Skip to content

Hellblazer704/ADAPT_index_framework

Repository files navigation

📊 ADAPT Framework: Regime-Aware, Personalized Indexing System

Welcome to the official repository for the Enhanced ADAPT Framework — a next-generation indexing solution designed to outperform traditional benchmarks like NIFTY 50 by integrating real-time regime detection, factor-driven alpha sleeves, and investor personalization.


🧠 What is ADAPT?

ADAPT stands for:

Alpha-aware
Defensive
Actively-tilted
Personalized
Tactical Indexing

Unlike static indices, ADAPT dynamically reallocates based on:

  • Market regime (Bull / Bear / Sideways)
  • Behavioral personas (Conservative, Moderate, Aggressive)
  • Factor scores (Quality, Momentum, Value, Growth)
  • Real-world frictions (slippage, commissions, turnover constraints)

🔧 Features

  • Regime Detection Engine using price/MAs and volatility signals
  • 🧮 Mathematical Portfolio Construction: Core, Tactical, Defensive sleeves
  • 🏗️ Optimization Techniques: Mean-variance, Risk parity, Turnover control
  • 🔄 Rebalancing Framework: Monthly, Bi-weekly, or Quarterly as per profile
  • 📈 Comprehensive Backtesting: Alpha, beta, Sharpe, Sortino, drawdowns
  • 🧾 Realistic Costs: Transaction cost modeling (slippage + commission)
  • 📊 Benchmarking vs NIFTY 50 TRI

📁 Folder Structure

adapt-framework/
├── data/                    # Raw price & rebalance inputs
├── src/                     # Regime detection, optimizers, backtester
│   ├── regime/              # Bull/Bear logic
│   ├── portfolio/           # Factor scoring & weighting
│   └── utils/               # Helpers, logger, constants
├── results/                 # Output CSVs, plots, summary tables
├── notebooks/               # Exploratory analysis & validation
├── README.md
└── requirements.txt

About

ADAPT is a regime-aware portfolio framework for Indian equities that combines market classification, multi-sleeve allocation, and factor-based optimization to deliver consistent, risk-adjusted outperformance vs NIFTY 50 TRI, with built-in cost modeling, constraints, and tactical adaptability.

Resources

Stars

0 stars

Watchers

0 watching

Forks

Releases

No releases published

Packages

 
 
 

Contributors

Languages