Custom quantitative research and alpha discovery for serious traders. We apply institutional research methodologies to find genuine market inefficiencies.
This Is Our Most Advanced Service
Quantitative research is time-intensive and requires significant expertise. Most research projects don\'t result in profitable strategies. We only take on research projects for serious clients with realistic expectations and adequate budgets.
high
Systematic discovery and validation of trading signals and market inefficiencies
Factor research
Pattern mining
Signal discovery
Market microstructure
Typical Pricing
$5,000 - $20,000
Timeline
4-12 weeks
very high
Machine learning and AI-based prediction models for trading
Feature engineering
Ensemble methods
Model training
Deep learning
Typical Pricing
$7,500 - $25,000
Timeline
6-16 weeks
high
Build completely new strategies from scratch based on your research ideas
Hypothesis testing
Full development
Strategy prototyping
Validation & testing
Typical Pricing
$5,000 - $30,000
Timeline
6-20 weeks
high
Deep analysis of market mechanics, order flow, and execution dynamics
Order flow analysis
Spread analysis
Liquidity modeling
Impact studies
Typical Pricing
$4,000 - $15,000
Timeline
4-10 weeks
Pairs trading
Mean reversion
Correlation trading
Statistical patterns
Predictive models
Classification algorithms
Neural networks
Reinforcement learning
Bid-ask optimization
Inventory management
Adverse selection
Market impact
Momentum strategies
Breakout systems
Regime detection
Volatility filters
Volatility trading
Greeks optimization
Skew exploitation
Delta hedging
Sentiment analysis
News processing
Social media signals
Satellite imagery
Random Forests
Gradient Boosting (XGBoost, LightGBM)
Neural Networks (LSTM, Transformers)
Support Vector Machines
Ensemble Methods
Reinforcement Learning
Clustering & Dimensionality Reduction
Time Series Forecasting
Natural Language Processing
Computer Vision for Charts
Genetic Programming
Bayesian Methods
Define research objectives, data requirements, and success criteria
Acquire, clean, and engineer features from relevant datasets
Systematically test ideas with statistical rigor
Build and validate profitable strategies from research findings
Research Documentation
Comprehensive write-up of methodology, findings, and statistical evidence
Working Code
Production-ready implementation of strategies discovered
Backtest Results
Rigorous validation with realistic assumptions
Data & Features
Engineered features and data pipelines
Statistical Analysis
Significance tests, robustness checks, and sensitivity analysis
Deployment Guide
Instructions for taking strategies live
Most research hypotheses fail. That\'s normal and expected in quant research.
Finding genuine alpha is difficult. There are no guarantees of profitability.
Research requires significant time and computational resources.
Successful strategies often have modest returns (10-30% annually) with controlled drawdowns
Schedule a consultation to discuss your research objectives and feasibility.