Optimize Quantitative Futures Position Management
Budget: $30 – $250 USD
I am working with a trading strategy that already has defined entry conditions, fixed stop loss, fixed take profit, and controlled risk per trade.
The focus of this project is not to improve entry signals, but to optimize position management after a trade is already executed.
I have conducted a backtest over approximately 6 years of historical data.
The current results show a profit factor around 1.0, with both winning and losing years, indicating that the strategy does not yet express a clear statistical edge.
Project Goal
The goal is to deeply analyze trade data in order to:
Understand how trades behave after entry
Identify meaningful structures and behaviors in the data without relying on predefined assumptions
Determine, based on evidence, which position management approaches are actually effective
Identify concrete opportunities to improve the profit factor
Target: increase profit factor from ~1.0 to at least 1.3, with higher improvement preferred, based on solid statistical evidence.
Expected Approach
I am looking for someone who works from data to conclusions, not from assumptions.
Expected workflow:
Define required data
You should clearly specify:
What data you need
Required format
Deep analysis
Based on the data:
Analyze full trade behavior
Detect relevant relationships
Identify inefficiencies in current position management
Clear interpretation (critical)
I need the output to act as a “data translator”, meaning:
Clear explanation of what the data actually shows
Actionable insights
Identification of performance bottlenecks
Recommendations grounded in data
(Not just code or charts — interpretation is essential.)
Expected Deliverables
Clear list of required data
Analysis report (PDF, notebook, or presentation)
Relevant visualizations
Concrete position management recommendations
The focus of this project is not to improve entry signals, but to optimize position management after a trade is already executed.
I have conducted a backtest over approximately 6 years of historical data.
The current results show a profit factor around 1.0, with both winning and losing years, indicating that the strategy does not yet express a clear statistical edge.
Project Goal
The goal is to deeply analyze trade data in order to:
Understand how trades behave after entry
Identify meaningful structures and behaviors in the data without relying on predefined assumptions
Determine, based on evidence, which position management approaches are actually effective
Identify concrete opportunities to improve the profit factor
Target: increase profit factor from ~1.0 to at least 1.3, with higher improvement preferred, based on solid statistical evidence.
Expected Approach
I am looking for someone who works from data to conclusions, not from assumptions.
Expected workflow:
Define required data
You should clearly specify:
What data you need
Required format
Deep analysis
Based on the data:
Analyze full trade behavior
Detect relevant relationships
Identify inefficiencies in current position management
Clear interpretation (critical)
I need the output to act as a “data translator”, meaning:
Clear explanation of what the data actually shows
Actionable insights
Identification of performance bottlenecks
Recommendations grounded in data
(Not just code or charts — interpretation is essential.)
Expected Deliverables
Clear list of required data
Analysis report (PDF, notebook, or presentation)
Relevant visualizations
Concrete position management recommendations