Understand stock screening for trades

4.2.8 · Stock-Market › What to Trade

Why Screening Matters (The Economics)

Time efficiency: Manual screening mein ~2 min/stock × 5,000 stocks = 166 hours lagte hain. Automated screening mein 10 seconds lagte hain.

Consistency: Insaan thak jaate hain, criteria bhool jaate hain, rules inconsistently apply karte hain. Screeners har stock par, har baar identical logic apply karte hain.

Opportunity cost: Jo har ghanta non-qualifying stocks par waste hota hai, woh ghanta high-probability setups analyze karne ya open positions manage karne mein nahi jaata.

The Two Screening Paradigms

1. Fundamental Screening (Value/Growth Investing)

Example fundamental screen (Value + Quality):

Filter 1: Market Cap > ₹500 crore (liquidity)
Filter 2: P/E < 12 (undervalued)
Filter 3: ROE > 18% (high quality)
Filter 4: Debt/Equity < 0.3 (safe balance sheet)
Filter 5: Revenue growth > 10% YoY (growth momentum)

Yeh kyun kaam karta hai: Tum quality businesses (ROE > 18%) ko discount prices (P/E < 12) par low risk ke saath (D/E < 0.3) khareed rahe ho. Value-quality combo historically outperform karta raha hai.

2. Technical Screening (Swing/Momentum Trading)

Example technical screen (Momentum Breakout):

Filter 1: Price >50-day MA (uptrend)
Filter 2: Volume today > 2× 20-day avg volume (breakout confirmation)
Filter 3: Close within 5% of 52-week high (near resistance)
Filter 4: ADX > 30 (strong trend)
Filter 5: Market cap > ₹1,000 crore (pump-and-dump se bachao)

Yeh kyun kaam karta hai: Tum woh stocks pakad rahe ho jo volume confirmation (institutional buying) ke saath new highs tod rahe hain, strong trends ke dauran. Momentum continuation edge.

Deriving a Screen from Strategy (First Principles)

Step 1: Apna edge hypothesis define karo

Example: "Oversold quality stocks mein mean reversion 3-6 months mein outperform karta hai."

Step 2: Hypothesis ko measurable filters mein translate karo

"Oversold" → RSI < 35 YA Price < 20-day BB lower band
"Quality" → ROE > 15%, consistent earnings growth
"3-6 months" → Daily data use karo, intraday nahi (time horizon se match karta hai)

Step 3: Universe boundaries set karo

Market cap > ₹500 crore (tumhari position size ke liye liquidity)
Average volume > 500,000 shares (slippage ke bina enter/exit kar sako)

Step 4: Output ko rank karo

Primary: RSI (sabse zyada oversold pehle)
Secondary: ROE (sabse zyada quality pehle)

Result: Top 10 stocks SABSE ZYADA oversold HIGHEST quality names hain → catalysts/news ke liye manual review.

Worked example Worked Example: Designing a Dividend Growth Screen

Strategy hypothesis: "Jo companies consistently dividends grow karti hain woh outperform karti hain kyunki unke paas pricing power, disciplined capital allocation, aur management confidence hoti hai." Filters mein translate karo:

Filter 1: Dividend yield > 2% (meaningful income)
Filter 2: 5-year dividend CAGR > 10% (growth, sirf yield nahi)
Filter 3: Payout ratio < 60% (sustainable, grow karne ki jagah)
Filter 4: ROE > 15% (dividends fund karne ke liye cash generate karta hai)
Filter 5: Debt/Equity < 0.5 (downturn mein dividends cut nahi karega)

Har filter kyun?

Yield > 2%: Growth stocks ko eliminate karta hai jo token dividends dete hain. Hume income + growth chahiye.
5Y CAGR > 10%: Payout grow karne ki historical commitment (management quality signal).
Payout < 60%: Agar payout 90% hai, grow karne ki jagah nahi hai. <60% matlab woh AUR pay kar sakte hain lekin retain choose karte hain—reinvestment mein confidence signal karta hai.
ROE > 15%: Dividends fund karne ke liye returns generate karne chahiye. Low ROE = dividend unsustainable hai.
D/E < 0.5: High debt → cash interest mein jaata hai, dividends mein nahi. Recession mein dividend cut ho jaata hai.

Output: NSE 500 par run karo → 12 stocks return karta hai → manually check karo: koi recent dividend cuts? Management credibility? Sector headwinds?

Yeh specific combo kyun kaam karta hai: Tumne yield (abhi income), growth (kal ki income), quality (sustainable business), aur safety (blow up nahi hoga) combine kiya hai. Har filter ek failure mode remove karta hai.

Common Screening Platforms

Platform	Best For	Key Feature
Screner.in	Indian fundamental	Free, clean financial data, custom screens
Chartink.com	Indian technical	Real-time intraday scans, pattern detection
Finviz	US stocks	Fast, visual momentum/value presets
TradingView	Global technical	Pine Script custom indicators, backtest screens

The Multi-Stage Screening Funnel

Example funnel (Momentum strategy):

Stage 1 (5,000 → 300):
- Price > ₹20 (penny stocks se bachao)
- Avg volume > 100,000 shares (500 shares trade kar sako without slippage)

Stage 2 (300 → 25):
- Price > 50 MA (uptrend)
- Volume spike > 2× average (breakout)
- RSI < 70 (abhi overbought nahi)

Stage 3 (25 → 5):
- News check karo: kya volume spike scandal ki wajah se hai? (eliminate karo)
- Chart check karo: clean breakout hai ya false break? (visual confirmation)
- Sector check karo: kya poora sector spike kar raha hai? (context)
- Position size: kya main is stop level par 1% risk kar sakta hoon? (risk management)

Funnel kyun matter karta hai: Tum computational power (stages 1-2) use karte ho 95% kaam ke liye, human judgment (stage 3) reserve karte ho un 5% ke liye jo sabse zyada matter karte hain.

Common mistake Common Screening Mistakes

Mistake 1: Over-optimization (curve-fitting)

Students kya karte hain: 15 filters add karte hain, 10 saal par backtest karte hain, paper par 95% win rate achieve karte hain.
Yeh sahi kyun lagta hai: Zyada filters = zyada precision, right?
Yeh galat kyun hai: Tumne noise fit ki hai, signal nahi. Har added filter sample size reduce karta hai. 15-filter screen sirf 3 stocks kabhi match kar sakta hai—pure luck.
The fix: 3-5 core filters use karo jo EK edge se aligned hon. Out-of-sample data par test karo (alag time period). Agar performance collapse ho jaaye, tumne overfit kiya.

Mistake 2: Base rates ignore karna

Students kya karte hain: Daily "stocks up 10%+ on high volume" screen karte hain, 50 stocks dhundhte hain, assume karte hain sab continue karenge.
Yeh sahi kyun lagta hai: Bada move + volume = momentum continue karna chahiye.
Yeh galat kyun hai: 10% spike ke baad continuation ka base rate ~40% hai (zyaadatar revert karte hain). Tumhara screen BAHUT SAARE false positives dhundhta hai.
The fix: Screening ke baad calculate karo: "Mere screen ne jo last 100 stocks dhundhe, unme se kitne actually continue hue?" Agar <50%, toh discriminating filters add karo (trend strength, sector momentum).

Mistake 3: Dynamic markets mein static screens

Students kya karte hain: 2025 bull market mein wahi screen use karte hain (jaise "P/E < 10") jo 2020 bear market mein use ki thi.
Yeh sahi kyun lagta hai: P/E < 10 kisi bhi market mein undervalued hai.
Yeh galat kyun hai: Bull markets mein, P/E < 10 often = broken business (itna sasta kyun hai?). Bear markets mein, P/E < 10 = opportunity. Context matter karta hai.
The fix: Market regime ke hisaab se thresholds adjust karo. Bull market: valuation tighten karo, momentum add karo. Bear market: valuation loosen karo, quality/safety add karo.

Building a Custom Screen: Step-by-Step

Example: "Post-Earnings Momentum" screen banana

Step 1: Hypothesis "Woh stocks jo earnings beat karte hain AUR guidance raise karte hain, woh 1-2 weeks tak upar continue karte hain kyunki analysts targets upward revise karte hain."

Step 2: Measurable criteria

Earnings announcement last 5 days mein
EPS consensus se >5% beat kiya
Forward guidance raised (manual check chahiye)
Announcement day par price >3% up
Announcement day par volume >2× average

Step 3: Implementation

Screner.in ya similar par:
1. Filter → Corporate Actions → Earnings Date (last 5 days)
2. Filter → EPS vs. Estimate > 5%
3. Filter → Price Change (earnings day) > 3%
4. Filter → Volume (earnings day) / Avg Volume > 2
5. List export karo → manually guidance check karo (press release padho)

Step 4: Ranking

Primary: EPS beat ka size (bada surprise = stronger momentum)
Secondary: Volume ratio (higher = zyada conviction)

Step 5: Backtest

Screen 3 months tak weekly run karo
Track karo: Screen mein se kitne stocks 2 weeks baad 5%+ up hain?
Agar <60%, toh filters revise karo (shayad >5% beat bahut kam hai, >10% chahiye)

Step 6: Live trading

Har Monday morning screen run karo (post-weekend earnings)
Top 3 lo, Tuesday open par enter karo
Stop loss: -3% (earnings day low ke neeche)
Target: +7% ya 10 days baad exit karo

Integrating Screening Into Your Workflow

Daily routine example (Swing trader):

8:00 AM: Momentum screen run karo (kal ke breakouts)
8:15 AM: Top 10 ka manual review (news, chart quality)
8:30 AM: Watchlist banao (5 stocks)
9:15 AM: Market open, watchlist ko entry triggers ke liye monitor karo
3:30 PM: Market close, end-of-day screen run karo (kal ke liye new setups)
Evening: Week ke results ke basis par screen backtest/refine karo

Yeh rhythm kyun kaam karta hai: Tum screens use kar rahe ho ideas generate karne ke liye (morning), phir discretion apply karte ho (chart/news check), phir market ko confirm karne ka wait karte ho (entry trigger). Screening decision nahi karta—sirf candidates surface karta hai.

Recall Ek 12-Saal-Ke Bachche Ko Explain Karo

Imagine karo tum ek bade shahar mein ek acha restaurant dhundhna chahte ho jahan 5,000 restaurants hain. Tum sab visit nahi kar sakte, toh tum food app par filters use karte ho:

"Sirf Indian restaurants dikhao" (ab 800 bache)
"4+ star ratings" (ab 150 bache)
"₹500 per person se kam" (ab 30 bache)
"Abhi khule hain" (ab 8 bache) Ab tum un 8 ke menus aur reviews actually PADH sakte ho aur best choose kar sakte ho. Filters ke bina, tum random restaurants visit karte hue weeks waste kar dete.

Stock screening bilkul yahi hai. Stock market mein 5,000+ stocks hain. Tum filters banate ho jaise "price upar ja raha hai," "company profit bana rahi hai," "bahut expensive nahi," etc. Screen tumhe shayad 10-20 stocks dikhata hai jo match karte hain. TAB tum un 10-20 ko carefully study karte ho 1-2 best trades dhundhne ke liye.

Screen tumhare liye stock nahi chunata—yeh sirf un 4,990 stocks ko hatata hai jo definitely KAAM NAHI KARENGE, taaki tum apna dimag un 10 par focus kar sako jo KAAM KAR SAKTE HAIN.

Connections

4.2.01-Types-of-stocks-to-trade – Screening stock type se filter karne mein help karta hai (large/mid/small cap)
4.2.07-Understand-stock-selection-criteria – Selection criteria screen filters ban jaate hain
4.3.02-Technical-indicators-and-patterns – Technical screens RSI, MACD jaise indicators use karte hain
4.4.01-Fundamental-analysis-basics – Fundamental screens P/E, ROE, etc. use karte hain
4.5.03-Position-sizing-strategies – Screen output ko position-sizing constraints se filter karna zaroori hai
4.6.01-Backtesting-trading-strategies – Screens ko live use se pehle ZAROOR backtest karna chahiye

#flashcards/stock-market

Stock screening kya hai? :: Hazaron stocks ko quantitative criteria (price, volume, ratios) ke through filter karne ka systematic process taaki tumhari specific trading strategy se match karne wale 10-50 candidates identify ho sakein, 90% elimination ka kaam automate karta hai.

Hum saare 5,000 stocks analyze karne ki jagah screen kyun karte hain?

Time efficiency (manual screening mein 166 hours lagte hain), consistency (human bias/fatigue eliminate hoti hai), aur opportunity cost (analysis time sirf high-probability setups par focus karo).

Do main screening paradigms kya hain?

(1) Fundamental screening jo P/E, ROE, Debt/Equity jaise financial ratios use karta hai value/growth investing ke liye, aur (2) Technical screening jo RSI, ADX, moving averages jaise price/volume patterns use karta hai momentum/swing trading ke liye.

Screen filter ke roop mein P/E < 15 kya achieve karta hai?

Earnings ke relative "cheap" valuations par trade karne wale stocks identify karta hai (value hypothesis: undervalued stocks time ke saath mean-revert upward karte hain).

Screen filter ke roop mein ROE > 15% kya achieve karta hai?

Woh companies dhundhta hai jo shareholder equity se efficiently profit generate karti hain (quality screen: high ROE competitive advantage aur sustainable growth suggest karta hai).

3-stage screening funnel kya hai?

Stage 1 (broad filter: liquidity/universe → 5,000 se 200 stocks), Stage 2 (strategy filter: core criteria → 200 se 20 stocks), Stage 3 (manual filter: discretion/context → 20 se 5 trades).

Technical screen mein RSI 30-40 ke beech kyun use karte hain?

Oversold conditions identify karta hai (price sharply gira) bina catastrophic crashes ke (RSI < 20 often matlab broken business), mean-reversion edge capture karta hai.

Momentum screen mein ADX > 25 kyun add karte hain?

Ensure karta hai ki strong trending market exist karta hai (momentum strategies ko kaam karne ke liye trends chahiye; ADX < 20 choppy sideways market indicate karta hai jahan momentum fail hota hai).

Screening mein over-optimization kya hai?

Bahut zyada filters add karna (10+) jo historical noise fit karte hain signal nahi, resulting mein woh screens jo sirf 2-3 stocks kabhi match karte hain (curve-fitting) aur out-of-sample fail ho jaate hain.

Market regime ke hisaab se screens kyun adjust karne chahiye?

Same thresholds (jaise P/E < 10) bull vs. bear markets mein alag matlab rakhte hain (bulls mein, ultra-low P/E often = broken business; bears mein = opportunity), isliye context-aware adjustment edge improve karta hai.

Screening mein "base rate" mistake kya hai?

Criteria match karne wale bahut saare stocks dhundhna (jaise 50 stocks 10%+ up) lekin yeh ignore karna ki historical continuation rate sirf 40% hai, leading to false positives—solution hai screen ka actual hit rate calculate karo aur refine karo.

Trading strategy se screen kaise derive karte hain?

(1) Edge hypothesis define karo, (2) Measurable filters mein translate karo, (3) Universe boundaries set karo (liquidity/cap), (4) Primary/secondary criteria se output rank karo, (5) Backtest karo aur refine karo.

Screen mein filtering aur ranking mein kya fark hai?

Filtering un stocks ko remove karta hai jo criteria meet nahi karte (binary yes/no), ranking survivors ko priority se order karta hai (jaise sabse zyada oversold pehle, highest ROE pehle) taaki analysis best candidates par focus ho.

Screens mein market cap > ₹500 crore kyun include karte hain?

Liquidity ensure karta hai—positions mein excessive slippage ya counterparty dhundhne ki mushkil ke bina enter/exit kar sako, jo screen ke output ko actually trade karne ke liye zaroori hai.

Screening workflow mein "discretionary overlay" kya hai?

Manual Stage 3 review jisme tum 10-20 algorithmic survivors par news, chart quality, sector context, aur risk/reward check karte ho final 3-5 trade decisions karne se pehle.

What is Stock Screening?