Variables & Data

Variables are how you turn raw market inputs into readable strategy logic.

In practice, good PyPScript code is mostly:

• clear variable names
• reusable conditions
• simple pattern rules built from those variables

Built-In Data

These values are available automatically:

• open
• high
• low
• close
• volume
• pair
• timeframe

These are the base inputs for indicators and conditions.

Example:

fast_ma = sma.evaluate(close, 10)
range_size = atr.evaluate(high, low, close, 14)

Basic Assignment

The assignment form is:

name = expression

Examples:

fast_ma = sma.evaluate(close, 10)
slow_ma = sma.evaluate(close, 20)
momentum = rsi.evaluate(close, 14)

What Variables Usually Hold

In PyPScript, variables commonly hold:

• indicator outputs
• literal thresholds
• reusable boolean conditions

Indicator Values

trend_ma = sma.evaluate(close, 50)
entry_ma = sma.evaluate(close, 20)
rsi_value = rsi.evaluate(close, 14)
atr_value = atr.evaluate(high, low, close, 14)

Reusable Conditions

trend_up = above(close, trend_ma)
momentum_up = above(rsi_value, 55)
pullback_zone = below(close, entry_ma)
volatility_ok = above(atr_value, 0.0)

This is one of the cleanest habits in PyPScript:

• calculate once
• name the meaning
• reuse the named condition in patterns

Build Conditions In Layers

Instead of writing one giant expression, build named layers.

Less readable:

patterns {
    above(close, sma.evaluate(close, 50)) and above(rsi.evaluate(close, 14), 55) and cross(close, sma.evaluate(close, 20)) -> UP(confidence: 0.85)
    default -> HOLD(confidence: 0.95)
}

Better:

trend_ma = sma.evaluate(close, 50)
entry_ma = sma.evaluate(close, 20)
rsi_value = rsi.evaluate(close, 14)

trend_up = above(close, trend_ma)
momentum_up = above(rsi_value, 55)
entry_reclaim = cross(close, entry_ma)

patterns {
    trend_up and momentum_up and entry_reclaim -> UP(confidence: 0.85)
    default -> HOLD(confidence: 0.95)
}

Naming Conventions

Prefer names that explain meaning, not implementation noise.

Good:

trend_ma
fast_ma
slow_ma
trend_up
trend_down
momentum_up
breakout_ready
volatility_ok

Weak:

x1
calc2
signal_thing
my_var
temp

Distinguish Measurements From Conditions

A useful pattern is to name numeric series and boolean conditions differently.

Numeric series:

rsi_value = rsi.evaluate(close, 14)
atr_value = atr.evaluate(high, low, close, 14)
trend_ma = sma.evaluate(close, 50)

Boolean conditions:

rsi_bullish = above(rsi_value, 55)
volatility_ok = above(atr_value, 0.0)
price_above_trend = above(close, trend_ma)

That keeps the script mentally clean.

Use Variables To Express Market Logic

This:

price_above_trend = above(close, trend_ma)
momentum_confirmed = above(rsi_value, 55)

is much easier to review than:

above(close, trend_ma) and above(rsi_value, 55)

both in code review and later when you revisit the strategy.

Reusing Variables Across Multiple Outcomes

Variables help when long and short logic share common pieces.

fast_ma = sma.evaluate(close, 20)
slow_ma = sma.evaluate(close, 50)
rsi_value = rsi.evaluate(close, 14)

trend_up = above(fast_ma, slow_ma)
trend_down = below(fast_ma, slow_ma)
momentum_up = above(rsi_value, 55)
momentum_down = below(rsi_value, 45)

patterns {
    trend_up and momentum_up -> UP(confidence: 0.82)
    trend_down and momentum_down -> DOWN(confidence: 0.82)
    default -> HOLD(confidence: 0.95)
}

Variables In Config Context

Pair and timeframe are part of the environment, and config sets what the strategy targets.

config {
    pairs: ["XAUUSD"]
    timeframes: ["1m"]
}

Keep the script consistent with the markets it is written for. Do not call a script gold_scalper and then leave the config vague.

Common Mistakes

1. Recomputing The Same Thing Repeatedly

Avoid:

patterns {
    above(close, sma.evaluate(close, 50)) -> UP(confidence: 0.70)
    below(close, sma.evaluate(close, 50)) -> DOWN(confidence: 0.70)
    default -> HOLD(confidence: 0.95)
}

Better:

trend_ma = sma.evaluate(close, 50)

patterns {
    above(close, trend_ma) -> UP(confidence: 0.70)
    below(close, trend_ma) -> DOWN(confidence: 0.70)
    default -> HOLD(confidence: 0.95)
}

2. Naming A Condition Like A Number

Avoid:

rsi_55 = above(rsi_value, 55)

Better:

momentum_up = above(rsi_value, 55)

3. Stuffing Everything Into patterns

Avoid building unreadable one-liners when named variables would make intent obvious.

A Clean Example

#!pyp/1.0
engine: pyp-1.0

import indicators.sma
import indicators.rsi
import indicators.atr
import functions.above
import functions.below
import functions.cross

strategy "clear_variables" {
    fast_ma = sma.evaluate(close, 20)
    slow_ma = sma.evaluate(close, 50)
    rsi_value = rsi.evaluate(close, 14)
    atr_value = atr.evaluate(high, low, close, 14)

    trend_up = above(fast_ma, slow_ma)
    trend_down = below(fast_ma, slow_ma)
    long_momentum = above(rsi_value, 55)
    short_momentum = below(rsi_value, 45)
    volatility_ok = above(atr_value, 0.0)
    long_trigger = cross(close, fast_ma)
    short_trigger = cross(fast_ma, close)

    patterns {
        trend_up and long_momentum and long_trigger and volatility_ok -> UP(confidence: 0.84)
        trend_down and short_momentum and short_trigger and volatility_ok -> DOWN(confidence: 0.84)
        default -> HOLD(confidence: 0.96)
    }
}

Rule Of Thumb

If a non-programmer trader can read your variable names and roughly explain what the script is doing, your naming is good.

If not, simplify it.