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Quick Start Guide

Welcome to Tickersnap - your gateway to Indian stock market analysis! 🚀

This guide will get you up and running in minutes... whether you're an experienced trader, developer, investor, enthusiast, or just a beginner getting started with market analysis!

🚀 Getting Started

Don't let complex financial jargon intimidate you! Tickersnap makes Indian stock market analysis accessible to everyone!

It's simple... just understand the basic concepts, pick sample code and run them to see things in action!

📦 Install the package

To get started, just install the package, run usage example codes (available throughout the docs), and see the results for yourself.

Experiment and add your modifications... and when you understand the library, build cool and more complex stuff for market analysis!

🧠 Basic Concepts

Tickersnap has 3 core modules that work together:

Module What it does Example Learn More
📈 MMI Tracks market sentiment Market fear or greed today? MMI
📋 Assets Lists all stocks & ETFs Get all 5,000+ Indian stocks Assets
📊 Scorecard Analyzes individual stocks TCS performance: Good/Bad? Stocks

💡 The Power of Combination

Use Assets to find stocks → Scorecard to analyze them → MMI for market timing

⚡️ Quick Start Examples

Example

from tickersnap.mmi import MarketMoodIndex

# Get market sentiment
mmi = MarketMoodIndex()
current = mmi.get_current_mmi()
changes = mmi.get_mmi_changes()

print(f"📊 Market Mood: {current.value:.1f} ({current.zone.value})")
print(f"📈 vs Yesterday: {changes.vs_last_day:+.1f}")
print(f"📅 vs Last Week: {changes.vs_last_week:+.1f}")

# Investment signal
if current.zone.value in ["Extreme Fear", "Fear"]:
    print("🟢 Consider buying opportunities")
elif current.zone.value in ["Extreme Greed", "Greed"]:
    print("🔴 Be cautious, market may be overheated")

from tickersnap.stock import StockScorecard

# Analyze a stock
scorecard = StockScorecard()
analysis = scorecard.get_scorecard("TCS")

print(f"📈 TCS Stock Analysis:")
if analysis.performance:
    print(f"Performance: {analysis.performance.value} ({analysis.performance.rating.value})")
if analysis.valuation:
    print(f"Valuation: {analysis.valuation.value} ({analysis.valuation.rating.value})")
if analysis.growth:
    print(f"Growth: {analysis.growth.value} ({analysis.growth.rating.value})")
if analysis.entry_point:
    print(f"Entry Point: {analysis.entry_point.value} ({analysis.entry_point.rating.value})")

# Quick quality check
good_categories = sum(1 for cat in [
    analysis.performance, analysis.valuation, 
    analysis.growth, analysis.profitability
] if cat and cat.rating.value == "good")

print(f"Quality Score: {good_categories}/4 categories are good")

🎯 Understanding Output

MMI Zones Meaning Implications
0-30 Extreme Fear 🟢 Good buying opportunity
30-50 Fear ⚪ Monitor trends
50-70 Greed 🟡 Be selective
70-100 Extreme Greed 🔴 Avoid new positions
Stock Ratings Meaning Implications
good 🟢 Positive signal must consider analysing the stock further!
okay 🟡 Neutral/Average most common case scenario for most stocks!
bad 🔴 Negative signal something is wrong with the stock!
unknown ⚪ Insufficient data probably new or banned, data missing!

🎓 Next Steps

Happy Coding & Happy Analyzing! ✨