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Cryptocurrency Price Prediction

Developed a Bidirectional LSTM model for predicting next day closing prices with a MAPE of 19% and built a dynamic portfolio optimization algorithm incorporating profit-taking and stop-loss strategies.





 


Overview:

  • Developed a Bidirectional Long Short-Term Memory (BiLSTM) model for predicting next-day closing prices of cryptocurrencies.

  • Achieved a Mean Absolute Percentage Error (MAPE) of 19%.

  • Built a dynamic portfolio optimization algorithm incorporating profit-taking and stop-loss strategies to maximize returns and minimize losses.


Introduction:

  • Cryptocurrencies are highly volatile, with 75% of Bitcoin investors and 90% of stock investors losing money.

  • Investors face challenges in predicting returns and optimizing portfolios due to the market's dynamic nature.

  • The solution aims to provide AI-driven predictions and optimized trading strategies to help investors make informed decisions.


Solution Approach:


Exploratory Data Analysis (EDA):

  • Analyzed a dataset containing 24 million rows and 10 columns, focusing on trends and patterns.

  • Identified Bitcoin as the most volatile asset with a high standard deviation in closing prices.


Data Preprocessing:

  • Created a daily-level dataset from minute-level data.

  • Handled data anomalies and performed feature engineering, including scaling features and creating additional variables like RSI indicators and lagged features for close price and volume.


Predictive Modeling:

  • Implemented a Sequential Neural Network with ReLU activation and Adam optimizer.

  • Developed a BiLSTM model with 64 units in each layer and a dropout rate of 0.2 to prevent overfitting.

  • The model effectively captured dependencies in both forward and backward directions of the time series.


Portfolio Optimization:

  • Created a buy/sell logic to optimize the portfolio and maximize results.

  • Implemented dynamic stop-loss and take-profit strategies to manage risks.

  • Tracked metrics such as wallet balance, portfolio current value, and asset performance daily.


Conclusion:

The project showcases the application of advanced AI techniques to predict cryptocurrency prices and optimize investment portfolios. By leveraging a BiLSTM model and dynamic trading strategies, the solution aims to enhance investors' decision-making processes and improve their chances of profitability in the volatile cryptocurrency market.

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