Benchmarks

Performance tests, throughput measurements, and the methodology behind them.

Overview

The infrastructure is built for quantitative research workloads — not one-off backtests. The benchmark data below reflects real execution performance on production infrastructure. All results are reproducible.

ℹ

Execution time is measured from the moment the API receives the request to the moment the result is available — including data retrieval, strategy parsing, full candle evaluation, and metrics computation.

Reference Benchmark

The primary benchmark is BTC/USDC over 5 years at 15M timeframe. This represents approximately 175,200 candles — a meaningful workload for evaluating infrastructure performance.

~200ms

Execution time

BTC/USDC · 5 years · 15M

175,200

Candles processed

5 years × ~35,040 candles/year

1,000+

Concurrent executions

Horizontal scaling

Timeframes

15M to 1D

Execution Time by Timeframe

All measurements use BTC/USDC over a 5-year period. Shorter timeframes have more candles and therefore take longer.

Timeframe	Candles (5y)	Approx. execution time
1D	~1,825	~45ms
4H	~10,950	~85ms
2H	~21,900	~110ms
1H	~43,800	~150ms
30M	~87,600	~175ms
15M	~175,200	~200ms

Times are approximate and may vary based on strategy complexity (number of indicators, lookback periods) and current infrastructure load.

#Methodology

What is measured

Total wall-clock time from API request receipt to result availability.
Data is pre-loaded and cached on the infrastructure. Cold-start times are not included.
Measurements exclude network latency between client and API server.
Results are the median of 100 consecutive executions of the same strategy.

Test strategy used

Benchmarks use a standard strategy: EMA 9/21 crossover + RSI confirmation + volume spike, on 1H timeframe. This is a representative strategy with medium computational complexity (3 indicators, 3 conditions).

Reproducibility

All benchmark results are reproducible. Given the same strategy, asset pair, and date range, the execution time will be within the published ranges. You can verify this by running your own benchmarks via the API and comparing against the published numbers.

Throughput & Concurrency

The infrastructure supports concurrent execution. Multiple backtests can run simultaneously without blocking each other. This is designed for:

Parameter optimization loops (running 100 variations of the same strategy in parallel).
AI agent workflows that generate and test multiple hypotheses simultaneously.
Multi-asset testing (running the same strategy on BTC, ETH, and SOL in parallel).
Multi-timeframe testing (running the same strategy across 15M, 1H, and 4H simultaneously).

💡

For iterative research, consider batching submissions: submit all variations first, then poll all results, rather than waiting for each one before submitting the next. This maximizes infrastructure throughput.