cubiczan-ml 0.1.0

High-performance ML library for finance/DeFi — Rust-powered Python bindings

CubicZan ML

Shared Rust ML layer for the Cubiczan AI / DeFi / Finance ecosystem.

A high-performance, zero-dependency-on-Python machine learning library written in pure Rust. Provides the foundational ML infrastructure shared across all Cubiczan projects — from commodity price prediction and critical mineral traceability to on-chain inference and financial sentiment analysis.

Architecture

cubiczan-ml/
├── Cargo.toml                          # Workspace root
├── crates/
│   ├── cubiczan-ml-core/               # Foundation layer
│   │   └── math · time_series · signal · risk · preprocessing · utils
│   ├── cubiczan-ml-nlp/                # Natural language processing
│   │   └── tokenizer · sentiment · classifier · ner · embeddings · summarizer
│   ├── cubiczan-ml-dl/                 # Deep learning (Candle)
│   │   └── models · inference · on_chain · time_series · training
│   ├── cubiczan-ml-rl/                 # Reinforcement learning
│   │   └── agents · environment · policy · exploration · backtest
│   └── cubiczan-ml-tf/                 # TensorFlow bridge
│       └── session · bridge · models

Crates

cubiczan-ml-core — Foundation

Financial math, time series, trading signals, risk management, and data preprocessing. The bedrock every other crate builds on.

Module	Highlights
math	Moving averages (SMA/EMA/WMA/DEMA), Bollinger Bands, RSI, MACD, portfolio metrics, correlation, statistical tests
time_series	OHLCV candles, resampling, returns (log/simple), stationarity tests (ADF), seasonality detection, rolling stats
signal	Trading signal types, strength/confidence scoring, signal aggregation, consensus voting, conflict detection
risk	Kelly criterion, position sizing, Value-at-Risk, CVaR, max drawdown tracking, exposure limits, margin calculation
preprocessing	MinMax/Standard/Robust scalers, label encoders, train/test splits, feature engineering (lags, rolling stats), NaN handling
utils	Softmax, sigmoid, ReLU, one-hot encoding, MSE, clipping, dense parameter counting
device	Compute device enumeration (CPU, CUDA), device-aware dispatch
error	Unified MlError enum with Result<T> alias, serde/bincode interop
metrics	Training metrics (loss, accuracy, epoch, timing), serialization support
normalization	Online normalization stats with incremental mean/std computation

cubiczan-ml-nlp — Financial NLP

Text analysis specialized for financial documents — SEC filings, earnings calls, crypto social media, commodity reports.

Module	Highlights
tokenizer	HuggingFace tokenizers wrapper, financial-aware preprocessing, subword tokenization, padding/truncation, batch encode
sentiment	Sector-specific lexicons, Fed-speak decoder, emoji/emoticon handling, cashtag detection, confidence scoring, aggregate scoring
classifier	Zero-shot classification, multi-label pipeline, keyword-based, FinBERT-ready integration, thresholded confidence
ner	Named entity recognition for companies (ORG), currencies (MONEY), dates (DATE), percentages, SEC filing entities
embeddings	Sentence embeddings with in-memory cache, cosine similarity search, TF-IDF fallback, batch processing
summarizer	Extractive summarization (TextRank-style), abstractive hooks, sentence scoring, configurable length limits

cubiczan-ml-dl — Deep Learning

Neural network architectures and inference powered by HuggingFace Candle — pure Rust, no GPU required for inference.

Module	Highlights
models	LSTM, Transformer, Autoencoder, MLP, Conv1D architectures with configurable hyperparameters
inference	Fast inference engine, framework abstraction layer, batch prediction, model checkpointing
on_chain	Blockchain transaction analysis, on-chain ML inference, wallet behavior profiling, fraud detection
time_series	DL-based time series forecasting, feature normalization, sliding window datasets
training	Learning rate schedules (step decay, cosine annealing, warmup), early stopping, gradient clipping, Adam optimizer config

cubiczan-ml-rl — Reinforcement Learning

A complete RL framework for building autonomous trading agents. Train, evaluate, and backtest strategies in simulated market environments.

Module	Highlights
agents	Q-learning, Deep Q-Network (DQN), Policy Gradient, Actor-Critic, ensemble agents with weight averaging
environment	Simple trading (long/short/hold), portfolio management with multi-asset support, order book simulation, configurable commissions/slippage
policy	Kelly criterion, momentum, mean-reversion, risk parity, adaptive policy switching, policy chaining
exploration	Epsilon-greedy, Boltzmann softmax, UCB1, Thompson sampling, entropy-regularized exploration
backtest	Event-driven backtesting engine, equity curve tracking, trade logging, performance metrics (Sharpe, Sortino, max DD, win rate)

cubiczan-ml-tf — TensorFlow Bridge

Load and run Python-trained TensorFlow/Keras models from Rust. Bridges existing ML pipelines into the Cubiczan ecosystem without rewriting.

Module	Highlights
session	SavedModel and frozen graph loading, batch inference, session pooling for concurrency, inference stats tracking
bridge	PyTfBridge for importing Python-trained models, ONNX import/validation, auto-generated Rust wrapper code
models	Pre-built interfaces for TF LSTM, Transformer, Classifier, and Risk Model inference

Quick Start

Prerequisites

• Rust 1.80+ (tested on 1.95.0 stable)
• No Python runtime required

Add as dependency

# In your Cargo.toml
[dependencies]
cubiczan-ml-core = { git = "https://github.com/Cubiczan/cubiczan-ml", branch = "main" }
cubiczan-ml-nlp  = { git = "https://github.com/Cubiczan/cubiczan-ml", branch = "main" }
cubiczan-ml-dl   = { git = "https://github.com/Cubiczan/cubiczan-ml", branch = "main" }
cubiczan-ml-rl   = { git = "https://github.com/Cubiczan/cubiczan-ml", branch = "main" }
cubiczan-ml-tf   = { git = "https://github.com/Cubiczan/cubiczan-ml", branch = "main" }

Build from source

git clone https://github.com/Cubiczan/cubiczan-ml.git
cd cubiczan-ml
cargo build
cargo test

Usage examples

use cubiczan_ml_core::{
    math::{MovingAverage, MovingAverageType},
    time_series::OhlcvCandle,
    risk::KellyCriterion,
    preprocessing::MinMaxScaler,
};

// Compute moving averages
let prices = vec![100.0, 102.0, 101.0, 103.0, 105.0, 104.0, 106.0];
let sma = MovingAverage::compute(&prices, 3, MovingAverageType::SMA);
let ema = MovingAverage::compute(&prices, 3, MovingAverageType::EMA);

// Kelly criterion position sizing
let kelly = KellyCriterion::new(0.6, 2.0);
let fraction = kelly.compute_fraction();

// Scale features for ML
let mut scaler = MinMaxScaler::new();
let scaled = scaler.fit_transform(&data)?;

use cubiczan_ml_nlp::{
    sentiment::FinSentimentAnalyzer,
    tokenizer::FinTokenizer,
    classifier::TextClassifier,
};

// Analyze financial sentiment
let analyzer = FinSentimentAnalyzer::new();
let result = analyzer.analyze("Fed signals potential rate cut in Q3")?;
println!("Sentiment: {:?} (confidence: {:.2})", result.label, result.confidence);

use cubiczan_ml_rl::{
    agents::QLearningAgent,
    environment::SimpleTradingEnv,
    exploration::EpsilonGreedy,
};

// Build a trading agent
let env = SimpleTradingEnv::new(prices, 100_000.0);
let exploration = EpsilonGreedy::new(0.1, 0.995, 1000);
let mut agent = QLearningAgent::new(
    env.state_size(),
    env.action_count(),
    0.1,    // learning rate
    0.99,   // discount factor
    exploration,
);

// Train
for episode in 0..500 {
    let mut state = env.reset();
    let mut total_reward = 0.0;
    loop {
        let action = agent.select_action(&state);
        let (next_state, reward, done) = env.step(action);
        agent.update(&state, action, reward, &next_state, done);
        state = next_state;
        total_reward += reward;
        if done { break; }
    }
}

Key Dependencies

Crate	Purpose
ndarray / nalgebra	n-dimensional arrays and linear algebra
candle-core / candle-nn	Pure-Rust deep learning (HuggingFace)
tokenizers	HuggingFace tokenizers (bindings)
serde / serde_json	Serialization framework
statrs	Statistical distributions and tests
tracing	Structured logging and diagnostics
anyhow / thiserror	Ergonomic error handling
chrono	Date/time for financial time series
rand	RNG for exploration strategies

Stats

Metric	Value
Total lines of Rust	17,667
Source files	41
Crates	5
Tests passing	265 / 265
Build errors	0
Minimum Rust version	1.80+ (tested 1.95.0)

Integration Targets

This shared ML layer is designed to be integrated into the following Cubiczan ecosystem projects:

• Commodity-Price-Analyzer — Price prediction and signal generation
• closed-loop-finance — Autonomous finance loop with ML-driven decisions
• FinFlowRL — RL-based trading strategies
• minescope-signal — Mining signal processing and anomaly detection
• sec-earnings-workbench — SEC filing NLP analysis
• Stellar-critical-metal-traceability — Supply chain traceability ML
• consensus-hardening-protocol — Multi-agent decision governance
• courtvision-ai — Sports analytics with ML
• greenverify-ai — ESG verification and scoring

License

MIT

Author

Shyam Desigan <[e-mail]>

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Built with Candle, ndarray, and tokenizers.