Quant Investing: Simple Algorithms

What is Quantitative Investing?

Quantitative investing uses mathematical models, statistical analysis, and computer algorithms to make investment decisions. Instead of relying on gut feelings or stories, quant investors use data to identify patterns, build models, and systematically execute strategies.

This approach has gained popularity in India as markets become more efficient and data becomes more accessible. For retail investors, simple quant strategies can provide disciplined, emotion-free investing.

Core Concepts of Quant Investing

1. Factor-Based Investing

Factors are characteristics that explain stock returns:

Style Factors:

• Value: P/E, P/B, EV/EBITDA ratios
• Quality: ROE, debt levels, profit margins
• Growth: Earnings growth, revenue growth
• Momentum: Price trends, earnings revisions
• Low Volatility: Stocks with lower price swings
• Profitability: Operating margins, return metrics

Size Factor:

• Small Cap: Smaller companies tend to outperform
• Market Cap Effect: Risk premium for size

2. Multi-Factor Models

Combine multiple factors for better risk-adjusted returns:

3. Risk Models

Understanding and managing various types of risk:

• Market Risk: Overall market movements
• Sector Risk: Industry-specific factors
• Style Risk: Factor tilts (value vs growth)
• Specific Risk: Company-specific factors

Building Your First Quant Strategy

Step 1: Define Investment Universe

Set clear criteria for stocks to include:

• Market Cap: >₹500 crores (liquidity filter)
• Trading Volume: >₹1 crore daily average
• Listing Age: >2 years (sufficient data)
• Financial Data: Complete financial statements available

Step 2: Choose Factors

Start with proven factors:

Value Factors:

• P/E ratio (lower is better)
• P/B ratio (lower is better)
• EV/EBITDA (lower is better)
• Price-to-Sales (lower is better)

Quality Factors:

• ROE (higher is better)
• Debt-to-Equity (lower is better)
• Interest Coverage (higher is better)
• Profit Margin stability

Step 3: Create Factor Scores

Standardize each factor for comparison:

Percentile Rank	Score	Interpretation
90-100%	5	Excellent
70-90%	4	Good
30-70%	3	Average
10-30%	2	Below Average
0-10%	1	Poor

Step 4: Combine Factors

Weight factors based on historical performance and conviction:

Balanced Approach:

• Value: 25%
• Quality: 25%
• Momentum: 25%
• Growth: 25%

Conservative Approach:

• Quality: 40%
• Value: 30%
• Momentum: 20%
• Growth: 10%

Simple Quant Strategies for Beginners

Strategy 1: Magic Formula (Joel Greenblatt)

Simple 2-factor strategy combining quality and value:

Ranking System:

1. Rank stocks by Earnings Yield (EBIT/Enterprise Value) - higher is better
2. Rank stocks by Return on Capital (EBIT/Invested Capital) - higher is better
3. Add rankings together (lower combined rank is better)
4. Buy top 20-30 stocks
5. Rebalance annually

Indian Implementation:

• Use EBITDA instead of EBIT (Indian accounting)
• Filter out companies with negative earnings
• Add minimum market cap filter
• Rebalance quarterly instead of annually

Strategy 2: Quality at Reasonable Price (QARP)

Focus on high-quality companies at reasonable valuations:

Quality Metrics:

• ROE >15%
• ROA >7%
• Debt-to-Equity <0.5
• Interest Coverage >5
• Consistent earnings growth

Value Filters:

• P/E <25
• P/B <4
• PEG <2 (P/E to Growth ratio)

Strategy 3: Low Volatility

Buy stocks with historically low volatility:

Implementation:

1. Calculate 1-year standard deviation of daily returns
2. Rank stocks from lowest to highest volatility
3. Buy bottom 50 stocks (lowest volatility)
4. Weight by inverse volatility
5. Rebalance quarterly

Enhancements:

• Add quality filters (profitable companies only)
• Exclude penny stocks (price >₹10)
• Sector diversification rules

Data Sources and Tools

Free Data Sources

• Screener.in: Financial ratios, historical data
• BSE/NSE Websites: Price and corporate action data
• Company Annual Reports: Detailed financial information
• RBI/Government: Economic and sector data

Paid Data Sources

• Bloomberg/Reuters: Professional-grade data
• FactSet: Comprehensive financial database
• Capitaline: Indian market specialist
• ACE Equity: Historical and real-time data

Analysis Tools

Spreadsheet Tools:

• Microsoft Excel: Data analysis, basic modeling
• Google Sheets: Cloud-based collaboration
• Templates: Pre-built quant analysis sheets

Programming Tools:

• Python: pandas, numpy, scikit-learn libraries
• R: Statistical analysis and modeling
• MATLAB: Advanced mathematical modeling

Platforms:

• QuantInsti: Educational and research platform
• AlgoZ: Indian quant platform
• Streak: Strategy building without coding

Backtesting Fundamentals

What is Backtesting?

Backtesting tests how a strategy would have performed using historical data. It's crucial for validating strategies before risking real money.

Backtesting Best Practices

1. Use Point-in-Time Data

• Only use data available at the time of decision
• Avoid look-ahead bias
• Account for restatements and data revisions

2. Include All Costs

• Brokerage: ₹20 per trade or 0.1%
• STT: 0.1% on equity delivery
• GST: 18% on brokerage
• Market Impact: Price movement due to trading

3. Realistic Assumptions

• Use closing prices, not intraday extremes
• Account for liquidity constraints
• Consider capacity constraints (strategy size limits)
• Include corporate actions (splits, dividends)

Common Backtesting Mistakes

Risk Management in Quant Strategies

Portfolio-Level Risk Controls

1. Diversification Rules

• Maximum Position Size: 5% per stock
• Sector Limits: 25% per sector
• Minimum Positions: 20+ stocks
• Correlation Limits: Avoid highly correlated stocks

2. Rebalancing Discipline

• Fixed Schedule: Monthly or quarterly rebalancing
• Threshold-Based: Rebalance when positions drift >2%
• Volatility Adjustment: Reduce position sizes during high volatility

3. Drawdown Controls

• Maximum Drawdown: 20% limit
• Position Sizing: Reduce size after losses
• Strategy Shutdown: Stop if drawdown exceeds historical worst

Factor Risk Management

Factor Timing

Adjust factor exposure based on market conditions:

Market Condition	Preferred Factors	Avoid
Bull Market	Momentum, Growth	Value, Quality
Bear Market	Quality, Low Vol	Momentum, Growth
Recovery	Value, Small Cap	Momentum
Late Cycle	Quality, Dividend	Growth, High Beta

Avoiding Overfitting

What is Overfitting?

Creating models that work perfectly on historical data but fail in real markets. The model "memorizes" past data instead of learning generalizable patterns.

Signs of Overfitting

• Too-Good Results: Backtest returns much higher than theoretical factor returns
• Complex Models: Too many parameters relative to data points
• Unstable Performance: Performance changes dramatically with small data changes
• No Economic Logic: Strategy works but makes no intuitive sense

Prevention Techniques

1. Out-of-Sample Testing

• Split data: 70% for model development, 30% for testing
• Walk-forward analysis: test strategy on rolling future periods
• Reserve recent 2-3 years for final validation

2. Cross-Validation

• Test strategy on different time periods
• Verify performance across market cycles
• Check robustness across different universes

3. Keep It Simple

• Start with 2-3 factors maximum
• Use simple, intuitive combinations
• Avoid complex mathematical transformations
• Prefer strategies with economic rationale

Performance Measurement

Key Performance Metrics

Return Metrics:

• Total Return: Absolute performance
• Excess Return: Performance vs benchmark
• Risk-Adjusted Return: Return per unit of risk

Risk Metrics:

• Standard Deviation: Volatility measure
• Maximum Drawdown: Worst peak-to-trough decline
• Beta: Sensitivity to market movements

Efficiency Metrics:

• Sharpe Ratio: (Return - Risk-free Rate) / Standard Deviation
• Information Ratio: Excess Return / Tracking Error
• Sortino Ratio: Like Sharpe but only considers downside risk

Benchmark Selection

Choose appropriate benchmarks for comparison:

• Market Benchmark: Nifty 50, Nifty 500
• Size Benchmark: Large-cap, mid-cap, small-cap indices
• Factor Benchmark: Value, growth, quality indices
• Strategy Benchmark: Similar quant strategies

Implementation Considerations

Transaction Costs

Accurate cost modeling is crucial:

Direct Costs:

• Brokerage: ₹20 per trade or 0.03% (discount brokers)
• STT: 0.1% on delivery (equity)
• Exchange Fees: 0.0035% approximately
• GST: 18% on brokerage and exchange fees

Indirect Costs:

• Bid-Ask Spread: 0.1-0.5% for liquid stocks
• Market Impact: Price movement due to large orders
• Opportunity Cost: Delay between decision and execution

Portfolio Turnover

Balance rebalancing benefits with transaction costs:

Strategy Type	Typical Turnover	Rebalancing Frequency
Buy and Hold	20-30%	Annual
Value/Quality	50-80%	Quarterly
Momentum	100-200%	Monthly
Mean Reversion	200-400%	Weekly/Daily

Building Your Quant Toolkit

Excel-Based Approach

Basic Setup:

1. Data Sheet: Stock prices and financial ratios
2. Factor Calculation: Compute factor scores
3. Portfolio Sheet: Current holdings and weights
4. Performance Tracking: Returns and metrics
5. Rebalancing Sheet: Buy/sell recommendations

Key Formulas:

• PERCENTRANK: Convert values to percentile ranks
• VLOOKUP: Match data across sheets
• SUMPRODUCT: Calculate weighted averages
• Array Formulas: Complex calculations

Python-Based Approach

Essential Libraries:

• pandas: Data manipulation
• numpy: Numerical computations
• matplotlib: Data visualization
• scikit-learn: Machine learning tools
• yfinance: Stock price data

Sample Code Structure:

```python import pandas as pd import numpy as np # Load data prices = pd.read_csv('stock_prices.csv') fundamentals = pd.read_csv('fundamentals.csv') # Calculate factors value_score = fundamentals['PE'].rank(ascending=False) quality_score = fundamentals['ROE'].rank(ascending=True) # Combine factors total_score = 0.5 * value_score + 0.5 * quality_score # Select top stocks top_stocks = total_score.nlargest(20) ```

Real-World Quant Strategy Example

Conservative Multi-Factor Strategy

Universe:

• Nifty 500 stocks
• Market cap >₹1,000 crores
• Daily volume >₹5 crores
• Positive earnings last 4 quarters

Factors and Weights:

Factor	Weight	Metric	Direction
Quality	40%	ROE + Debt/Equity	High ROE, Low Debt
Value	30%	P/E + P/B	Low ratios
Momentum	20%	6-month return	High returns
Growth	10%	Earnings growth	High growth

Portfolio Construction:

• Top 25 stocks by combined score
• Equal weight initially (4% each)
• Quarterly rebalancing
• Maximum 8% in any single stock
• Maximum 30% in any sector

Expected Results:

• Return: 12-16% annually
• Volatility: 18-22% annually
• Sharpe Ratio: 0.6-0.8
• Maximum Drawdown: 25-35%

Common Challenges and Solutions

Data Quality Issues

Strategy Decay

Capacity Constraints

Getting Started: Your Action Plan

Phase 1: Learning (Months 1-2)

1. Study factor investing basics
2. Learn Excel/Google Sheets for analysis
3. Download and analyze historical data
4. Implement simple Magic Formula strategy

Phase 2: Development (Months 3-4)

1. Build comprehensive factor database
2. Develop 2-3 different strategies
3. Backtest thoroughly with realistic assumptions
4. Set up monitoring and rebalancing process

Phase 3: Implementation (Months 5-6)

1. Start with small allocation (10-20% of portfolio)
2. Execute first strategy with real money
3. Monitor performance and costs
4. Refine and improve based on experience

Phase 4: Scaling (Months 7-12)

1. Increase allocation based on results
2. Add complementary strategies
3. Automate more of the process
4. Consider advanced techniques

Learning Resources

Books

• "Quantitative Equity Portfolio Management" by Chincarini & Kim
• "The Little Book of Common Sense Investing" by Jack Bogle
• "Factor-Based Investing" by Berkin & Swedroe
• "Quantitative Portfolio Management" by Fabozzi

Online Courses

• QuantInsti: Algorithmic trading courses
• Coursera: Financial engineering and risk management
• NSE Academy: Certification in financial markets
• YouTube: Free tutorials on Excel and Python

Communities

• QuantInsti Community: Indian quant investors
• Reddit r/SecurityAnalysis: Value and quant discussions
• LinkedIn Groups: Quantitative investing in India
• Local Meetups: Find quant investors in your city

Remember: Quantitative investing is not about finding perfect strategies, but about systematic, disciplined approaches that work over the long term. Start simple, validate thoroughly, and always keep learning.