Practical guides, printable references, and reproducible backtesting research. Free to read.
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NOTE 27The blog mixes guides, printable references, tool comparisons, and backtesting experiments. A reader looking for the best place to begin should choose by task: learn the method, define a rule, run a controlled test, or review evidence.
A useful archive helps readers reject the wrong article quickly.
A practical Saturday backtesting routine measured by completion, rule adherence, clean observations, review quality, and a four-week comparison.
A reproducible 100-trade support-and-resistance protocol with pre-drawn levels, touch rules, zone width, costs, skipped signals, and a baseline.
A careful M3 scalping backtest protocol covering candle aggregation, intrabar ambiguity, spread, commission, slippage, opportunity count, and controls.
A controlled comparison of fixed-pip, ATR, and candle-structure stops using identical entries, equal account risk, matched targets, and adverse excursion.
A matched protocol for comparing AUDCAD and EURUSD using the same setup, dates, sessions, risk, costs, opportunity counts, and holdout period.
A realistic six-month micro-lot account simulation covering lot increments, pip value, costs, margin, compounding, drawdown paths, and uncertainty.
A controlled 300-observation protocol for comparing breakout, pullback, and reversal setups with exclusive definitions, matched exits, costs, and holdout data.
A neutral 30-day journaling protocol for measuring revenge-trading behaviours, cooling-off adherence, risk changes, and decision quality without fake testimony.
A careful NFP event-study protocol for EURUSD and XAUUSD covering release calendars, timing, spread and slippage stress, controls, and small samples.
A reproducible ten-run prop-challenge simulation using dated official terms, unseen periods, consistent risk, breach logging, and distribution reporting.
A reproducible 200-observation Fibonacci retracement test that controls swing selection, confirmation rules, costs, baselines, and hindsight bias.
A controlled protocol for comparing London and New York forex sessions using matched rules, UTC windows, costs, opportunity counts, and holdout data.
A reproducible GBPUSD M5 backtesting protocol covering frozen entry rules, costs, skipped signals, sample audits, and honest result reporting.
A statistically sound coin-flip entry experiment explaining boundary probabilities, random timestamps, costs, repeated samples, and comparison with a real rule.
A reproducible 50-session GBPJPY volatility study using fixed clocks, range distributions, costs, event tags, equal risk, and tail reporting.
A matched bank-holiday forex study using official calendars, market-specific closures, comparable weekdays, session clocks, costs, and small-sample caveats.
A cautious forex news-trading protocol covering event calendars, order timing, spread, slippage, data limits, matched controls, and repeated random baselines.
A reproducible 150-pattern double-top backtest defining peaks, tolerance, neckline, confirmation, invalidation, near-misses, costs, and a baseline.
A matched 30-day phone-versus-desktop trading experiment measuring interruptions, input errors, rule adherence, task time, and decision quality.
A prospective boredom-trade journal protocol using pre-entry reason tags, restraint events, rule adherence, matched setups, and cautious interpretation.
Compare one strategy across forex, metals, indices and crypto with risk-normalised rules, matched dates, costs and multiple-testing control.
Test forex hour effects with UTC and local clocks, daylight saving, event controls, true bar boundaries, costs and multiple-testing discipline.
Test stop-loss widths with volatility scaling, fixed account risk, competing exits, costs, MAE and MFE instead of survival rate alone.
Test forex weekday effects with fixed day boundaries, matched weeks, costs, multiple-comparison control, rolling stability and a later holdout.
Compare forex timeframes using one base feed, true bar boundaries, translated rules, matched dates, equal risk, realistic costs and holdouts.
Measure BTC and SPX500 correlation with exact instrument identity, aligned returns, weekend handling, rolling regimes, lags and tail dependence.
Estimate backtest confidence using the correct trade denominator, Wilson or bootstrap intervals, clustered dependence and predeclared stopping.
Test profit factor out of sample with a complete search ledger, concentration checks, bootstrap uncertainty, costs and untouched validation.
Measure near-stop reversals with bid-ask triggers, precise MAE paths, fixed account risk, counterfactual sizing, costs and holdout validation.
Test immediate and delayed London-open entries with DST-safe clocks, defined reversals, matched sessions, event tags, costs and no-trade outcomes.
Compare XAUUSD and EURUSD with matched dates, contract and volatility normalisation, equal account risk, realistic costs and regime analysis.
Simulate trading drawdown with complete net trades, block resampling, sizing feedback, explicit seeds, percentile risk and stressed assumptions.
Compare ETHUSD and forex using venue-specific data, matched clocks, percentage returns, weekend regimes, costs, funding and holdout tests.
No Python. No MT4. No Pine Script. 6-step manual backtesting process with zero programming.
4 reasons Smart Money Concepts backtests overstate win rates by 20-40% — and how to fix it.
5 specific reasons your backtest lied to you — and a 7-step fix checklist to make your results match reality.
Stop guessing. Exact confidence intervals at 30, 50, 100, 200, 500, and 1000 trades. Monte Carlo table inside.
5 ways you overfit your strategy without realizing it — and how to get a win rate number you can trust.
Backtesting saves money, accelerates learning, and builds genuine edge. Data-driven argument.
Revenge trading, no SL, no journal. Data shows exactly how backtesting fixes each one.
Complete 7-step walkthrough for profitable intraday backtesting on GBPUSD.
Beginner exploration
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This page focuses on “FXAbsolute Blog”.Browse practical forex backtesting guides, reproducible research protocols, strategy-testing checklists, printable references, and trading-journal exercises.For “FXAbsolute Blog”, a beginner should identify what the research note measures, assumes or teaches before acting on its conclusion.Treat this page's account of “FXAbsolute Blog” as a learning reference rather than a prediction, signal or promise of future performance.
For “FXAbsolute Blog”, identify the exact experiment or observation the article reports before borrowing its conclusion.While exploring “FXAbsolute Blog”, check whether the result came from measured data, an illustrative example or a personal workflow.Keep your “FXAbsolute Blog” record honest: write down the condition that would make the lesson fail on a different pair or period.Before leaving “FXAbsolute Blog”, re-test the idea independently instead of treating one article as a universal trading rule.
Turn one idea from “FXAbsolute Blog” into a rule with explicit inputs, dates, costs and pass-or-fail conditions.Ask AI to expose missing assumptions in that “FXAbsolute Blog” test, not to guess the next market move.Use the FXAbsolute AI Backtesting Lab to inspect calculations connected to “FXAbsolute Blog” and the assumptions behind them.Reproduce any important “FXAbsolute Blog” result and reserve unseen data before deciding that an apparent pattern is useful.
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