Скачать или смотреть Market Regime Detection Using ML: Dynamic Capital Allocation Strategy Explained

In this video, Aparna Singhal explains how she built a machine-learning-based market regime detection strategy using market breadth and Python.

This video features an in-depth conversation between Aparna Singhal, an independent trader, and Mohak Pachisia, discussing her EPAT final project focused on regime classification, capital allocation, and drawdown control.

Aparna walks through her journey from discretionary trading to systematic thinking, where she observed that markets move through distinct regimes rather than in a single direction. To address this, she designed a long-only strategy on the Nifty 500 using market breadth indicators and multiple machine learning classifiers.

Key topics covered:
Market regime detection with ML
Feature engineering using market breadth
Random Forest classification
Regime-based position sizing
Backtesting vs buy-and-hold

This is a technical, educational walkthrough, not a performance pitch. The focus is on process, modeling decisions, and risk management, making it relevant for traders, quants, and professionals exploring systematic trading.

➡️ Download the codes from the link below: https://bit.ly/4kkKYm4

Join EPAT - Executive Programme in Algorithmic Trading: https://bit.ly/3ZOQPXr

Learn to apply AI and ML in trading in a practical hands-on manner
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🎯 What You’ll Learn:
-The framework for building a Machine Learning model to detect four distinct market regimes: Bull, Bear, High Volatility, and Low Volatility.
-How to utilize Market Breadth indicators—such as participation rates and stocks above moving averages—to find a trading edge.
-Technical methods to eliminate look-ahead bias and manage transaction costs using signal smoothing and persistence filters.
-A comparison between Active (4-way) and Passive (2-regime) strategies to mitigate drawdowns and outperform a buy-and-hold benchmark.

Timestamps / Chapters
0:00 - Introduction: The importance of price and features
1:32 - Why Market Breadth is a trading edge
2:47 - Strategy Overview: Nifty 500 Long-only approach
5:50 - The role of mentorship in building ML models
7:01 - 5-Step Project Framework: Data to Backtesting
8:02 - Handling Survivorship Bias in historical data
9:57 - Defining Target Variables & Z-Score Standardization
12:12 - Why High Volatility requires immediate protection
13:16 - Feature Engineering: Momentum & Participation Rate
15:56 - Managing Look-Ahead Bias in Python
19:03 - Analyzing Feature Correlation & Histograms
20:59 - Training the Random Forest Classifier
23:47 - Smoothing Signals to reduce transaction costs
27:40 - Strategy 1 Results: Active Regime Management
31:13 - Strategy 2 Results: Passive Investor Crash Protection

🎓 About the Speaker: Aparna Singhal
Aparna Singhal is a quantitative research and trading professional and an EPATian with 3+ years of experience across equities, commodities, and cryptocurrency markets, as well as equity research and market analysis. She also has a strong foundation in credit analysis from her previous role in Wholesale Banking at IDFC FIRST Bank.

🎓 About the Speaker: Mohak Pachisia
Mohak Pachisia is a Senior Quantitative Researcher at QuantInsti, specializing in trading strategy development, financial modeling, and quantitative research. Before joining QuantInsti, he worked in the Risk and Quant Solutions division at Evalueserve, where he also led the learning and development function for the Quant team.

About EPAT
The EPAT program by QuantInsti is a structured learning track focused on algorithmic and quantitative trading. It emphasizes Python-based strategy development, backtesting, risk management, and applied projects guided by mentors.

💡 Key Takeaways:
-Market Breadth vs. Index Price: The Nifty 500 is used because it captures the market essence across small, mid, and large-cap stocks more effectively than index price alone.
-The Smoothing Effect: Implementing a persistence filter (requiring a signal for 4 out of 5 days) reduces "spikes" and lowers unnecessary transaction costs.
-Volatility Sensitivity: High volatility regimes are intentionally not smoothed because they require "instant protection" to save capital from rapid falls.
-Risk-Adjusted Performance: The primary contribution of this ML model is lowering the downside and mitigating drawdowns rather than just increasing upside.

Keywords
Machine Learning, Market Regime Detection, Random Forest Classifier, Algorithmic Trading, Capital Allocation, Nifty 500, Python for Finance, Market Breadth, Sharpe Ratio, Risk Management, Quantitative Analysis, Drawdown Mitigation

Hashtags
#MachineLearning #AlgorithmicTrading #MarketRegimes #PythonTrading #RiskManagement #Nifty500 #QuantFinance #DataScience #tradingstrategy