TT-Autotune · data 2026-09-26
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.
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 (+45.4% mean), and its best single regime is Chop at +110.6%. 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 Bear-, which is a weak downtrend: a slow bleed with sharp bounces and frequent short squeezes.
- Bull+
- tuned +31.2% vs default -0.1% (beats default by 31.2 pts)
- Bull-
- tuned +46.1% vs default -7.9% (beats default by 54.0 pts)
- Bear+
- tuned +28.7% vs default +1.3% (beats default by 27.4 pts)
- Bear-
- tuned +50.7% vs default -5.7% (beats default by 56.4 pts)
- Chop
- tuned +31.8% vs default -8.7% (beats default by 40.6 pts)
- Quiet
- tuned +7.1% vs default -2.3% (beats default by 9.5 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 +28.7% vs default +1.3% (beats default by 27.4 pts)
- Bull-
- tuned +42.7% vs default -18.2% (beats default by 60.9 pts)
- Bear+
- tuned +12.8% vs default -0.7% (beats default by 13.5 pts)
- Bear-
- tuned +20.7% vs default +1.1% (beats default by 19.5 pts)
- Chop
- tuned +30.9% vs default -2.2% (beats default by 33.1 pts)
- Quiet
- tuned +52.7% vs default -20.8% (beats default by 73.6 pts)
LIT on the 1h chart
On 1h, 5 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 +23.4% vs default -23.3% (beats default by 46.7 pts)
- Bull-
- tuned +31.2% vs default +9.4% (beats default by 21.8 pts)
- Bear+
- tuned +65.1% vs default -13.0% (beats default by 78.1 pts)
- Bear-
- tuned +40.3% vs default +17.3% (beats default by 23.0 pts)
- Chop
- tuned +110.6% vs default -24.3% (beats default by 134.9 pts)
- Quiet
- tuned +1.8% vs default +1.8% (trails default by 0.0 pts)
LIT on the 4h chart
On 4h, 5 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 +28.3% vs default +0.0% (beats default by 28.3 pts)
- Bull-
- tuned +21.1% vs default +0.0% (beats default by 21.1 pts)
- Bear+
- tuned +24.2% vs default -3.7% (beats default by 27.9 pts)
- Bear-
- tuned +22.1% vs default +0.0% (beats default by 22.1 pts)
- Chop
- tuned +42.2% vs default -2.0% (beats default by 44.3 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.