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[**https://www.tradingview.com/script/9SgtsBck-Machine-Learning-Supertrend-Aslan/**](https://www.tradingview.com/script/9SgtsBck-Machine-Learning-Supertrend-Aslan/)
Hey everyone 👋, This is an indicator I've been working on for a long time. Its not really finished yet, but I think it has huge potential. It's really still in beta.
It uses a SuperTrend foundation layer with momentum filtering, volume confirmation, and, most importantly, an adaptive engine that continuously adjusts its own parameters based on recent performance. It fires entry arrows on the chart while running a background simulation to score how the current settings are performing — then quietly adjusts itself over time.
Two systems run in parallel: the signal engine that produces the arrows you see, and a background test matrix that opens and closes simulated trades to feed the optimizer. The optimizer adjusts parameters, which produces better signals, which feeds back into better learning.
**The Core Concept**
At its heart this is a SuperTrend indicator — a volatility band that flips bullish or bearish as price crosses it. Signals fire either when that flip occurs (Reversal mode) or when price pushes to a new extreme within the current trend (Breakout mode).
On top of this, three adaptive learning layers run continuously:
**Global Optimizer** — rolling window of recent trade results that proposes parameter changes based on win rate, Sortino ratio, and profit factor
**Micro-Batch Processor** — a faster layer that groups results into mini-batches for quicker adjustments
**Regime Grid** — a 2D memory map that remembers what settings worked under similar market conditions (trend strength × volatility) and biases proposals accordingly
**State Snapshots**
The State Snapshot system acts as a persistence layer for the adaptive engine, allowing you to save and reload the indicator’s learned state instead of restarting from scratch on every chart refresh. At any point, you can export a serialized snapshot string that captures the current parameter set, optimizer state, and regime grid memory. This string can then be stored and later reinserted via the restore field, where it is applied on the next confirmed bar to fully reconstruct the indicator’s internal state. In practice, this means the system retains its “experience” — including what it has learned about specific market conditions — across sessions, symbols, or even different charts, eliminating the need for repeated warm-up periods.
I would love to get some feedback on it.
**You can access the indicator for free here:** [**https://www.tradingview.com/script/9SgtsBck-Machine-Learning-Supertrend-Aslan/**](https://www.tradingview.com/script/9SgtsBck-Machine-Learning-Supertrend-Aslan/)