Live · NSE · Nifty 200

Institutional Momentum.
Built for India.

Regime-adaptive quantitative strategy combining cross-sectional momentum, dynamic sector rotation, and live yfinance data.

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Portfolio Today
This Month
Strategy Hybrid Dynamic
Momentum 30
Universe Nifty 200
+ ETFs

Model Portfolio

Hybrid Dynamic Momentum 30

Regime-adaptive · Nifty 200 universe · Buffer-based selection · Quarterly rebalance

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Today
1 Week
1 Month
6 Month
1 Year
vs Nifty 50

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Current Holdings

Stock Weight Price Today 1 Month

Top Performers (1M)

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Strategy Analytics (1Y)

Sharpe Ratio
Max Drawdown
Alpha vs Nifty
Ann. Volatility

Live prices via yfinance · NSE data · Returns are simulated buy-and-hold, not actuals · Past performance is not indicative of future results · Not investment advice

How it works

Three steps to institutional alpha

Our end-to-end quant pipeline runs monthly and adapts to market regimes in real time.

01
📡

Regime Detection

Classify the market as GROWTH or DEFENSIVE using 3-month NiftyBeES momentum. Shift allocation to equities or gold accordingly.

02

Momentum Ranking

Score each stock in the Nifty 200 using MR12 × MR6 composite momentum. Cross-sectional Z-scores normalize across sectors.

03
🎯

Portfolio Construction

Buffer selection picks the top 15 mandatory stocks and retains any rank ≤ 45 from last cycle. Iterative weight capping ensures diversification.

Strategy Methodology

Quantitative rigor,
institutional grade

Every parameter is academically grounded and India-market calibrated.

📈
Regime Engine

Adaptive Regime Detection

3-month NiftyBeES return determines market regime. GROWTH regime overweights equities; DEFENSIVE shifts to GoldBeES and low-beta names.

Regime = GROWTH if R(NiftyBeES, 3M) > 0 else DEFENSIVE Equity_wt = 0.85 (GROWTH) | 0.50 (DEFENSIVE) Gold_wt = 0.15 (GROWTH) | 0.30 (DEFENSIVE)
⚖️
Momentum Score

Composite Momentum Ratio

Dual-horizon momentum captures both trend persistence and recent acceleration. Stocks are ranked by a geometric composite of 12-month and 6-month returns.

MR12 = P_now / P_12M_ago MR6 = P_now / P_6M_ago Composite = MR12 × MR6 Z_score = (Composite − μ) / σ
🔒
Buffer Selection

Turnover-Controlled Selection

Buffer logic reduces unnecessary churn: any stock ranked ≤ 45 from the last cycle is automatically retained, keeping portfolio turnover below 30% per quarter.

Mandatory: top 15 by Z_score Retained: prev_rank ≤ 45 ∩ curr_rank ≤ 45 Target: 30 stocks total Max weight: 5% per stock (iterative cap)
🏗️
Weight Construction

FFMC-Weighted Allocation

Free-float market cap weights ensure the portfolio is investable at scale. Iterative redistribution of excess weight from capped stocks prevents concentration risk.

w_i = FFMC_i / Σ FFMC_j (raw weight) Iterative cap: while any w_i > 5%: excess = Σ max(w_i − 5%, 0) redistribute excess ∝ FFMC of uncapped re-normalize

Community

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Pricing

Institutional access,
retail pricing

Explorer

Free

Forever

  • Live Hybrid Momentum 30 portfolio
  • NAV chart vs Nifty 50
  • Performance metrics dashboard
  • Community portfolio leaderboard
  • Strategy methodology docs

Institutional

Custom

Bespoke strategy

  • Custom factor model development
  • Dedicated quant analyst support
  • White-label portfolio reports
  • Sharekhan API integration
  • SLA-backed data pipeline

Execute signals with Sharekhan

Connect your Sharekhan account and trade the momentum rebalances automatically with one click.

Open Sharekhan Account