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Faber Timing Model

Faber's tactical timing model: holds while the close sits above its 10-month (210-bar) simple moving average, but only checks the rule once every 21-bar month — the monthly review keeps turnover minimal.

Total score 60/ 100 rank 03 / 42 · trend 03 / 20

How It Works

  1. Compute a 10-month simple moving average of the close — 210 bars, treating every 21 bars as one month.
  2. Check the rule only once a month, on every 21st bar; between reviews, nothing the market does matters. This monthly cadence is Faber's key idea — it ignores intra-month noise entirely and is what keeps the trade count tiny.
  3. At a review: if the close is above the 10-month average, be in the market; if below, be out. Buy or sell only when the answer changes from the previous review.

Worked example. At this month's review the close is 108 against a 10-month average of 104 — above, so buy. The next four reviews price stays above the (rising) average, including a mid-month crash that recovered before review day — the model never saw it. At the fifth review the close is 111 against an average of 113 — below — so the position is sold after roughly five months held with zero trades in between.

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.

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.

Real Data

56/ 100Composite score

Metrics Per Trade

Final Metrics

Scores

Resampled Data

63/ 100Composite score

Metrics Per Trade

Final Metrics

Scores