Identify, backtest, and trade seasonal market patterns across assets with technical confirmation, strict risk controls, and statistical validation.
Read More...Posts in the "Trading" Category
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Evaluate trading strategies using profit factor, win rate, risk-reward, Sharpe ratio, drawdown, and execution costs to separate robust systems from overfitted backtests.
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Stress-test equity curves with Monte Carlo simulations: reshuffle or resample trades, estimate 95% max drawdowns, risk of ruin, and capital needs.
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Automated pre-trade controls, real-time surveillance and gapless audit trails are essential to prevent regulatory breaches in algorithmic trading.
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Use account-risk ÷ (entry‑stop) to size trades, risk 1–2% per trade, and adjust for volatility (ATR) and risk-to-reward to protect capital.
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Compare grid and random search for tuning trading strategy parameters — tradeoffs in speed, coverage, reproducibility, and overfitting prevention.
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Survivorship bias can inflate backtests. Use delisted data, time‑varying universes, test across market regimes, and document assumptions for realistic results.
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How raw (unadjusted) and adjusted historical prices impact backtesting—trade signals, commissions, look-ahead bias, and a practical dual approach.
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Evaluate trading strategies with Sharpe, max drawdown, profit factor, win rate and CAGR; use backtesting, Monte Carlo, and walk‑forward tests to avoid overfitting.
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Rigorous backtesting — define exact rules, use clean data, and stress-test strategies with out-of-sample, walk-forward and Monte Carlo analysis before risking capital.
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Practical methods to refine trading systems with realistic backtests, risk-aware position sizing, overfitting checks, walk-forward validation, and multi-market testing.
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Practical methods to prevent overfitting in trading rule backtests: proper data splits, simple rules, walk-forward testing, Monte Carlo sims, and stress tests.
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Compare six backtesting platforms by data quality, customization, execution realism, analytics, and pricing to find the best fit for your skill level.
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Compare manual vs algorithmic trading: pros and cons, speed, costs, tools, and when to use each, plus backtesting and hybrid approaches.
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Practical 12-step checklist for building and validating algorithmic trading strategies: goals, data, rules, risk, backtesting, optimization, execution, safety.
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Overfitting makes backtests look great but fail live; learn causes, signs, and fixes like out-of-sample testing, walk-forward, and Monte Carlo.
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Five practical steps to optimize trading strategy parameters: define rules, clean data, run optimizations, validate stability, and monitor live performance.
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Entrepreneur, Founder and CEO at AlgoLab Capital Management Ltd Description In this interview Mike Townsend interviews Greg Kolodziejzyk, founder of AlgoLab. In his 20’s Greg quit his job at a local oil company based out of Calgary Albert and started his own business as a freelance graphic designer. In 1994 his company, Image Club, was purchased for millions and […]
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Record unemployment numbers and the market goes up. Rising COVID19 death rates and community spread showing up around the world and the market goes up. For the first time in history oil prices dip into negative territory, and the market goes up. Earning projections as we go into earnings season are pessimistic at best, and […]
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Read Greg’s free e-book “Disrupting Wall Street”
