🌲 PineScript to Python Translator

.pine

When to use this skill

Workflow

1. Deconstruction & Classification (The IR Build)
   • Parse the

     .pine

     script to identify

     input()

     ,

     input.int()

     ,

     input.float()

     , and

     input.bool()

     .
   • Classify parameters into three buckets:
     • Signal: Parameters that dictate entries (e.g.,

       length

       ,

       crossover_threshold

       ).
     • Risk: Parameters that dictate exits (e.g.,

       stop_ticks

       ,

       trail_offset

       ).
     • Display: Parameters used only for plotting/UI (discard these).
2. Boundary Extraction
   • For every Signal and Risk parameter, extract the

     minval

     ,

     maxval

     , and

     step

     if provided.
   • Format these into Optuna trial suggestions (e.g.,

     trial.suggest_int('length', 10, 50)

     ).
3. Logic Translation
   • Translate PineScript technical analysis functions (

     ta.sma

     ,

     ta.ema

     ,

     ta.rsi

     ) into their

     pandas-ta

     or

     numpy

     equivalents.
   • Vectorize the entry and exit conditions. Do not use standard

     for

     loops over rows unless path-dependency strictly requires it. Use

     np.where

     and

     .shift()

     wherever possible.
4. Hardening & Auditing
   • Repaint Risk: Scan the translation for anything relying on the current unclosed bar data. Force the use of

     .shift(1)

     for signal generation.
   • Division by Zero: Wrap all denominators in a

     np.maximum(denominator, 1e-8)

     guard to prevent NaN explosions during optimization.

Output Format

1. An

   extract_features(df, params)

   function that builds all the indicators based on a parameter dictionary.
2. A

   generate_signals(df)

   function that creates a

   signal

   column (1 for Long, -1 for Short, 0 for Flat).
3. A

   get_optuna_space(trial)

   function that returns the hyperparameter search space dictionary.