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English
$
USD
I will develop quantitative trading systems for polymarket and kalshi
Quantitative Developer: Trading Systems from Research to Production
I build quantitative trading systems for professional traders, prop firms, hedge funds, asset managers, and fintechs, from alpha discovery through validation to production deployment. I turn market hy...
About this Gig
I turn prediction market theses and inefficiencies into proprietary quantitative systems for Polymarket, Kalshi, and other prediction markets, combining statistical rigor, robust engineering, and operational efficiency.
I work end to end: from hypothesis formulation and data acquisition to modeling, validation, automation, and deployment. Projects can start with a thesis, existing strategy, or market opportunity.
Scope may include data pipelines, feature engineering, probabilistic models, probability forecasting systems, event-driven strategies, order book analysis, market making, arbitrage across related markets, REST/WebSocket integrations, realistic backtesting, out-of-sample and robustness tests, risk controls, execution logic, monitoring, and logs.
I develop primarily in Python, with modular architecture, clean code, auditable assumptions, and technical documentation.
No generic bots or promises of profit. I turn hypotheses into reliable, testable quantitative infrastructure for professional prediction market operations.
NDA available. Before ordering, send your hypothesis, markets, data, execution requirements, infrastructure, and project goals.
Programming language:
Python
•
SQL
•
Other
Frameworks:
Scikit-learn
•
PyTorch
•
Panda
•
Other
APIs:
Other
Tools:
Jupyter Notebook
•
TensorFlow
•
Other
FAQ
What types of systems can you develop?
Probabilistic models, event-driven strategies, market making, liquidity provision, arbitrage, and custom quantitative systems. Cross-market comparisons account for resolution rules, costs, liquidity, and execution risk.
What is the difference between the packages?
The Blueprint provides technical specifications and a research plan. The Prototype implements the logic in Python with backtesting and documented tests. Production adds execution, operational controls, and deployment assistance.
What do I receive, and how does support work?
You receive technical documentation, source code where applicable, usage instructions, and results from the agreed-upon tests. Revisions and support are defined in the proposal; ongoing maintenance, data, and hosting are quoted separately.
What data and integrations can you use?
I integrate APIs from Polymarket, Kalshi, and other compatible platforms, plus relevant market data and external sources. I assess coverage, granularity, history, and permissions. Data availability may limit backtesting.
What technology stack do you use?
I use Python, SQL, pandas, NumPy, SciPy, statsmodels, and scikit-learn. Depending on the project, I also use XGBoost, PyTorch, FastAPI, PostgreSQL, Docker, pytest, asyncio, REST/WebSocket APIs, and Web3 tools as needed.
How do you validate models and strategies?
I assess data quality, data leakage, costs, slippage, and liquidity. Testing may include out-of-sample, walk-forward, Monte Carlo, and sensitivity analysis. For probabilistic models, I evaluate calibration, Brier score, and log loss.
How do you prepare the system for production?
I implement order and position management and reconciliation, exposure limits, failure handling, logging, and monitoring. Deployment includes integration tests and acceptance criteria aligned with your infrastructure.
What should I send before placing an order?
Send your idea or strategy, platform, target markets, objectives, and any available data or code. From there, we assess feasibility and define the scope, execution requirements, and acceptance criteria for the project.
Do you guarantee profits, ROI, or future performance?
No. My commitment is to the quality of the agreed-upon research, implementation, and testing. Validation may show that a hypothesis is not viable. Historical or simulated results do not guarantee future performance.
How do you handle communication and confidentiality?
I prioritize Fiverr chat and written documentation to keep decisions traceable. Brief calls can be used when needed. An NDA is available; your strategy, code, data, and results remain confidential.
