4 Ensemble ML Models
Premium Feature

Machine learning.
Trained on PSX history.

Four ML models run in parallel on every stock — Linear Regression, Logistic Regression, KNN, and a Neural Network. Their consensus produces a high-conviction directional prediction no single model could match.

Four models. One consensus.

Each model approaches the prediction from a different mathematical angle.

Linear Regression

Supervised Learning

Fits a linear relationship between 20+ technical features and future price movement. Predicts the next-day price target and 5-day projected level.

Next-day price target
5-day price projection
Predicted % change

Logistic Regression

Binary Classification

Classifies each stock as UP or DOWN for the next trading day. Outputs a probability score (0–1) indicating confidence in the directional call.

UP / DOWN direction
Probability score (0–1)
Confidence in direction

K-Nearest Neighbours

Instance-Based Learning

Finds the K most similar historical periods in the stock's data and uses their outcomes to predict what will happen next. Excels at recognising repeated patterns.

Pattern match score
Historical outcome consensus
Nearest neighbour similarity

Neural Network

3-Layer Backpropagation

A 3-layer artificial neural network trained on historical PSX data to capture non-linear relationships between technical features and future price behaviour.

Non-linear pattern signal
Activation-based confidence
Direction + magnitude estimate

How the ML engine is built

Rigorous feature engineering and consensus scoring — not off-the-shelf signals.

20+ engineered features

RSI, MACD histogram, MACD signal delta, Bollinger Band position, price vs SMA 20/50, EMA cross, volume ratio, momentum (1d/3d/5d), volatility, ATR, trend slope, and more.

Consensus mechanism

All four models vote on direction. When 3 or 4 agree, that consensus signal carries higher confidence. Disagreement is flagged as uncertain.

Minimum data requirement

Requires 61+ trading days of historical data per stock to train and run predictions. Stocks with insufficient history are skipped gracefully.

Runs alongside AI analysis

ML predictions run as a separate layer alongside AI analysis and technical indicators. The three sources can agree or diverge — both are shown to you.

From raw data to ML prediction

Five steps from historical OHLCV to a consensus directional call.

01

Historical data loaded

Up to 120 days of OHLCV data is loaded for the stock being analysed.

02

20+ features engineered

Technical features are computed from the raw data: momentum, volatility, indicator values, price ratios.

03

All 4 models run

Each model independently processes the feature set and produces a directional prediction.

04

Consensus score calculated

Agreement level across models is quantified. 4/4 agreement = very high confidence. 2/4 = uncertain.

05

Result added to your dashboard

ML prediction (direction + consensus score) is displayed alongside the AI signal and technical indicators.

ML predictions + advanced AI — better together

ML models find statistical patterns in historical price data. advanced AI reasons about current conditions, macro context, and fundamental factors. When both agree, conviction is high. When they diverge, you see both — so you can weigh the evidence yourself.

AI engine handles macro + qualitative reasoning
ML models handle statistical pattern recognition
Consensus score combines all sources
Disagreement flagged — never hidden

Related features

Four ML models running on your stocks.

Upgrade to Premium and unlock ML consensus predictions alongside AI analysis for every stock in your watchlist.