Blog
This blog focuses on how to build trading strategies, how to verify them properly, and how to think about expectancy and risk management in FX and futures trading. Every article is written with practical backtesting on Delver in mind.
Does any MACD cross setting hold up over 25 years of FX data? Results from 2 million+ parameter combinations — which currency pairs and timeframes show positive expectancy and which conditions consistently fail.
Read article →Does a moving average golden cross / dead cross strategy hold up over 25 years? Results from 1.36 billion EMA combinations across 9 currency pairs and 4 timeframes — which conditions show structural edge and which consistently fail.
Read article →Does any RSI mean reversion setting hold up over 25 years of FX data? Results from 4.5 million combinations on EURUSD M15 — which parameter ranges show positive expectancy and how they perform across different market regimes.
Read article →Does any Bollinger Bands mean reversion setting hold up over 25 years of FX data? Results from testing 10,000 parameter combinations — including which conditions show positive expectancy and which don't.
Read article →Analyzing and explaining the winning structure in trading from a mathematical and statistical perspective. While a single Holy Grail may not exist, entries with mathematically high expectancy certainly do.
Read article →Five cognitive biases that make FX backtest results fail to hold up in real trading. A structured breakdown of hindsight bias, survivorship bias, confirmation bias, and two more — with data and real examples.
Read article →A step-by-step guide to FX backtesting across three approaches: manual, MT4/MT5, and SaaS tools. Covers the workflow for each and how reproducibility differs between them.
Read article →How a positive win rate and expectancy can still result in account ruin. Explains the relationship between max drawdown as a probability distribution and position sizing, with simulations showing how the collapse unfolds.
Read article →A positive expectancy does not guarantee a steady upward equity curve. We explain how the variance and sequence of trade results dictate capital trends and your ability to stay in the game, using simulations and diagrams.
Read article →What TradingView Bar Replay can and can't do for backtesting. Covers the paid plan data range limits, bar-level TP/SL precision on higher timeframes, and what happens to your trade logs between sessions.
Read article →Discover why strategies with high win rates and Profit Factors (PF) are often the most dangerous. We break down equity curves, outliers, and drawdown distributions to help you look past the vanity metrics.
Read article →A comparison of the main FX backtesting tools — TradingView, MT4/MT5, and dedicated SaaS platforms. Evaluated by reproducibility, data range, and use case to help you choose the right environment.
Read article →Why do you lose in FX despite using RSI? It’s not a problem with settings or values. We break down the structural reasons why RSI results fluctuate in discretionary trading.
Read article →Even with a seemingly impressive 60% win rate, your capital will vanish if you ignore R-multiple and Max Drawdown. Let’s break down why discretionary FX fails from a backtesting perspective.
Read article →Is automated or discretionary trading more profitable? We compare win rates, reproducibility, and risk structures under identical backtesting conditions.
Read article →Why manual backtesting on MT5 or TradingView doesn't translate to consistent real-world results. Three design flaws — in entry criteria, exit rules, and record-keeping — explained with concrete examples.
Read article →Why do results fluctuate when backtesting discretionary trading under identical conditions? We analyze 5 factors—judgment shifts, subconscious filters, and environmental bias—from a data perspective.
Read article →We've handpicked 5 free FX backtesting tools. Compare their uses, limits, and ideal user profiles before committing to a paid subscription.
Read article →Win rate alone tells you very little about a strategy. A practical guide to reading backtest results using PF, max drawdown, return distribution, and losing streak data to evaluate whether a strategy is actually robust.
Read article →The strategies and ideas explained in this blog can be backtested directly on Delver. Reading while verifying with real numbers significantly deepens your understanding.