Simple, systematic trading strategies that work

Every strategy on EdgeLab is tested on data it has never seen before it gets published — full rules, honest backtests and the code to run them. The rules are plain English, simple enough to run on any platform or code in any language. Most strategies fail our tests. These didn't.

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Two simple strategies and their 50/50 combination, 2005-2026
Two simple strategies, combined — real backtest, 2005–2026

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Two free strategies that work better together

Subscribers get two complete strategy reports — exact entry and exit rules, full backtests, sensitivity analysis and Monte Carlo testing. The rules are written in plain English: a few lines each, nothing platform-specific, so you can run them manually or code them in TradingView, MetaTrader, Python — whatever you already use. Which strategies they are stays between us. What they do:

  • Strategy 1 — short-term mean reversion on stock indices. In the market about a quarter of the time, out-of-sample Sharpe above 1.
  • Strategy 2 — the golden cross on gold: hold when the 50-day average is above the 200-day, cash when it isn't. One rule.

They're built to be combined. Each works on its own — and they barely correlate (monthly correlation since 2005: -0.04). Split 50/50 and rebalanced monthly, the pair earns nearly the same as Strategy 1 alone with a Sharpe ratio above 1 and a clearly smaller worst case:

Equity curves of a stock strategy alone and combined 50/50 with an uncorrelated gold strategy, 2005–2026
Backtest 2005–2026, 0.05% commission per side, rebalanced monthly 50/50. The combination: CAGR 10.6%, Sharpe 1.02, max drawdown -16.4% — versus 11.2%, 0.82 and -20.3% for Strategy 1 alone. You trade some raw return for half the worst case — which is what lets you size up.

“Why not just trade Strategy 1? It earns more.”

Because your position size isn’t capped by returns — it’s capped by the worst drawdown you can survive, financially and mentally. Strategy 1 alone turns every $100k into a $20k hole at its worst. The combination caps that hole at $16k — while earning nearly as much.

That headroom is spendable. Size the combination up 1.25x and the worst case is back at Strategy 1’s level — while the return lands around 13% a year instead of 11%. More return for the same pain. Same pain budget, more return, calmer ride. That’s why professionals judge strategies on risk-adjusted return instead of raw CAGR: the smooth curve is the one you can actually size up, stick with through the bad months, and compound.

How we test

Every strategy published on EdgeLab has passed the same three filters:

  1. Out-of-sample validation. The most recent 20–30% of price history is locked away during development and used exactly once. If the strategy fails there, it's rejected.
  2. Sensitivity analysis. Every parameter is shifted ±20–30%. If the edge only exists at one magic setting, it's curve fitting — rejected.
  3. Monte Carlo simulation. Trade order is randomized across thousands of runs to stress-test drawdowns beyond what the historical sequence happened to produce.

And every backtest pays its way: we charge 0.05% per side in commission and slippage — roughly two to three times what a liquid ETF actually costs to trade at a decent broker. If an edge can't survive costs it will never pay in real life, so we'd rather understate every number on this site than flatter one.

More than 80% of the strategies we develop are rejected. Here's why that rejection rate is the whole point →

Who's behind this

I'm Robin Eriksson. I spent my first five years in the markets losing money on discretionary trading before switching to systematic strategies — and testing everything. EdgeLab is where I publish the research: what works, what doesn't, and the process for telling the difference. More about me and the methodology →