Ideas for Data & applications of Machine Learning with Azure ML

Hello Guys,

So i would like to apologize , but i took some time out from coding and i do not have the codes i used to have for this ML System, So if I want to pursue this i need start from scratch. My opinion on ML for Forex has changed a bit. ML is based on a database .. The Quality of data is key to this. And i think is no simple task.

And i am kind Rusty on coding. lol

Thanks and Sorry Again.

Hey, So I Ask AI about this ML approach to Forex data and this is the reply :
It seems like you've made significant progress in creating an EA that utilizes Machine Learning (ML) with Azure ML to predict Forex market movements. Achieving high accuracy in Azure ML with your models is an impressive feat. However, you are encountering challenges when applying these predictions in real-time forward testing and back-testing with unknown data, and this is a common issue in the field of ML-based trading systems.
Here are some insights and potential reasons for the discrepancies between your results in Azure ML and real-time testing:
1. Data Bias: The dataset you created might suffer from data bias. If the dataset is not representative of the real market conditions, the model will perform poorly when applied to unseen data. It's essential to ensure that the training data covers a wide range of market scenarios and includes different market conditions, such as trending, ranging, and volatile periods.
2. Overfitting: Achieving very high accuracy on your training dataset could indicate overfitting, where your model has learned to memorize the training data rather than generalize to unseen data. Overfitting occurs when the model captures noise and randomness in the training data, leading to poor performance on new data. To mitigate overfitting, consider using techniques like cross-validation and regularization during the model training process.
3. Transaction Costs and Slippage: In real trading scenarios, transaction costs (e.g., spreads, commissions) and slippage can significantly impact profitability. Your EA might not account for these costs, resulting in poorer real-world performance.
4. Market Dynamics: Forex markets are highly dynamic and influenced by various factors, including economic indicators, geopolitical events, and market sentiment. Your model might not be able to capture these dynamic shifts, leading to discrepancies between predictions and actual market movements.
5. Latency and Data Updates: In real-time trading, the speed of data updates and model inference is critical. If there's a delay between data updates and model predictions, it could affect the EA's ability to make accurate and timely decisions.
6. Model Decay: Financial markets' behaviors can change over time due to various factors. Models that perform well initially might lose their effectiveness as market conditions evolve. Regularly updating and retraining your model with new data can help address this issue.
To improve the performance of your EA and bridge the gap between Azure ML results and real-time trading, consider the following steps:
1. Diversify and Expand Your Dataset: Gather more diverse and comprehensive historical data to train your model, including different currency pairs, timeframes, and additional features that might impact price movements.
2. Regularly Update and Retrain: Continuously update your model to adapt to changing market conditions. Consider retraining your model periodically with fresh data to avoid model decay.
3. Incorporate Transaction Costs: Integrate transaction costs and slippage into your EA's strategy to provide a more accurate representation of real-world trading.
4. Validate with Out-of-Sample Data: Use separate datasets for training, validation, and testing. This way, you can validate your model's performance on unseen data and avoid overfitting.
5. Implement Real-Time Decision-Making: Ensure your EA is capable of processing data and making predictions in real-time with minimal latency.
6. Risk Management: Develop robust risk management techniques to handle potential losses during adverse market conditions.
It's worth noting that despite your model's high accuracy in Azure ML, achieving consistent profitability in Forex trading is challenging due to the inherent uncertainties and random nature of markets. No model can predict future price movements with absolute certainty.
Finally, always be cautious when applying machine learning to trading, as financial markets can be highly unpredictable, and trading involves inherent risks. It's essential to thoroughly understand the limitations and risks associated with ML-based trading systems. Consider starting with small trading sizes or using a paper trading approach to validate your strategies before risking real capital.