Regulatory Disclosure Keins Finance is a market research platform and NOT registered with SEBI. Quantitative and algorithmic models are strictly for market research. Not financial or investment advice.
AI RESEARCH / MODEL PERFORMANCE

Algorithmic Forecasts

Explore market forecasts and see how predictions compare with actual prices. Clear performance metrics, historical results, and a closer look at every forecast.

Research use. Algorithmic predictions are not financial advice. Algorithms cannot foresee black swan events.
Forecast Accuracy Report — Predicted vs Actual

A transparent view of model performance against market outcomes.

Click "Load Report" to compare predictions vs actuals.

Compare actual and predicted closing prices on the left axis, with percentage error on the right. Hover over a date for details; daily values appear below.

Current Model Output

Artificial Intelligence & Machine Learning Model Forecast (5-Day Rolling)
Backtest Ledger
Specify parameters and Query Ledger.
Machine Learning Methodology & Research Architecture

1. Multi-Factor Feature Pipeline

Our quantitative time-series models ingest multi-dimensional feature matrices computed from historical tick-level and daily OHLCV market feeds across the National Stock Exchange (NSE) and Bombay Stock Exchange (BSE). Key engineered inputs include exponential moving average differentials (EMA 12/26), Relative Strength Index (RSI-14), Bollinger Band percentage bandwidth (%B), Average True Range (ATR volatility envelopes), and multi-timeframe volume-weighted momentum.

2. Directional Classification & Probability Calibration

The artificial intelligence architecture evaluates short-term directional regime probabilities (UP, DOWN, or NEUTRAL) rather than attempting to guess exact price targets. Directional probabilities are calibrated using cross-entropy loss functions and walk-forward historical validation splits to prevent lookahead bias and statistical overfitting.

3. Walk-Forward Backtesting & Performance Ledger

All forecasting models are evaluated against out-of-sample test windows. We measure model efficacy using balanced accuracy, precision/recall curves, Brier probability calibration scores, and maximum drawdown simulations. Compare historical predictions with observed outcomes to understand how estimates performed. Past performance does not guarantee future results.

Research Research Note: Machine learning forecasts cannot predict exogenous geopolitical shocks or unexpected black swan events. All model outputs are strictly for statistical and quantitative research demonstration.