AI-Powered Trading Strategy Generator & Backtester
Budget: $1,500 – $3,000 USD
Project Overview:
I am seeking a highly skilled AI Developer and Python Programmer to develop a web-based application for creating and backtesting algorithmic trading strategies written in EasyLanguage. The final product will be fully owned by me, including 100% retention of copyright and intellectual property rights.
Key Functional Requirements:
The application must accept pre-uploaded market data (daily Open, High, Low, Close) in CSV format.
Implement AI functionality to:
Automatically generate trading strategies in EasyLanguage.
Conduct iterative backtesting until specified optimization criteria are satisfied.
For example: Continue testing until both In-Sample and Out-of-Sample Return-to-Drawdown (RR/DD) ratios exceed 3.0.
Allow user-defined segmentation of the dataset into In-Sample and Out-of-Sample periods.
Example: From a 10-year dataset, allocate 8 years for In-Sample training and 2 years for Out-of-Sample validation.
Backtest Output Requirements:
Generate an equity curve comparable to those found in TradeStation, clearly differentiating In-Sample and Out-of-Sample periods.
At a minimum, the output must include:
Return-to-Drawdown Ratio
Profit Factor
Ideally, the backtest reporting features should closely emulate those available in TradeStation.
Technical Skill Requirements:
The ideal candidate should demonstrate proficiency in:
Python programming and web application development.
Artificial Intelligence (AI) and Machine Learning (ML) techniques, particularly for strategy generation and optimization.
Experience with algorithmic trading concepts and familiarity with EasyLanguage is a significant plus.
Trading Strategy Scope:
While I am particularly interested in strategies based on:
Trend following
Mean reversion
Statistical arbitrage
…my preference is for the AI to autonomously determine the optimal strategy type using machine learning methods.
Additional Notes:
Creativity and deep technical expertise in AI/ML model selection and application are highly valued, as I am open to recommendations on which models to implement.
The solution must prioritize interpretability and performance in real-world financial data environments.
I am seeking a highly skilled AI Developer and Python Programmer to develop a web-based application for creating and backtesting algorithmic trading strategies written in EasyLanguage. The final product will be fully owned by me, including 100% retention of copyright and intellectual property rights.
Key Functional Requirements:
The application must accept pre-uploaded market data (daily Open, High, Low, Close) in CSV format.
Implement AI functionality to:
Automatically generate trading strategies in EasyLanguage.
Conduct iterative backtesting until specified optimization criteria are satisfied.
For example: Continue testing until both In-Sample and Out-of-Sample Return-to-Drawdown (RR/DD) ratios exceed 3.0.
Allow user-defined segmentation of the dataset into In-Sample and Out-of-Sample periods.
Example: From a 10-year dataset, allocate 8 years for In-Sample training and 2 years for Out-of-Sample validation.
Backtest Output Requirements:
Generate an equity curve comparable to those found in TradeStation, clearly differentiating In-Sample and Out-of-Sample periods.
At a minimum, the output must include:
Return-to-Drawdown Ratio
Profit Factor
Ideally, the backtest reporting features should closely emulate those available in TradeStation.
Technical Skill Requirements:
The ideal candidate should demonstrate proficiency in:
Python programming and web application development.
Artificial Intelligence (AI) and Machine Learning (ML) techniques, particularly for strategy generation and optimization.
Experience with algorithmic trading concepts and familiarity with EasyLanguage is a significant plus.
Trading Strategy Scope:
While I am particularly interested in strategies based on:
Trend following
Mean reversion
Statistical arbitrage
…my preference is for the AI to autonomously determine the optimal strategy type using machine learning methods.
Additional Notes:
Creativity and deep technical expertise in AI/ML model selection and application are highly valued, as I am open to recommendations on which models to implement.
The solution must prioritize interpretability and performance in real-world financial data environments.
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