Code first
Building a predictive model on market data normally starts with Python, a data pipeline and a machine learning library. That requirement filters out most of the people who actually have a market thesis worth testing.
Institutional Quant Research, Without Writing Code
Velo Lab lets an investor build, train and validate machine learning models that predict market movement — XGBoost, LightGBM, Random Forest and LSTM — entirely through a browser, with walkforward simulation standing in for the engineering team a hedge fund would otherwise need.
Build Your AI Research PlatformAbout the Project
Velo Lab is a research platform for quantitative trading strategies. It gives individual investors and analysts the model-building workflow that has historically belonged to institutional quant desks — feature selection, model training, backtesting and walkforward validation — without requiring them to write Python or run their own compute.
Industry
FinTech and Quantitative Research
Market research tooling: the software layer between raw market data and a trading decision, where the output of a model has to be trusted before any money follows it.
Users
Investors and analysts who have the market thesis but not the engineering team
The platform targets people who understand markets and want to test a hypothesis systematically, rather than developers looking for a Python library.
Model Library
Gradient boosting, ensembles and sequence models behind one no-code interface
XGBoost, LightGBM and Random Forest cover tabular feature sets; LSTM covers sequence behaviour in price series. The interface exposes them as configurable experiments rather than as code.
Validation
Walkforward simulation, not a single backtest that flatters the model
Models are validated against historical data using walkforward runs that retrain and re-test in sequence, so the result reflects how a strategy would have behaved as new data arrived rather than how well it fits the past.
Platform
Python and FastAPI services behind a Next.js application, training on scalable cloud compute
Training and backtesting run on cloud infrastructure so a user is not limited by their own hardware, and results return to the browser as charts rather than as files.
Build your idea
Talk to our experts
Scope a machine learning research or backtesting platform for your market, your data and your users.
The Hard Part of Quant Research Is Not the Model. It Is Everything Around the Model
An investor with a good hypothesis still needs feature engineering, training infrastructure, and a validation method honest enough to reject their own idea.
Overfitting
A model tuned until it explains history perfectly tells you nothing about tomorrow. Research tooling has to make the honest test — train on what was known then, evaluate on what came next — the default path rather than an advanced option.
Compute
Training gradient boosted trees and sequence models across long histories and many parameter sets is not a laptop workload. Without managed compute behind it, a research tool limits every user to the experiments their own machine can finish.
From a market hypothesis
to a validated model,
without a line of Python
Start Your AI Platform
How the Platform Works
-
Configure the Experiment
Configure the Experiment
- Select the instrument, the history window and the prediction target through the interface rather than in code
- Choose the model family — gradient boosting, ensemble or sequence — and set its parameters as form inputs
- Each configuration is an experiment that can be repeated, adjusted and compared against earlier runs
-
Train on Cloud Compute
Train on Cloud Compute
- Training is dispatched to scalable cloud infrastructure, so model size and history length are not bounded by the user's hardware
- Python services handle the machine learning work; the browser application stays responsive while runs execute
- FastAPI sits between the interface and the training layer, exposing long-running jobs as tracked, resumable work
-
Validate With Walkforward Simulation
Validate With Walkforward Simulation
- Models are tested against historical data in walkforward sequence, mirroring how a strategy meets data it has never seen
- Validation is part of the standard flow rather than an expert setting, so a result that only fits the past is visible as one
- Runs are comparable across configurations, which is what makes an experiment worth keeping or discarding
-
Read the Result as a Dashboard
Read the Result as a Dashboard
- Performance charts and dashboards explain what the model did, not just what it scored
- Results are presented so a non-programmer can judge whether a pattern is real or an artefact of the window chosen
- Next.js delivers the analysis surface, keeping the research loop inside one browser session
What This Build Demonstrates
Applied machine learning delivered as a product, not as a notebook.
Machine Learning as a Product Surface
Model configuration, training, validation and interpretation are presented as an ordinary application workflow. The engineering effort goes into making the pipeline usable, not into demonstrating the algorithm.
Python Services, JavaScript Product
A FastAPI and Python layer for training and evaluation sits behind a Next.js application, which keeps the data science stack and the product stack independently deployable.
Validation Built Into the Default Path
Walkforward testing is the standard route through the product rather than an advanced toggle, which is what makes the platform suitable for research rather than for demonstration.
Cloud Compute Without Cloud Expertise
Scalable training runs are exposed to the user as a button. The infrastructure decisions — dispatch, capacity, teardown — stay inside the platform.
More Case Studies
Kotak 811: Zero-Contact Digital Account Opening
A digital savings account opened end to end from a phone, with video KYC replacing the branch visit.
Agalia: Workforce, Payroll and Banking for Nursing Homes
Shift scheduling, NFC and geofenced clock in, CMS PBJ compliance, in house payroll and embedded banking across five client apps.
Global presence
