🌲 PineScript to Python Translator

This skill is designed to take raw TradingView PineScript files (.pine) and rigorously deconstruct them into Python-native components so they can be optimized using vectorization and tools like Optuna.

When to use this skill

Use this skill when you want to migrate a backtest from the TradingView ecosystem into a headless Python environment. This is critical for running multi-fold Walk-Forward Analysis (WFA) and Monte Carlo simulations that TradingView cannot handle.

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

The output should be a single Python file containing:

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.