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Documentation Index

Fetch the complete documentation index at: https://docs.novosky.app/llms.txt

Use this file to discover all available pages before exploring further.

All results are out-of-sample (OOS). The OOS window is never used to fit hyperparameters — it is a blind evaluation on data the models have not seen.

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Current results

Phase 17.3 — May 2026
MetricValue
OOS period37 days (2026-03-30 to 2026-05-05)
Win rate71.7%
Profit factor5.46
Max drawdown4.3%
Sharpe ratio59.54
Return+194.6%
Trades184
Score18.48
Configconf=0.65, risk=2.0%, TP=1.0×ATR, SL=1.0×ATR

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Performance history

DateScoreWRPFMaxDDReturnTradesConfig summary
2026-05-0854.4771.7%5.464.5%+195.7%99conf=0.65, risk=3.0%, TP=1.0×ATR — single-source + recency weighting
2026-05-0718.4860.3%2.694.3%+137.3%184conf=0.65, risk=2.0%, TP=1.0×ATR — single-source + recency weighting
2026-04-2935.1367.6%2.837.3%+394.5%179conf=0.65, risk=1.75%, TP=1.0×ATR
2026-04-2446.8062.8%2.2614.5%+1154.6%156conf=0.633, risk=3.0%, TP=1.2×ATR
2026-04-2421.3465.8%3.037.1%+390.9%114Phase 16 fixes (RF weight, TP<SL)
2026-04-247.7750.3%1.713.6%+71.5%344conf=0.633, risk=2.0%, TP=0.8×ATR
2026-04-246.7050.7%1.614.1%+66.5%343conf=0.633, risk=2.5%, TP=0.8×ATR
2026-04-2021.3478.5%2.431.8%+50.7%274conf=0.6, risk=2.5%, TP=0.8×ATR
2026-04-194.8467.4%1.383.7%+51.6%717conf=0.6, risk=2.5%, TP=0.8×ATR
2026-04-184.8271.2%1.877.6%+323.0%483conf=0.6, risk=1.0%, TP=0.8×ATR

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Score formula

The optimizer maximizes a single composite score that balances return against risk: 
score = profit_factor × sharpe × (1 - max_drawdown) × log(1 + trades)
A score improvement of ≥ 2% over baseline is required to commit new models. This prevents the pipeline from committing marginal or noise-driven improvements. A new week’s baseline is the score recorded at the end of the previous week’s run.

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OOS validation methodology

1

Data split

The dataset is split into in-sample (IS) and out-of-sample (OOS) segments. The IS window is used for training and Optuna tuning. The OOS window is never touched during training.
2

Sweep on first 70%

The config sweep (scripts/sweep.py) runs only on the first 70% of the OOS window. This is the “search” OOS.
3

Final eval on full OOS

After the best config is identified, the full OOS window is backtested. The last 30% of OOS data — the true holdout — is never seen during the sweep.
4

Rollback check

If the full-OOS score does not beat the stored baseline by ≥ 2%, all models and configs roll back to the previous version.
Always use python backtest.py --oos-only for realistic validation. Avoid generic lookback backtests — they are subject to in-sample contamination.

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Reading the backtest output

python backtest.py --balance 500 --leverage 500 --spread 14.0 --oos-only --no-chart
Key output fields:
FieldWhat it means
win_rate% of trades closed profitably
profit_factorGross profit ÷ gross loss — above 1.5 is good
max_drawdownLargest peak-to-trough equity drop during the OOS period
sharpeRisk-adjusted return (annualized)
returnTotal % return over the OOS period
scoreComposite score used by the optimizer

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Hardware note

Backtest results shown here used:
  • Spread: 14.00 pts (RoboForex BTCUSD typical)
  • Leverage: 500
  • Starting balance: 500 USC
  • Swap: enabled (RoboForex applies overnight swap)
Results on other brokers or with different spread/swap will differ. Historical results through Apr 2026 were on RoboForex BTCUSD (14.59 pts spread, no swap).