Institutional Accumulation & Distribution Analyzer

Raw price action often hides the intentions of larger market participants. This skill evaluates volume, market structure, and order-flow data to identify evidence consistent with accumulation, distribution, or absorption. It moves beyond basic indicators to assess the relationship between trading effort and price result, helping you distinguish between ordinary rotations and potential institutional footprints.

What it does

• Market structure mapping identifies trading range boundaries, trend direction, and price location relative to higher-timeframe zones.
• Volume participation analysis compares current volume against rolling medians and session-specific baselines to detect abnormal activity.
• Accumulation and distribution screening evaluates support defense, high-volume absorption, and relative strength against benchmarks.
• Effort-versus-result assessment analyzes if high volume or aggressive buying/selling is resulting in meaningful price progress or being absorbed.
• Participation scoring provides a qualitative confidence read based on price, volume, VWAP, and available order-flow metrics.

How it works

1. Data ingestion confirms the instrument, venue, timeframe, and asset class while validating OHLCV and optional flow data.
2. Context mapping establishes the current market phase, including range duration and key support/resistance levels.
3. Signal evaluation runs multi-session checks for absorption, volume-price divergence, and relative performance.
4. Classification assigns a qualitative read (e.g., Accumulation-leaning) with a specific confidence level based on data completeness.

Frameworks & tools

This skill utilizes standard OHLCV data, volume profiles, VWAP, and order-flow metrics such as CVD and bid/ask delta. It is designed to work with data exported from TradingView, MetaTrader, or exchange APIs.

Why this beats prompting it yourself

Generic prompts often hallucinate institutional "certainty" or miss the nuance of volume absorption. This skill uses a structured multi-factor framework that forces the analysis to consider alternative explanations and data gaps, ensuring a disciplined, evidence-based read.

Use cases

• Range analysis to determine if a multi-week consolidation is more likely to resolve as a breakout or a fakeout.
• Post-earnings review to see if high-volume selling was met with passive absorption at key levels.
• Relative strength checks to find instruments holding value during broader market drawdowns.
• Order-flow auditing for crypto or futures traders who need to synthesize CVD and open interest with price action.

Known limitations

Cannot identify specific trader identities or hidden dark pool activity. Signals are probabilistic hypotheses based on observable public data and do not guarantee future price direction.