Crate fin_primitives 

fin-primitives

The basic parts of trading software, done once and checked: exact price and size types that refuse bad values, an order book, candles built from trades, indicators, option prices and risk limits.

Add it (the dec! macro for decimal literals needs both rust_decimal crates):

cargo add fin-primitives rust_decimal rust_decimal_macros

The main types: Price, Quantity and Symbol (validated values), OrderBook, OhlcvAggregator (ticks to candles), the Signal trait and its indicators, PositionLedger, RiskMonitor, BlackScholes, and one error type, FinError.

A first look

Build a book from sequenced deltas, read the top of book, and price a market order by walking the levels:

use fin_primitives::orderbook::{BookDelta, DeltaAction, OrderBook};
use fin_primitives::types::{Price, Quantity, Side, Symbol};
use rust_decimal_macros::dec;

let mut book = OrderBook::new(Symbol::new("BTC-USD")?);
let levels = [
    (Side::Ask, dec!(64250.50), dec!(0.842)),
    (Side::Ask, dec!(64251.00), dec!(1.310)),
    (Side::Bid, dec!(64250.00), dec!(1.204)),
    (Side::Bid, dec!(64249.50), dec!(0.655)),
];
for (seq, (side, price, qty)) in (1..).zip(levels) {
    book.apply_delta(BookDelta {
        side,
        price: Price::new(price)?,
        quantity: Quantity::new(qty)?,
        action: DeltaAction::Set,
        sequence: seq,
    })?;
}

assert_eq!(book.spread(), Some(dec!(0.50)));
assert_eq!(book.mid_price(), Some(dec!(64250.25)));

// Buying 1 BTC takes all 0.842 at the best ask and 0.158 at the next level.
let vwap = book.vwap_for_qty(Side::Ask, Quantity::new(dec!(1))?)?;
assert_eq!(vwap, dec!(64250.579));

// A delta that would cross the book is rejected and rolled back.
let crossing = BookDelta {
    side: Side::Bid,
    price: Price::new(dec!(64251.00))?,
    quantity: Quantity::new(dec!(2))?,
    action: DeltaAction::Set,
    sequence: 5,
};
assert!(book.apply_delta(crossing).is_err());
assert_eq!(book.sequence(), 4);

Ticks become bars, and bars feed indicators. Indicators return SignalValue::Unavailable until they have enough history, never a NaN or a zero:

use fin_primitives::ohlcv::{OhlcvAggregator, Timeframe};
use fin_primitives::signals::indicators::Sma;
use fin_primitives::signals::{BarInput, Signal, SignalValue};
use fin_primitives::tick::Tick;
use fin_primitives::types::{NanoTimestamp, Price, Quantity, Side, Symbol};
use rust_decimal_macros::dec;

let sym = Symbol::new("ETH-USD")?;
let mut agg = OhlcvAggregator::new(sym.clone(), Timeframe::Minutes(1))?;
let mut sma = Sma::new("sma3", 3)?;

let mut values = Vec::new();
for (minute, close) in [(0, dec!(3180)), (1, dec!(3190)), (2, dec!(3200)), (3, dec!(3230))] {
    let tick = Tick::new(
        sym.clone(),
        Price::new(close)?,
        Quantity::new(dec!(1))?,
        Side::Bid,
        NanoTimestamp::from_secs(1_767_625_200 + minute * 60),
    );
    for bar in agg.push_tick(&tick)? {
        values.push(sma.update(&BarInput::from(&bar))?);
    }
}
// Three bars have closed; the fourth is still open.
assert_eq!(values[0], SignalValue::Unavailable);
assert_eq!(values[1], SignalValue::Unavailable);
assert_eq!(values[2], SignalValue::Scalar(dec!(3190)));

Runnable examples

The repository ships examples that print formatted, colored output:

Command	Shows
cargo run --example order_book	depth ladder, spread, micro-price, VWAP fill, rejected deltas
cargo run --example candles	ticks to 1-minute candles, EMA and RSI with warm-up
cargo run --example position_risk	fills, mark-to-market, drawdown and equity-floor breaches
cargo run --example option_chain	Black-Scholes chain with Greeks and an implied-vol round trip

Where things live

Module	What it provides
types	Price, Quantity, Symbol, NanoTimestamp, Side
tick	Tick, TickFilter, TickReplayer
orderbook	OrderBook: L2 book with sequence checks and crossed-book rollback
ohlcv	OhlcvBar, OhlcvAggregator, OhlcvSeries analytics
signals	the Signal trait, SignalPipeline, and several hundred indicators in signals::indicators
position	Fill, Position, PositionLedger, Kelly sizing
risk	DrawdownTracker, the RiskRule trait, RiskMonitor, VaR and stress tools
greeks	BlackScholes pricing, Greeks, implied volatility, multi-leg spreads
backtest	bar-by-bar Backtester, Strategy trait, WalkForwardOptimizer
async_signals	Tokio-based StreamingSignalPipeline
regime	Hurst exponent, GARCH(1,1), correlation-breakdown regime detection

Further modules cover portfolio optimization, factor models, yield curves, fixed income, credit, derivatives, execution cost, microstructure, Monte Carlo, pairs trading, tax lots and more; see the module list below.

Design

• Validated at construction. Price::new rejects zero and negative values; Quantity::new rejects negatives; Symbol::new rejects empty or whitespace strings. Code that holds one of these types can trust it.
• Decimal where money is. Prices, quantities, P&L and order-book math use rust_decimal::Decimal. Statistical models (GARCH, optimizers, Black-Scholes internals) compute in f64 and convert at the boundary.
• Typed errors. Fallible operations return Result<_, FinError>, and the crate’s Clippy config warns on unwrap, expect and panic.
• Traits at the seams. risk::RiskRule, signals::Signal and tick::TickFilter are traits, so your own rules and indicators plug in next to the built-in ones.
• No unsafe code. The crate is #![forbid(unsafe_code)].

Sister crate: fin-stream handles real-time market data ingestion on top of these types.

Re-exports

pub use error::FinError;

Modules

alternative_data

Alternative data integration: social sentiment, web traffic, satellite imagery, credit card data, job postings, and patent filings. Provides AltDataAggregator with composite signals, Pearson correlation, z-score anomaly detection, and staleness checking.

arbitrage

Cross-market arbitrage detection: ArbitrageOpportunity, TriangularArb, StatisticalArb, ArbitrageScanner (scan_cross_market, scan_triangular, filter_by_min_profit, rank_by_confidence).

async_signals

Tokio-based streaming signal pipeline: push bars in, receive SignalUpdates out.

attribution

Portfolio performance attribution: Brinson-Hood-Beebower decomposition, multi-factor attribution, marginal risk contribution, and comprehensive performance tearsheet.

backtest

Bar-by-bar backtester, the Strategy trait, and a walk-forward optimizer.

clustering

Asset clustering using k-means on return correlations.

correlation

Streaming Pearson correlation matrix for indicator redundancy detection.

credit

Credit analytics module.

cross_asset

Cross-asset rolling correlations and PCA-based dimensionality reduction.

crypto

Crypto-specific financial metrics: funding rates, perpetual basis, open-interest ratio, liquidation heatmap, and Fear & Greed index.

derivatives

Derivative pricing modules.

error

Error types for the fin-primitives crate.

events

Event Study Framework

execution

Execution cost estimation and turnover optimization.

execution_cost

Execution cost models: commission (Fixed, Proportional, Tiered, ZeroCommission), SpreadCost, MarketImpact (linear, sqrt, Almgren-Chriss), TotalExecutionCost, ExecutionCostBreakdown.

factor

Fama-French style multi-factor regression and portfolio factor exposure.

fixed_income

Fixed income analytics: bond pricing, duration, convexity, and yield calculations.

funding

Funding rate calculations for perpetual futures: premium index, clamped funding rate, payment computation, annualization, exponentially-weighted rate prediction, and rolling history with avg/volatility/cumulative-payment aggregation.

greeks

Black-Scholes pricing, the five Greeks, implied volatility and multi-leg spreads.

impact

Almgren-Chriss optimal order execution and market impact model.

latency

Order latency tracking: measures submit→ack, ack→fill, fill→book-update phases.

liquidity

Liquidity measures: bid-ask spread, market depth, composite liquidity scoring, and Amihud (2002) illiquidity ratio with rolling window averaging.

microstructure

Tick-level microstructure metrics: bid-ask spread, Amihud illiquidity, Kyle’s lambda, Roll implied spread.

ml

ML feature vector builder: snapshot N indicator outputs, normalize, and serialize for ML pipelines.

ml_features

ML feature engineering: price features, microstructure features, feature vectors, z-score normalization, cross-sectional ranking, and lagged feature construction.

montecarlo

Monte Carlo price-path simulation: GBM, VaR, CVaR, and percentile paths.

ohlcv

OHLCV bars, tick-to-bar aggregation by timeframe, and OhlcvSeries analytics.

options

Black-Scholes options pricing engine with Greeks and implied volatility solver.

orderbook

Level-2 order book with sequence-checked deltas and crossed-book rollback.

pairs_trading

Statistical pairs trading: Engle-Granger cointegration, ADF stationarity test, spread z-score signal generation, and Welford online mean/variance tracking.

performance

Portfolio performance metrics.

pnl

Streaming P&L attribution: decomposes realized P&L into alpha and cost components.

portfolio

Portfolio construction and optimization.

position

Fills, positions and a multi-symbol PositionLedger with realized and unrealized P&L.

rebalancing

Portfolio rebalancing: drift calculation, threshold and calendar triggers, trade generation, turnover estimation, and tax-aware rebalancing.

regime

Market regime engine: Hurst exponent, GARCH(1,1), cross-asset correlation breakdown, RegimeConditionalSignal (regime-adaptive RSI), and full RegimeHistory audit trail.

risk

Drawdown tracking, pluggable RiskRules, RiskMonitor, VaR and stress tools.

scenario

Risk scenario backtesting: replays historical bars through risk rules.

signals

The Signal trait, signal pipelines, composition, warm-up contracts and the indicator library.

tax

Tax lot accounting with FIFO, LIFO, SpecificLot, MinTax, and AverageCost disposal methods.

technical

Technical analysis indicators for OHLCV price series.

tick

Trade ticks, composable tick filters and a timestamp-ordered tick replayer.

types

Validated newtypes: Price, Quantity, Symbol, NanoTimestamp and Side.

volatility

Realised volatility estimators: Close-to-Close, Parkinson, Garman-Klass, Rogers-Satchell, Yang-Zhang. Also provides volatility::garch with GARCH(1,1) MLE fitting, conditional variance, multi-step forecasting, and volatility term structure.

yield_curve

Yield Curve Modeler