RESEARCH CENTER
Research and insights
Research on algorithmic risk management, systematic strategy portfolios, futures, CFDs and disciplined trading.
Applied research and analysis for professional traders and investors.
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LATEST INSIGHTS
Research publications
A practical view of robust trading systems and portfolio management.
Risk Management in Algorithmic Trading: How Systems Control Losses
Position sizing, risk per trade, stop orders, drawdown control, risk of ruin and an algorithm-evaluation checklist.
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Strategy Diversification: How to Build an Algorithmic Portfolio
Strategy correlation, diverse markets and logics, capital allocation, rebalancing and portfolio drawdown reduction.
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Futures or CFDs: Which Is Better for Algorithmic Trading?
Exchange and broker structure, liquidity, commissions, spreads, slippage, margin, MetaTrader and TradeStation.
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How to Evaluate a Trading Algorithm Before Buying: An Investor Checklist
A practical checklist covering backtest quality, real trading costs, forward tests, drawdown, martingale exposure and verified data.
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Strategy Backtesting: How to Test a Trading Algorithm Properly
How to backtest a strategy properly: historical data, commissions, spreads, slippage, overfitting and result reliability.
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Maximum Strategy Drawdown: How to Assess Algorithmic Risk
How to evaluate drawdown depth and duration, capital recovery, algorithm risk and portfolio exposure.
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How to Evaluate a Trading Algorithm Using Performance Metrics
Profit, maximum drawdown, CAGR, profit factor, Sharpe, Sortino and Calmar ratios, average trade and performance stability.
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Strategy Overfitting: How to Identify Curve Fitting
How to distinguish robust optimisation from curve fitting using parameters, out-of-sample tests, walk-forward analysis and forward testing.
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Monte Carlo Analysis for Trading Strategies: Drawdown and Robustness
How trade-order randomisation, parameter variation, spreads, commissions and slippage help estimate drawdown and algorithmic risk.
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Out-of-Sample Testing and Walk-Forward Analysis
In-sample and validation periods, the walk-forward matrix, out-of-sample equity, parameter robustness and forward testing.
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Trading Algorithms Without Martingale: Controlling Risk
Martingale, grids, averaging, fixed risk per trade, equity curves, hidden position escalation and algorithm review.
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Commissions, Spreads and Slippage in Backtests
How to model trading costs, bid/ask prices, order types, swaps and stress tests—and why live trading differs from a backtest.
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Types of Algorithmic Trading Strategies: An Overview
A guide to trend, breakout, momentum, counter-trend, mean-reversion, volatility and portfolio approaches.
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Explore algorithmic portfolios designed around risk control and diversification.