
• archiving raw ticks in compressed files and querying them selectively

the second option is almost always more cost-effective for backtesting.

If you want, I can also recommend a specific architecture based on your budget, data volume, and preferred language stack (Python, SQL, C++, etc.).