WE SOLVE INFINITYStatistical Models
Advanced statistical and econometric models for alpha generation.
CUSTOM SOFTWARE · VIPRASOL
Quant Development
Advanced quantitative development for hedge funds, prop firms, and institutional traders. Statistical models, risk analytics, and alpha generation.
Agreed scope · Reviewable milestones · Documented handover
BUILT AROUND YOUR REQUIREMENTS
Statistical Arbitrage
Risk Modeling
Alpha Research
Portfolio Optimization
THE WORK IN PRACTICEWhat we can build
What we can build
with you.
We build institutional-grade quantitative systems that power algorithmic trading strategies. From statistical arbitrage to market-making algorithms, our quant solutions leverage advanced mathematics, machine learning, and high-performance computing.
ML Integration
Machine learning models for pattern recognition and prediction.
Risk Analytics
VaR, stress testing, and comprehensive risk metrics.
Data Infrastructure
High-performance data pipelines for market and alternative data.
Low Latency
Optimized execution for minimal market impact.
Compliance Ready
Built-in audit trails and regulatory compliance.
A CLOSER LOOK
Inside quant development.
Our quantitative trading development service builds alpha research, backtesting frameworks, and execution systems, with walk-forward validation and realistic transaction-cost modelling.
Quantitative development sits at the intersection of mathematics, computer science, and financial theory. At Viprasol, we build quantitative trading systems that go beyond simple technical analysis — we develop alpha research frameworks, statistical arbitrage models, factor-based portfolio construction systems, and high-frequency execution engines that operate at institutional standards.
The quantitative development landscape has evolved dramatically over the past decade. What was once the exclusive domain of large hedge funds and investment banks is now accessible to smaller firms and sophisticated individual traders. However, the technical barriers remain significant. Building a reliable quant system requires expertise in time series analysis, stochastic calculus, optimisation theory, and robust software engineering practices.
Our quant development services cover the full spectrum from research to production. On the research side, we help clients discover and validate alpha signals using rigorous statistical methods. We build backtesting frameworks that avoid common pitfalls like look-ahead bias, survivorship bias, and data snooping. Our backtests use realistic transaction cost models, account for market impact, and employ walk-forward analysis to assess out-of-sample performance.
On the production side, we build execution infrastructure that can handle everything from daily rebalancing of a factor portfolio to sub-second order routing for intraday strategies. We integrate with major brokers and exchanges via FIX protocol, REST APIs, and WebSocket connections. Our systems include real-time risk monitoring, position tracking, PnL attribution, and automated compliance checks.
We have built quantitative systems for asset managers running systematic equity strategies, crypto trading firms deploying market-making algorithms, commodity trading houses optimising their hedging programs, and fintech companies building robo-advisory platforms. Each engagement is tailored to the client specific requirements, risk tolerance, and regulatory environment.
THE DELIVERY PROCESSA clear path from
A clear path from
brief to handover.
We agree the requirements and review working milestones together. The process below is adapted to the systems, testing and operating needs of your project.
- 01
Alpha Hypothesis Stress Test
Before touching data: is this edge real, or is it mined from noise? We will review your hypothesis critically — most quant strategies fail because the alpha was never there to begin with, just a statistical artefact from overfitting. We'd rather challenge it now than after 6 weeks of build.
- 02
Data Infrastructure — The Unsexy Foundation
This phase takes longer than most clients expect, and that's fine. Garbage in, garbage out. Quality historical tick data, survivorship-bias-free universes, and proper corporate action adjustments are expensive and slow to get right — but they're what separates real quant work from backtesting theatre.
- 03
Signal Research with Honest Statistics
Factor modelling, signal generation, and — critically — out-of-sample validation. We use walk-forward analysis and bootstrap testing rather than cherry-picked in-sample periods. If the signal doesn't hold out-of-sample, we go back to the drawing board, not to production.
- 04
Transaction Cost Reality Check
Most backtest alphas disappear the moment you add realistic transaction costs. We model slippage, market impact, and financing costs specific to your execution venue. If the edge survives, great. If it doesn't, better to know now.
- 05
Execution & Live Risk Controls
Smart order routing, kill switches, position limits, and drawdown alerts baked in before a single live trade. Going live without these is how funds blow up. The execution system is as important as the signal.
BEFORE WE STARTLet’s define
Let’s define
a useful first version.
Share the workflow you want to improve, the platforms you use and any existing code or examples. We’ll work through the requirements, dependencies and acceptance checks with you before proposing a build.
Each custom project is quoted individually. Hosting, external services and ongoing maintenance are made clear in the agreed scope.
Talk through your requirementsPRACTICAL QUESTIONSBefore we
Before we
get started.
Answers to the questions that help define your project.
What's the minimum data requirement?
Depends on the strategy type. Equity factors need 5–10 years of daily data minimum. Intraday strategies require 2+ years of tick data. Alternative data varies by source. We'll assess your data availability before quoting so you know exactly what you're working with.
How do you prevent overfitting?
Walk-forward analysis, out-of-sample holdout periods, bootstrap testing, and explicit data leakage checks. We also run Monte Carlo analysis on the backtest results. If the alpha doesn't survive these tests, it doesn't go to production.
Do you provide ongoing alpha research?
Yes. Monthly retainer packages are available for ongoing signal development, strategy monitoring, and performance attribution. Many quant clients stay on retainer after the initial build.
What execution infrastructure do you support?
FIX protocol, Interactive Brokers TWS API, Alpaca, Binance, and custom broker integrations. We'll assess latency requirements and recommend the appropriate setup for your strategy type.
Can you work with our existing codebase?
Yes. We work in Python, R, and C++ and can extend or refactor existing quant stacks. We'll do a code review first to understand what you have before committing to scope.
Who owns the models and code?
You do, entirely. All models, signals, backtesting frameworks, and execution code are delivered to you at project end with full documentation.
How is a quant development project quoted?
We review your requirements, integrations and delivery scope before preparing a project-specific quote. The proposal sets out the work, milestones and any ongoing costs. Changes are discussed and agreed before additional work begins.
Our team combines deep mathematical knowledge with production engineering skills. We do not just build models that work in Jupyter notebooks — we build systems that run reliably in production, handle edge cases gracefully, and scale as trading volumes grow.
We take a research-first approach to every engagement. Before writing production code, we validate the underlying alpha hypothesis using rigorous statistical methods. This saves our clients from the expensive mistake of deploying systems built on spurious correlations.
We build with institutional-grade practices: version-controlled research, reproducible backtests, automated testing pipelines, and comprehensive logging. Every decision in the system can be traced back to its rationale.
Our pricing reflects the specialised nature of quantitative work. We charge fair rates for genuinely expert-level work, and we scope engagements carefully to avoid scope creep and cost overruns.
We maintain strict confidentiality around our clients trading strategies and intellectual property. We sign comprehensive NDAs and implement access controls to ensure sensitive information is protected.
How the work holds together.
Our quantitative development methodology follows the scientific method applied to financial markets. We start with hypothesis formation — identifying potential alpha sources through market microstructure analysis, academic literature review, or client-provided insights. We then move to data collection and cleaning, which typically consumes 30 to 40 percent of total project time because data quality directly determines model quality. Next comes model development using appropriate statistical and machine learning techniques, followed by rigorous backtesting with walk-forward validation, realistic transaction costs, and out-of-sample testing. Only strategies that pass these hurdles advance to paper trading and eventually live deployment with appropriate risk controls and monitoring infrastructure.
LET’S TALK ABOUT YOUR PROJECTWhat would you
What would you
like to build?
Tell us what you need from quant development. We’ll work through the scope and next steps together.