Adventures of Greg

Identify, backtest, and trade seasonal market patterns across assets with technical confirmation, strict risk controls, and statistical validation.

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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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