Are Retail Investors like us Accidentally Becoming Exit Liquidity in Every Bull Cycle?

General
1

Are we automatically selected by a smart system where retail like us provides exit liquidity to institutions every cycle?
Every rally now sees:

  • Heavy SIP inflows
  • SME IPO oversubscriptions
  • Momentum chasing
    and we see lot of news paper article says
  • Record demat accounts
  • F&O volumes dominated by small traders
  • and we talk about grey market ipo pricing also unlisted companies worth
    Are we trapped by smart heavy investors or opposite?
2

All of the above the retail investor is doing right? So why blame the institution.

If I remember right The SEBI chief had mentioned that retail investors are at a massive disadvantage compared to institutions, primarily due to the dominance of algorithmic trading. I think they introduced some rule then.

3

It’s not just “blame” — it’s a mechanism in markets where later buyers provide the buying interest that earlier holders need to sell without sharp price drops.
Here small investor like us have more advantage than institutional or large buyer. We havee small quantity of shares . If we can sell that before the massive quantity from large players then it will be helpful.
Identifying the large orders volume , trend reversal etc will mostly help us from huge loss.
Zerodha have kite MCP , claude have required infra to help us to identify the trend reversal. If these changes happen in the realtime market is updated via alert for our portfolio then we can do some decision making do the required changes in the portfolio.
But we need some help or guidence to create daily routine to identify this.
Zerodha conduct many boot camps , if one boot camp based on this will help us and tutorials on AI implementation using kite MCP will be great help.
Others can add more fine tune the topic…

4

Thinking about this for more than a few seconds,
isn’t it evident that any technology that a retail individual has access to
institutions have earlier access to a larger/more-efficient/faster version of that technology?

Then, what edge does any of this give the retailer? :thinking:

Adding all these complexities to the market,
adds the baseline participation costs (time and infrastructure)
and benefits entities with economies of scale (usually institutions not individuals).

Similar shortsightedness is in play when folks think that they will be able to profit if markets stayed open longer during the day/night or on weekends. It would only add to the costs/challenges faced by individuals, and at their cost, end-up further favoring institutions.

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5

More time the market is open - the more participation , yes this will increase more cost to companies or larger institutions not for individual investors .
An example I can tell you , Since I am working in a profession where I get the fews day only to participate in the market . GTT/ATO or some times mobile app help to do some trades. But when market available in the 7 - 10 PM ,I can check my protfolio and do the required changes …

1 Like
6

That’s just wishful thinking.
It might, it might not either.

What leads you to beleive that
the market conditions present in a 6 hour per day market,
will be extended to a 9-hour-per-day market? :thinking:

Is it possible (or even likely) that
extending operating hours will lead to requiring people to
participate 9 hours a day (instead of just 6)
to eek out the same edge/volume/value that they currently do? :sweat:

Also, while the cost for an institution to operate additional hours in the day is limited to monetary costs (cost of additional technology to scale, cost of additional folks hired to and coordination overheads), those 3 hours in a day are now gone from the hands of an individual who previously could have otherwise engaged in some other productive activity (or spent in preparation for market participation).

Even if an individual attemtps to mimic technology adoption to scale,
the fixed costs associated with tech,
being relatively larger to an individuals assets/income, compared to an institution,
such trends end-up bearing relatively more expensive for an individual.

7

Not sure , How I can create an automation using claude + Kite MCP.
My daily routine I am using this prompt to understand my portfolio

First run
Nifty 50:

  • Current price:
  • 5-day change %:
  • Distance from recent 20-day high:
  • 20 EMA slope (up/down/flat):
  • Advance/Decline ratio:
    Classify market state strictly as:
    Crash / Bearish / Bull / Neutral

Second Run

  1. Holdings table:

    • Stock/ETF Name
    • Quantity
    • Average Price
    • Current Price
    • Sector
    • Market Cap (Large/Mid/Small)
    • Beta (if available)

  2. Portfolio-level data:

    • Total capital invested
    • Current total value
    • Cash position
    • Benchmark index used (e.g., Nifty 50)
    • Benchmark return for same period
    • Time horizon of portfolio

  3. Optional:

    • Historical monthly returns (if available)
    • Max drawdown data (if available)

Then Third Run

  1. STRUCTURE ANALYSIS
  • Calculate weight of each holding
  • Identify concentration risk (top 3, top 5 weight %)
  • Sector allocation %
  • Market-cap allocation %
  • Equity vs ETF vs Cash distribution

  1. PERFORMANCE ANALYSIS

    • Total return %
    • Benchmark comparison (relative performance)
    • Best and worst contributors (weighted contribution)
    • Unrealised gain/loss per holding

  2. RISK ANALYSIS

    • Portfolio beta (weighted)
    • Volatility estimation (if return data provided)
    • Drawdown behaviour (if data provided)
    • Correlation clustering (if enough data)

  3. CAPITAL EFFICIENCY

    • Idle cash %
    • Capital trapped in underperformers (define underperformer as below benchmark return)
    • Risk-adjusted return proxy (Return / Beta)

  4. STRUCTURAL WEAKNESSES

    • Overexposure flags (>30% single sector, >40% top 3, etc.)
    • High beta concentration
    • Excess small-cap risk
    • Lack of diversification patterns

OUTPUT FORMAT:

A. Summary Snapshot (5–7 bullet points)
B. Allocation Tables (weights, sector %, cap %)
C. Risk Metrics Table
D. Concentration & Structural Flags
E. Data Gaps (if any)

Do not assume missing numbers.
If any data is missing, clearly state “insufficient data”.

8

@Siva,
Referring to Zerodha Kite Connect MCP Server | Docker MCP Catalog — can I view previous orders or transactions using get_order_history? If so, how can I retrieve the transaction breakdown to check whether any leg of a multi-legged transaction is in a loss state?