The Core Idea
This guide breaks down From Manual to Automated: Mastering Automation Ethics into rules you can execute, backtest, and automate.
In crypto, small frictions compound: taker fees, slippage, funding, and missed exits. Treat them as first-class inputs.
- Core concepts: AI crypto trading, algorithmic trading, and realistic execution
- A checklist you can apply on any exchange in 2024
- Concrete numbers for sizing, fees, and slippage (so backtests stay honest)
Mistakes That Kill the Edge
Most strategies fail at the edges: regime shifts, liquidity, and “small” frictions like fees and slippage.
If you can’t define invalidation and size correctly, the best signal in the world won’t save the PnL curve.
- Vague stops: “I’ll exit if it looks bad”
- Sizing that ignores volatility (same size in calm and chaos)
- Optimizing multiple knobs at once (classic overfitting)
Implementation Checklist
Start with one simple idea and one filter. Then test across different conditions (trend, chop, high volatility).
The goal is not max backtest profit—it’s stable performance with controlled drawdowns and enforceable rules.
- Write entry/exit rules in one sentence each (no exceptions)
- Add a filter (volatility or trend) to avoid the worst regimes
- Backtest with fees + slippage (don’t assume perfect fills)
Execution & Automation
Automation helps when it removes discretion and enforces risk limits—especially during fast moves.
Execution quality matters as much as the signal. Design for reliability, not “perfect fills”.
- Prefer predictable execution over “perfect fills”
- Add guardrails: max loss/day, kill switch, cooldown timers
- Monitor: spreads, outages, and partial-fill behavior
Risk Management Rules
Risk is the main lever you control. If you can’t survive drawdowns, you can’t compound.
Keep rules enforceable: size small enough to stay rational, and automate the limits.
- Size from stop distance (risk ÷ stop%)
- Cap correlated exposure (altcoins move together)
- Scale down after drawdowns; scale up only after stability
Metrics That Matter
Track a small set of metrics: win rate, average win/loss, expectancy, max drawdown, and execution costs.
Iterate one change at a time. If results improve, keep it. If not, revert and move on.
- Expectancy > win rate (a high win rate can still lose money)
- Separate signal quality from execution quality
- Re-test after major market regime changes
Case Study (Numbers)
Example sizing (spot or perp): account $25,000, risk per trade 1.25%, stop distance 2.8%.
Risk budget is $313. Position size ≈ $11,179 (risk ÷ stop).
Model frictions: taker fee 0.06% and slippage 0.08%. If your expected edge is smaller than fees+slippage, the strategy is noise.
- Checklist: entry rule → stop rule → sizing → fee/slippage model → automation guardrails
- If the model doesn’t survive fees+slippage, it won’t survive live trading
Practical FAQ
Is Automation Ethics still relevant in 2024?
Yes, but only when you account for real execution costs and market regimes. Treat it as a framework, then validate on current data with fees, slippage, and risk limits baked in.
Do I need leverage for Automation Ethics to work?
No. Leverage increases variance and liquidation risk. Start with spot or low leverage until the process is stable and drawdowns are controlled.
How do I size positions without blowing up?
Pick a fixed risk per trade (for example 0.5%–1.25%), define a real stop distance, then size as risk ÷ stop%. Add a daily loss limit and reduce size after drawdowns.
What is the single biggest mistake with automation ethics?
Ignoring frictions (fees, slippage, funding) and over-optimizing parameters. A smaller but more robust edge often outperforms a fragile “perfect backtest”.