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Backtest · Simulation

Backtesting Software Development with Realistic Simulation

We build custom backtesters with tick/bar modes, slippage models, partial fills, funding, and corporate actions—so promotion to paper trading is a validation step, not a leap of faith.

production / validated
pre_trade.validate()
execution.route(order)
risk.enforce_limits()
log.audit(event_id)

Most backtests lie politely

Look-ahead bias, survivorship-free data, zero-cost fills, and optimized parameters on the same slice produce curves that live trading shatters.

  • ×Bar-based tests intrabar paths unknowable—false precision.
  • ×Crypto backtests ignore funding and fee tiers.
  • ×FX ignores rollover and session spread widening.
  • ×No walk-forward framework—just one glistening equity line.

Custom backtesting software with explicit assumptions

Documented fill models, point-in-time datasets, and parity hooks exporting same signals live system will consume.

  • Tick and event-driven modes where data supports it.
  • Parameter sweeps with walk-forward and holdout reporting.
  • Cost models: spread, commission, slippage, funding, borrow.
  • Export signals to production format for parity tests.

Teams serious about research governance

Funds and traders who need backtests as decision tools—not marketing charts.

Quant funds

Multi-asset backtester integrated with data lake.

Crypto systematic traders

Funding-aware perp simulation.

FX EA developers

MT5-aligned tick tests with broker spread profiles.

Fintech strategy marketplaces

Verified backtests for listed strategies.

Real-world delivery examples

Crypto perp backtester

Fund needed funding and tiered fee model on 3 years tick data.

Live paper drift under 5% vs sim on 90-day validation.

Prop EA verification reports

Vendor needed PDF backtests with spread stress scenarios for marketing.

Automated report gen cut manual Excel work from 4h to 12m per EA.

What you get

Configurable fill models

Market, limit, queue position, and partial fill logic.

Walk-forward engine

Rolling train/test windows with aggregated metrics.

Monte Carlo modules

Shuffle returns or resample trades for DD distributions.

Data quality checks

Gap detection, outlier flags, and survivorship notes.

Report generator

HTML/PDF with metrics table, DD chart, and assumptions appendix.

Live parity export

Same signal JSON production consumes for diff tests.

Technology stack

TechnologyRole in your build
Python / C++Simulation core for speed and flexibility
Pandas / PolarsVectorized bar engines
Parquet / ArcticHistorical data storage
DuckDBAd hoc research queries on tick archives
Plotly / MatplotlibReport visualizations

Development process

  1. 01

    Assumption workshop

    Asset class, bar/tick, costs, and data sources.

  2. 02

    Data pipeline

    Ingest, clean, point-in-time store.

  3. 03

    Engine MVP

    Single strategy path with reports.

  4. 04

    Parity hook

    Export format matched to live runner.

  5. 05

    Validation study

    Compare paper vs backtest on holdout period.

Frequently asked questions

Build vs QuantConnect/Amibroker?+

Custom when you need parity with proprietary live stack or non-standard assets.

Tick data included?+

We integrate your vendors or recommend sources; licensing is client responsibility.

GPU acceleration?+

Available for massive parameter sweeps when justified.

Can you backtest MT5 EAs?+

Yes—align tester settings or external tick replay with same logic.

Machine learning backtests?+

Walk-forward and purged cross-validation supported.

Related services

Build backtesting software you can trust

Share asset classes and data sources—we propose simulation assumptions upfront.