Quant Strategy
Investment Philosophy
Take investment decisions based on data analysis, minimising emotional bias
Optimise strategies based on rigorous backtesting, using machine learning techniques
Diversify across factor models, sectors and instruments to mitigate risk
Monitor and adapt quant models based on evolving market conditions
Investment Process
Data acquisition and processing
Collect and curate high quality financial data from stock exchanges, data providers, and regulatory filings
Factor model development
Develop factor models tailored to the Indian market, incorporating domestic factors and their historical performance
Backtesting and optimisation
Backtest and optimise the models and portfolio construction methodology to ensure effectiveness
Portfolio construction
Construct a diversified portfolio based on the signals generated by the quantitative models, considering liquidity constraints
Reweighting and monitoring
Continuously monitor market movements and rebalance the portfolio strategically to capture evolving opportunities and manage risk
Techniques:
Multi-factor investing: Exploits a combination of factors like value, momentum, quality, volatility, and liquidity specific to the Indian market
Mean reversion strategies: Capitalises on temporary price deviations of Indian stocks from their historical averages using statistical arbitrage techniques
Statistical arbitrage: Identifies short-term price discrepancies between correlated assets and capitalise on them
Machine learning: Utilises advanced ML algorithms to uncover complex relationships within vast datasets and generate alpha
Tech Stack:
Quantitative programming languages: Utilises Python, R for data analysis, model development, and backtesting
Cloud computing: Utilises cloud-based platforms for efficient data storage, processing, and model training
Machine learning libraries: Leverages libraries like TensorFlow, PyTorch, and scikit-learn for building and deploying ML models
Rich data APIs: Incorporates quantitative, fundamental and technical data sources
Machine learning model development: Trains ML models on historical data to predict future asset prices and market movements
Risk Management
Liquidity filters
Ensure sufficient portfolio liquidity to meet redemption needs and exploit attractive trading opportunities
Position sizing ceiling
Volatility based position sizing strategies to limit potential losses on any individual trade. This will involve position sizing strategies and dynamic portfolio adjustments
Diversification
Construct well diversified portfolio across multiple stocks/instruments. No position is allowed to grow beyond risk caps
Portfolio level drawdown limit
Market data and rules ensure portfolio adjustments are made before drawdowns grow to uncontrollable levels
Strategy Facts
Securities
- Multicap Indian stocks
- Tactical allocation to US ETFs, commodity ETFs and debt instruments
- Derivatives for hedging
Benchmark
S&P BSE 500 TRI
Partners
- Custodian: Orbis Financial
- Brokers: Zerodha, Emkay
Minimum investment
INR 50 lakhs (total across strategies)