Polymarket Strategy Optimization & Execution

MAX $10-$30 for this project: I have had this Type of work done in the past with specialists QuantConnect LEAN Specialist — Polymarket Strategy Optimization & Execution PROJECT OVERVIEW I have an existing local Polymarket QuantConnect LEAN research and execution bridge that has already been developed. This is not a request to build the entire system from scratch. The existing architecture already includes: Polymarket API → Event Layer → States Layer → QuantConnect LEAN Bridge → Strategy Discovery → Validation → Decision → Execution I am looking for a developer who is a specialist in QuantConnect LEAN, quantitative strategy research, optimization, and automated execution. The primary goal of this project is to take the existing system and significantly improve its ability to discover, validate, optimize, and execute high-quality Polymarket strategies. PRIMARY OBJECTIVE I want the system optimized toward producing a strong, smooth, consistently growing equity curve, ideally approaching the characteristics of a highly consistent equity curve rather than simply maximizing the number of trades or raw historical profit. I understand that no developer can guarantee future profits. I am not asking for a guarantee. I am asking for a specialist who can use the existing data architecture and LEAN framework to determine what research, optimization, statistical modeling, machine learning, feature engineering, regime detection, or other quantitative techniques can realistically improve the quality and consistency of the strategies being discovered. I am specifically interested in achieving the type of consistency demonstrated by highly successful Polymarket traders, while ensuring that the resulting strategy is genuinely supported by statistical evidence rather than simply curve-fitted. EXISTING SYSTEM The current system already has: Polymarket API integration Structured event layer State classification layer QuantConnect LEAN bridge Automated strategy discovery Strategy validation Strategy decision logic Automated execution capability Local research environment Historical research data The developer's first responsibility will be to understand the existing architecture before making major changes. Do not unnecessarily rebuild functioning components. WHAT I NEED THE SPECIALIST TO DO 1. AUDIT THE EXISTING LEAN BRIDGE Review the entire current pipeline and determine: How data enters LEAN How events are represented How states are represented How features are generated How strategies are generated How parameters are optimized How validation is performed How strategies are ranked How decisions are produced How orders are executed Where information may currently be lost Where the system may be generating excessive or low-quality trades Where optimization is currently insufficient Where overfitting may exist Where execution assumptions differ from actual Polymarket execution Provide practical improvements based on the existing architecture. 2. IMPROVE STRATEGY DISCOVERY The system should search intelligently rather than simply generating large numbers of loosely filtered strategies. Research should consider, where appropriate: Entry timing Exit timing Probability/price Market duration Time remaining until settlement Position sizing Expected value Market liquidity Spread Volume Price movement Momentum Mean reversion Volatility Market regime Event/state relationships Historical settlement behavior Trade frequency Capital utilization Risk-adjusted return The goal is to discover high-quality opportunities, not simply more opportunities. 3. OPTIMIZE FOR EQUITY-CURVE QUALITY Do not optimize solely for: Net profit Win rate Number of trades Instead, investigate metrics such as: Risk-adjusted return Expectancy Profit factor Sharpe Sortino Maximum drawdown Recovery factor Equity-curve smoothness Return consistency Trade distribution Consecutive losses Consecutive wins Average trade Capital efficiency Stability across different market periods A strategy with lower total profit but substantially better consistency and risk-adjusted performance may be preferable to a strategy with higher historical profit and unstable results. 4. REDUCE UNNECESSARY TRADE FREQUENCY The system should not be rewarded for producing hundreds of marginal trades. Where statistically justified, implement research methods that identify only the highest-quality setups. Possible approaches may include: Confidence thresholds Multiple feature agreement Regime filters Market-quality filters Minimum expected-value thresholds Multi-factor confirmation Trade-spacing rules Capital-efficiency filters Opportunity ranking Probability thresholds The specialist should determine the appropriate implementation rather than blindly applying arbitrary thresholds. 5. ADVANCED QUANTITATIVE METHODS I am open to advanced methods if they genuinely improve out-of-sample robustness. Depending on what the data supports, investigate: Machine learning Classification models Regression Probability estimation Feature selection Feature importance Ensemble models Regime classification Clustering Bayesian methods Reinforcement-learning research where appropriate Walk-forward optimization Adaptive parameter selection Meta-models for strategy selection I do not want AI or machine learning added simply because it sounds advanced. Every additional technique should have a measurable research justification. 6. PREVENT OVERFITTING This is extremely important. The objective is not to find the strategy that performed best on historical data. The objective is to discover a strategy that has a reasonable probability of continuing to perform on unseen data. Use appropriate methods such as: In-sample / out-of-sample separation Walk-forward testing Monte Carlo analysis Parameter sensitivity Parameter stability Multiple historical periods Different market regimes Stress testing Transaction-cost assumptions Slippage assumptions Execution realism Robustness testing The system should reject strategies whose performance depends excessively on one narrow parameter combination or one historical period. POLYMARKET EXECUTION REALISM Because this is ultimately intended for automated Polymarket execution, the research engine must account for actual market mechanics wherever possible. Research should consider: Actual executable prices Liquidity Spread Slippage Order availability Market expiration Settlement timing Partial fills Failed orders Latency Capital availability Existing positions A backtest should not assume that an order can always be filled at an ideal historical price. STRATEGY SELECTION The final system should produce a ranked strategy library. Each strategy should contain clearly defined: Entry conditions Exit conditions Filters Position sizing Risk rules Expected performance Historical performance Validation results Drawdown Trade frequency Confidence/stability measurements The system should make it easy to understand why a strategy was selected. DECISION & EXECUTION The final architecture should allow the validated strategy to flow automatically through: Data → Events → States → Features → Strategy → Validation → Ranking → Decision → Execution The execution layer must use the actual Polymarket API and accurately reconcile: Orders Fills Positions Available balance Market status Settlement P/L Execution should never rely solely on assumptions from the backtester. RESEARCH REPEATABILITY A major requirement is that the improved system remain repeatable. When new historical data becomes available, I want to be able to run the research pipeline again and have it: Process the new data. Generate features. Test existing strategies. Discover new strategies. Re-optimize where appropriate. Validate against unseen data. Compare new strategies against the existing strategy library. Rank candidates. Identify whether the existing live strategy should remain active. Recommend replacement or retraining only when statistically justified. DELIVERABLE The final deliverable should be the improved existing Polymarket QuantConnect LEAN system, not merely a research report. I want the developer to modify and optimize the existing architecture so that it can perform: Research → Discovery → Validation → Optimization → Strategy Selection → Decision → Execution → Monitoring with as much automation as is technically and statistically responsible. I am open to significant architectural improvements if they are justified by the data and improve the system. I am also open to specialized machine learning or other quantitative techniques if the developer determines they are appropriate. IDEAL DEVELOPER I am specifically looking for someone with strong experience in: QuantConnect LEAN Algorithmic trading Quantitative research Strategy optimization Backtesting Walk-forward analysis Machine learning for trading Statistical validation Automated execution Python API-driven trading systems QuantConnect LEAN expertise is particularly important. Please explain your relevant experience and provide examples of quantitative trading systems or LEAN projects you have personally worked on. The existing system is already substantially built. I am looking for a specialist who can understand what is already there, identify its limitations, and use advanced quantitative research and optimization techniques to make the overall system substantially better. The goal is a robust, repeatable, statistically defensible strategy research and execution system with an exceptionally smooth equity curve, rather than simply a strategy that looks impressive in one backtest.

Project ID: 40648376