Every “beginner strategy” thread devolves into the same indicator salad and backtest screenshots. Most of it ignores the two things that actually kill novices: friction and execution error. If a strategy only works with perfect fills, zero slippage, and monk-like discipline, it isn’t a beginner strategy-it’s marketing.
I want to crowdsource something different: a design brief for beginner strategies that are intentionally built as training wheels. Not to maximize P&L, but to teach the right skills under live conditions while surviving real costs and inevitable mistakes.
Constraints I think any genuine beginner strategy should meet:
- Rooted in a structural phenomenon (auction mechanics, scheduled flows, calendar effects), not vague “momentum/mean reversion” with arbitrary thresholds.
- Robust to worst-case retail friction: assume 1-2 ticks of slippage, full fees, and occasional partial fills.
- Executable with simple order types (market/limit/stop, OCO brackets) on liquid products.
- Clear, enforceable risk envelope: 1R definition, per-day stop, weekly pause rule.
- Defined “no-trade” conditions to avoid chop and news landmines.
- A pre-registered validation plan: in-sample constraints, out-of-sample forward test, minimum trade count to detect a modest edge with reasonable power.
- A skill focus per strategy (e.g., tape-reading, patience during opens, exit discipline), with observable metrics beyond P&L.
Unpopular take: paper trading should be limited and short. If the strategy can’t function on micro-size live with a fixed “tuition per lesson” cap (e.g., $5-$20 risk per trade), it’s not suitable for beginners. Also, strategies should be resilient to sloppiness; if a single late exit flips expectancy, it’s brittle.
What I’m asking the community to contribute:
1) Structural anchors. What simple, observable, persistent flows can a beginner align with?
- Examples to vet: opening auction range behaviors, end-of-day liquidity/imbalance effects, earnings gap regimes, monthly/quarterly rebalance drift, weekly seasonality around macro prints. No indicator soup; tie it to a mechanism.
2) Minimal viable playbooks. For one chosen phenomenon, specify:
- Market(s) and session window.
- Entry trigger and explicit “stand down” conditions.
- Bracket logic (initial stop, profit target, time stop) that doesn’t require discretion to function.
- Assumed friction and worst-case fill model.
- Maximum daily/weekly loss and pause rules.
3) Proof-of-edge protocol. How does a beginner know it’s not self-delusion?
- Sample size needed to detect a small edge (say 0.15-0.25R) with 80% power.
- Forward test ladder: SIM for mechanics -> micro live -> scale steps with guardrails.
- Kill-switch criteria (variance spike, slippage exceeding X, adverse news behavior).
4) Skill telemetry. P&L aside, what KPIs predict future success with that play?
- Examples: average adverse excursion vs stop, time-in-trade distribution vs plan, slippage vs liquidity at entry, checklist adherence rate, error cost as % of R.
5) Disqualifiers. What makes a “beginner strategy” unethical or misleading?
- Reliance on illiquid names, hidden gamma/overnight gap risk, path dependency that punishes small mistakes, overfitting to one regime.
If you believe there are exactly three “scaffold” strategies that can carry most beginners from zero to competent execution, what are they under these constraints? Name the structural phenomenon, the precise rules, and the friction assumptions. If you think the right answer is “no strategy, just risk frameworks and market selection” until regime-reading improves, defend that with a concrete curriculum.
Let’s retire the RSI/MACD crossover starter pack and define starter playbooks that can survive 100 trades of real-world messiness.