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TT-Autotune · data 2026-10-11

HYPEHYPE trading bot: TT-Autotune tuned results by timeframe and regime

Tuned vs default Lorentzian Classification results for HYPE across 4 timeframes and six market regimes, and how to automate HYPE from TradingView paper first.

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HYPE with TT-Autotune

TT-Autotune tunes the Lorentzian Classification strategy separately for every token, chart timeframe and market regime, then switches to that regime's champion settings live inside one TradingView strategy alert. This page summarizes what the optimizer found for HYPE: 4 trained timeframes (15m, 30m, 1h, 4h) and 24 regime cells, of which 22 beat the stock default settings.

On tuned ROI averaged across regimes, the strongest HYPE timeframe is 4h (+52.9% mean), and its best single regime is Bull+ at +69.0%. ROI here is the sum of per-trade ROI over each cell's walk-forward window, scored with fees and slippage, not a live result.

HYPE on the 15m chart

On 15m, 5 of 6 regimes beat the default settings, led by Quiet, which is no trend and low volatility: tight ranges where fees can eat most edges.

Bull+
tuned +13.5% vs default +3.6% (beats default by 9.8 pts)
Bull-
tuned +14.3% vs default +3.2% (beats default by 11.1 pts)
Bear+
tuned +4.7% vs default +1.0% (beats default by 3.7 pts)
Bear-
tuned +14.6% vs default -1.5% (beats default by 16.1 pts)
Chop
tuned +0.0% vs default +0.0% (trails default by 0.0 pts)
Quiet
tuned +15.0% vs default -7.3% (beats default by 22.3 pts)

HYPE on the 30m chart

On 30m, 5 of 6 regimes beat the default settings, led by Quiet, which is no trend and low volatility: tight ranges where fees can eat most edges.

Bull+
tuned +10.9% vs default +1.8% (beats default by 9.2 pts)
Bull-
tuned +11.8% vs default -5.6% (beats default by 17.5 pts)
Bear+
tuned +3.6% vs default -1.3% (beats default by 4.9 pts)
Bear-
tuned +13.9% vs default -2.0% (beats default by 16.0 pts)
Chop
tuned +0.0% vs default +0.0% (trails default by 0.0 pts)
Quiet
tuned +23.1% vs default -0.0% (beats default by 23.2 pts)

HYPE on the 1h chart

On 1h, 6 of 6 regimes beat the default settings, led by Chop, which is no trend but high volatility: big swings in both directions where breakouts often fail.

Bull+
tuned +31.2% vs default -0.2% (beats default by 31.4 pts)
Bull-
tuned +34.5% vs default -2.5% (beats default by 37.0 pts)
Bear+
tuned +19.0% vs default +2.1% (beats default by 16.9 pts)
Bear-
tuned +29.3% vs default -7.3% (beats default by 36.6 pts)
Chop
tuned +35.7% vs default -6.7% (beats default by 42.4 pts)
Quiet
tuned +18.5% vs default -3.8% (beats default by 22.3 pts)

HYPE on the 4h chart

On 4h, 6 of 6 regimes beat the default settings, led by Bull+, which is a strong uptrend: ADX of 25 or more with a strong positive slope, where buyers are in control and pullbacks stay shallow.

Bull+
tuned +69.0% vs default -8.2% (beats default by 77.3 pts)
Bull-
tuned +58.8% vs default +10.1% (beats default by 48.6 pts)
Bear+
tuned +65.5% vs default -5.3% (beats default by 70.7 pts)
Bear-
tuned +39.0% vs default -6.9% (beats default by 45.9 pts)
Chop
tuned +67.1% vs default +14.2% (beats default by 53.0 pts)
Quiet
tuned +18.3% vs default +1.5% (beats default by 16.7 pts)

How to read these HYPE numbers

These are optimizer results on historical data, and every champion had to pass gates before publication: positive Sortino and ROI in its regime, beating the default on both, enough trades spread across walk-forward windows, and a plateau rather than a spike. Longer timeframes cover longer calendar windows, so compare timeframes with care. TensorTrader's forward testnet books have not yet shown an edge after fees, so treat any HYPE cell as a hypothesis to paper trade, not a forecast.

Trade HYPE from TradingView

  1. 1Connect a trade-only key on a live-ready venue that lists HYPE perpetuals, on testnet first.
  2. 2Open the TensorTrader extension's TT-Autotune tab, select that key and browse the HYPE cells.
  3. 3Enroll the HYPE timeframes you want; each becomes one strategy alert with its regime champions injected.
  4. 4Watch fills and closed-trade accounting on paper before any live opt-in.

Not financial advice. Figures are TensorTrader optimizer or backtest results with the method stated; past results do not predict future returns.

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