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Institutional Rolling VWAP with RVOL

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Institutional Rolling VWAP (IRVWAP) combines a time-based Rolling VWAP with liquidity-adjusted volatility bands, ADV analysis, and relative volume metrics to estimate institutional fair value and identify accumulation, distribution, and price extremes.

Unlike a standard VWAP, which only tracks the volume-weighted average price over a rolling window, IRVWAP adapts its volatility bands using liquidity and volume participation metrics inspired by institutional trading behavior.

Institutional Logic
Research on market impact suggests that large orders are typically executed over multiple days or weeks rather than in a single session.
IRVWAP incorporates this idea by comparing recent trading activity against long-term average volume.​

When market participation increases:
Bands contract;
Price deviations become more meaningful;

When participation decreases:
Bands expand;
Noise is filtered more aggressively.

Inputs
• Rolling VWAP time window;
• ADV length;
• Flow length;
• Adaptive band toggle;
• Standard deviation multipliers.

Features:
• Rolling VWAP based on a time-based moving window;
• Volume-weighted standard deviation bands;
• Adaptive volatility bands based on Relative Volume (RVOL);
• Average Daily Volume (ADV) monitoring;
• Institutional Activity Score (Z-Score);
• Designed to identify potential accumulation and distribution zones.

Suggested Uses
| Condition | Interpretation |
| Above +2σ & RVOL > 1.3 | Bullish continuation |
| Above +3σ & RVOL > 1.5 | Strong bullish continuation |
| Above +2σ & RVOL < 0.8 | Bearish mean reversion |
| Below -2σ & RVOL > 1.3 | Bearish continuation |
| Below -3σ & RVOL > 1.5 | Strong bearish continuation |
| Below -2σ & RVOL < 0.8 | Bullish mean reversion |
| Between ±1σ | Fair value zone |

Code is bugged, fix the scale: right click --> fix the axis

Based on the papers:
Slow decay of impact in equity markets - Brokmann et al. (2014)
VWAP Execution as an Optimal Strategy - Takashi Kato (2017)
Mean Variance Optimal: VWAP Trading - McCulloch & Kazakov (2012)

Ps: I built this code with zero coding knowledge using GPT. If I made mistakes or some things does not make sense please contact me.

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