From hypothesis to strategy.
Every candidate strategy undergoes rigorous quant verification by Elvaris Capital. No results are curve-fitted, parameters are locked before out-of-sample evaluation, and execution latency is accounted for.
Generate
AI-assisted research turns a quantitative hypothesis into strictly codified testable rules.
Backtest
Rules run on tick-level historical data with realistic fixed costs and zero look-ahead bias.
Improve
Weak spots and regime transitions are reviewed; improvements are committed as immutable versions.
Validate
Multi-window robustness, Monte Carlo stress testing, and out-of-sample periods decide listing approval.
Questions, answered.
Are these results real or hypothetical?
Metrics in the marketplace reflect systematic backtest and paper simulation evaluations using historical tick data. All models are clearly labeled with their validation type (Backtested, Forward-Tested, Simulated). Live trading performance is never fabricated.
What is out-of-sample testing?
A strategy's rules and parameters are discovered exclusively on the training window (70% of dataset). The strategy is then executed blindly on the remaining unseen out-of-sample period (30%) to guarantee it has not memorized historical market noise.
Do strategies get versioned?
Yes. Every parameter change or logic update produces a new immutable version identifier (e.g. v1.2.0). Previous versions, reports, and provenance remain accessible to users so historical accountability is preserved.
Where do the algorithmic strategies run?
The algorithmic strategy signals run in an isolated execution container on an Elvaris Capital 24/7 VPS with sub-millisecond connectivity to institutional liquidity feeds. Real-time TradingView execution bridges communicate with this runner over authenticated, low-latency secure channels.
How is slippage and commission calculated?
Every report incorporates realistic transaction costs (e.g. $2.50/lot commission + 0.5 pip spread) and dynamic slippage assumptions (1.0 pip on entry and stop triggers) to ensure metrics reflect real-world execution drag.