Ichimoku Conversion/Base Cross
Positive risk-adjusted return in 14 of 18 cells — strongest on GOLD · 4H (Sharpe 1.23); 4 cells lose after the 0.05% per-side fee. Median cell trails buy-and-hold by 81.9pp; 4 of 18 cells beat it.
§ Verdict rationale
Composite Sharpe 0.65 across 18 cells. The edge concentrates in GOLD 4H (Sharpe 1.23); 4 of 18 cells are negative after the 0.05% per-side fee.
§ Alpha score breakdown
alpha = edge (0-40: clamp(compositeSharpe/2,0,1)*40) + stability (0-25: positiveCells/18*25) + flaw penalty (0 to -25: static findings, 0 across v1 catalog) + reproduction (0-10: engine oracle parity vs pine2py, 251/257 scripts = 9)
§ Equity vs buy-and-hold — fee-adjusted, per cell
§ All 18 cells — including the ugly ones
| Market · TF | Sharpe | Return | vs B&H | Max DD | Win | Trades |
|---|---|---|---|---|---|---|
| BTCUSD 1H | 0.73 | +951.6% | -429.6pp | -49.1% | 40% | 1053 |
| BTCUSD 4H | 0.66 | +585.6% | -781.7pp | -57.6% | 42% | 225 |
| BTCUSD 1D | 0.83 | +1228.5% | -160.9pp | -47.8% | 43% | 28 |
| ETHUSD 1H | 0.63 | +722.7% | +194.5pp | -61.9% | 41% | 984 |
| ETHUSD 4H | 0.88 | +2990.6% | +2475.4pp | -37.4% | 41% | 241 |
| ETHUSD 1D | 0.87 | +2431.8% | +1904.4pp | -41.8% | 52% | 33 |
| SPX500 1H | 0.15 | +2.6% | -69.6pp | -13.8% | 43% | 67 |
| SPX500 4H | 0.80 | +17.3% | -54.3pp | -12.7% | 58% | 26 |
| SPX500 1D | 0.55 | +101.9% | -481.4pp | -18.4% | 55% | 49 |
| QQQ 1H | -0.07 | -3.4% | -96.3pp | -14.3% | 37% | 60 |
| QQQ 4H | -0.08 | -3.3% | -95.1pp | -15.7% | 42% | 24 |
| QQQ 1D | 0.76 | +240.6% | -1169.0pp | -21.6% | 58% | 48 |
| GOLD 1H | 0.82 | +34.8% | -94.2pp | -20.6% | 52% | 62 |
| GOLD 4H | 1.23 | +61.1% | -67.4pp | -13.7% | 70% | 20 |
| GOLD 1D | 0.55 | +121.9% | -103.0pp | -16.0% | 48% | 44 |
| EURUSD 1H | -1.43 | -17.0% | -26.1pp | -19.4% | 39% | 208 |
| EURUSD 4H | 0.06 | +0.5% | -8.6pp | -6.0% | 40% | 50 |
| EURUSD 1D | -0.23 | -14.9% | +3.8pp | -23.3% | 30% | 40 |
14 of 18 cells positive · best GOLD · 4H · cells are not equal length — the composite is a median
§ The exact source that ran — Pine v5
//@version=5strategy("Ichimoku Conversion/Base Cross", initial_capital=100000, default_qty_type=strategy.percent_of_equity, default_qty_value=100)conv = (ta.highest(high, 9) + ta.lowest(low, 9)) / 2base = (ta.highest(high, 26) + ta.lowest(low, 26)) / 2spanA = (conv + base) / 2cloudA = spanA[26]longSig = ta.crossover(conv, base) and close > cloudAexitSig = ta.crossunder(conv, base)if longSig strategy.entry("L", strategy.long)if exitSig strategy.close("L")plot(strategy.equity, "equity")plot(strategy.position_size, "pos")plot(strategy.closedtrades, "closed")plot(strategy.wintrades, "wins")plot(strategy.grossprofit, "gp")plot(strategy.grossloss, "gl")This source was executed verbatim by the wavealgo JS engine — there is no port step to drift. The engine is oracle-verified bar-by-bar (1e-9 tolerance) against an independent Python implementation on 251/257 corpus scripts.
§ Flaw checklist
Verdict rules: pass: composite Sharpe >= 0.9 and >= 12/18 cells positive · cond: composite Sharpe > 0 and best cell Sharpe >= 0.9 · fail: otherwise · rep: static repaint/look-ahead finding (none in the authored v1 catalog: confirmed-bar signals, next-bar-open fills)
§ What the numbers mean
- Sharpe ratio
- Risk-adjusted return: mean daily return divided by its standard deviation, annualized ×√252. The composite figure is the median of the 18 cell Sharpes, not the mean — cells differ in length by an order of magnitude, and one long cell should not carry the other seventeen.
- Max drawdown
- The worst peak-to-trough equity loss over the window, measured on the fee-adjusted curve. What you would have sat through, not what you would have ended with. The headline figure is the deepest of the 18 cells, not their average.
- Profit factor (PF)
- Gross profit divided by gross loss, with fees charged to the loss side. Above 1.0 means gross gains exceeded gross losses; 1.0–1.2 is usually noise.
- Win rate
- Share of closed trades that closed profitable. A high win rate with a profit factor near 1 means many small wins and a few large losses — common in mean-reversion.
- vs buy-and-hold
- Strategy total return minus the return of simply holding the asset over the same window — the benchmark any active strategy must beat to justify existing. Shown as the median of the 18 cells plus how many cells beat it; a mean would let one long cell stand in for the rest.
- Alpha score
- The desk’s 0–100 composite: out-of-sample edge (0–40; the methodology page defines what out of sample means for the catalog), regime stability across cells (0–25), flaw penalty (0 to −25), reproduction accuracy (0–10). The exact formula ships with the data.
Bring your own. We grade it the same.
Engine: resin 0.2.2 (next-bar-open fills, TV rule) · scored UTC 2026-11-07 10:58 · deterministic rerun — same data, same numbers. Backtested results are measurements of the past, not investment advice; nothing here is a recommendation to trade. wavealgo is operated by Streamize LLC.