Adaptive Lorentzian Classification [Quantum Algo]

Jun 11

Quantum ML Engine — Adaptive Lorentzian Classification [Quantum Algo]

█ OVERVIEW

Quantum ML Engine is a machine-learning classifier that predicts the direction of price over a configurable horizon using an Approximate Nearest Neighbors (ANN) search across historical feature vectors. Instead of relying on a single oscillator, it compares the current bar's "fingerprint" — a vector of up to six normalized features — against thousands of past bars, finds the most similar market conditions, and lets those historical outcomes vote on what is likely to happen next.

By default the engine measures similarity with Lorentzian distance, log(1 + |Δ|), rather than Euclidean distance. Market data is heavily distorted around major events (CPI prints, FOMC, black swans), and Lorentzian distance naturally compresses these outliers — analogous to how mass warps space-time — so a single extreme bar cannot dominate the neighbor selection.

This is an original, fully self-contained implementation written from scratch with zero library imports. The concept of applying Lorentzian distance to kNN classification on charts was pioneered in the open-source work of jdehorty (Machine Learning: Lorentzian Classification), building on earlier kNN studies by @capissimo. Full credit to both for the foundational research. This script does not reuse their code; it re-derives the approach independently and extends it in the ways described below.

█ WHAT IS DIFFERENT IN THIS IMPLEMENTATION

1 — Time-aligned training set
Each training sample pairs the feature vector recorded AT a given bar with the realized outcome over the following H bars. Features and labels are stored on the same time axis, so the classifier learns from correctly matched cause-and-effect pairs. There is no lookahead: a sample only enters the training set once its outcome is fully realized.

2 — ATR neutral-zone labeling
Historical moves smaller than a configurable multiple of ATR are labeled NEUTRAL instead of long/short. Sideways noise therefore never teaches the model a false directional lesson. Set the multiplier to 0 to disable.

3 — Six engineered features with importance weights
RSI, WaveTrend, CCI, ADX, MFI (volume flow) and Fisher Transform, each normalized to a common 0–1 scale. Every feature slot has its own weight input, so you can tell the engine which dimensions matter more for your market without removing features entirely.

4 — Four selectable distance metrics
Lorentzian (default), Manhattan, Euclidean, and a 50/50 Lorentzian-Manhattan Hybrid. Switching metrics changes the geometry of the neighborhood and is a powerful tuning lever per asset class.

5 — Distance-weighted voting with a confidence score
Closer neighbors vote louder (weight = 1 / (1 + distance)). The agreement between neighbors is expressed as a 0–100% confidence value printed on every bar, and a minimum-confidence gate suppresses low-conviction signals entirely.

6 — Adaptive K
The neighbor count automatically shrinks (up to 40%) when volatility ranks high over the last 100 bars, making the model more reactive in fast markets, and expands back in quiet regimes for stability. Can be disabled for a fixed K.

7 — Sliding training window
The engine always trains on the most recent N bars rather than the oldest bars in chart history, so the model reflects current market structure.

8 — Configurable prediction horizon
The training/holding horizon is an input (1–20 bars) instead of a hardcoded constant.

9 — Three exit modes
Fixed-horizon exits, dynamic kernel-slope exits, and an optional ATR trailing stop with the stop level plotted on the chart.

10 — Higher-timeframe confluence filter
Optionally require price to be above (longs) or below (shorts) an EMA on a higher timeframe of your choice.

█ HOW IT WORKS

1. On every bar, six features are computed and normalized.
2. The bar's feature vector is compared against samples inside the sliding training window, sampled with a minimum chronological spacing (default 4 bars) so neighbors come from distinct market episodes rather than one cluster.
3. A monotonic distance threshold maintains a stable pool of approximate nearest neighbors; when the pool exceeds K, the threshold resets to the 75th-percentile distance, allowing genuinely closer samples to rotate in over time.
4. Neighbors vote long / short / neutral, weighted by proximity. The weighted sum becomes the prediction; the degree of agreement becomes the confidence.
5. The raw signal is then passed through optional filters: volatility regime (recent ATR vs long-run ATR), trend regime (EMA separation normalized by ATR), ADX, EMA/SMA trend, higher-timeframe trend, and a Nadaraya-Watson kernel regression filter (rational quadratic estimate with a Gaussian crossover mode for smoother color transitions).
6. Entries print only when the ML signal, the confidence gate, and all enabled filters agree.

█ SETTINGS GUIDE

General — source, training window size, prediction horizon, neutral-zone width.
ML Engine — K, adaptive K toggle, chronological spacing, distance metric, distance weighting, minimum confidence.
Feature Engineering — feature type, parameters and weight for each of the six slots.
Filters — volatility, regime, ADX, EMA/SMA, higher-timeframe confluence.
Kernel — lookback, relative weighting, regression level, lag, smoothing mode.
Exits — fixed vs dynamic exits, ATR trailing stop and multiplier.
Display — bar colors, prediction labels (value + confidence), dashboard, color compression.

█ DASHBOARD

The on-chart panel shows the live signal, prediction confidence, current adaptive K, volatility and trend regime states, kernel bias, and a calibration win-rate. The calibration statistic simply checks whether price moved in the predicted direction over the horizon after each signal. It exists ONLY to give feedback while tuning features — it is not a backtest, includes no costs or risk management, and must not be treated as a performance claim.

█ USAGE NOTES

— Works on any symbol and timeframe; intraday (15m–4H) and daily charts are typical starting points. Crypto, FX, indices and equities all behave differently — retune the features and metric per market.
— Higher minimum confidence = fewer but more selective signals. Raising chronological spacing diversifies neighbors on lower timeframes.
— Signals are evaluated on bar close. Like any bar-close logic, the in-progress bar can change until it closes.
— Best used as a confluence layer inside a complete trading plan with your own risk management, not as a standalone buy/sell system.

█ CREDITS

Concept inspiration: jdehorty (Machine Learning: Lorentzian Classification) and capissimo (kNN implementations). This script is an independent, original implementation with the extensions listed above.

█ DISCLAIMER

This script is provided for educational and informational purposes only. It is not financial advice, and past behavior — including the on-chart calibration statistics — does not guarantee future results. Trading involves substantial risk of loss. Always do your own research and manage risk responsibly.

Jun 29

Release Notes

On-chart symbol + timeframe tag

Jun 29

Release Notes

On-chart symbol + timeframe tag