Advanced Strategy Development for QuantConnect LEAN
Budget: $10 – $30 USD
This project is simple for anyone who understands Trading and strategy creation with existing data!!!!!! Max $10 - $20
PROJECT: QuantConnect LEAN Strategy Development Using Custom Market Structure Research Engine (If you have a better Quantitative Research engine than QuantConnect Lean, that is open source, I am open to it. But I believe QuantConnect Lean is currently the best free option).
OBJECTIVE
I have already developed a custom Python research platform that processes Interactive Brokers Time & Sales data into proprietary Non-Print Bid/Ask Market Structure engines.
I am looking for an experienced QuantConnect LEAN / quantitative trading developer to build a complete research and strategy development workflow around this engine. "ALL PROPRIETARY WORK WILL BE DONE FROM MY DESKTOP"
The goal is not simply to code one strategy.
The goal is to create a repeatable quantitative research framework that can continually discover, test, rank, optimize, and deploy high-quality trading strategies using my proprietary market structure data.
This project should become the foundation for all future strategy development.
IMPORTANT
This is NOT a standard indicator-based trading system.
The strategy must be built primarily from my custom structural data generated by my research engine.
Traditional indicators (EMA, ATR, RSI, VWAP, etc.) may be used only if they objectively improve performance and survive validation.
The strategy should allow the data to determine what works—not force preconceived trading ideas.
EXPERIENCE REQUIRED
Please apply only if you have strong experience with:
QuantConnect LEAN
Python
Algorithmic Trading
Quantitative Research
Feature Engineering
Strategy Optimization
Walk-Forward Analysis
Monte Carlo Validation
Statistical Testing
Futures Trading
Market Microstructure
Machine Learning (optional but preferred)
Please include examples of previous quantitative strategy work.
DATA PROVIDED
I will provide my custom research engine and exported datasets.
The engine produces proprietary structural information including:
Bid Non-Print Structure Engine
Ask Non-Print Structure Engine
Multi-Resolution Line Break Structures (1–100)
Structure States
Structure Phases
Structural Events
Compression / Expansion States
Efficiency Metrics
Event Counts
Structural Counts
Void Metrics
Velocity
Acceleration
Persistence
Historical Event Database
Replayable Historical Data
CSV exports
The data is already processed and ready for research.
PROJECT REQUIREMENTS
Phase 1 — Data Integration
Import my exported datasets into QuantConnect LEAN.
Create a clean and reusable data pipeline.
The pipeline should make it easy to replace datasets in the future without rewriting the strategy.
Future workflow should simply be:
Export new research data →
Import into LEAN →
Generate new strategy research →
Evaluate →
Deploy.
Phase 2 — Feature Engineering
Create as many meaningful research features as possible from my data.
Examples include (but are not limited to):
Structure Alignment
Bid vs Ask Imbalance
Compression Ratios
Expansion Ratios
Event Density
Event Frequency
Structure Persistence
Structure Velocity
Structure Acceleration
Structural Momentum
Phase Transitions
Phase Duration
Resolution Agreement
Resolution Disagreement
Multi-Timeframe Consensus
Structural Efficiency
Structural Stability
Event Clustering
Trend Persistence
Structural Exhaustion
Mean Reversion Characteristics
Breakout Characteristics
The objective is to extract as much useful information from the data as possible.
Phase 3 — Automated Strategy Discovery
I do not want a manually designed strategy.
Instead, I want an intelligent research process that systematically discovers profitable trading rules.
Examples include:
Testing thousands of rule combinations
Testing parameter combinations
Testing different entry logic
Testing different exit logic
Testing different stop methods
Testing different profit targets
Testing filters
Testing combinations of structural metrics
Ranking all discovered strategies
The process should be repeatable.
Phase 4 — Validation
Any discovered strategy must pass rigorous validation before being accepted.
Validation should include:
In-Sample Testing
Out-of-Sample Testing
Walk-Forward Analysis
Monte Carlo Analysis
Parameter Robustness Testing
Stability Testing
Sensitivity Testing
Overfitting Detection
Cross Validation where appropriate
Only robust strategies should be retained.
Phase 5 — Strategy Ranking
Rank discovered strategies using objective performance metrics such as:
Net Profit
Profit Factor
Sharpe Ratio
Sortino Ratio
Win Rate
Average Trade
Expectancy
Maximum Drawdown
Recovery Factor
Ulcer Index
Risk-Adjusted Return
Trade Stability
Equity Curve Smoothness
Parameter Stability
Walk-Forward Performance
Out-of-Sample Performance
The goal is to identify strategies that are statistically reliable rather than those with the highest historical profit.
Phase 6 — Final Strategy Output
For each accepted strategy, provide:
Complete entry rules
Complete exit rules
Position sizing rules
Stop-loss rules
Profit target rules
Time-based exit rules
Risk management rules
Required parameters
Feature descriptions
Performance report
Validation report
Code ready to run in QuantConnect LEAN
PROP FIRM REQUIREMENTS
The final strategy must be designed to comply with prop firm trading rules.
Current requirements include:
$3,000 Profit Target
$2,000 End-of-Day Drawdown
No Daily Loss Limit during Evaluation/Simulation
Maximum Position Size: 5 Contracts
10:1 Micro Scaling
50% Consistency Rule
Every position must remain open for at least 10–11 seconds before exiting
The strategy should account for these constraints during development and testing, rather than treating them as an afterthought.
FUTURE RESEARCH WORKFLOW
One of the most important goals of this project is creating a repeatable research process.
My ideal workflow should be:
Capture new market data.
Generate updated structural datasets with my research engine.
Import the new datasets into QuantConnect LEAN.
Automatically generate candidate strategies.
Validate all strategies.
Rank the best strategies.
Produce a complete report with trading rules and performance statistics.
Select a strategy for forward testing and eventual live deployment.
This process should require minimal manual intervention and be reusable whenever new research data becomes available.
DELIVERABLES
QuantConnect LEAN project
Clean data import pipeline
Automated feature engineering pipeline
Automated strategy generation framework
Strategy optimization framework
Validation framework
Robustness testing framework
Strategy ranking system
Complete source code
Documentation explaining the workflow
Instructions for running the process with future datasets
PROJECT: QuantConnect LEAN Strategy Development Using Custom Market Structure Research Engine (If you have a better Quantitative Research engine than QuantConnect Lean, that is open source, I am open to it. But I believe QuantConnect Lean is currently the best free option).
OBJECTIVE
I have already developed a custom Python research platform that processes Interactive Brokers Time & Sales data into proprietary Non-Print Bid/Ask Market Structure engines.
I am looking for an experienced QuantConnect LEAN / quantitative trading developer to build a complete research and strategy development workflow around this engine. "ALL PROPRIETARY WORK WILL BE DONE FROM MY DESKTOP"
The goal is not simply to code one strategy.
The goal is to create a repeatable quantitative research framework that can continually discover, test, rank, optimize, and deploy high-quality trading strategies using my proprietary market structure data.
This project should become the foundation for all future strategy development.
IMPORTANT
This is NOT a standard indicator-based trading system.
The strategy must be built primarily from my custom structural data generated by my research engine.
Traditional indicators (EMA, ATR, RSI, VWAP, etc.) may be used only if they objectively improve performance and survive validation.
The strategy should allow the data to determine what works—not force preconceived trading ideas.
EXPERIENCE REQUIRED
Please apply only if you have strong experience with:
QuantConnect LEAN
Python
Algorithmic Trading
Quantitative Research
Feature Engineering
Strategy Optimization
Walk-Forward Analysis
Monte Carlo Validation
Statistical Testing
Futures Trading
Market Microstructure
Machine Learning (optional but preferred)
Please include examples of previous quantitative strategy work.
DATA PROVIDED
I will provide my custom research engine and exported datasets.
The engine produces proprietary structural information including:
Bid Non-Print Structure Engine
Ask Non-Print Structure Engine
Multi-Resolution Line Break Structures (1–100)
Structure States
Structure Phases
Structural Events
Compression / Expansion States
Efficiency Metrics
Event Counts
Structural Counts
Void Metrics
Velocity
Acceleration
Persistence
Historical Event Database
Replayable Historical Data
CSV exports
The data is already processed and ready for research.
PROJECT REQUIREMENTS
Phase 1 — Data Integration
Import my exported datasets into QuantConnect LEAN.
Create a clean and reusable data pipeline.
The pipeline should make it easy to replace datasets in the future without rewriting the strategy.
Future workflow should simply be:
Export new research data →
Import into LEAN →
Generate new strategy research →
Evaluate →
Deploy.
Phase 2 — Feature Engineering
Create as many meaningful research features as possible from my data.
Examples include (but are not limited to):
Structure Alignment
Bid vs Ask Imbalance
Compression Ratios
Expansion Ratios
Event Density
Event Frequency
Structure Persistence
Structure Velocity
Structure Acceleration
Structural Momentum
Phase Transitions
Phase Duration
Resolution Agreement
Resolution Disagreement
Multi-Timeframe Consensus
Structural Efficiency
Structural Stability
Event Clustering
Trend Persistence
Structural Exhaustion
Mean Reversion Characteristics
Breakout Characteristics
The objective is to extract as much useful information from the data as possible.
Phase 3 — Automated Strategy Discovery
I do not want a manually designed strategy.
Instead, I want an intelligent research process that systematically discovers profitable trading rules.
Examples include:
Testing thousands of rule combinations
Testing parameter combinations
Testing different entry logic
Testing different exit logic
Testing different stop methods
Testing different profit targets
Testing filters
Testing combinations of structural metrics
Ranking all discovered strategies
The process should be repeatable.
Phase 4 — Validation
Any discovered strategy must pass rigorous validation before being accepted.
Validation should include:
In-Sample Testing
Out-of-Sample Testing
Walk-Forward Analysis
Monte Carlo Analysis
Parameter Robustness Testing
Stability Testing
Sensitivity Testing
Overfitting Detection
Cross Validation where appropriate
Only robust strategies should be retained.
Phase 5 — Strategy Ranking
Rank discovered strategies using objective performance metrics such as:
Net Profit
Profit Factor
Sharpe Ratio
Sortino Ratio
Win Rate
Average Trade
Expectancy
Maximum Drawdown
Recovery Factor
Ulcer Index
Risk-Adjusted Return
Trade Stability
Equity Curve Smoothness
Parameter Stability
Walk-Forward Performance
Out-of-Sample Performance
The goal is to identify strategies that are statistically reliable rather than those with the highest historical profit.
Phase 6 — Final Strategy Output
For each accepted strategy, provide:
Complete entry rules
Complete exit rules
Position sizing rules
Stop-loss rules
Profit target rules
Time-based exit rules
Risk management rules
Required parameters
Feature descriptions
Performance report
Validation report
Code ready to run in QuantConnect LEAN
PROP FIRM REQUIREMENTS
The final strategy must be designed to comply with prop firm trading rules.
Current requirements include:
$3,000 Profit Target
$2,000 End-of-Day Drawdown
No Daily Loss Limit during Evaluation/Simulation
Maximum Position Size: 5 Contracts
10:1 Micro Scaling
50% Consistency Rule
Every position must remain open for at least 10–11 seconds before exiting
The strategy should account for these constraints during development and testing, rather than treating them as an afterthought.
FUTURE RESEARCH WORKFLOW
One of the most important goals of this project is creating a repeatable research process.
My ideal workflow should be:
Capture new market data.
Generate updated structural datasets with my research engine.
Import the new datasets into QuantConnect LEAN.
Automatically generate candidate strategies.
Validate all strategies.
Rank the best strategies.
Produce a complete report with trading rules and performance statistics.
Select a strategy for forward testing and eventual live deployment.
This process should require minimal manual intervention and be reusable whenever new research data becomes available.
DELIVERABLES
QuantConnect LEAN project
Clean data import pipeline
Automated feature engineering pipeline
Automated strategy generation framework
Strategy optimization framework
Validation framework
Robustness testing framework
Strategy ranking system
Complete source code
Documentation explaining the workflow
Instructions for running the process with future datasets