Systematization Path: Strip Discretion, Backtest, Then Forward-Test With Reconciliation
Sources: 1 • Confidence: Medium • Updated: 2026-04-11 18:21
Key takeaways
- Mabe reports that in his early trading community, backtesting was often frowned upon because it was believed not to reflect reality and because traders believed intuition could not be modeled.
- Mabe asserts that even with fully automated execution, trading remains emotionally difficult because the discretionary pressure shifts to how to respond to drawdowns.
- Mabe says that for short-selling strategies he prefers a 'pristine' backtest that includes commissions but does not explicitly model slippage or locate costs, treating those as post-backtest degradations.
- Mabe describes his core traded setup as a gapping-stock breakout that enters on a breakout from a narrowing post-open range and places a stop on the opposite side of that tightening range, with an intraday holding period.
- Mabe reports that, in his backtests, taking partial profits and moving stops to breakeven materially reduces performance versus holding full size to the strategy's natural exit.
Sections
Systematization Path: Strip Discretion, Backtest, Then Forward-Test With Reconciliation
- Mabe reports that in his early trading community, backtesting was often frowned upon because it was believed not to reflect reality and because traders believed intuition could not be modeled.
- Mabe asserts that the work of validating a strategy effectively begins after the backtest, because live trading (even at tiny size) reveals important differences between simulation and reality.
- Mabe says his first attempt to backtest his discretionary process produced better results than his manual trading, changing his view of his discretion.
- Mabe says live automated results were close to but not identical to backtest results because backtests assume fills that are not always achievable in real markets.
- Mabe says he created an automated reconciliation loop by logging trades (including slippage) to an online journal and generating daily reports of missed backtest trades to diagnose and improve capture.
- Mabe says converting a discretionary approach into a backtest can start by stripping discretion and applying the underlying rules as a purely systematic strategy.
Automation Shifts The Human Bottleneck To Governance And Drawdown Decisions
- Mabe asserts that even with fully automated execution, trading remains emotionally difficult because the discretionary pressure shifts to how to respond to drawdowns.
- A speaker in the episode asserts that predefining drawdown thresholds and actions reduces emotional decision-making when a system enters a drawdown.
- Mabe says he increased automation in stages by automating sizing, then exit orders, then computer-generated entry orders with manual transmit, rather than enabling full auto execution immediately.
- Mabe asserts that scaling becomes harder as trade size increases because the larger numbers change psychology and can reintroduce errors even with automation.
- Mabe recommends approaching automation as an additive side project rather than trying to immediately replace a working discretionary approach, because full cutovers create excessive pressure and take longer than expected.
- Mabe claims the only two ways to build confidence in a trading system are long-term repetition of live trading and backtesting, with backtesting acting as a shortcut to the confidence needed to scale size.
Backtest Realism Limits: Fills, Slippage Modeling Tradeoffs, And Stop-Related Bias
- Mabe says that for short-selling strategies he prefers a 'pristine' backtest that includes commissions but does not explicitly model slippage or locate costs, treating those as post-backtest degradations.
- Mabe asserts that very tight stops can create overly optimistic backtests due to bar-resolution and entry-bar assumptions about whether a stop could be hit immediately after entry.
- Mabe says live automated results were close to but not identical to backtest results because backtests assume fills that are not always achievable in real markets.
- Mabe asserts that trying to model slippage and real-world execution perfectly inside a backtest is generally futile.
- Mabe says tick-by-tick backtesting can address some precision issues but is costly and resource-intensive, so it requires cost-benefit judgment.
Risk Framework: R-Multiples, Fixed-Dollar Risk Sizing, And Stop-Distance-Driven Position Size
- Mabe describes his core traded setup as a gapping-stock breakout that enters on a breakout from a narrowing post-open range and places a stop on the opposite side of that tightening range, with an intraday holding period.
- Mabe asserts that tightening ranges enable larger share size for the same fixed dollar risk because the stop distance is smaller.
- Mabe says he evaluates performance using expectancy and R-multiples and treated this as a non-negotiable prerequisite before making his first day trade.
- Mabe says his process sets stop distance from the setup and sizes positions by risking a fixed dollar amount per trade, which he increased gradually as confidence grew.
Trade Management Claims: Partial Exits And Stops Often Reduce Backtested Performance
- Mabe reports that, in his backtests, taking partial profits and moving stops to breakeven materially reduces performance versus holding full size to the strategy's natural exit.
- Mabe reports that, in his backtests, stops generally worsen strategy performance, while also stating stops remain necessary for practical live-trading risk control.
- Mabe recommends a backtest sanity check: the core strategy should still work without stops or targets before adding them.
Unknowns
- What were the actual pre- and post-automation performance statistics (returns, drawdowns, volatility, and risk-adjusted measures) supporting the reported fivefold profit increase?
- What exact backtest assumptions were used for fills, latency, spreads, and commissions, and how sensitive were results to those assumptions?
- How were universes selected (e.g., which gappers, which liquidity thresholds), and were survivorship and corporate action handling addressed in the backtests?
- What were the concrete, codified filters (the trade-skipping rules) that Mabe found valuable, and how stable were they out-of-sample?
- What were the operational controls for automation failures (kill-switch logic, max-loss limits, order validation, and monitoring), and how often were they triggered?