AI-Powered Trading Prediction Model Development
Budget: $40 – $150 USD
I am looking to hire an AI expert freelancer to develop a predictive model that will help me maximize my profits in financial trading. The model will be trained on historical data, and I want it to continuously learn from new decisions in real-time to improve its accuracy. The freelancer should also be available to monitor and follow the model's performance as needed.
I have prepared csv file with OHLC and key indicators I want to base on for decision. The file consists of 74.000 rows (dates 19.06-01.09) and each row has got my manual decision: Go long, Go short or Don’t trade. You can either consider these my manual proposed trades or ignore them. After you make a model and it has >95% accuracy, I will run this model on new data (dates: 01.09-07.10) to check the possible profit. After that I will run the model on new data (07.10-27.10) for additional verification.
You should teach me how to behave at each day on the market – if the model works for 3 months, it won’t work forever, this is why it is important to continuously improve it and provide new data from the market – you should explain the method and procedure how to update the model so it is always feeding on the newest data from the market (daily? weekly?).
Project Objectives:
• Develop an AI-based trading prediction model that can analyze historical data to make buy (long), sell (short), or hold (out of trading) recommendations.
• Implement a mechanism for the model to adapt and learn from real-time trading decisions to improve its predictive capabilities.
• Optimize the model for real-time performance and accuracy.
I need the model to work for a long term and keep give predictions with high accuracy, the model should learn from the data I have in CSV files (history) and it should start learning from the new daily data based on the best choice.
I have prepared csv file with OHLC and key indicators I want to base on for decision. The file consists of 74.000 rows (dates 19.06-01.09) and each row has got my manual decision: Go long, Go short or Don’t trade. You can either consider these my manual proposed trades or ignore them. After you make a model and it has >95% accuracy, I will run this model on new data (dates: 01.09-07.10) to check the possible profit. After that I will run the model on new data (07.10-27.10) for additional verification.
You should teach me how to behave at each day on the market – if the model works for 3 months, it won’t work forever, this is why it is important to continuously improve it and provide new data from the market – you should explain the method and procedure how to update the model so it is always feeding on the newest data from the market (daily? weekly?).
Project Objectives:
• Develop an AI-based trading prediction model that can analyze historical data to make buy (long), sell (short), or hold (out of trading) recommendations.
• Implement a mechanism for the model to adapt and learn from real-time trading decisions to improve its predictive capabilities.
• Optimize the model for real-time performance and accuracy.
I need the model to work for a long term and keep give predictions with high accuracy, the model should learn from the data I have in CSV files (history) and it should start learning from the new daily data based on the best choice.
Related categories:
Neural Networks
AI (Artificial Intelligence) HW/SW
Financial Modeling
Trading
Data Modeling