Functions Reference

Complete reference for all comparison, logical, and relationship functions in PyPScript

Functions are the building blocks of pattern matching in PyPScript. They compare indicators, detect relationships, and create the conditions that trigger trading signals.

Comparison Functions

above()

Tests if the first value is greater than the second value.

import functions.above

strategy "above_example" {
    ma_20 = sma.evaluate(close, 20)
    
    patterns {
        above(close, ma_20) -> UP(confidence: 0.75)
        default -> HOLD(confidence: 0.95)
    }
}

below()

Tests if the first value is less than the second value.

import functions.below

strategy "below_example" {
    ma_20 = sma.evaluate(close, 20)
    
    patterns {
        below(close, ma_20) -> DOWN(confidence: 0.75)
        default -> HOLD(confidence: 0.95)
    }
}

between()

Tests if a value is within a specified range (inclusive).

import functions.between

strategy "between_example" {
    rsi_14 = rsi.evaluate(close, 14)
    
    patterns {
        between(rsi_14, 40, 60) -> HOLD(confidence: 0.90)
        default -> HOLD(confidence: 0.95)
    }
}

Relationship Functions

cross()

Detects when the first line crosses above the second line.

import functions.cross

strategy "cross_example" {
    fast_ma = sma.evaluate(close, 10)
    slow_ma = sma.evaluate(close, 20)
    
    patterns {
        // fast_ma crosses above slow_ma
        cross(fast_ma, slow_ma) -> UP(confidence: 0.85)
        
        // fast_ma crosses below slow_ma (slow_ma crosses above fast_ma)
        cross(slow_ma, fast_ma) -> DOWN(confidence: 0.85)
        
        default -> HOLD(confidence: 0.95)
    }
}

bounce()

Detects when price touches a level and reverses direction.

import functions.bounce

strategy "bounce_example" {
    support = support.evaluate(low, 20)
    
    patterns {
        bounce(low, support) -> UP(confidence: 0.85)
        default -> HOLD(confidence: 0.95)
    }
}

reject()

Detects when price approaches a level but fails to break through.

import functions.reject

strategy "reject_example" {
    resistance = resistance.evaluate(high, 20)
    
    patterns {
        reject(high, resistance) -> DOWN(confidence: 0.85)
        default -> HOLD(confidence: 0.95)
    }
}

Mathematical Functions

• min(v1, v2): Returns the smaller of two values.
• max(v1, v2): Returns the larger of two values.
• average(v1, v2, ...): Returns the arithmetic mean of 2-10 values.
• change(value): Returns the rate of change from the previous period.

Logical Operators

and

All conditions must be true.

above(fast, slow) and above(rsi, 50) -> UP(confidence: 0.85)

or

At least one condition must be true.

below(rsi, 30) or below(stoch, 20) -> UP(confidence: 0.75)

not

Inverts the following condition.

not above(rsi, 70) -> UP(confidence: 0.70)

Decision Primitives

PyPScript supports various signal types, all of which accept a confidence value:

• Directional: UP, DOWN, HOLD
• Actions: BUY, SELL, EXIT
• Conviction: STRONG_BUY, WEAK_BUY, STRONG_SELL, WEAK_SELL
• Risk: STOP_LOSS, TAKE_PROFIT

Best Practices

1. Order of Logic: Place the most likely conditions first in and chains for efficiency.
2. Grouping: Use parentheses ( ) to clarify complex logical expressions.
3. Confidence: Ensure your default pattern has a high confidence level (e.g., 0.95) to prevent false signals.