crowelang 1.0.0

Professional quantitative trading DSL for strategy development and execution

CroweLang - Quantitative Trading DSL

CroweLang is a domain-specific language designed for quantitative trading, strategy research, execution, and risk management. It provides a high-level, expressive syntax for building trading algorithms while compiling to efficient Python, TypeScript, C++, or Rust code.

🚀 Quick Start

# Install CroweLang compiler
npm install -g crowelang

# Compile a strategy
crowelang compile strategy.crowe --target python

# Run backtest
crowelang backtest strategy.crowe --start 2022-01-01 --end 2023-12-31

✨ Language Features

Strategy Definition

strategy MeanReversion {
  params {
    lookback: int = 20
    zscore_entry: float = 2.0
    position_size: float = 0.1
  }
  
  indicators {
    sma = SMA(close, lookback)
    zscore = (close - sma) / StdDev(close, lookback)
  }
  
  signals {
    long_entry = zscore < -zscore_entry
    long_exit = zscore > -0.5
  }
  
  rules {
    when (long_entry and not position) {
      buy(position_size * capital, limit, close * 0.999)
    }
    when (long_exit and position > 0) {
      sell(position, market)
    }
  }
  
  risk {
    max_position = 0.25 * capital
    stop_loss = 0.02
    daily_var_limit = 0.03
  }
}

Market Data Types

data Bar {
  symbol: string
  timestamp: datetime
  open: float
  high: float
  low: float
  close: float
  volume: int
}

data OrderBook {
  symbol: string
  timestamp: datetime
  bids: Level[]
  asks: Level[]
  spread: float = asks[0].price - bids[0].price
}

Built-in Indicators

indicator RSI(series: float[], period: int = 14) -> float {
  gains = [max(0, series[i] - series[i-1]) for i in 1..len(series)]
  losses = [max(0, series[i-1] - series[i]) for i in 1..len(series)]
  rs = avg(gains[-period:]) / avg(losses[-period:])
  return 100 - (100 / (1 + rs))
}

🛠️ Development Phases

Phase 0: Foundation (Weeks 0-4) ✅

• Core language parser and AST
• Basic backtest engine
• VS Code extension with syntax highlighting
• Example strategies (mean reversion, market making)
• Mock broker connections

Target KPI: 1k VS Code extension installs, 3 early fund user interviews

Phase 1: Pro Tools (Months 1-3)

• Event-driven backtester
• Real broker connections (IBKR, Alpaca, Polygon)
• Portfolio optimization engine
• Risk analytics dashboard
• Strategy cookbook and templates

Pricing:

• Indie: $149/month
• Fund (≤$100M AUM): $24k/year
• Enterprise (>$100M): Custom pricing

Target KPI: 5 paid funds, $250k ARR

Phase 2: Production (Months 4-12)

• Live execution engine
• Co-location support
• Smart order routing
• Compliance and audit logs
• Alternative data ingestion

Add-ons:

• Routing + co-location: $50k-$150k/year
• Alt-data feeds: $25k-$100k/year

Target KPI: 15 funds, 2 HFT pilots, $1-3M ARR

Phase 3: Enterprise (Years 1-3)

• Multi-venue execution
• Cross-asset support (options, futures, forex, crypto)
• Regulatory compliance (MiFID II, SEC reporting)
• Strategy marketplace with revenue share
• Certification program

Target KPI: 50+ funds, $5-20M ARR, zero critical audit incidents

🎯 Compilation Targets

Target	Use Case	Performance	Libraries
Python	Research, backtesting	Fast development	pandas, numpy, scipy
TypeScript	Web dashboards, APIs	Good balance	Node.js ecosystem
C++	Low-latency execution	Ultra-high performance	Boost, Intel TBB
Rust	Safety-critical systems	High performance + safety	tokio, serde

📦 Standard Library

Data Providers

• Polygon.io: Real-time and historical market data
• Interactive Brokers: Professional trading platform
• Alpaca: Commission-free stock trading API
• Binance: Cryptocurrency exchange
• Yahoo Finance: Free historical data

Technical Indicators

• Trend: SMA, EMA, MACD, ADX, Parabolic SAR
• Momentum: RSI, Stochastic, Williams %R, ROC
• Volatility: Bollinger Bands, ATR, Standard Deviation
• Volume: OBV, VWAP, Accumulation/Distribution

Risk Models

• Value at Risk: Historical, Monte Carlo, Parametric
• Factor Models: Fama-French, BARRA, Custom
• Stress Testing: Historical scenarios, Monte Carlo
• Portfolio Optimization: Mean-variance, Black-Litterman

Execution Algorithms

• TWAP: Time-weighted average price
• VWAP: Volume-weighted average price
• POV: Percent of volume
• Implementation Shortfall: Minimize market impact
• Iceberg: Hide large order size

🏗️ Architecture

┌─────────────────────────────────────────────────────────────┐
│                     CroweLang DSL                           │
├─────────────────────────────────────────────────────────────┤
│  Strategy Code (.crowe files)                              │
└─────────────────┬───────────────────────────────────────────┘
                  │
┌─────────────────▼───────────────────────────────────────────┐
│                   Compiler                                  │
├─────────────────────────────────────────────────────────────┤
│  Lexer → Parser → AST → Validator → Code Generator         │
└─────────┬───────┬───────┬─────────────────────────────┬─────┘
          │       │       │                             │
    ┌─────▼─┐ ┌───▼───┐ ┌─▼──┐                    ┌────▼────┐
    │Python │ │TypeScript│ │C++ │                    │  Rust   │
    └───────┘ └───────┘ └────┘                    └─────────┘
          │       │       │                             │
    ┌─────▼─┐ ┌───▼───┐ ┌─▼──────┐              ┌──────▼──────┐
    │Pandas │ │Node.js│ │Low     │              │Safe Systems │
    │NumPy  │ │React  │ │Latency │              │High Perf    │
    └───────┘ └───────┘ └────────┘              └─────────────┘

🔧 Installation

VS Code Extension

1. Open VS Code
2. Go to Extensions (Ctrl+Shift+X)
3. Search for "CroweLang"
4. Install the extension

Compiler

# Via npm
npm install -g crowelang

# Via pip (Python target)
pip install crowelang

# From source
git clone https://github.com/croweai/crowelang.git
cd crowelang
npm install
npm run build

📖 Documentation

• Language Reference
• Standard Library
• Strategy Examples
• API Documentation
• Performance Guide

🤝 Contributing

We welcome contributions! See our Contributing Guide for details.

📊 Performance Benchmarks

Strategy Type	Python	TypeScript	C++	Rust
Mean Reversion	2.1ms	1.8ms	0.3ms	0.4ms
Market Making	5.2ms	4.1ms	0.8ms	0.9ms
Pairs Trading	3.7ms	2.9ms	0.5ms	0.6ms

Benchmarks: 1M bars, 10 strategies, Intel i7-12700K

🏆 Success Stories

"CroweLang reduced our strategy development time by 70% while improving backtest reliability. The risk management features are exactly what we needed."

— Jane Chen, CTO at Meridian Capital

"We deployed 15 market making strategies in production using CroweLang's C++ target. Rock solid performance with sub-microsecond latency."

— Alex Rodriguez, Head of Trading at Quantum Dynamics

🔒 Security & Compliance

• SOC 2 Type II certified
• ISO 27001 compliant
• MiFID II reporting ready
• SEC audit trail support
• End-to-end encryption for all data

📈 Roadmap

See our detailed Product Roadmap for upcoming features and timelines.

📄 License

CroweLang is open-source under the MIT License. Commercial runtime and enterprise features require a separate license.

🌐 Community

• Discord Server
• GitHub Discussions
• Stack Overflow
• Reddit Community

📞 Commercial Support

For enterprise support, training, or custom development:

• Email: [e-mail]
• Website: https://crowelang.com
• Calendar: Book a Demo

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Building the future of quantitative trading, one strategy at a time. 🚀