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Monte Carlo Protocol in 15 Verifiable Facts

Embargo, temporal splits, Sharpe_p5, bootstrap, and validated cells — live VPS figures.

⚠️ Paper trading only. Strategy Arena is a public research lab. Not financial advice. Simulated past performance does not guarantee future results.
How many strategies does the live Monte Carlo tracker follow?

7 configured MC strategies (2026-07-07).

How many strategy×asset cells are MC-validated?

36 validated cells in live_mc_results_snapshot.json.

What temporal embargo between MC windows?

48-bar embargo between OOS windows (smart_money_evolved_v2_params.json, May 2026).

What temporal holdout split discipline?

Phase 1: top candidates on 70% train; phase 2: 30 MC OOS splits between 20% and 70% of bars.

Sharpe_p5 threshold for robust validation?

Batch5 filter: Sharpe_p5 > 0.5; 8 cells exceed Sharpe_p5 > 1.0 (best: SNX 1.526).

How many Monte Carlo splits per asset?

30 MC splits by default, minimum 10 valid splits (generic_strategy_optimizer.py).

Minimum simulated trades threshold?

n_trades_mean ≥ 20 trades per validated cell (batch5_mc_optimizer.py).

How many live vs MC aligned cells?

2 aligned cell, 21 major drift (2026-07-07).

How many cells with insufficient live data?

9 cells (< 5 closed live trades).

Flagship MC-validated example?

Smart Money Evolved: 15 validated assets, best Sharpe_p5 1.526 on SNX.

Bootstrap resampling method?

Random OOS anchors + GPU replay of top-train combos on each window (temporal bootstrap, not i.i.d.).

Cyclic cross-temporal validation?

30 independent OOS windows; robustness = Sharpe distribution across splits, p5 percentile.

Fee model in batch5 MC?

0.10% entry + 0.10% exit (0.20% round-trip) — batch5_mc_optimizer.py.

How many MC params files on the VPS?

16 *_mc_params.json / *_v2_params.json files in /data/.

Does Monte Carlo guarantee real profits?

No — public paper trading; MC filters historical robustness, not a return promise.

Last generated: 2026-07-07T01:59:17.419421+00:00 UTC · Source: live_mc_results_snapshot.json, *_mc_params.json, batch5_mc_optimizer.py