How It Works
- Each bar, compute the 20-bar simple moving average of the close and its Bollinger Bands two standard deviations above and below.
- Buy when the close drops below the lower band — price is unusually cheap relative to its own recent range, and stretched moves tend to snap back.
- Hold through the bounce and sell only when the close rises above the upper band, capturing the full ride from one edge of the channel to the other.
Worked example. Say the 20-bar average is 100 with σ = 2, so the bands sit at 96 and 104. A close at 95.5 triggers the buy. Days later the average has drifted up to 101 and price closes at 105.5 — above the upper band — so the position is sold for roughly a 10% gain.
The Math Behind The Indicators
Everything runs on closing prices of the traded timeframe: P is a close, Pt today's close, and N counts bars — one bar is one candle of that timeframe, so 20 bars on a 1h chart is 20 hours.
- Simple Moving Average (SMA)
- The plain average of the last N closing prices: add them up, divide by N. It smooths out bar-to-bar noise so the underlying direction is easier to see — a rising SMA means recent prices sit above where they used to be.
- SMAN = P1 + P2 + … + PNN
- Example: With N = 3 and closes 100, 102, 104 the SMA is (100 + 102 + 104) / 3 = 102.
- Bollinger Bands
- A 20-bar SMA with an envelope two standard deviations above and below it. The standard deviation σ measures how far closes have recently strayed from their average, so the bands widen when the market is choppy and tighten when it is calm — a close outside a band is a statistically unusual move.
- Bands = SMA20 ± 2σ20, σ20 = √12020∑i=1(Pi − SMA20)2
- Example: If the last 20 closes average 100 and typically stray about 1.5 from it (σ = 1.5), the bands sit at 100 ± 3, i.e. 97 and 103. A close at 96.5 is below the lower band — unusually cheap relative to the recent range.
Example Chart
Example Chart
The Metrics
| Metric | Calculation | What it shows |
|---|---|---|
| Price Change % | change = Plast − P0P0 × 100 The traded market's own close against its first close over the same window, as a percentage. | What the market did while the rule was running — the benchmark every other row here is read against. A rule that made 40% in a market that made 120% lost to doing nothing. |
| Trades | N = count(closed positions) How many positions the rule opened and closed over the window. | The sample behind every other figure, and what the fees are charged on. Two rules with the same return are not the same rule if one took nine trades and the other took nine hundred. |
| Win Rate % | W%n = winsnn × 100 Of the first n trades, how many closed above the cash they opened with after fees. Plotted trade by trade, so the line is the rate so far rather than a final figure. | How often the rule is right, which is not how much it makes. A rule can win a third of its trades and still lead, if the third it wins pays for the two it loses. |
| Cumulative P&L % | PnL%n = n∑i=1(fi − 1) × 100 Each trade's percentage result added up, net of fees. A sum rather than a compounding, so a 10% gain and a 10% loss cancel. | What the rule returned per trade, with position size taken out of it. It answers whether the edge is in the trades themselves, where the equity curve answers what the account did with them. |
| Equity | En = E0 n∏i=1fi The account compounded through every trade — the whole balance goes into the next position. Drawn net of fees as a solid line and gross of them as a dotted one. | The account itself, which is the only figure a reader actually ends up with. The gap between the two lines is what the fees took, and it widens with every trade rather than staying a fixed share. |
| Cumulative Fees | Fn = n∑i=1(Ci φ + Xi φ) Fee charged on the way into each position and again on the way out, at rate phi, on the capital actually committed — so the bill grows with the account as well as with the trade count. | The cost of trading, in the account's own units. It is the one line here that only ever rises, and the one a rule cannot trade its way out of. |
| Rolling Sharpe | Sharpet = mean(rdaily)sd(rdaily) × √365 Mean daily return over its deviation, annualized on a 365-day year because crypto has no weekend. Taken on the account marked to market every bar — open positions included, not just closed ones — and read off at each trade's exit. | Return per unit of the swing it took to get it. It is the heaviest weight in the composite score, because an account that doubled calmly and one that doubled violently are not the same result. |