NinjaTrader 8 Machine-Learning Trading System Development
Budget: $2 – $8 USD
We are hiring a developer to build a machine-learning trading system for NinjaTrader 8 focused on futures trading and intraday market analysis.
This is not a simple indicator or rule-based strategy project. We are looking for someone who can build a complete workflow that combines NinjaTrader/NinjaScript execution with a Python-based machine-learning pipeline for training, validation, and live signal generation.
The ideal candidate should have experience with:
* NinjaTrader 8 / NinjaScript / C#
* Python machine-learning workflows
* futures market data and quantitative trading systems
* backtesting, walk-forward testing, and model validation
* integrating ML models into simulated or live trading environments
The project includes:
* building a NinjaTrader 8 execution strategy
* creating a feature engineering and market data pipeline
* training ML models to generate long, short, or no-trade signals
* connecting trained Python models back into NinjaTrader for live inference
* implementing risk management inside NinjaTrader, including stop loss, profit targets, trading session filters, daily loss limits, and one-position-at-a-time logic
* building tools for backtesting, walk-forward validation, and simulation testing
* delivering documentation and full source code
We are specifically looking for someone who understands the difference between traditional automated strategies and true machine-learning systems, including how to properly prevent:
* look-ahead bias
* overfitting
* data leakage
* unrealistic backtest assumptions
* weak validation methods
Important for MVP:
* The MVP will focus on simulation trading only using live market data
* No live capital deployment is required initially
* Initial development should focus on intraday futures trading using 5-minute and/or tick-based data
* The ML system should generate signals or probabilities while risk management remains inside NinjaTrader
* Reliability, validation quality, and stability are more important than aggressive profit targets during the MVP stage
MVP Deliverables:
* NinjaTrader 8 data export and feature pipeline
* baseline Python ML model
* walk-forward and out-of-sample validation framework
* live inference connection between Python and NinjaTrader
* simulated live testing environment
* full source code handoff
* setup, retraining, and deployment documentation
This role is best suited for a developer who can build a robust, maintainable, and well-documented trading system rather than a black-box trading bot.
This is not a simple indicator or rule-based strategy project. We are looking for someone who can build a complete workflow that combines NinjaTrader/NinjaScript execution with a Python-based machine-learning pipeline for training, validation, and live signal generation.
The ideal candidate should have experience with:
* NinjaTrader 8 / NinjaScript / C#
* Python machine-learning workflows
* futures market data and quantitative trading systems
* backtesting, walk-forward testing, and model validation
* integrating ML models into simulated or live trading environments
The project includes:
* building a NinjaTrader 8 execution strategy
* creating a feature engineering and market data pipeline
* training ML models to generate long, short, or no-trade signals
* connecting trained Python models back into NinjaTrader for live inference
* implementing risk management inside NinjaTrader, including stop loss, profit targets, trading session filters, daily loss limits, and one-position-at-a-time logic
* building tools for backtesting, walk-forward validation, and simulation testing
* delivering documentation and full source code
We are specifically looking for someone who understands the difference between traditional automated strategies and true machine-learning systems, including how to properly prevent:
* look-ahead bias
* overfitting
* data leakage
* unrealistic backtest assumptions
* weak validation methods
Important for MVP:
* The MVP will focus on simulation trading only using live market data
* No live capital deployment is required initially
* Initial development should focus on intraday futures trading using 5-minute and/or tick-based data
* The ML system should generate signals or probabilities while risk management remains inside NinjaTrader
* Reliability, validation quality, and stability are more important than aggressive profit targets during the MVP stage
MVP Deliverables:
* NinjaTrader 8 data export and feature pipeline
* baseline Python ML model
* walk-forward and out-of-sample validation framework
* live inference connection between Python and NinjaTrader
* simulated live testing environment
* full source code handoff
* setup, retraining, and deployment documentation
This role is best suited for a developer who can build a robust, maintainable, and well-documented trading system rather than a black-box trading bot.
Related categories:
C Programming
Python
Software Architecture
C++ Programming
Risk Management
Market Analysis
Backtesting
NinjaTrader