CORE SERVICES
Forex Turnkey Solution
End-to-end brokerage launch package
Regulatory Licensing
SCA, FCA, CySEC & global licensing
Trading Platforms
MT4, MT5 & proprietary platforms
Liquidity Solution
Top-tier multi-LP aggregation
Risk Management
Real-time exposure monitoring
TRADING TECHNOLOGY
Quantitative Strategy
Alpha research & systematic models
MT5 Manager API
Full broker-side MT5 management API
Automated Strategies
Algo execution & HFT infrastructure
Signals Application
Live forex, metals & indices signals
Our Story
The journey behind Algoment
Our Philosophy
Principles that guide every decision
Our Process
How we deliver from brief to launch
Our Partners
Technology partners we trust
Test Before You
Trade. Always.
Algoment's backtesting infrastructure gives quantitative traders and strategy developers tick-level historical data, a high-speed simulation engine, and institutional-grade analytics — before a single dollar goes live.
A backtesting environment built for serious quants
Every tool you need to develop, validate, and deploy systematic trading strategies with confidence.
Tick-Level Data Pipelines
Access to 10+ years of institutional-quality tick data across 70+ FX pairs, indices, commodities, and crypto — sourced from tier-1 LPs.
Tick-Level Data Pipelines
Access to 10+ years of institutional-quality tick data across 70+ FX pairs, indices, commodities, and crypto — sourced from tier-1 LPs.
High-Speed Simulation
Vectorised backtesting engine capable of processing millions of data points per second — test years of strategy history in minutes, not hours.
High-Speed Simulation
Vectorised backtesting engine capable of processing millions of data points per second — test years of strategy history in minutes, not hours.
Walk-Forward Optimisation
Avoid over-fitting with built-in walk-forward analysis, Monte Carlo simulation, and out-of-sample robustness testing frameworks.
Walk-Forward Optimisation
Avoid over-fitting with built-in walk-forward analysis, Monte Carlo simulation, and out-of-sample robustness testing frameworks.
Realistic Execution Modelling
Simulate live spread widening, slippage, partial fills, requotes, and swap costs — so backtest results reflect real-world trading conditions.
Realistic Execution Modelling
Simulate live spread widening, slippage, partial fills, requotes, and swap costs — so backtest results reflect real-world trading conditions.
Multi-Strategy Portfolio Testing
Combine multiple strategies in a single backtest run to analyse correlation, diversification benefit, and aggregate risk metrics.
Multi-Strategy Portfolio Testing
Combine multiple strategies in a single backtest run to analyse correlation, diversification benefit, and aggregate risk metrics.
Deploy to Live Markets
One-click deployment of validated strategies to live MT4/MT5 or cTrader environments — no code rewriting required.
Deploy to Live Markets
One-click deployment of validated strategies to live MT4/MT5 or cTrader environments — no code rewriting required.
Every metric that matters
Comprehensive performance reporting across all the metrics sophisticated investors need to evaluate strategy robustness.
The Problem
Why Most Strategies
Fail Before They Even Launch
Strategies Deployed Without Validation
Trading strategies launched based on intuition or limited visual chart analysis consistently underperform — or blow up — because the logic was never rigorously tested against historical data.
Strategies Deployed Without Validation
Trading strategies launched based on intuition or limited visual chart analysis consistently underperform — or blow up — because the logic was never rigorously tested against historical data.
Curve-Fitting Creates False Confidence
Over-optimised backtests that look perfect on historical data often fail immediately in live trading. Without walk-forward testing, most backtest results are meaningless.
Curve-Fitting Creates False Confidence
Over-optimised backtests that look perfect on historical data often fail immediately in live trading. Without walk-forward testing, most backtest results are meaningless.
Ignoring Execution Costs Kills Returns
Backtests that don't model realistic spreads, slippage, swap costs, and partial fills consistently overstate returns by 30–60% — leading to massive disappointment in live trading.
Ignoring Execution Costs Kills Returns
Backtests that don't model realistic spreads, slippage, swap costs, and partial fills consistently overstate returns by 30–60% — leading to massive disappointment in live trading.
Slow Backtesting Kills Research Velocity
Bar-by-bar backtesting engines that take hours to test a single strategy across 5 years of data force quant teams to test fewer hypotheses — reducing the chance of finding genuine alpha.
Slow Backtesting Kills Research Velocity
Bar-by-bar backtesting engines that take hours to test a single strategy across 5 years of data force quant teams to test fewer hypotheses — reducing the chance of finding genuine alpha.
Poor Analytics Miss Real Risk
Reporting only return and win rate without Sharpe, Sortino, max drawdown, and drawdown duration gives a dangerously incomplete picture of strategy risk — especially in tail-event scenarios.
Poor Analytics Miss Real Risk
Reporting only return and win rate without Sharpe, Sortino, max drawdown, and drawdown duration gives a dangerously incomplete picture of strategy risk — especially in tail-event scenarios.
No Path From Backtest to Live
When rewriting code is required to move from a backtesting environment to a live execution system, strategies lose weeks to deployment delays — during which market conditions change.
No Path From Backtest to Live
When rewriting code is required to move from a backtesting environment to a live execution system, strategies lose weeks to deployment delays — during which market conditions change.
FAQ
Frequently Asked
Questions
Validate your strategy before it goes live
Connect with our quant infrastructure team to get access to the Algoment backtesting environment and historical data archive.
Get Framework Access