Crypto Price Movement Predicition
Github Link:
https://github.com/kunthshah21/Crypto-Price-Movement-Prediction
The Crypto Price Movement Prediction project addresses the challenge of forecasting cryptocurrency price fluctuations in a highly volatile and dynamic market. With the surge in global cryptocurrency adoption and the limitations of traditional fiat systems, investors and financial institutions face difficulty in making informed decisions. This project leverages Recurrent Neural Networks (RNNs), specifically LSTM-based architectures, to predict whether crypto prices will move up or down across three horizons—daily, monthly, and yearly. A user-friendly Streamlit dashboard makes these models accessible for traders, analysts, and retail investors.
Client
Personal
Client
Personal
Technology
Deep Learning - CNN LSTM
Technology
Deep Learning - CNN LSTM
Timeline
3 Weeks
Timeline
3 Weeks
The primary goal of this project is to build a multi-horizon predictive system that provides actionable insights into cryptocurrency price movements by:
Utilizing deep learning models (LSTM, CNN+LSTM, stacked LSTMs) tailored for short-term (daily), medium-term (monthly), and long-term (yearly) predictions.
Enabling binary classification (up/down) with probability scores to guide investment and trading strategies.
Offering an interactive deployment platform through Streamlit, where users can select prediction horizons, upload custom data, and visualize trends.
Supporting practical applications such as portfolio management, algorithmic trading, financial advisory services, and retail investment apps.
The project demonstrates that while cryptocurrency prediction remains inherently challenging due to volatility and macroeconomic uncertainties, multi-horizon modelling provides layered insights that can aid different types of investors.
Daily models offer quick trade signals but are constrained by high volatility.
Monthly models show moderate accuracy (around 54%), making them more reliable for tactical decision-making.
Yearly models provide long-term perspectives but are affected by global events, limiting accuracy.
By deploying the system in an accessible Streamlit app, the project bridges the gap between advanced AI models and practical financial decision-making. Future improvements could focus on integrating macroeconomic data, cross-market signals, and richer sentiment analysis, potentially enhancing predictive accuracy and business applicability.
Crypto Price Movement Predicition
Github Link:
https://github.com/kunthshah21/Crypto-Price-Movement-Prediction
The Crypto Price Movement Prediction project addresses the challenge of forecasting cryptocurrency price fluctuations in a highly volatile and dynamic market. With the surge in global cryptocurrency adoption and the limitations of traditional fiat systems, investors and financial institutions face difficulty in making informed decisions. This project leverages Recurrent Neural Networks (RNNs), specifically LSTM-based architectures, to predict whether crypto prices will move up or down across three horizons—daily, monthly, and yearly. A user-friendly Streamlit dashboard makes these models accessible for traders, analysts, and retail investors.
Client
Personal
Client
Personal
Technology
Deep Learning - CNN LSTM
Technology
Deep Learning - CNN LSTM
Timeline
3 Weeks
Timeline
3 Weeks
The primary goal of this project is to build a multi-horizon predictive system that provides actionable insights into cryptocurrency price movements by:
Utilizing deep learning models (LSTM, CNN+LSTM, stacked LSTMs) tailored for short-term (daily), medium-term (monthly), and long-term (yearly) predictions.
Enabling binary classification (up/down) with probability scores to guide investment and trading strategies.
Offering an interactive deployment platform through Streamlit, where users can select prediction horizons, upload custom data, and visualize trends.
Supporting practical applications such as portfolio management, algorithmic trading, financial advisory services, and retail investment apps.
The project demonstrates that while cryptocurrency prediction remains inherently challenging due to volatility and macroeconomic uncertainties, multi-horizon modelling provides layered insights that can aid different types of investors.
Daily models offer quick trade signals but are constrained by high volatility.
Monthly models show moderate accuracy (around 54%), making them more reliable for tactical decision-making.
Yearly models provide long-term perspectives but are affected by global events, limiting accuracy.
By deploying the system in an accessible Streamlit app, the project bridges the gap between advanced AI models and practical financial decision-making. Future improvements could focus on integrating macroeconomic data, cross-market signals, and richer sentiment analysis, potentially enhancing predictive accuracy and business applicability.
Crypto Price Movement Predicition
Github Link:
https://github.com/kunthshah21/Crypto-Price-Movement-Prediction
The Crypto Price Movement Prediction project addresses the challenge of forecasting cryptocurrency price fluctuations in a highly volatile and dynamic market. With the surge in global cryptocurrency adoption and the limitations of traditional fiat systems, investors and financial institutions face difficulty in making informed decisions. This project leverages Recurrent Neural Networks (RNNs), specifically LSTM-based architectures, to predict whether crypto prices will move up or down across three horizons—daily, monthly, and yearly. A user-friendly Streamlit dashboard makes these models accessible for traders, analysts, and retail investors.
Client
Personal
Client
Personal
Technology
Deep Learning - CNN LSTM
Technology
Deep Learning - CNN LSTM
Timeline
3 Weeks
Timeline
3 Weeks
The primary goal of this project is to build a multi-horizon predictive system that provides actionable insights into cryptocurrency price movements by:
Utilizing deep learning models (LSTM, CNN+LSTM, stacked LSTMs) tailored for short-term (daily), medium-term (monthly), and long-term (yearly) predictions.
Enabling binary classification (up/down) with probability scores to guide investment and trading strategies.
Offering an interactive deployment platform through Streamlit, where users can select prediction horizons, upload custom data, and visualize trends.
Supporting practical applications such as portfolio management, algorithmic trading, financial advisory services, and retail investment apps.
The project demonstrates that while cryptocurrency prediction remains inherently challenging due to volatility and macroeconomic uncertainties, multi-horizon modelling provides layered insights that can aid different types of investors.
Daily models offer quick trade signals but are constrained by high volatility.
Monthly models show moderate accuracy (around 54%), making them more reliable for tactical decision-making.
Yearly models provide long-term perspectives but are affected by global events, limiting accuracy.
By deploying the system in an accessible Streamlit app, the project bridges the gap between advanced AI models and practical financial decision-making. Future improvements could focus on integrating macroeconomic data, cross-market signals, and richer sentiment analysis, potentially enhancing predictive accuracy and business applicability.