Linear Regression
Classic statistical model fitting a linear trend to historical price movements. Calculates slope and intercept using least squares method. Ideal for trending markets with R² accuracy scoring.
Generate 0-100 stock scores with 5 machine learning models, backtesting validation, and legal-compliant predictions
Ensemble learning with diversified algorithms for robust stock predictions
Classic statistical model fitting a linear trend to historical price movements. Calculates slope and intercept using least squares method. Ideal for trending markets with R² accuracy scoring.
Non-linear model capturing complex price patterns with cubic polynomials (degree 3). Solves augmented matrix systems for coefficient optimization. Handles market reversals effectively.
Double exponential smoothing (Holt's method) with α and β parameters for level and trend. Weighted averages giving more importance to recent data. Adaptive to changing market conditions.
Instance-based learning comparing current patterns to historical lookback windows (5 periods). Euclidean distance calculation for similarity matching. Predicts based on K=5 nearest historical scenarios.
Single hidden layer (10 neurons) with ReLU activation. Gradient descent training over 100 epochs. Learns non-linear relationships with normalized inputs. Backpropagation for weight optimization.
Comprehensive 0-100 scoring system combining momentum, ML consensus, technicals, volatility, and trend quality
Analyzes recent 20-period returns to measure price acceleration. Averages return percentages and normalizes to 0-100 scale. Captures short-term bullish/bearish trends effectively.
Aggregates directional signals (+1/-1/0) from 5 ML models. Calculates consensus strength as percentage agreement. Highest weight for model convergence reliability.
Combines RSI (14-period), MACD histogram, and Bollinger Band position. Evaluates overbought/oversold conditions and momentum indicators. Professional technical analysis integration.
Calculates risk-adjusted returns using Sharpe-like ratio. Penalizes high volatility predictions for stability scoring. Ensures score reflects risk/reward balance.
Averages R² scores from all models to measure prediction confidence. Higher R² indicates stronger trend reliability. Filters out noisy, unpredictable price movements.
Real-time ML predictions with backtesting validation and legal-compliant scoring
From stock symbol to AI-generated score in under 10 seconds
Search any ticker (AAPL, TSLA, NVDA, etc.) with intelligent autocomplete. System loads 6-month historical data from Twelve Data API. Select prediction horizon (7/14/30 days).
Engine trains Linear, Polynomial, Exponential, KNN, and Neural Network models. Each model generates normalized predictions (no raw prices). R² accuracy and RMSE calculated.
System calculates 0-100 score from 5 weighted components: Momentum (25%), ML Consensus (30%), Technical (20%), Volatility (15%), Trend Quality (10%). Backtesting validation + recommendation generated.
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