TT-Autotune · data 2026-11-11
LIT trading bot: TT-Autotune tuned results by timeframe and regime
Tuned vs default Lorentzian Classification results for LIT across 4 timeframes and six market regimes, and how to automate LIT from TradingView paper first.
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LIT 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 LIT: 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 LIT timeframe is 1h (+64.0% mean), and its best single regime is Chop at +151.0%. 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.
LIT on the 15m chart
On 15m, 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 +32.7% vs default -6.7% (beats default by 39.4 pts)
- Bull-
- tuned +20.8% vs default +2.1% (beats default by 18.7 pts)
- Bear+
- tuned +26.2% vs default -0.0% (beats default by 26.2 pts)
- Bear-
- tuned +17.3% vs default -12.4% (beats default by 29.7 pts)
- Chop
- tuned +43.4% vs default +3.4% (beats default by 40.0 pts)
- Quiet
- tuned +20.9% vs default -0.3% (beats default by 21.3 pts)
LIT on the 30m chart
On 30m, 6 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 +26.1% vs default -2.6% (beats default by 28.8 pts)
- Bull-
- tuned +53.2% vs default -12.0% (beats default by 65.1 pts)
- Bear+
- tuned +30.0% vs default -0.7% (beats default by 30.7 pts)
- Bear-
- tuned +27.7% vs default +3.2% (beats default by 24.5 pts)
- Chop
- tuned +35.9% vs default -2.2% (beats default by 38.1 pts)
- Quiet
- tuned +54.5% vs default -20.9% (beats default by 75.4 pts)
LIT 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 +43.9% vs default -30.2% (beats default by 74.1 pts)
- Bull-
- tuned +57.4% vs default +13.6% (beats default by 43.7 pts)
- Bear+
- tuned +86.1% vs default -6.2% (beats default by 92.3 pts)
- Bear-
- tuned +34.2% vs default +2.2% (beats default by 32.0 pts)
- Chop
- tuned +151.0% vs default -7.7% (beats default by 158.8 pts)
- Quiet
- tuned +11.3% vs default +1.8% (beats default by 9.5 pts)
LIT on the 4h chart
On 4h, 4 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 +24.9% vs default +0.0% (beats default by 24.9 pts)
- Bull-
- tuned +37.3% vs default +0.0% (beats default by 37.3 pts)
- Bear+
- tuned +4.7% vs default +4.7% (trails default by 0.0 pts)
- Bear-
- tuned +31.9% vs default +0.0% (beats default by 31.9 pts)
- Chop
- tuned +67.7% vs default -2.0% (beats default by 69.7 pts)
- Quiet
- tuned +0.0% vs default +0.0% (trails default by 0.0 pts)
How to read these LIT 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 LIT cell as a hypothesis to paper trade, not a forecast.
Trade LIT from TradingView
- 1Connect a trade-only key on a live-ready venue that lists LIT perpetuals, on testnet first.
- 2Open the TensorTrader extension's TT-Autotune tab, select that key and browse the LIT cells.
- 3Enroll the LIT timeframes you want; each becomes one strategy alert with its regime champions injected.
- 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.