/ Built for Systematic Investing

The quantitative layer for every portfolio

Explain returns, control risk, and optimize outcomes on one robust SaaS platform.

Request demo

/ What is Systematic Investing?

A disciplined, data-driven playbook using advanced models to transform market insight into repeatable, risk-aware edge and smarter, consistent investing decisions.

Learn More

/ What is MethodTech?

MethodTech is a full-stack systematic investment platform for institutional investors. Built with a practitioner-first approach and global best practices, it unifies structured data, an in-house multi-factor risk model, research & backtesting, portfolio optimization, and advanced analytics—on one robust SaaS platform.

Risk Model

Built in-house and integrated across the platform

A Risk Model sits at the
Core of Systematic Investing

A Risk Model sits at
the Core of Systematic Investing

What is a Risk Model?

A statistical framework that converts every position into systematic factor exposures and stock specific (idiosyncratic) risk, letting you gauge future volatility and return drivers before you trade.

What is a Risk Model?

A statistical framework that converts every position into systematic factor exposures and stock specific (idiosyncratic) risk, letting you gauge future volatility and return drivers before you trade.

Why use a Risk Model?

Decomposes each holding into Factor vs Idiosyncratic Risk.

Adjusts weights in real time to hit your target IR, instantly reflected on the dashboard.

Quantify risk in a standardised manner.

Lets you size bets, not just rupees, before they hit the blotter.

Why use a Risk Model?

Decomposes each holding into Factor vs Idiosyncratic Risk.

Quantify risk in a standardised manner.

Adjusts weights in real time to hit your target
IR, instantly reflected on the dashboard.

Lets you size bets, not just rupees, before
they hit the blotter.

/ Built for Systematic Investing

The quantitative layer for every portfolio

Explain returns, control risk, and optimize outcomes on one robust SaaS platform.

Request demo

/ What is Systematic Investing?

A disciplined, data-driven playbook using advanced models to transform market insight into repeatable, risk-aware edge and smarter, consistent investing decisions.

Learn More

/ What is MethodTech?

Risk Model

Built in-house and integrated across the platform

A Risk Model sits at the
Core of Systematic Investing

What is a Risk Model?

A statistical framework that converts every position into systematic factor exposures and stock specific (idiosyncratic) risk, letting you gauge future volatility and return drivers before you trade.

Why use a Risk Model?

Decomposes each holding into Factor vs Idiosyncratic Risk.

Adjusts weights in real time to hit your target IR, instantly reflected on the dashboard.

Quantify risk in a standardised manner.

Lets you size bets, not just rupees, before they hit the blotter.

Why use a Risk Model?

Decomposes each holding into Factor vs Idiosyncratic Risk.

Adjusts weights in real time to hit your target IR, instantly reflected on the dashboard.

Quantify risk in a standardised manner.

Lets you size bets, not just rupees, before they hit the blotter.

Use Cases For

Use Cases

Build systematic strategies without the stack. India-first data, a 39-factor risk model, explicit Objectives and Constraints, and point-in-time backtests all in one platform. Understand portfolio risks, decompose to single stocks, and isolate idiosyncratic returns with advanced analytics.

Quant PMs

Build, validate, and scale systematic strategies—end to end.

What you can do with MethodTech:

What you can do with MethodTech

What you can do with MethodTech:

Create alphas from signals/descriptors, then optimise with Objectives like Alpha, Factor Alpha, Risk Minimisation, Benchmark Tracking, Maximise GMV, Transaction Cost Minimisation—all grounded in our India first multi-factor risk model (40 factors) for stable covariance and exposure control.
Enforce real world Constraints: Maximum Capital, Maximum Number of Positions, Position Size Bounds, Portfolio Risk Budget, Beta/Factor Exposure bands, Sub Portfolio Capital, Single Name Idiosyncratic Contribution, Portfolio Turnover—validated against factor exposures from the risk model.
Backtest with Start/End Dates, Rebalance Frequency, and optional Stop Loss on point-in-time data, with costs and our multi-factor model applied at each step.
Monitor exposures, tracking error, factor lens attribution, and drift with the live multi-factor model.

Create alphas from signals/descriptors, then optimise with Objectives like Alpha, Factor Alpha, Risk Minimisation, Benchmark Tracking, Maximise GMV, Transaction Cost Minimisation—all grounded in our India first multi-factor risk model (40 factors) for stable covariance and exposure control.
Enforce real world Constraints: Maximum Capital, Maximum Number of Positions, Position Size Bounds, Portfolio Risk Budget, Beta/Factor Exposure bands, Sub Portfolio Capital, Single Name Idiosyncratic Contribution, Portfolio Turnover—validated against factor exposures from the risk model.
Backtest with Start/End Dates, Rebalance Frequency, and optional Stop Loss on point-in-time data, with costs and our multi-factor model applied at each step.
Monitor exposures, tracking error, factor lens attribution, and drift with the live multi-factor model.

Create alphas from signals/descriptors, then optimise with Objectives like Alpha, Factor Alpha, Risk Minimisation, Benchmark Tracking, Maximise GMV, Transaction Cost Minimisation—all grounded in our India first multi-factor risk model (40 factors) for stable covariance and exposure control.
Enforce real world Constraints: Maximum Capital, Maximum Number of Positions, Position Size Bounds, Portfolio Risk Budget, Beta/Factor Exposure bands, Sub Portfolio Capital, Single Name Idiosyncratic Contribution, Portfolio Turnover—validated against factor exposures from the risk model.
Backtest with Start/End Dates, Rebalance Frequency, and optional Stop Loss on point-in-time data, with costs and our multi-factor model applied at each step.
Monitor exposures, tracking error, factor lens attribution, and drift with the live multi-factor model.

Create alphas from signals/descriptors, then optimise with Objectives like Alpha, Factor Alpha, Risk Minimisation, Benchmark Tracking, Maximise GMV, Transaction Cost Minimisation—all grounded in our India first multi-factor risk model (40 factors) for stable covariance and exposure control.
Enforce real world Constraints: Maximum Capital, Maximum Number of Positions, Position Size Bounds, Portfolio Risk Budget, Beta/Factor Exposure bands, Sub Portfolio Capital, Single Name Idiosyncratic Contribution, Portfolio Turnover—validated against factor exposures from the risk model.
Backtest with Start/End Dates, Rebalance Frequency, and optional Stop Loss on point-in-time data, with costs and our multi-factor model applied at each step.
Monitor exposures, tracking error, factor lens attribution, and drift with the live multi-factor model.

Create alphas from signals/descriptors, then optimise with Objectives like Alpha, Factor Alpha, Risk Minimisation, Benchmark Tracking, Maximise GMV, Transaction Cost Minimisation—all grounded in our India first multi-factor risk model (40 factors) for stable covariance and exposure control.
Enforce real world Constraints: Maximum Capital, Maximum Number of Positions, Position Size Bounds, Portfolio Risk Budget, Beta/Factor Exposure bands, Sub Portfolio Capital, Single Name Idiosyncratic Contribution, Portfolio Turnover—validated against factor exposures from the risk model.
Backtest with Start/End Dates, Rebalance Frequency, and optional Stop Loss on point-in-time data, with costs and our multi-factor model applied at each step.
Monitor exposures, tracking error, factor lens attribution, and drift with the live multi-factor model.

Outcomes

Save years of tech stack + financial model build. Access hedge-fund grade tools without the overhead.
- Faster idea → portfolio
- Higher IR via risk budgets
- Lower slippage with turnover/cost control
- Audit-ready lineage tied to the risk model

Save years of tech stack + financial model build. Access hedge-fund grade tools without the overhead.
- Faster idea → portfolio
- Higher IR via risk budgets
- Lower slippage with turnover/cost control
- Audit-ready lineage tied to the risk model

Save years of tech stack + financial model build. Access hedge-fund grade tools without the overhead.
- Faster idea → portfolio
- Higher IR via risk budgets
- Lower slippage with turnover/cost control
- Audit-ready lineage tied to the risk model

Save years of tech stack + financial model build. Access hedge-fund grade tools without the overhead.
- Faster idea → portfolio
- Higher IR via risk budgets
- Lower slippage with turnover/cost control
- Audit-ready lineage tied to the risk model

Save years of tech stack + financial model build. Access hedge-fund grade tools without the overhead.
- Faster idea → portfolio
- Higher IR via risk budgets
- Lower slippage with turnover/cost control
- Audit-ready lineage tied to the risk model

Fundamental PMs

Keep conviction. Remove unintended bets.

What you can do with MethodTech:

Diagnose where risk sits and where returns come from using the multi-factor risk model: market/style/industry exposures. Slice and dice across dimensions to get a 360 degree view.
Tracking a specific stock? Find true drivers of price change—factors vs stock specific news—and make informed entry and exit decisions.
Find and reduce factor winds: Use Objectives like Risk Minimisation or Factor Exposure Minimisation; keep single-name views with Constraints for Position Size, Idiosyncratic Contribution, Beta/Factor bands, and Turnover, all measured through the risk model.
Spot low quality factors: Identify styles with persistently low Sharpe or poor regime behaviour via the multi-factor lens, and reshape exposures toward intended tilts.
Backtest hedge/sizing changes (dates, rebalance, optional stop loss) and see instant impact on drawdown and capture—with factor paths tracked throughout.

Diagnose where risk sits and where returns come from using the multi-factor risk model: market/style/industry exposures. Slice and dice across dimensions to get a 360 degree view.
Tracking a specific stock? Find true drivers of price change—factors vs stock specific news—and make informed entry and exit decisions.
Find and reduce factor winds: Use Objectives like Risk Minimisation or Factor Exposure Minimisation; keep single-name views with Constraints for Position Size, Idiosyncratic Contribution, Beta/Factor bands, and Turnover, all measured through the risk model.
Spot low quality factors: Identify styles with persistently low Sharpe or poor regime behaviour via the multi-factor lens, and reshape exposures toward intended tilts.
Backtest hedge/sizing changes (dates, rebalance, optional stop loss) and see instant impact on drawdown and capture—with factor paths tracked throughout.

Diagnose where risk sits and where returns come from using the multi-factor risk model: market/style/industry exposures. Slice and dice across dimensions to get a 360 degree view.
Tracking a specific stock? Find true drivers of price change—factors vs stock specific news—and make informed entry and exit decisions.
Find and reduce factor winds: Use Objectives like Risk Minimisation or Factor Exposure Minimisation; keep single-name views with Constraints for Position Size, Idiosyncratic Contribution, Beta/Factor bands, and Turnover, all measured through the risk model.
Spot low quality factors: Identify styles with persistently low Sharpe or poor regime behaviour via the multi-factor lens, and reshape exposures toward intended tilts.
Backtest hedge/sizing changes (dates, rebalance, optional stop loss) and see instant impact on drawdown and capture—with factor paths tracked throughout.

Diagnose where risk sits and where returns come from using the multi-factor risk model: market/style/industry exposures. Slice and dice across dimensions to get a 360 degree view.
Tracking a specific stock? Find true drivers of price change—factors vs stock specific news—and make informed entry and exit decisions.
Find and reduce factor winds: Use Objectives like Risk Minimisation or Factor Exposure Minimisation; keep single-name views with Constraints for Position Size, Idiosyncratic Contribution, Beta/Factor bands, and Turnover, all measured through the risk model.
Spot low quality factors: Identify styles with persistently low Sharpe or poor regime behaviour via the multi-factor lens, and reshape exposures toward intended tilts.
Backtest hedge/sizing changes (dates, rebalance, optional stop loss) and see instant impact on drawdown and capture—with factor paths tracked throughout.

Diagnose where risk sits and where returns come from using the multi-factor risk model: market/style/industry exposures. Slice and dice across dimensions to get a 360 degree view.
Tracking a specific stock? Find true drivers of price change—factors vs stock specific news—and make informed entry and exit decisions.
Find and reduce factor winds: Use Objectives like Risk Minimisation or Factor Exposure Minimisation; keep single-name views with Constraints for Position Size, Idiosyncratic Contribution, Beta/Factor bands, and Turnover, all measured through the risk model.
Spot low quality factors: Identify styles with persistently low Sharpe or poor regime behaviour via the multi-factor lens, and reshape exposures toward intended tilts.
Backtest hedge/sizing changes (dates, rebalance, optional stop loss) and see instant impact on drawdown and capture—with factor paths tracked throughout.

Outcomes

Cleaner alpha — separated from factor tides
Fewer style surprises — more consistent manager selection
Sharper single-name diagnostics — better entry/exit decisions
IC-ready, audit-clean narratives — anchored in the risk model

Cleaner alpha — separated from factor tides
Fewer style surprises — more consistent manager selection
Sharper single-name diagnostics — better entry/exit decisions
IC-ready, audit-clean narratives — anchored in the risk model

Cleaner alpha — separated from factor tides
Fewer style surprises — more consistent manager selection
Sharper single-name diagnostics — better entry/exit decisions
IC-ready, audit-clean narratives — anchored in the risk model

Cleaner alpha — separated from factor tides
Fewer style surprises — more consistent manager selection
Sharper single-name diagnostics — better entry/exit decisions
IC-ready, audit-clean narratives — anchored in the risk model

Cleaner alpha — separated from factor tides
Fewer style surprises — more consistent manager selection
Sharper single-name diagnostics — better entry/exit decisions
IC-ready, audit-clean narratives — anchored in the risk model

Wealth Manager

Evaluate managers on skill vs luck. Design clientfit baskets.

What you can do with MethodTech:

Run multi-period diagnostics (persistence, downside, regime drift) using the multi-factor model to separate idiosyncratic skill from factor beta and crowding.
Build model baskets with Objectives like Benchmark Tracking, Maximise GMV, or Risk Minimisation, bounded by Constraints for Capital, Positions, Factor/Beta bands, and Turnover—and verify fit via factor exposures.
Backtest to set expectations and produce client ready explanations with factor-aware attribution.
View client assets through a consolidated lens (stocks + mutual funds + PMS) and keep them aligned to target factor profiles.

Run multi-period diagnostics (persistence, downside, regime drift) using the multi-factor model to separate idiosyncratic skill from factor beta and crowding.
Build model baskets with Objectives like Benchmark Tracking, Maximise GMV, or Risk Minimisation, bounded by Constraints for Capital, Positions, Factor/Beta bands, and Turnover—and verify fit via factor exposures.
Backtest to set expectations and produce client ready explanations with factor-aware attribution.
View client assets through a consolidated lens (stocks + mutual funds + PMS) and keep them aligned to target factor profiles.

Run multi-period diagnostics (persistence, downside, regime drift) using the multi-factor model to separate idiosyncratic skill from factor beta and crowding.
Build model baskets with Objectives like Benchmark Tracking, Maximise GMV, or Risk Minimisation, bounded by Constraints for Capital, Positions, Factor/Beta bands, and Turnover—and verify fit via factor exposures.
Backtest to set expectations and produce client ready explanations with factor-aware attribution.
View client assets through a consolidated lens (stocks + mutual funds + PMS) and keep them aligned to target factor profiles.

Run multi-period diagnostics (persistence, downside, regime drift) using the multi-factor model to separate idiosyncratic skill from factor beta and crowding.
Build model baskets with Objectives like Benchmark Tracking, Maximise GMV, or Risk Minimisation, bounded by Constraints for Capital, Positions, Factor/Beta bands, and Turnover—and verify fit via factor exposures.
Backtest to set expectations and produce client ready explanations with factor-aware attribution.
View client assets through a consolidated lens (stocks + mutual funds + PMS) and keep them aligned to target factor profiles.

Run multi-period diagnostics (persistence, downside, regime drift) using the multi-factor model to separate idiosyncratic skill from factor beta and crowding.
Build model baskets with Objectives like Benchmark Tracking, Maximise GMV, or Risk Minimisation, bounded by Constraints for Capital, Positions, Factor/Beta bands, and Turnover—and verify fit via factor exposures.
Backtest to set expectations and produce client ready explanations with factor-aware attribution.
View client assets through a consolidated lens (stocks + mutual funds + PMS) and keep them aligned to target factor profiles.

Outcomes

Better manager selection — fewer style surprises
Goal-aligned baskets — tailored to client risk, style, and factor targets
Higher trust — through transparent, plain-English, factor-aware analytics

Better manager selection — fewer style surprises
Goal-aligned baskets — tailored to client risk, style, and factor targets
Higher trust — through transparent, plain-English, factor-aware analytics

Better manager selection — fewer style surprises
Goal-aligned baskets — tailored to client risk, style, and factor targets
Higher trust — through transparent, plain-English, factor-aware analytics

Better manager selection — fewer style surprises
Goal-aligned baskets — tailored to client risk, style, and factor targets
Higher trust — through transparent, plain-English, factor-aware analytics

Better manager selection — fewer style surprises
Goal-aligned baskets — tailored to client risk, style, and factor targets
Higher trust — through transparent, plain-English, factor-aware analytics

Product & ETF Teams

Launch and maintain factor funds, SIFs, and passive/quant products with industrial-strength analytics and monitoring.

Evaluate managers on skill vs luck. Design clientfit baskets.

Launch and maintain factor funds, SIFs, and passive/quant products with industrial-strength analytics and monitoring.

What you can do with MethodTech:

Design, simulate, and validate factor or thematic products directly through the multifactor risk model—ensuring each ETF or SIF has intentional exposures and low overlap with existing products.
Set Objectives like Benchmark Tracking, Factor Exposure Targeting, or Risk Minimization, bounded by Constraints for Capital, Turnover, Tracking Error, and Factor/Beta bands.
Model pre-launch performance via backtests with Start/End Dates, Rebalance Frequency, and execution cost modelingat each rebalance step.

Design, simulate, and validate factor or thematic products directly through the multifactor risk model—ensuring each ETF or SIF has intentional exposures and low overlap with existing products.
Set Objectives like Benchmark Tracking, Factor Exposure Targeting, or Risk Minimization, bounded by Constraints for Capital, Turnover, Tracking Error, and Factor/Beta bands.
Model pre-launch performance via backtests with Start/End Dates, Rebalance Frequency, and execution cost modelingat each rebalance step.

Run multi-period diagnostics (persistence, downside, regime drift) using the multi-factor model to separate idiosyncratic skill from factor beta and crowding.
Build model baskets with Objectives like Benchmark Tracking, Maximise GMV, or Risk Minimisation, bounded by Constraints for Capital, Positions, Factor/Beta bands, and Turnover—and verify fit via factor exposures.
Backtest to set expectations and produce client ready explanations with factor-aware attribution.
View client assets through a consolidated lens (stocks + mutual funds + PMS) and keep them aligned to target factor profiles.

Design, simulate, and validate factor or thematic products directly through the multifactor risk model—ensuring each ETF or SIF has intentional exposures and low overlap with existing products.
Set Objectives like Benchmark Tracking, Factor Exposure Targeting, or Risk Minimization, bounded by Constraints for Capital, Turnover, Tracking Error, and Factor/Beta bands.
Model pre-launch performance via backtests with Start/End Dates, Rebalance Frequency, and execution cost modelingat each rebalance step.

Design, simulate, and validate factor or thematic products directly through the multifactor risk model—ensuring each ETF or SIF has intentional exposures and low overlap with existing products.
Set Objectives like Benchmark Tracking, Factor Exposure Targeting, or Risk Minimization, bounded by Constraints for Capital, Turnover, Tracking Error, and Factor/Beta bands.
Model pre-launch performance via backtests with Start/End Dates, Rebalance Frequency, and execution cost modelingat each rebalance step.

Outcomes

Robust, factor-aware ETFs and SIFsGoal-aligned
Transparent performance attribution

Robust, factor-aware ETFs and SIFsGoal-aligned
Transparent performance attribution

Better manager selection — fewer style surprises
Goal-aligned baskets — tailored to client risk, style, and factor targets
Higher trust — through transparent, plain-English, factor-aware analytics

Robust, factor-aware ETFs and SIFsGoal-aligned
Transparent performance attribution

Robust, factor-aware ETFs and SIFsGoal-aligned
Transparent performance attribution

/ From first data pull to live decisions - six ways MethodTech upgrades the workflow.

Data you can defend

(point-in-time)

We store data exactly as it appears each day, with audit trails and point-in-time snapshots, so backtests and decisions remain fully reproducible and regulator-ready.

Data you can defend

(point-in-time)

Easily grow your site with a built-in CMS, AI-powered localization,

and maintenance tools, designed to handle projects of any size.

Expose Hidden Risks before They Hit P&L

Real-time factor tilts, ownership metrics, and idiosyncratic flags highlight vulnerabilities early, so managers can neutralise blind spots instead of explaining losses.

Expose Hidden Risks Before They Hit P&L

Real-time factor tilts, ownership metrics, and idiosyncratic flags highlight vulnerabilities early, so managers can neutralise blind spots instead of explaining losses.

From idea to mandate-checked portfolio

Optimize under objectives—maximize IR, minimize variance or tracking error, target factors, or cap drawdowns—within constraints for single-name, sector, liquidity, turnover, derivatives, and benchmark bands.

Validate with history and stress

Re-run every idea on long, audited point-in-time history and event stresses to quantify robustness, reveal hidden sensitivities, and understand outcomes before real capital moves.

Validate Every Strategy with 15 Years of Robust Back-Tests & Stress Scenarios

Event-driven simulations and

point-in-time data let you de-risk concepts long before real capital is deployed, accelerating conviction cycles.

Explain performance, fast

Attribution across any period separates factor, sector, and stock contributions; drill from portfolio to position to clearly see which names actually drove each factor.

Empower Fundamental Desks without Extra Headcount, while Quants Keep Their APIs

No-code dashboards deliver factor insight to fundamental PMs; Python endpoints slot straight into quant pipelines, everyone works in their native habitat with zero heavy lifting.

From idea to mandate-checked portfolio

Explain performance, fast

Attribution across any period separates factor, sector, and stock contributions; drill from portfolio to position to clearly see which names actually drove each factor.

Explain performance, fast

Attribution across any period separates factor, sector, and stock contributions; drill from portfolio to position to clearly see which names actually drove each factor.

Built for fundamental and quant teams

No-code dashboards for PMs and risk, with Python APIs for quants, so each team works natively while staying aligned through shared data and controls.

Built for fundamental and quant teams

No-code dashboards for PMs and risk, with Python APIs for quants, so each team works natively while staying aligned through shared data and controls.

/ About Us

MethodTech, founded in 2024, melds state-of-the-art machine-learning with rigorous quantitative finance to deliver an ML-powered operating system for institutional portfolio managers, whether they pursue systematic or fundamentally driven strategies.

Rachit Parasrampuria, a Computer Science and Economics grad from Cornell University, Rachit led Portfolio Monetization within Citadel’s Equity Quant Research team, building systematic strategies and scalable investment pipelines for multibillion dollar portfolios.

Siddhartha Kochar, an MBA from Columbia University, Siddhartha established and led the India business for a global credit infrastructure firm and previously co-founded an additive manufacturing venture. He brings fundamental investing experience of over a decade.

Siddhartha Kochar, an MBA from Columbia University, Siddhartha established and led the India business for a global credit infrastructure firm and previously co-founded an additive manufacturing venture. He brings fundamental investing experience of over a decade.

FAQs

What is Methodtech?

MethodTech is India's first full-stack platform for systematic investing and it combines clean financial data, quant research tools, portfolio optimizers, execution infrastructure, and analytics, all in one modular SaaS platform.

Who is MethodTech built for?

We serve asset managers, PMS/AIF firms, brokers, and distributors looking to modernize their investment processes. Whether you're running complex quant strategies or advising retail clients, MethodTech offers tailored tools to fit your workflow.

Do I need a technical team to use the platform?

Not at all. MethodTech features a no-code/low-code interface designed for portfolio managers, analysts, and advisors. Quant teams can still access deeper control via code and APIs.

Can I use only specific modules of the platform?

Yes. We're built with Indian market regulation in mind. Our execution stack, analytics tools, and audit logs are designed to align with SEBI guidelines from day one.

What is Methodtech?

Who is MethodTech built for?

Do I need a technical team to use the platform?

Not at all. MethodTech features a no-code/low-code interface designed for portfolio managers, analysts, and advisors. Quant teams can still access deeper control via code and APIs.

Can I use only specific modules of the platform?

Yes. We're built with Indian market regulation in mind. Our execution stack, analytics tools, and audit logs are designed to align with SEBI guidelines from day one.

Experience MethodTech,

Book a founder demo and

elevate portfolios today.

Request demo

FAQs

What is MethodTech?

MethodTech is India’s first full-stack platform for systematic investing. It combines clean financial data, quant research tools, portfolio optimizers, execution infrastructure, and analytics—all in one modular SaaS platform.

Who is MethodTech built for?

Do I need a technical team to use the platform?

Not at all. MethodTech features a no-code/low-code interface designed for portfolio managers, analysts, and advisors. Quant teams can still access deeper control via code and APIs.

Can I use only specific modules of the platform?

Our entire platform is modular. We offer each component separately, in two different formats - UI and API.

Experience MethodTech,

Book a founder demo and

elevate portfolios today.

Request demo

/ Join Our Newsletter

Be a part of the MethodTech network. Subscribe now to get information about updates, new releases and more.

/ From first data pull to live decisions - six ways MethodTech upgrades the workflow.

Data you can defend

(point-in-time)

We store data exactly as it appears each day, with audit trails and point-in-time snapshots, so backtests and decisions remain fully reproducible and regulator-ready.

Data you can defend

(point-in-time)

We store data exactly as it appears each day, with audit trails and point-in-time snapshots, so backtests and decisions remain fully reproducible and regulator-ready.

See risk before it hits P&L

Live factor and exposure dashboards flag liquidity pockets and stock-specific outliers, with a factor view that shows which stocks load onto which underlying drivers.

See risk before it hits P&L

Live factor and exposure dashboards flag liquidity pockets and stock-specific outliers, with a factor view that shows which stocks load onto which underlying drivers.

From idea to mandate-checked portfolio

Validate with history and stress

Re-run every idea on long, audited point-in-time history and event stresses to quantify robustness, reveal hidden sensitivities, and understand outcomes before real capital moves.

Validate with history and stress

Re-run every idea on long, audited point-in-time history and event stresses to quantify robustness, reveal hidden sensitivities, and understand outcomes before real capital moves.

Explain performance, fast

Attribution across any period separates factor, sector, and stock contributions; drill from portfolio to position to clearly see which names actually drove each factor.

Explain performance, fast

Attribution across any period separates factor, sector, and stock contributions; drill from portfolio to position to clearly see which names actually drove each factor.

Built for fundamental and quant teams

No-code dashboards for PMs and risk, with Python APIs for quants, so each team works natively while staying aligned through shared data and controls.

Built for fundamental and quant teams

No-code dashboards for PMs and risk, with Python APIs for quants, so each team works natively while staying aligned through shared data and controls.

/ About Us

Rachit Parasrampuria, a Computer Science and Economics grad from Cornell University, Rachit led Portfolio Monetization within Citadel’s Equity Quant Research team, building systematic strategies and scalable investment pipelines for multibillion dollar portfolios.

Siddhartha Kochar, an MBA from Columbia University, established and led the India business at Nova Credit and co-founded an additive manufacturing venture prior to that. He brings venture building and fundamental investing experience.

FAQs

What is MethodTech?

Who is MethodTech built for?

Do I need a technical team to use the platform?

Not at all. MethodTech features a no-code/low-code interface designed for portfolio managers, analysts, and advisors. Quant teams can still access deeper control via code and APIs.

Can I use only specific modules of the platform?

Our entire platform is modular. We offer each component separately, in two different formats - UI and API.

What is MethodTech?

Who is MethodTech built for?

Do I need a technical team to use the platform?

Not at all. MethodTech features a no-code/low-code interface designed for portfolio managers, analysts, and advisors. Quant teams can still access deeper control via code and APIs.

Can I use only specific modules of the platform?

Our entire platform is modular. We offer each component separately, in two different formats - UI and API.

Experience MethodTech,

Book a founder demo and

elevate portfolios today.

Request demo

/ Join Our Newsletter

Be a part of the MethodTech network. Subscribe now to get information about updates, new releases and more.

/ What is Systematic Investing?

A disciplined, data-driven playbook using advanced models to transform market insight into repeatable, risk-aware edge and smarter, consistent investing decisions.

Learn More

/ What is MethodTech?

Risk Model

Built in-house and integrated across the platform

A Risk Model sits at the
Core of Systematic Investing

What is a Risk Model?

A statistical framework that converts every position into systematic factor exposures and stock specific (idiosyncratic) risk, letting you gauge future volatility and return drivers before you trade.

Why use a Risk Model?

Decomposes each holding into Factor vs Idiosyncratic Risk.

Adjusts weights in real time to hit your target IR, instantly reflected on the dashboard.

Quantify risk in a standardised manner.

Lets you size bets, not just rupees, before they hit the blotter.

Why use a Risk Model?

Decomposes each holding into Factor vs Idiosyncratic Risk.

Adjusts weights in real time to hit your target IR, instantly reflected on the dashboard.

Quantify risk in a standardised manner.

Lets you size bets, not just rupees, before they hit the blotter.

/ Built for Systematic Investing

The quantitative layer for every portfolio

Explain returns, control risk, and optimize outcomes on one robust SaaS platform.

Request demo

/ From first data pull to live decisions - six ways MethodTech upgrades the workflow.

Data you can defend

(point-in-time)

We store data exactly as it appears each day, with audit trails and point-in-time snapshots, so backtests and decisions remain fully reproducible and regulator-ready.

Data you can defend

(point-in-time)

We store data exactly as it appears each day, with audit trails and point-in-time snapshots, so backtests and decisions remain fully reproducible and regulator-ready.

See risk before it hits P&L

Live factor and exposure dashboards flag liquidity pockets and stock-specific outliers, with a factor view that shows which stocks load onto which underlying drivers.

See risk before it hits P&L

Live factor and exposure dashboards flag liquidity pockets and stock-specific outliers, with a factor view that shows which stocks load onto which underlying drivers.

From idea to mandate-checked portfolio

Validate with history and stress

Re-run every idea on long, audited point-in-time history and event stresses to quantify robustness, reveal hidden sensitivities, and understand outcomes before real capital moves.

Validate with history and stress

Re-run every idea on long, audited point-in-time history and event stresses to quantify robustness, reveal hidden sensitivities, and understand outcomes before real capital moves.

Explain performance, fast

Attribution across any period separates factor, sector, and stock contributions; drill from portfolio to position to clearly see which names actually drove each factor.

Explain performance, fast

Attribution across any period separates factor, sector, and stock contributions; drill from portfolio to position to clearly see which names actually drove each factor.

Built for fundamental and quant teams

No-code dashboards for PMs and risk, with Python APIs for quants, so each team works natively while staying aligned through shared data and controls.

Built for fundamental and quant teams

No-code dashboards for PMs and risk, with Python APIs for quants, so each team works natively while staying aligned through shared data and controls.

/ About Us

Rachit Parasrampuria, a Computer Science and Economics grad from Cornell University, Rachit led Portfolio Monetization within Citadel’s Equity Quant Research team, building systematic strategies and scalable investment pipelines for multibillion dollar portfolios.

Siddhartha Kochar, an MBA from Columbia University, established and led the India business at Nova Credit and co-founded an additive manufacturing venture prior to that. He brings venture building and fundamental investing experience.

FAQs

What is MethodTech?

Who is MethodTech built for?

Do I need a technical team to use the platform?

Not at all. MethodTech features a no-code/low-code interface designed for portfolio managers, analysts, and advisors. Quant teams can still access deeper control via code and APIs.

Can I use only specific modules of the platform?

Our entire platform is modular. We offer each component separately, in two different formats - UI and API.

What is MethodTech?

Who is MethodTech built for?

Do I need a technical team to use the platform?

Not at all. MethodTech features a no-code/low-code interface designed for portfolio managers, analysts, and advisors. Quant teams can still access deeper control via code and APIs.

Can I use only specific modules of the platform?

Our entire platform is modular. We offer each component separately, in two different formats - UI and API.

Experience MethodTech,

Book a founder demo and

elevate portfolios today.

Request demo

/ Join Our Newsletter

Be a part of the MethodTech network. Subscribe now to get information about updates, new releases and more.

/ Built for Systematic Investing

/ Built for Systematic Investing

The quantitative layer for every portfolio

The quantitative layer for every portfolio

/ What is Systematic Investing?

/ What is Systematic Investing?

A disciplined, data-driven playbook using advanced models to transform market insight into repeatable, risk-aware edge and smarter, consistent investing decisions.

A disciplined, data-driven playbook using advanced models to transform market insight into repeatable, risk-aware edge and smarter, consistent investing decisions.

/ What is MethodTech?

/ What is MethodTech?

Risk Model

Risk Model

Built in-house and integrated across the platform

Built in-house and integrated across the platform

A Risk Model sits at the Core of Systematic Investing

A Risk Model sits at the Core of Systematic Investing

Why use a Risk Model?

Why use a Risk Model?

/ Built for Systematic Investing

The quantitative layer for every portfolio

/ What is Systematic Investing?

A disciplined, data-driven playbook using advanced models to transform market insight into repeatable, risk-aware edge and smarter, consistent investing decisions.

/ What is MethodTech?

Risk Model

Built in-house and integrated across the platform

A Risk Model sits at the Core of Systematic Investing

Why use a Risk Model?

Why use a Risk Model?

Use Cases

Build systematic strategies without the stack. India-first data, a 39-factor risk model, explicit Objectives and Constraints, and point-in-time backtests all in one platform. Understand portfolio risks, decompose to single stocks, and isolate idiosyncratic returns with advanced analytics.

Quant PMs

Quant PMs

Quant PMs

Quant PMs

Quant PMs

Build, validate, and scale systematic strategies—end to end.

Build, validate, and scale systematic strategies—end to end.

Build, validate, and scale systematic strategies—end to end.

Build, validate, and scale systematic strategies—end to end.

Build, validate, and scale systematic strategies—end to end.

Fundamental PMs

Fundamental PMs

Fundamental PMs

Fundamental PMs

Fundamental PMs

Keep conviction. Remove unintended bets.

Keep conviction. Remove unintended bets.

Keep conviction. Remove unintended bets.

Keep conviction. Remove unintended bets.

Keep conviction. Remove unintended bets.

Wealth Manager

Wealth Manager

Wealth Manager

Wealth Manager

Wealth Manager

Evaluate managers on skill vs luck. Design clientfit baskets.

Evaluate managers on skill vs luck. Design clientfit baskets.

Evaluate managers on skill vs luck. Design clientfit baskets.

Evaluate managers on skill vs luck. Design clientfit baskets.

Evaluate managers on skill vs luck. Design clientfit baskets.

Product & ETF Teams

Product & ETF Teams

Product & ETF Teams

Product & ETF Teams

Product & ETF Teams

Launch and maintain factor funds, SIFs, and passive/quant products with industrial-strength analytics and monitoring.

Launch and maintain factor funds, SIFs, and passive/quant products with industrial-strength analytics and monitoring.

Evaluate managers on skill vs luck. Design clientfit baskets.

Launch and maintain factor funds, SIFs, and passive/quant products with industrial-strength analytics and monitoring.

Launch and maintain factor funds, SIFs, and passive/quant products with industrial-strength analytics and monitoring.

/ From first data pull to live decisions - six ways MethodTech upgrades the workflow.

/ From first data pull to live decisions - six ways MethodTech upgrades the workflow.

/ About Us

/ About Us

Rachit Parasrampuria, a Computer Science and Economics grad from Cornell University, Rachit led Portfolio Monetization within Citadel’s Equity Quant Research team, building systematic strategies and scalable investment pipelines for multibillion dollar portfolios.

Rachit Parasrampuria, a Computer Science and Economics grad from Cornell University, Rachit led Portfolio Monetization within Citadel’s Equity Quant Research team, building systematic strategies and scalable investment pipelines for multibillion dollar portfolios.

Siddhartha Kochar, an MBA from Columbia University, Siddhartha established and led the India business for a global credit infrastructure firm and previously co-founded an additive manufacturing venture. He brings fundamental investing experience of over a decade.

Siddhartha Kochar, an MBA from Columbia University, Siddhartha established and led the India business for a global credit infrastructure firm and previously co-founded an additive manufacturing venture. He brings fundamental investing experience of over a decade.

FAQs

FAQs

A Risk Model sits at the
Core of Systematic Investing

A Risk Model sits at
the Core of Systematic Investing

A Risk Model sits at the
Core of Systematic Investing

A Risk Model sits at the
Core of Systematic Investing