AI indicators on SAHI charts

A k-nearest-neighbours classifier decides the current direction, plotting green for up, red for down and orange for flat, with a teal baseline underneath for confirmation.

Best for a clean trend read without adding oscillators to the chart.

Adds machine-learning trend signals to the price chart, helping you read changes in direction alongside the candles and your other studies.

Best for comparing directional signals with the trend you see in price.

An adaptive take on SuperTrend that uses machine learning to respond to changing market conditions. Follow its trend line alongside price to assess the current direction.

Best for following an adaptive trend line through changing conditions.

Uses clustering to organise different SuperTrend settings into groups, offering another way to read the trend as market conditions change.

Best for exploring a clustering-based view of trend direction.

Brings volume into an AI-assisted SuperTrend view, adding participation context to the direction shown on the price chart.

Best for reading trend direction with volume context.

A machine-learning take on the moving average that offers a reference line for reading price direction and changes in the trend.

Best for using a model-based reference line alongside price.

Scans thousands of historical bars, finds the past market states closest to the current one, and reads likely direction from what followed them. Colours each bar by the classifier's prediction and shows live confidence scores with a session hit-rate box.

Best for intraday F&O traders on 5-minute Nifty and Bank Nifty charts.

Uses a logistic-regression model to show directional signals on the chart. Read those signals with price action and your chosen risk controls.

Best for adding a model-based directional signal to your chart analysis.

Displays bullish and bearish signals from a neural-network model directly on the price chart, giving you another view of potential changes in direction.

Best for comparing bullish and bearish model signals with price action.

A standard RSI blended with a kNN prediction that matches past states on momentum, volatility and slope. Reacts less to random noise while still catching real momentum shifts.

Best for fewer false overbought and oversold readings in trending markets.

Shows a machine-learning view of momentum in a separate panel, helping you compare the strength of a move with what price is doing.

Best for comparing momentum with the direction of a price move.

An oscillator that uses machine learning to track shifts in momentum. Compare its swings with the price chart when assessing a developing move.

Best for following momentum swings in a dedicated chart panel.

Uses kernel-based smoothing to present momentum, helping you follow changes in its direction alongside the underlying price action.

Best for reading a smoothed view of momentum changes.

Support and resistance mapped automatically as the session runs, by SAHI's own algorithm. Not available on any other platform.

Best for traders who want levels on the chart without drawing them by hand.

Groups price observations with K-means clustering to highlight potential support and resistance areas. Use the levels as context for your own chart analysis.

Best for finding potential price zones without plotting every level manually.

Uses a k-nearest-neighbours approach to show market structure on the chart, helping you examine the areas around which price is moving.

Best for adding a model-based view of market structure.

Plots a smoothed price estimate with surrounding bands using Nadaraya-Watson regression. The envelope gives context to how far price has moved from that estimate.

Best for viewing price in relation to a smoothed estimate and its bands.

Estimates how far price is likely to move after a signal by weighting past similar conditions across eight features, including volatility, volume and RSI, then places a take-profit and stop-loss at that distance.

Best for data-backed targets instead of estimating how far a move might run.