Neural Trader Predictor

Conformal prediction SDK for neural trading with guaranteed prediction intervals

A high-performance Rust library providing distribution-free prediction intervals with provable statistical guarantees. Perfect for quantitative trading, risk management, and any application requiring reliable uncertainty quantification.

Core Principle

Conformal prediction provides a mathematical guarantee:

P(y ∈ [lower, upper]) ≥ 1 - α

Rather than uncertain point estimates, get intervals with provable coverage regardless of the underlying data distribution.

🎯 Key Features

• Split Conformal Prediction: Distribution-free prediction intervals with (1-α) coverage guarantee
• Adaptive Conformal Inference (ACI): PID-controlled dynamic coverage adjustment for streaming data
• Conformalized Quantile Regression (CQR): Quantile-based prediction intervals with conformal guarantees
• Hybrid Integration: Best-of-both-worlds combining our optimized implementation with advanced features from conformal-prediction crate
  • CPD (Conformal Predictive Distributions): Full probability distributions with CDF queries for risk/reward analysis
  • PCP (Posterior Conformal Prediction): Regime-aware predictions with cluster-based adaptation (bull/bear/sideways)
  • Formal Verification: Lean4 mathematical proofs for coverage guarantees
  • KNN Nonconformity: Advanced distance-based scoring
• Multiple Nonconformity Scores: Absolute, normalized, and quantile-based scoring functions
• High-Performance Optimizations:
  • Nanosecond-precision scheduling for microsecond-level recalibration
  • Sublinear O(log n) streaming updates
  • Temporal lead solving for predictive pre-computation
  • Parallel processing with rayon
• Production-Ready: Comprehensive error handling, logging with tracing, type-safe design
• CLI Tool: Standalone neural-predictor command-line interface for batch processing

📊 Performance Benchmarks

Implementation	Prediction	Calibration	Memory
Rust (Native)	<100μs	<50ms	<10MB
Rust (Parallel)	<50μs	<25ms	~15MB
Streaming Update	<10μs	-	-

Performance targets for typical trading scenarios:

• Prediction latency: <1ms with guaranteed interval
• Calibration time: <100ms for 5,000 samples
• Memory footprint: <10MB for 2,000 calibration points
• Throughput: 10,000+ predictions/second

🚀 Quick Start

Basic Conformal Prediction

use neural_trader_predictor::{ConformalPredictor, scores::AbsoluteScore};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create predictor with 90% coverage (alpha = 0.1)
    let mut predictor = ConformalPredictor::new(0.1, AbsoluteScore);

    // Calibrate with historical predictions and actuals
    let predictions = vec![100.0, 105.0, 98.0, 102.0, 101.0];
    let actuals = vec![102.0, 104.0, 99.0, 101.0, 100.5];
    predictor.calibrate(&predictions, &actuals)?;

    // Make prediction with guaranteed interval
    let interval = predictor.predict(103.0);
    println!(
        "Prediction: {} with 90% confidence interval [{}, {}]",
        interval.point, interval.lower, interval.upper
    );
    println!("Interval width: {}", interval.width());

    // Update with new observation for improved calibration
    predictor.update(103.0, 102.5)?;

    Ok(())
}

Adaptive Trading Example

use neural_trader_predictor::{AdaptiveConformalPredictor, scores::AbsoluteScore};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create adaptive predictor that adjusts coverage in real-time
    let mut predictor = AdaptiveConformalPredictor::new(
        0.90,      // target 90% coverage
        0.02,      // learning rate (gamma)
        AbsoluteScore,
    );

    // Example: Streaming market predictions
    let predictions = vec![100.5, 101.2, 99.8, 102.1, 100.9];
    let actuals = vec![100.2, 101.5, 99.5, 102.3, 100.8];

    for (pred, actual) in predictions.iter().zip(actuals.iter()) {
        // Get interval and adapt coverage based on actual outcome
        let interval = predictor.predict_and_adapt(*pred, Some(*actual));

        // Trading signal based on interval confidence
        if interval.width() < 2.0 && interval.point > 100.0 {
            println!(
                "TRADE SIGNAL: Point={}, Interval=[{}, {}]",
                interval.point, interval.lower, interval.upper
            );
        }

        // Monitor coverage adaptation
        if pred == &101.2 {
            println!(
                "Empirical coverage: {:.2}%",
                predictor.empirical_coverage() * 100.0
            );
        }
    }

    Ok(())
}

📦 Installation

Add to your Cargo.toml:

[dependencies]
neural-trader-predictor = "0.1"

Optional Features

[dependencies]
neural-trader-predictor = { version = "0.1", features = ["cli"] }

Features:

• cli - Enables command-line interface (requires clap)
• wasm - WASM compilation target support

🔧 CLI Usage

Install with CLI support:

cargo install neural-trader-predictor --features cli

Calibrate a Predictor

neural-predictor calibrate \
    --model-path model.bin \
    --calibration-data calibration.csv \
    --alpha 0.1 \
    --output predictor.bin

Make Predictions

neural-predictor predict \
    --predictor predictor.bin \
    --features features.csv \
    --format json

Stream Predictions with Adaptive Coverage

neural-predictor stream \
    --predictor predictor.bin \
    --input-stream data.jsonl \
    --adaptive \
    --gamma 0.02

Evaluate Coverage on Test Data

neural-predictor evaluate \
    --predictor predictor.bin \
    --test-data test.csv

Benchmark Performance

neural-predictor benchmark \
    --predictor predictor.bin \
    --iterations 10000

🎯 Advanced: HybridPredictor with CPD/PCP

The HybridPredictor combines our fast implementation with advanced features from the conformal-prediction crate:

Risk/Reward Analysis with CPD

use neural_trader_predictor::{HybridPredictor, AbsoluteScore};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut predictor = HybridPredictor::new(0.1, AbsoluteScore)?;

    // Enable Conformal Predictive Distributions
    predictor.enable_cpd()?;

    // Calibrate
    predictor.calibrate(&predictions, &actuals)?;

    // Query full probability distribution
    let entry_price = 103.0;
    let take_profit = entry_price * 1.05;
    let stop_loss = entry_price * 0.97;

    // P(price > target)
    let prob_target = 1.0 - predictor.cdf(take_profit)?;
    // P(price < stop)
    let prob_stop = predictor.cdf(stop_loss)?;

    let risk_reward = prob_target / prob_stop;

    if risk_reward > 2.0 && prob_target > 0.4 {
        println!("✅ TRADE SIGNAL: Good risk/reward ratio");
        println!("   Entry: ${:.2}", entry_price);
        println!("   Target probability: {:.1}%", prob_target * 100.0);
        println!("   Stop probability: {:.1}%", prob_stop * 100.0);
    }

    Ok(())
}

Regime-Aware Trading with PCP

use neural_trader_predictor::{HybridPredictor, AbsoluteScore};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut predictor = HybridPredictor::new(0.1, AbsoluteScore)?;

    // Enable Posterior Conformal Prediction with 3 clusters
    predictor.enable_pcp(3)?;  // bull/bear/sideways

    // Calibrate with data from different market regimes
    predictor.calibrate(&predictions, &actuals)?;

    // Predictions automatically adapt to detected regime
    let interval = predictor.predict(current_price);

    // Strategy based on interval width
    let width_pct = interval.relative_width();

    if width_pct < 3.0 {
        println!("✅ NARROW INTERVAL: High confidence prediction");
        println!("   Execute aggressive strategy");
    } else if width_pct > 5.0 {
        println!("⚠️  WIDE INTERVAL: High uncertainty");
        println!("   Reduce position size or wait");
    }

    Ok(())
}

Run the complete examples:

cargo run --example hybrid_cpd    # Full probability distribution queries
cargo run --example hybrid_pcp    # Regime-aware predictions

📚 API Reference

Core Types

PredictionInterval

pub struct PredictionInterval {
    pub point: f64,      // Point prediction from base model
    pub lower: f64,      // Lower bound of interval
    pub upper: f64,      // Upper bound of interval
    pub alpha: f64,      // Miscoverage rate (1 - coverage)
    pub quantile: f64,   // Computed quantile threshold
}

impl PredictionInterval {
    pub fn width(&self) -> f64;           // Interval width
    pub fn contains(&self, value: f64) -> bool;  // Check containment
    pub fn relative_width(&self) -> f64;  // Width as % of point
    pub fn coverage(&self) -> f64;        // Expected coverage (1-α)
}

PredictorConfig

pub struct PredictorConfig {
    pub alpha: f64,                    // Miscoverage rate (default: 0.1)
    pub calibration_size: usize,       // Max calibration points (default: 2000)
    pub max_interval_width_pct: f64,   // Max width as % (default: 5.0)
    pub recalibration_freq: usize,     // Recalibrate after N predictions
}

Main Predictors

HybridPredictor<S: NonconformityScore> ⭐ NEW

Combines our fast implementation with advanced features from conformal-prediction crate.

pub struct HybridPredictor<S: NonconformityScore> {
    // Implementation details...
}

impl<S: NonconformityScore> HybridPredictor<S> {
    pub fn new(alpha: f64, score: S) -> Result<Self>;

    // Core operations (from our implementation)
    pub fn calibrate(&mut self, predictions: &[f64], actuals: &[f64]) -> Result<()>;
    pub fn predict(&mut self, point_pred: f64) -> PredictionInterval;
    pub fn update(&mut self, point_pred: f64, actual: f64) -> Result<()>;

    // Advanced features (from conformal-prediction crate)
    pub fn enable_cpd(&mut self) -> Result<()>;                      // Enable full probability distributions
    pub fn enable_pcp(&mut self, n_clusters: usize) -> Result<()>;  // Enable regime-aware predictions
    pub fn cdf(&mut self, threshold: f64) -> Result<f64>;            // Query P(Y ≤ threshold)
    pub fn quantile(&mut self, p: f64) -> Result<f64>;               // Query inverse CDF

    // Status queries
    pub fn cpd_enabled(&self) -> bool;
    pub fn pcp_enabled(&self) -> bool;
    pub fn n_clusters(&self) -> Option<usize>;
}

ConformalPredictor<S: NonconformityScore>

Split conformal prediction with fixed coverage.

pub struct ConformalPredictor<S: NonconformityScore> {
    // Implementation details...
}

impl<S: NonconformityScore> ConformalPredictor<S> {
    pub fn new(alpha: f64, score: S) -> Self;
    pub fn calibrate(&mut self, predictions: &[f64], actuals: &[f64]) -> Result<()>;
    pub fn predict(&self, point_pred: f64) -> PredictionInterval;
    pub fn update(&mut self, point_pred: f64, actual: f64) -> Result<()>;
    pub fn set_alpha(&mut self, alpha: f64) -> Result<()>;
}

AdaptiveConformalPredictor<S: NonconformityScore>

Adaptive conformal inference with PID control.

pub struct AdaptiveConformalPredictor<S: NonconformityScore> {
    // Implementation details...
}

impl<S: NonconformityScore> AdaptiveConformalPredictor<S> {
    pub fn new(target_coverage: f64, gamma: f64, score: S) -> Self;
    pub fn predict_and_adapt(&mut self, point_pred: f64, actual: Option<f64>) -> PredictionInterval;
    pub fn empirical_coverage(&self) -> f64;
    pub fn current_alpha(&self) -> f64;
}

Nonconformity Scores

AbsoluteScore

Absolute difference between prediction and actual value.

use neural_trader_predictor::scores::AbsoluteScore;

let score = AbsoluteScore;
// Uses: |y_pred - y_actual|

NormalizedScore

Normalized difference accounting for prediction magnitude.

use neural_trader_predictor::scores::NormalizedScore;

let score = NormalizedScore::new(epsilon: 1e-6);
// Uses: |y_pred - y_actual| / (|y_pred| + epsilon)

QuantileScore

Quantile-based conformity score for asymmetric intervals.

use neural_trader_predictor::scores::QuantileScore;

let score = QuantileScore::new(q_low: 0.05, q_high: 0.95);
// Uses: Conformalized quantile regression

Optimizers

High-Performance Features (Available in Rust)

use neural_trader_predictor::optimizers::{
    NanosecondScheduler,
    SublinearUpdater,
    TemporalLeadSolver,
};

// Microsecond-precision scheduling
let scheduler = NanosecondScheduler::new();

// O(log n) streaming updates
let updater = SublinearUpdater::new(predictor);

// Predictive pre-computation
let solver = TemporalLeadSolver::new(predictor);

🎯 Use Cases

Quantitative Trading

// Automated trading with confidence-based position sizing
let interval = predictor.predict(current_price);
let position_size = kelly_fraction * interval.coverage() / interval.relative_width();

Risk Management

// Value at risk (VaR) estimation
let interval = predictor.predict(portfolio_return);
println!("95% VaR: {}", interval.lower);

Regression with Uncertainty

// Any supervised learning task needing uncertainty bounds
let training_data = load_training_data();
predictor.calibrate(&model.predictions(&training_data), &training_data.targets())?;

// Get intervals for new predictions
let interval = predictor.predict(model.predict(new_features));

🧪 Testing & Benchmarking

Run Tests

cargo test --release

Run Benchmarks

cargo bench

Benchmarks measure:

• prediction_bench - Prediction latency across various calibration sizes
• calibration_bench - Calibration performance
• Coverage accuracy on synthetic and real market data

🔗 Integration with @neural-trader/neural

The JavaScript package @neural-trader/predictor provides seamless integration with the neural prediction package:

import { NeuralPredictor } from '@neural-trader/neural';
import { wrapWithConformal } from '@neural-trader/predictor';

const neural = new NeuralPredictor();
const conformal = wrapWithConformal(neural, { alpha: 0.1 });

// Now neural predictions have guaranteed intervals
const { point, lower, upper } = await conformal.predict(features);

📖 Examples

See /examples directory for complete working examples:

• basic_usage.rs - Simple conformal prediction
• adaptive_trading.rs - Adaptive coverage for market conditions
• hybrid_cpd.rs - Full probability distributions for risk/reward analysis ⭐ NEW
• hybrid_pcp.rs - Regime-aware predictions with clustering ⭐ NEW

Run examples:

cargo run --example basic_usage
cargo run --example adaptive_trading --features cli
cargo run --example hybrid_cpd          # CPD example
cargo run --example hybrid_pcp          # PCP example

📊 Mathematical Background

Conformal Prediction Guarantee

Given n calibration samples with nonconformity scores:

Quantile = ceil((n+1)(1-α)) / n

The prediction interval is:

[y_pred - Quantile, y_pred + Quantile]

This guarantees: P(y_actual ∈ [lower, upper]) ≥ 1 - α

Adaptive Coverage (ACI)

Uses PID control to adjust α dynamically:

α_new = α_old - γ × (observed_coverage - target_coverage)

Constraints: α_min ≤ α_new ≤ α_max

Conformalized Quantile Regression (CQR)

Adjusts quantile predictions with conformal calibration:

Q_new = Q_base + Quantile

Maintains guarantee while using quantile information from base model.

🚀 Performance Optimization

Nanosecond-Precision Scheduling

Microsecond-level timing for precise recalibration triggers:

scheduler.schedule_recalibration(
    Duration::from_micros(500),
    Box::new(|| predictor.recalibrate()),
)?;

Sublinear Streaming Updates

O(log n) updates instead of O(n) recalibration:

updater.stream_update(prediction, actual)?; // Fast!

Temporal Lead Solving

Pre-compute future intervals using temporal patterns:

let future = solver.solve_ahead(&features, lead_time_ms)?;

🔒 Error Handling

The library uses Result<T, Error> for all fallible operations:

#[derive(Debug)]
pub enum Error {
    InvalidAlpha(f64),
    InsufficientData,
    CalibrateError(String),
    // ...
}

pub type Result<T> = std::result::Result<T, Error>;

All errors are detailed and actionable for debugging.

📝 Logging

Uses the tracing crate for structured logging:

RUST_LOG=neural_trader_predictor=debug cargo run --example basic_usage

Log levels: error, warn, info, debug, trace

🤝 Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Ensure all tests pass: cargo test --release
5. Format code: cargo fmt
6. Submit a pull request

📄 License

Licensed under either of:

• Apache License, Version 2.0 (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)
• MIT license (LICENSE-MIT or http://opensource.org/licenses/MIT)

at your option.

🔗 Resources

• Conformal Prediction Theory
• Adaptive Conformal Inference
• Conformalized Quantile Regression
• Repository
• NPM Package

⚡ Roadmap

• Split conformal prediction
• Adaptive conformal inference
• Conformalized quantile regression
• CLI interface
• Integration with conformal-prediction crate ⭐ NEW
• CPD (Conformal Predictive Distributions) ⭐ NEW
• PCP (Posterior Conformal Prediction) ⭐ NEW
• Full CPD integration using conformal_prediction::cpd
• Full PCP integration using conformal_prediction::pcp
• Lean4 formal verification integration
• WASM bindings
• NAPI-rs native addon
• REST API server mode
• GPU acceleration (CUDA/OpenCL)
• Reinforcement learning for optimal α selection
• Integration with trading execution systems

💬 Support

For issues, questions, or suggestions:

• Open an issue on GitHub
• Check existing documentation
• Review examples

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