How a 34% Win Rate Can Still Be Profitable
We revisit a published backtest to explain how the size of winners and losers can matter more than the win-rate headline.
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Strategy Research
Research notes on strategy design, market structure, indicators, validation, failure modes and evidence from systematic trading.
← Explore by topicStrategy design • Validation • Evidence
Recent research
Why Optimization Cannot Rescue a Weak Trading Idea
We explain how we use optimization to examine a defined idea, and why a winning parameter set still needs a credible explanation.
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Python, Cointegration and Machine Learning in MT5 Workflows
We explain how our research and integration work brought Python models, time-aware data preparation and MT5's execution responsibilities into one discussion.
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Turning SMC and Price Action into Deterministic Code
We describe how we translated chart-reading ideas into repeatable rules for swings, breaks, zones and the timing of confirmation.
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Why More Filters Do Not Always Help
We explain how we assess an extra filter: what mistake it is meant to prevent, which opportunities it removes and whether it adds new information.
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Why Closed-Bar Logic Matters
We explain why we often anchor a decision to a completed candle, and what we still need to define about timing, repeated events and higher timeframes.
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Why a Good Backtest Can Still Fail
We explain why a promising backtest leads us to more questions about data, costs and stability, and how those questions shape our research process.
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Hidden Ambiguity in Indicator Changes
We show how a familiar instruction—“trade when the indicator changes”—led us to clarify the exact event, timing and reset rule behind a signal.
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