docs

Documentation

Complete guide to installing, configuring, and using Diffusion Zones CLI for professional-grade trading signals and backtesting.

Quick Start

Prerequisites

▸Deno 2.x - Install Deno
▸Alpaca Markets API Key - Get free API keys (200 req/min on free tier)

Installation

# Download the binary # After purchase, you'll receive a license key and download link # Make executable (macOS/Linux) chmod +x dz # Move to PATH mv dz /usr/local/bin/ # Verify installation dz --version

Configuration

# Set environment variables export ALPACA_API_KEY=your-key-here export ALPACA_SECRET_KEY=your-secret-key-here # Optional export ALPACA_DATA_FEED=iex  # iex (free) or sip (paid) export LOG_LEVEL=warn         # debug, info, warn, error

Generate Your First Signals

# Get signals for multiple symbols dz SPY NVDA TSLA # Backtest a strategy dz backtest SPY --days 90 --monte-carlo # Optimize parameters dz optimize QQQ --strategy regime-switching

Trading Strategies

Conservative

1.5% risk per trade, tight stops. Win Rate: 45-50%.

--mt-strategy conservative

Moderate

2% risk per trade, balanced approach. Win Rate: 47-52%.

--mt-strategy moderate

Aggressive

4% risk per trade, wider stops. Win Rate: 50-55%.

--mt-strategy aggressive

Regime-Switching

Auto-switches between momentum and mean reversion based on market ADX.

--strategy regime-switching

Multi-Timeframe Aligned

Requires 1H, 4H, 1D trend alignment. Win Rate: 52-58%.

--mt --mt-strategy aligned

Momentum with Volume

Trades trends with volume confirmation (STRATEGY-001).

--strategy momentum-volume

⚠️ All strategies include walk-forward validation and Monte Carlo simulation to prevent overfitting and provide statistical confidence intervals.

Backtesting & Analysis

Professional Backtesting

# Basic backtest dz backtest SPY --days 90 # With Monte Carlo simulation (1000 iterations) dz backtest SPY --days 90 --monte-carlo --monte-carlo-simulations 1000 # Multi-strategy comparison dz multibacktest --strategies "conservative,moderate,aggressive" SPY

Walk-forward validation prevents look-ahead bias. Monte Carlo provides 95% confidence intervals for all metrics.

Parameter Optimization

# Grid search with walk-forward validation dz optimize SPY --days 60 # Optimize specific parameter dz optimize SPY --optimize-param signal.momentum5Weight --optimize-min 0.3 --optimize-max 0.7 # Maximize win rate dz optimize SPY --optimize-objective winrate --optimize-validation walk-forward

Grid search explores parameter combinations systematically. Walk-forward validation ensures robustness across different market regimes.

Validation Suite

# Run comprehensive validation (200+ backtests) dz validation-suite SPY --validation-timeout 120 # Test specific strategies dz validation-suite --validation-strategies "conservative,moderate" SPY # Use watchlist dz validation-suite --watchlist tech --validation-timeout 300

Tests across multiple market periods (COVID-2020, Recovery-2021, RateHike-2022, etc.) and regimes (bull, bear, sideways, high volatility).

Diffusion Zones Algorithm

The Diffusion Zones algorithm calculates dynamic support and resistance zones based on price diffusion, volatility-adjusted momentum, and multi-timeframe trend analysis.

Key Components

▸ATR (Average True Range) - Volatility measurement
▸EMA (Exponential Moving Average) - Trend smoothing
▸Momentum Score - 5-period and 20-period
▸Volume Confirmation - Entry validation
▸Regime Detection - ADX-based market state

Signal Confidence

▸75-100% - Very Strong
▸65-74% - Strong
▸55-64% - Moderate
▸<55% - Weak (avoid)

⚠️ The algorithm is designed for research and educational purposes. Past performance does not guarantee future results. Always validate strategies with walk-forward backtesting before risking real capital.

Output Formats

Terminal Table (Default)

🎯 DIFFUSION ZONES™ SIGNALS ╔════════════════════════════════════════╗ ║ SYMBOL  TREND    BUY    CURRENT   SELL  ║ ╠════════════════════════════════════════╣ ║▲ SPY     BULL    478.32  482.15   488.91 ║ ║▲ NVDA    BULL    485.20  492.80   512.30 ║ ║▼ TSLA    BEAR    238.50  245.20   258.90 ║ ╚════════════════════════════════════════╝

JSON Output

{
  "signals": [
    {
      "symbol": "SPY",
      "trend": "BULLISH",
      "buy_trade": 478.32,
      "sell_trade": 488.91,
      "confidence": 0.92
    }
  ]
}

CSV Output

symbol,trend,buy_trade,sell_trade,confidence SPY,BULLISH,478.32,488.91,0.92 NVDA,BULLISH,485.20,512.30,0.88 TSLA,BEARISH,238.50,258.90,0.78

Use --output json or --output csv to export signals for automation or further analysis.