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sigc

A Rust-based quantitative finance research platform

Build, test, and deploy trading strategies with a type-safe DSL, deterministic backtester, and production-ready infrastructure.

Get Started View on GitHub

Why sigc?

Quant teams lose hours every week to ad hoc Python notebooks: mismatched calendars, fragile joins, non-deterministic backtests, and duplicated factor code. sigc is a composable, shape-aware DSL backed by a columnar runtime and deterministic backtester.

Type-Safe DSL

Write signals in a concise, readable language with compile-time type checking. Catch errors before running expensive backtests.

Deterministic Backtesting

Get reproducible results every time. Content-addressed caching ensures identical inputs produce identical outputs.

120+ Operators

Time-series, cross-sectional, technical indicators, and portfolio construction operators built-in. No external dependencies needed.

Production Ready

Circuit breakers, position limits, Slack alerts, audit logging, and Prometheus metrics. Deploy with confidence.

Single Binary

No services to manage. Compile, run, and deploy with one sigc binary. Daemon mode available for long-lived services.

IDE Support

VS Code extension with syntax highlighting, code completion, error diagnostics, and 25+ code snippets.

Quick Example

Text Only
data:
  prices: load parquet from "data/prices.parquet"

params:
  lookback = 20
  top_pct = 0.2

signal momentum:
  returns = ret(prices, lookback)
  score = zscore(returns)
  emit winsor(score, p=0.01)

portfolio main:
  weights = rank(momentum).long_short(top=top_pct, bottom=top_pct)
  backtest from 2024-01-01 to 2024-12-31
Bash
$ sigc run strategy.sig

=== Backtest Results ===
Total Return:         15.23%
Annualized Return:    15.23%
Sharpe Ratio:          1.45
Max Drawdown:          8.12%
Turnover:            312.00%

Choose Your Path

For Quant Researchers

Learn to build and test trading strategies with sigc's DSL.

For Developers

Integrate sigc into your infrastructure and extend its capabilities.

Key Features

Signal Development

Write trading signals using a concise DSL with 120+ built-in operators:

Text Only
signal momentum:
  returns = ret(prices, 252)
  skip = ret(prices, 21)
  mom = returns - skip
  emit zscore(mom)
Text Only
signal mean_reversion:
  ma = rolling_mean(prices, 20)
  std = rolling_std(prices, 20)
  z = (prices - ma) / std
  emit -zscore(z)
Text Only
signal combo:
  mom = zscore(ret(prices, 60))
  vol = -zscore(rolling_std(ret(prices, 1), 20))
  emit 0.6 * mom + 0.4 * vol

Custom Functions and Macros

Create reusable components for your signal library:

Text Only
// Custom function
fn volatility(x, window=20):
  rolling_std(ret(x, 1), window)

// Reusable macro
macro vol_adj_momentum(px: expr, ret_window: number = 20, vol_window: number = 60):
  let r = ret(px, ret_window)
  let vol = rolling_std(r, vol_window)
  emit zscore(r / vol)

signal my_signal:
  emit vol_adj_momentum(prices, 30, 90)

Production Infrastructure

Deploy with enterprise-grade safety systems:

  • Circuit Breakers - Automatic trading halts on anomalies
  • Position Limits - Max weight, sector, and turnover constraints
  • Kill Switches - Emergency stop for all trading
  • Alerting - Slack, email, and console notifications
  • Audit Logging - Full compliance trail
  • Monitoring - Prometheus metrics and Grafana dashboards

Integrations

Connect to your existing infrastructure:

Integration Description
Python pysigc bindings for Jupyter notebooks
VS Code Syntax highlighting, LSP, and snippets
Alpaca Paper and live trading via broker API
Yahoo Finance Free historical and real-time data
PostgreSQL Async connection pooling for databases
S3 Cloud storage for data and results

Project Status

sigc is feature-complete with all 8 development phases finished:

  • Production Features (async DB, corporate actions, alerts)
  • Performance & Scale (SIMD kernels, memory mapping)
  • Integration & Polish (colored CLI, 330 tests)
  • Documentation (11-chapter Quant Guide, 23 strategies)
  • Advanced Analytics (factor models, regime detection)
  • Safety & Deployment (circuit breakers, Docker)
  • Language Enhancements (macros, type inference, LSP)
  • External Integrations (Yahoo Finance, Alpaca)

See the Changelog for release history.

Getting Help

**Ready to build your first strategy?** [Get Started](getting-started/quickstart.md){ .md-button .md-button--primary }