Video summary

कौनसे शेयर Stocks खरीदें? अत्याधुनिक स्टॉक स्क्रीनर खुद बनाना सीखें। Stock screener in Google Colab

Main summary

Key takeaways

Finance

Finance-focused summary (stock screener built in Google Colab)

Purpose / what the screener targets

  • The author teaches a “state-of-the-art” stock screener usable across multiple trading styles (cash delivery, swing trading, margin/MTF, and F&O-related liquidity).
  • The implementation discussed is mostly for identifying “breakout” setups using:
    • price action,
    • moving averages,
    • and a custom metric called CAR.
  • The screener universe is built from F&O underlying stocks that satisfy liquidity/float/size criteria, then filtered again by technical conditions.

Instruments / universe / tickers mentioned

Indexes / segments

  • Nifty 50
  • Nifty 100
  • Nifty 250
  • Nifty Bank
  • Nifty Financial Services Index
  • Nifty Midcap Select (referred to as “Nifty Mid Select”)
  • Nifty Next 50
  • F&O / futures & options (F&O)

Specific stock names (examples shown in the video output)

  • Lykam (name shown; ticker not clearly readable)
  • LKM (explicitly referenced with price/DMA-distance logic later)
  • Lodha (shown with a 17.63% figure; context suggests distance/position vs a DMA-based threshold)
  • Elkem (appears in a ranking list)

Note: Subtitles show some names, but ticker symbols are not consistently captured in the text.

Key selection criteria (screening methodology)

Step A — Universe selection: “210 F&O stocks”

  • The author describes selecting a base list of ~210 F&O underlying stocks from the NSE FO Stocks List (NSC/NSE site; spelling varies in subtitles).

Inclusion logic for the F&O universe (liquidity/float/size)

  • High trading volume
  • High fluctuations
  • Average delivery value > ₹35 crore over the last 6 months
  • Free-float holding (non-promoter) > 20%
  • Free-float value > ₹1500 crore
  • Market cap condition: in the top 500 companies
  • These conditions overlap heavily with Nifty 50 / Nifty 100 constituents and other index F&O underlyings.

Data source mentioned for the 210-stock list

  • “NSC/ NSE FO Stocks List” page (author suggests searching via Google)

Step B — Breakout/entry filters inside the screener

  • The screener aims to catch turning points after a long decline from “year high”, using a metric related to cumulative averages.

1) CAR (Cumulative Average Reversal) must turn positive

  • CAR stands for Cumulative Average Reversal.
  • Subtitle logic:
    • If the cumulative average has been falling, avoid buying.
    • A buy condition occurs when the cumulative average reverses.
  • Requirement: CAR should be positive for the past 10 days continuously.
  • The author emphasizes:
    • it should not trigger from “year low”, to avoid repeated new-lows behavior;
    • instead it monitors reversal after decline from year high.

2) Moving average trend filter (price must be above key DMAs)

A buy signal requires:

  • Price > 30-day DMA
  • Price > 50-day DMA
  • Price > 200-day DMA

3) “Avoid buying too close to extended highs” filter

After crossing above the 200-day moving average, the stock must not have run up too far:

  • Distance from 200-DMA ≤ 10%
    • (subtitles: “should not have gained more than 10% after crossing its 200-day moving average”)

Output / ranking / files / automation behavior

Ranking

  • Results are ranked by shortest distance from the 200-day DMA (closest first).

Example numeric output shown

  • For LKM (as of 19th):
    • Current price above 30/50/200 DMA
    • Distance from 200-DMA = 0.96%
  • Lodha shown with 17.63% (implied to be its distance/percentage relative to the DMA logic/threshold).

File output

  • The code generates an Excel file containing:
    • Date
    • Stock
    • Current price
    • 30/50/200 DMA values
    • Distance from 200-DMA
    • CAR status (positive/negative)

Timing / update rule

  • Runs only when you press “play” in Google Colab.
  • No automatic daily update—user must rerun the notebook the next day.

Practical workflow steps (as taught): “4 steps / ~5 minutes”

  • Open Google Colab
  • Create New Notebook and rename it
  • Paste the provided Python code (uses Yahoo Finance for historical data)
  • Run code (press play) → view results and download the Excel file

Risk management / trading rules discussed (MTF course logic)

Position sizing / dealing cadence (MTF)

  • Rule of thumb in the course:
    • Do not buy more than one share in a day
    • Do not sell more than one share in a day
  • Rationale described:
    • Buying one per day allows averaging opportunities during prolonged declines.
    • Selling one per day allows profit booking across days during a bull run.

Profit target mentioned

  • 6.28% (repeated as the MTF profit-taking target)

Disclosures / disclaimers (explicit)

  • Educational documentary; not investment advice.
  • Not a recommendation to buy the filtered stocks.
  • Viewers should consult a SEBI-registered investment advisor / registered investment advisor.
  • No guaranteed returns; stock market risk acknowledged.
  • Legal disclaimer near the end:
    • No system guarantees 100% profit
    • Losses are possible
    • Creator not liable for damages
  • Additional disclosure:
    • Creator claims personal involvement (invests/trades) and may have personal interest/holdings.

Presenters / sources (as stated)

Presenter

  • Mahesh Chandra Kaushik
    • SEBI registered Research Analyst; channel host

External sources / data mentioned

  • Yahoo Finance
    • Used via a Python library to download historical data
  • NSE/NSC “FO Stocks List”
    • Used to form the ~210-stock F&O underlying universe
  • SEBI rules / YouTube policy
    • Mentioned for compliance/disclosures

Original video