A walkthrough of a TradingView Pine Script implementation of the MTF RVOL concept.

Educational — the script is an indicator, not a trading system.


What the Script Does


`MTF RVOL Pro v4` is a Pine Script (v6) indicator that plots Relative Volume across **six

timeframes** on one chart:

  • TF1 = 1m, TF2 = 5m, TF3 = 15m, TF4 = 60m, TF5 = 240m, TF6 = Daily

  • It overlays effort/result scoring, support/resistance proximity (in ATR units), and a

    Volume Profile (VPOC) layer, then exposes a strategy engine for signal generation.


    The Repainting Problem (and the v4 Fix)


    Repainting happens when an indicator uses `lookahead` or future bars, so signals appear

    "perfect" in hindsight but vanish live. v4's changelog explicitly targets this:

  • Fixed nearest-TF logic so it reads the **closed** higher-timeframe bar, not the forming one.
  • Configurable TF weights so you control how much each timeframe contributes.
  • Strategy engine signals only fire on **confirmed** bars.

  • Core Inputs


    
    rvolLen = 20            // RVOL SMA length
    effortThreshold = 0.12 // min effort to count as committed
    rangeLookback = 20     // S/R range
    supportNearAtr = 0.60   // near-level ATR distance
    

    Why This Matters for Quant Research


    A Pine Script is the *fastest* way to validate an idea visually before committing to a Python

    backtest. The MTF RVOL concept — volume commitment across timeframes — transfers directly to

    the 117-column Python feature set (see the `h1_trend`, `h1_vol_ratio`, `h1_range_ratio`

    features). The indicator is the hypothesis; the ML pipeline is the test.


    Honest Limitation


    Pine Script cannot reproduce the options-surface or order-flow features (OI, PCR, greeks)

    that the Python build uses. Treat the script as a **directional filter**, not a standalone

    system. The real edge in the research build came from combining MTF context with options

    data — something no single indicator captures.


    *Educational only. Not investment advice.*





    More From Shakti Tiwari


  • 🌐 **Websites:** [shaktitiwari.github.io/shakti-tiwari-nse](https://shaktitiwari.github.io/shakti-tiwari-nse) · [OptionTradingWithAI.in](https://optiontradingwithai.in)
  • 📚 **Books:** *Build Your Own AI* · *Option Trading with AI* (on [Amazon India](https://www.amazon.in/)) — practical guides from a Nifty options trader and ML practitioner.
  • 💬 **Community:** [Join the Discord](https://discord.gg/shaktitiwari) for live discussion, code, and research.
  • 💻 **Code:** [GitHub/shaktitiwari](https://github.com/shaktitiwari) — open research, models, and tools.
  • Educational only — not SEBI-registered investment advice. NISM-Series-XII certified; not a SEBI-registered Research Analyst. Content is educational only.

    Home | About