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I will build quantitative trading systems in python for quantconnect
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 build quantitative trading systems in Python for QuantConnect, with research, validation, and implementation in the LEAN Engine.
I turn hypotheses and discretionary approaches into testable algorithms or review existing strategies, code, and notebooks.
Scope may include data engineering, asset selection, signals and factors, statistical modeling, machine learning, portfolio construction, risk controls, and execution.
Validation may include backtests accounting for costs, slippage, and liquidity; out-of-sample testing, walk-forward analysis, Monte Carlo simulations, sensitivity and regime analysis; and checks for overfitting, look-ahead bias, and data leakage.
I prepare systems for paper or live trading on QuantConnect Cloud or local LEAN, with data and broker integration, tests, logs, monitoring, and deployment support.
You receive modular code, documented assumptions, and reproducible results. Research may show that a hypothesis is not supported by the evidence.
No profit guarantees. NDA available.
Contact me before ordering to align on strategy, data, infrastructure, and deliverables.
FAQ
Can you create, migrate, or improve a strategy on QuantConnect?
Yes. I can turn hypotheses and discretionary rules into Python algorithms for LEAN, migrate strategies from other platforms, or review existing code and notebooks. Work may cover signals, asset selection, portfolios, risk, execution, and modular architecture.
What is the difference between the packages?
Foundation: research, Python/LEAN strategy, initial backtest, and report. Validated: modular system, realistic backtests, validation, risk, and paper trading preparation. Production: advanced validation, risk controls, execution, automation, monitoring, and deployment support.
How do you validate strategies and make backtests more realistic?
I configure fees, slippage, fills, and margin in LEAN. Validation may include out-of-sample, walk-forward, Monte Carlo, sensitivity, regime analysis, and stress tests. I assess liquidity, parameter stability, overfitting, look-ahead bias, and data leakage.
Do you use machine learning on QuantConnect?
Yes, when the hypothesis and data justify it. I use scikit-learn, XGBoost, or PyTorch for prediction, classification, feature selection, text analysis, or regime detection. I compare against simpler models using out-of-sample tests, costs, and controls for temporal leakage.
Which markets and exchanges do you develop systems for?
U.S. stocks/ETFs; equity/index options; futures and options on futures; FX, CFDs, spot crypto, and crypto futures. Exchanges include NYSE, Nasdaq, NYSE Arca/American, Cboe/CFE, CME, CBOT, COMEX, NYMEX, ICE, Eurex, HKFE/HKEX, KRX, and NSE/BSE. Coverage varies by asset, dataset, and connector.
Which brokers and execution platforms can you integrate?
LEAN connectors: IBKR, Charles Schwab, TradeStation, tastytrade, Alpaca, OANDA, Webull, Tradier, Public, Clear Street, Wolverine, Samco, Zerodha, Binance/Binance.US, Coinbase, Bitfinex, Bybit, Kraken, dYdX, Bloomberg EMSX/FIX, Trading Technologies, and SS&C Eze. Availability varies by environment.
Which data sources do you use with QuantConnect and LEAN?
QuantConnect datasets, broker feeds, and native integrations: Databento, Bloomberg BPIPE/Terminal Link, FactSet, Polygon, IQFeed, Theta Data, Alpha Vantage, Trading Technologies, and SS&C Eze. Market, fundamental, and alternative data depend on the dataset, subscription, and Cloud/local environment.
What technology stack do you use from research to production?
Python, QuantConnect/LEAN, QuantBook, Jupyter, pandas, NumPy, SciPy, Polars, and statsmodels. Local LEAN: LEAN CLI, Docker, Linux, SQL, Git, pytest, and CI/CD, with monitoring as needed. Deployment on QuantConnect Cloud or your own infrastructure, including AWS, Azure, or Google Cloud.
What do I need to provide to start the project?
Send your objectives, markets, assets, timeframe, hypothesis or rules, data, risk limits, and broker. Share existing code, notebooks, backtests, and documentation. Specify whether you plan to use QuantConnect Cloud or local LEAN and need research, paper trading, or live trading.
How do you handle communication and confidentiality?
I prioritize Fiverr chat and written documentation for clear communication and traceable decisions. Short Fiverr calls can clarify requirements or demonstrate results. NDA available; your strategy, code, data, documentation, and results remain confidential.
