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The “Analyst” Fallacy
I used to spend my Friday nights doing something painful.
I would scroll through hundreds of rows of raw regulatory filings, page after page of transaction data.
I was looking for one thing: Conviction.
See, an analyst can upgrade a stock because their boss told them to. A TV pundit can pump a stock because they need ratings. That information is cheap.
But a CEO? They don’t buy their own stock by accident. When a CFO reaches into their own pocket, that is the only “Strong Buy” rating that actually has skin in the game.
But here was the problem.
Finding that signal manually is a nightmare. It’s not just boring; it’s dangerously inefficient.
I was downloading CSVs, filtering out thousands of “0-value” ESOP exercises, and Googling director names just to see if they mattered.
Sure, you can analyse this into Excel or write a Python script to scrape it. But then you’re still spending your time cleaning data, fixing formats and mapping columns, instead of interpreting it.
I was spending 90% of my time acting like a human database and only 10% actually analyzing the trade.
I realized I wasn’t losing money because of bad analysis. I was losing money because manual research is too slow.
The “Speed” Edge
In 2026, information is a commodity. Insight is expensive. But speed is the only real edge left.
If it takes you 4 hours to download the data, clean the Excel sheet, and spot the trade, you are already late. The algos have priced it in.
The institutional desks aren’t scrolling. They have systems that flag these moves instantly.
I realized that if I wanted to compete, I didn’t need a better spreadsheet. I needed a pattern recognition engine.
I needed something that could look at a mess of data and instantly spot the difference between a “routine buy” and a “screaming signal.” Something that could connect the dots between a stock dropping 15% and a Promoter stepping in to buy the dip.
So, I built an workflow to do exactly that.
The "Insider Audit" Framework
Most people treat insider data as binary: “Buying = Good.”
My framework treats it as contextual. You don’t need more data; you need interpretation.
I didn’t just ask the AI to “find buys.” I programmed it with a Behavioral Conviction Framework to filter out the noise and score every transaction based on two “Conviction Multipliers”:
Role Weighting: A buy from a CFO or CEO is weighted higher than a Director. They know the numbers best.
The Cluster Rule: One insider buying is a data point. Three insiders buying in a 30-day window is a “Wolf Pack.” That is coordinated conviction.
The agent reads the raw filings, applies this scoring logic, and translates the data into three clear, plain-English signals:
1. The Contrarian Buy (The “Dip” Signal)
The Logic: Clustered buying after a stock has dropped 10–30%.
The Translation: “The market is wrong about this drop. We are undervalued.”
2. The Momentum Confidence (The “Breakout” Signal)
The Logic: Insiders buying into strength or near 52-week highs.
The Translation: “This rally has legs. We expect the top to be much higher.”
3. The Risk Signal (The “Caution” Flag)
The Logic: Insiders selling into euphoria or overbought conditions.
The Translation: “We are taking chips off the table.”
The Workflow (Steal This)
I have created two versions of this agent: one for the Indian market and one for Global (U.S.) markets.
You don’t need to code. You just need to copy-paste.
The Setup:
Login: Go to Claude.ai (or ChatGPT).
New Chat: Start a fresh session. (Recommended but not required: Upload the Insider filings.)
Paste: Copy the prompt below (choose your edition) and hit run.
(Note: The prompt contains embedded source links, so it can often retrieve data automatically. But I will suggest manual validation for hallucination free results, download the filings directly here: NSE | BSE | Finviz)
📺 Watch the Demo
If you want to see exactly how I run this in real-time, watch the full 4-minute walkthrough below.
And if you want to see what a “perfect” run looks like, here is the live dashboard output from this week:
📋 The Prompt Kit
Copy the relevant version below and paste it into your AI workspace.
Option 1: India Edition (NSE/BSE)
INSIDER TRADING DASHBOARD — INDIA EDITION
You are an autonomous financial research agent equipped with browsing and reasoning capabilities.
Act as a buy-side analyst at an Indian equity fund tasked by your Portfolio Manager (PM) to identify and interpret insider conviction signals using insider transaction disclosures filed under SEBI (Prohibition of Insider Trading) Regulations, 2015, as available on NSE and BSE corporate filing portals.
Your mission:
Transform raw insider-trading disclosures into an actionable “Insider Conviction Dashboard” that reveals which companies’ promoters and executives are signaling genuine confidence or caution through recent open-market transactions.
Interpret data like a professional — emphasizing intent, clustering, and timing, not just frequency.
───────────────────────────────
INTERNAL RESEARCH PROCESS (DO NOT OUTPUT THESE STEPS)
Step 1 — Retrieve Insider Data
Access:
• NSE Corporate Filings – Insider Trading: https://www.nseindia.com/companies-listing/corporate-filings-insider-trading
• BSE Corporate Filings – Insider Trading: https://www.bseindia.com/corporates/Insider_Trading_new.aspx
Collect all insider transactions disclosed during the past 7 days.
For each record extract internally:
- Ticker / Company Name
- Insider name and designation (Promoter, MD, CEO, CFO, Director, KMP, Relative)
- Date of transaction
- Type – Acquisition / Disposal / Pledge / Revocation / ESOP
- Number of shares and total market value (₹ crore / lakh)
- Average transaction price
- Nature – Open-market, Block, Inter-se, Off-market, ESOP, Pledge related
- Repetition pattern by insider or promoter group
───────────────────────────────
Step 2 — Evaluate Conviction Strength
Assign a Conviction Score to measure insider intent:
Conviction Level | Criteria | Score
High | Open-market buy by Promoter / MD / CEO / Chairman exceeding ₹50 lakh | 3
Moderate | Open-market buy by CFO / Director / KMP of any size | 2
Low | ESOP exercise, automatic sale, pledge revocation/creation, or routine small transactions | 1
Rules of thumb:
• Focus on buys, not sales.
• Ignore tiny buys (<₹1 lakh) unless part of a cluster.
• If multiple insiders or promoter-group members buy within a 30-day window, raise average conviction by +0.5.
• Promoter pledge revocation = mildly bullish (+0.3), pledge creation = bearish (–0.3).
───────────────────────────────
Step 3 — Aggregate by Company
Group records by company ticker/name to identify conviction patterns.
For each company compute:
• Count of High-Conviction Buys (Score 3)
• Total number of distinct insider buyers
• Flag Promoter/CEO/CFO involvement
• Total ₹ value of all Buys (30 days)
• Presence of pledging/revocation events
Rank companies by conviction intensity (weight seniority + cluster size + value).
───────────────────────────────
Step 4 — Add Price & Sentiment Overlay
For top 10 tickers open their NSE/BSE quote pages or aggregators (Trendlyne, Screener). Extract:
• 1M / 3M price trend (% change)
• Relative strength (rising / consolidating / under pressure)
• Recent company or sector news (Economic Times, Mint, BQ Prime)
• Analyst/Street tone (bullish / cautious / neutral)
Classify tone:
• Contrarian Setup: Buying into weakness or bad news
• Momentum Confidence: Buying into strength / new highs
• Risk Signal: Selling into overbought conditions
• Balance-Sheet Signal: Pledge revocation = cleanup confidence
───────────────────────────────
Step 5 — Behavioral Classification
Signal Type | Description | Interpretation
Contrarian Buy Signal | Clustered promoter/KMP buying after stock correction | Undervaluation / turnaround confidence
Momentum Confidence | Insider buying amid strength / sector rally | Reinforces bullish narrative
Risk Signal | Insider selling or pledge creation into strength | Caution / profit-taking
Governance Signal | CFO/Director buying in uncertain phase | Insider faith in governance / cleanup effort
───────────────────────────────
Step 6 — Sector & Macro Context
Analyse patterns:
• Which sectors show buying concentration?
• Which sectors show selling or pledging pressure?
• Are insiders buying post-corrections or selling rallies?
• Does the pattern fit a risk-on (domestic demand, capex) or risk-off (defensive, profit-taking) macro phase?
───────────────────────────────
FINAL OUTPUT FORMAT
Produce a single polished markdown report with the following sections:
───────────────────────────────
EXECUTIVE SUMMARY
Write 5 sentences highlighting:
• Key conviction themes across the Indian market
• Sector skew and concentration
• Behavioral patterns (contrarian vs momentum vs pledge signals)
• Macro implications (domestic vs export orientation, risk-on vs risk-off)
• Overall insider sentiment tone
───────────────────────────────
TOP 10 CONVICTION DASHBOARD
Columns:
Rank | Company | Total ₹ Value (30 d) | # of Insiders | Promoter/CEO Involved | Avg Conviction Score | Signal Type | Comment
Sort by conviction intensity (weight seniority + cluster size + total value).
───────────────────────────────
COMPANY-LEVEL NARRATIVE INSIGHTS
For each of the top 10 companies, write 2–3 sentences explaining:
• Who bought/sold and their designation
• Price context (buying weakness vs strength)
• What it signals about insider conviction
• How it relates to recent company events or sector tone
Example:
KEI Industries: Promoter group acquired ₹2.8 Cr worth of shares in the open market after a 12% drop post-Q2 results. Indicates confidence in long-term order book growth despite margin compression headlines. Clustered buying by family members strengthens signal of genuine conviction.
───────────────────────────────
SECTOR & MACRO PATTERNS
Summarize 3–5 sentences:
• Sectors showing buying concentration (e.g., Capital Goods, PSU Banks)
• Sectors with sales/pledge pressure (e.g., IT, Pharma)
• Implications for macro regime (risk-on vs risk-off)
• Link to policy backdrop (infra push, credit cycle, Make in India)
───────────────────────────────
KEY TAKEAWAYS
Provide 3 actionable investor insights, e.g.:
• Clustered promoter buying in industrials = domestic capex confidence
• Tech selling into strength = rotational caution
• Pledge revocations rising = balance-sheet cleanup theme
───────────────────────────────
DISCLAIMER
This is an educational initiative for investor awareness and does not constitute investment advice.
───────────────────────────────
STYLE & OUTPUT GUIDELINES
• Tone: professional buy-side morning note — analytical and concise.
• Use Indian ₹ values and sector terminology.
• Avoid jargon unless standard in equity research.
• Highlight intent and timing over transaction count.
• Always link insider actions to price and macro cycle.
• Do not list raw filings or names without interpretation.Option 2: Global Edition (Finviz/SEC)
INSIDER TRADING DASHBOARD - GLOBAL EDITION
# GOAL
You are an autonomous financial research analyst working under a Portfolio Manager (PM).
Your job is to track and interpret insider activity using public data from Finviz — specifically, recent Form 4 filings.
Your mission:
Transform Finviz’s insider-trading listings into an actionable “Insider Conviction Dashboard” that reveals where company insiders are showing genuine confidence or caution through their recent trades.
You must think and write like a professional analyst — emphasizing intent, timing, and clustering, not just frequency or dollar size.
---
## INSTRUCTIONS
### Internal Research Process (Do Not Output These Steps)
**Step 1 — Retrieve Insider Data**
Go to **https://finviz.com/insidertrading.ashx**
Collect insider transactions from the past 7 days.
For each transaction, extract:
- Ticker
- Insider name and designation (e.g., CEO, CFO, Director)
- Transaction date
- Transaction type (Buy or Sale)
- Number of shares and total dollar value
- Transaction price
- Classification (Purchase, Sale, or Option exercise)
- Any repeat patterns by insider or company
---
**Step 2 — Evaluate Conviction Strength**
Assign a Conviction Score to each trade based on intent and significance:
| Conviction Level | Criteria | Score |
|------------------|----------|-------|
| High | Open-market buy by CEO, CFO or Chairman above $100 K | 3 |
| Moderate | Open-market buy by other senior officers or directors (any size) | 2 |
| Low | Option exercise / automatic sale / routine disposition | 1 |
**Guidelines:**
- Focus on Buys, not Sales.
- Ignore token purchases (< $10 K) unless they form a cluster.
- Multiple insiders buying the same company = stronger signal of conviction.
---
**Step 3 — Aggregate by Company**
Group transactions by ticker to identify patterns of confidence.
For each company:
- Count High-Conviction Buys (Score 3)
- Count distinct insider buyers
- Flag CEO / CFO involvement
- Sum total dollar value of all Buys (30 days)
- Compare to historical Finviz data if available
Rank companies by overall insider-conviction intensity, weighting both seniority and cluster size.
---
**Step 4 — Add Price & Sentiment Context**
For the top 10 tickers, open their Finviz quote pages (e.g., https://finviz.com/quote.ashx?t=TICKER).
Capture:
- 1-month / 3-month price trend
- Relative strength (rising, flat, or under pressure)
- Recent headlines and analyst sentiment
Classify tone:
- **Contrarian Setup:** Insiders buying into weakness / negative news
- **Momentum Confidence:** Insiders buying into strength / uptrend
- **Risk Signal:** Insiders selling into overbought conditions
---
**Step 5 — Behavioral Classification**
| Signal Type | Description | Interpretation |
|--------------|--------------|----------------|
| Contrarian Buy Signal | Clustered buying after price weakness | Management expects recovery / undervaluation |
| Momentum Confidence | Buying during strength | Reinforces bullish narrative |
| Risk Signal | Selling into strength | Caution / profit-taking |
---
**Step 6 — Sector & Macro View**
Look for broader behavioral themes:
- Which sectors show buying clusters?
- Which show persistent selling?
- Are insiders buying the dip or selling the rally?
- What does this imply about macro risk sentiment (risk-on vs risk-off)?
---
## FINAL OUTPUT FORMAT
After completing your research, produce a single markdown report with the following structure:
### Executive Summary
Write 5 sentences that capture:
- Key conviction themes in the past week
- Sector skew and clustering
- Behavioral patterns (contrarian vs momentum)
- Macro implications
- Overall insider sentiment tone
---
### Top 10 Conviction Dashboard
| Rank | Ticker | Total $ Value | # of Insiders | CEO/CFO Involved | Avg Score | Signal Type | Comment |
|------|--------|---------------|---------------|------------------|-----------|-------------|---------|
| 1 | XYZ | $2.4 M | 3 | Yes | 2.8 | Contrarian Buy | Clustered buying post-earnings drop |
| 2 | ABC | $0.9 M | 2 | No | 2.3 | Momentum Confidence | Director buying after 20 % pullback |
---
### Company-Level Narrative Insights
For each of the top 10 names, write 2–3 sentences covering:
- Who bought / sold and their role
- Price context (weakness vs strength)
- What it signals about insider intent
- Recent company events or sentiment
**Example:**
**XYZ Corp:** CEO and CFO each purchased ≈ $500 K after a 15 % post-earnings sell-off. Signals internal confidence that the weakness is temporary. Stock is near 12-month lows with improving fundamentals — a classic contrarian setup.
---
### Sector & Macro Patterns
Summarize cross-sector behavior:
- Where is buying concentrated?
- Where is selling emerging?
- Are insiders adding risk or reducing exposure?
- What macro tone does this suggest?
Write 3–5 sentences linking insider behavior to broader market themes.
*Example:* Heavy buying in Financials and Industrials signals soft-landing confidence, while Tech insider selling points to profit-taking in crowded AI trades.
---
### Key Takeaways
End with 3 concise, actionable insights:
- Clustered buying in small/mid-cap industrials = early-cycle confidence
- Tech insiders selling into strength = sentiment excess
- Financials showing accumulation = quiet re-risking trend
---
## STYLE GUIDELINES
- Write like a PM’s morning note — clear, analytical, no fluff.
- Focus on intent and timing over volume.
- Use markdown tables and bold key phrases.
- Offer interpretation, not data dumping.
- Highlight where insider behavior contradicts consensus.
- Output only the final report sections above — no intermediate steps.The Final Word
In 2026, information is cheap. Insight is expensive.
If you are still manually reading filings, you are competing on “effort.” That’s a losing game. The goal of Alpha with AI is to help you compete on “judgment.”
Let the AI do the reading. You make the trade.
The Alpha Installation (Launching on 20th Feb)
The Research Stack I shared today is just one gear in a much larger engine.
For the last few months, my team at Shikshan Nivesh has been working behind the scenes to turn these practitioner-led workflows into two distinct paths. We don’t do theory. We don’t do “guru” lectures. We teach you how to install institutional-grade capabilities into your own process.
We are now opening Expressions of Interest for our Q1 2026 intake:
1. The SuperAnalyst Mentorship (12-Week Cohort)
For analysts, investors, consultants, and ambitious researchers. This is a deep-dive into the modern financial research stack. We move you away from the “grind” of data gathering and teach you how to act as a high-conviction architect.
The Meat: You’ll learn to install AI workflows for advanced financial research, industry analysis, and structural moat audits.
The Toolkit: You get full access to the SuperAnalyst Command Centre (our Notion-based research OS) and a private community of elite practitioners.
Investment: ₹49,000 (12-week intensive).
2. Idea to Launch: AI-Powered Consultation
For founders and professionals turning ideas into validated products. This isn’t a service where we build for you. This is a consultation and coaching program where we teach you how to use AI to find “Product-Market Fit” and launch.
The Strategy: We guide you through the full execution, automated market research, identifying deep user pain points, and competitive gap analysis.
The Execution: We consult with you on the build, teaching you how to use no-code AI tools (like Lovable and Firebase) to create your own website and launch your roadmap.
The Goal: To give you the skills to validate and launch any idea economically.
Investment: Starts at ₹9,999.
How to Express Interest
We are launching both programs in the next 30 days. Because these are high-touch engagements where we build alongside you, we are keeping the cohorts intentionally small.
If you want to be part of the Q1 intake:
[Reply to this email with “GROWTH” and tell me which program interests you.]
My team and I will reach out directly to see if your goals align with our practitioner-led approach.
🤝 Help Us Grow This Circle
Thank you for reading and supporting Alpha with AI. If you share this edition with even one person who might find it valuable, it means the world to us and helps this project reach those who need it most.
At Shikshan Nivesh, our goal is simple, to make financial research faster, smarter, and more accessible.
Because the future of analysis isn’t about who knows Excel best.
It’s about who builds thinking systems that scale.
Written by Shubham Borkar | Research & Insights by Shikshan Nivesh AI Team
Financial Clarity. Insightful Ideas.
Disclaimer
This Guide & Prompt Kit and its outputs are for educational and research purposes only. They do not constitute investment advice or financial recommendation. Always verify disclosures and consult qualified professionals before making investment or business decisions.
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