Audit #11 of 20·Strategy·@wavealgo·scored 2026-08-08

Stochastic Cross (14,3,3)

No alphaalpha score 34 / 100

Positive risk-adjusted return in 14 of 18 cells — strongest on SPX500 · 4H (Sharpe 0.82); 4 cells lose after the 0.05% per-side fee. Trails buy-and-hold by 479.0pp on average.

§ Verdict rationale

Composite Sharpe 0.28 across 18 cells — expectancy does not survive the 0.05% per-side fee. Best cell: SPX500 4H (Sharpe 0.82). The account is wiped out entirely in 2 of 18 cells — the fee drag alone, before any question of signal quality.

§ Alpha score breakdown

Out-of-sample edge
+6 /40
Regime stability
+19 /25
Flaw penalty
0 /25
Reproduction accuracy
+9 /10

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

strategy, fee-adjusted · ends ×1.00buy-and-hold · ends ×5.79normalized to ×1.00 at window start
Sharpe
0.28
vs B&H
-479.0pp
Max DD
-44.3%
PF
1.00
Win
69%
Trades
4365

§ All 18 cells — including the ugly ones

Market · TFSharpeReturnvs B&HMax DDWinTrades
BTCUSD 1H-1.42-100.0%-1509.0pp-100.0%65%1259
BTCUSD 4H0.14-28.7%-1423.4pp-80.0%69%342
BTCUSD 1D0.22+12.0%-1391.1pp-74.9%64%56
ETHUSD 1H-1.42-100.0%-635.8pp-100.0%62%1288
ETHUSD 4H-0.08-90.5%-613.1pp-95.0%63%339
ETHUSD 1D0.14-49.2%-580.4pp-82.7%68%53
SPX500 1H0.44+13.0%-59.7pp-12.5%71%86
SPX500 4H0.82+28.2%-44.6pp-17.5%89%28
SPX500 1D0.48+131.4%-458.0pp-28.7%74%66
QQQ 1H0.54+21.4%-71.6pp-12.5%66%83
QQQ 4H0.56+23.9%-68.9pp-17.6%79%29
QQQ 1D0.34+85.1%-1348.5pp-40.9%76%63
GOLD 1H0.58+20.8%-102.4pp-21.3%78%92
GOLD 4H0.69+26.2%-97.0pp-17.2%69%35
GOLD 1D0.17+22.4%-197.7pp-37.2%59%71
EURUSD 1H-2.80-30.5%-39.6pp-30.8%61%313
EURUSD 4H0.44+6.6%-1.7pp-6.6%65%88
EURUSD 1D0.06+3.3%+21.1pp-22.2%60%74

14 of 18 cells positive · best SPX500 · 4H · cells are not equal length — the composite is a median

§ The exact source that ran — Pine v5

1//@version=5
2strategy("Stochastic Cross (14,3,3)", initial_capital=100000, default_qty_type=strategy.percent_of_equity, default_qty_value=100)
3k = ta.sma(ta.stoch(close, high, low, 14), 3)
4d = ta.sma(k, 3)
5longSig = ta.crossover(k, d) and k < 30
6exitSig = ta.crossunder(k, d) and k > 70
7if longSig
8 strategy.entry("L", strategy.long)
9if exitSig
10 strategy.close("L")
11plot(strategy.equity, "equity")
12plot(strategy.position_size, "pos")
13plot(strategy.closedtrades, "closed")
14plot(strategy.wintrades, "wins")
15plot(strategy.grossprofit, "gp")
16plot(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

Repainting
None · signals bind to confirmed bars
Look-ahead bias
None · market orders fill at the next bar open
request.security
Not used
Sizing
100% of equity per entry · long-only
Fee model
0.05% per side on every fill · no slippage model in v1
Source
Open — the exact measured source is shown below

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 mean of the 18 cell Sharpes — one bad market drags it honestly.
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.
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.
Alpha score
The desk’s 0–100 composite: out-of-sample edge (0–40), 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.

↑ Rank #10
RSI Mean Reversion (14, 30/70)
Conditional · alpha 35
↓ Rank #12
RSI 50-Line Momentum (14)
Conditional · alpha 34

Engine: pine2js (next-bar-open fills, TV rule) · scored UTC 2026-08-08 04:53 · 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.