I need you to create a full cryptocurrency trading bot capable of analyzing market trends, performing technical analysis, making market predictions, and executing automated trades based on real-time data. The bot should include the following features and functionalities:

1. Data Collection
Historical Data: Integrate APIs from major cryptocurrency exchanges like Binance, Coinbase, and Kraken to collect historical price data, trading volumes, and other relevant market metrics. Use the CCXT library for easy integration.
Real-Time Market Data: Continuously fetch real-time market data for Bitcoin, Ethereum, and other major cryptocurrencies, updating every minute.
Sentiment Data: Scrape relevant social media platforms (e.g., Twitter, Reddit) and news outlets for sentiment analysis data, focusing on positive/negative mentions of popular cryptocurrencies.
2. Data Preprocessing
Data Cleaning: Remove any incomplete or erroneous data points and handle missing data by interpolation.
Normalization: Normalize all features to ensure that differences in scale do not affect model performance (e.g., use Min-Max scaling or Z-score standardization).
Feature Engineering: Derive additional features such as price momentum, volatility, trade volumes, and moving averages (SMA, EMA, Bollinger Bands).
3. Model Development
Time Series Forecasting: Build a predictive model using an LSTM (Long Short-Term Memory) neural network, capable of making price forecasts for the next hour, day, and week. Train the model using historical price data and evaluate its performance using metrics like Mean Squared Error (MSE) and Mean Absolute Error (MAE).
Supervised Learning for Market Prediction: Train a Random Forest or Gradient Boosting classifier on historical data to predict whether the market will be bullish or bearish over the next 24 hours. Include features such as price trends, technical indicators, and sentiment scores.
Reinforcement Learning for Trading Strategy: Implement a reinforcement learning agent that learns optimal trading strategies by interacting with the market environment. The bot should learn to maximize profit by executing trades and receiving rewards based on the outcomes.
4. Technical Analysis & Strategy
Technical Indicators: Compute popular technical indicators such as RSI, MACD, Bollinger Bands, Moving Averages, etc., and display them on charts.
Buy/Sell Signals: Generate real-time buy and sell signals based on technical indicators and predicted price trends.
Custom Strategies: Allow the user to configure custom trading strategies, such as trend following (buy in bullish trends) or mean reversion (buy when prices drop and sell when they rebound).
5. Automated Trading
Exchange API Integration: Use Binance’s API for executing real-time trades. The bot should handle both market and limit orders for buying and selling cryptocurrencies.
Order Execution: The bot should be able to place market orders, stop-loss orders, and take-profit orders automatically based on pre-set conditions.
Portfolio Management: Track the user’s crypto portfolio and provide an overview of current holdings, profit/loss, and trade history. The bot should also handle rebalancing the portfolio based on user-defined strategies.
6. Risk Management
Stop-Loss Orders: Implement automatic stop-loss functionality to limit potential losses during market downturns.
Take-Profit Orders: Set take-profit targets that automatically close positions when a specified profit level is reached.
Risk-Reward Assessment: Before executing any trades, the bot should assess the risk/reward ratio and only proceed with trades that meet a certain threshold (configurable by the user).
7. Sentiment Analysis
Social Media Monitoring: Implement a sentiment analysis system that monitors Twitter, Reddit, and news articles for mentions of popular cryptocurrencies. Use NLP models (e.g., BERT, GPT, or VADER) to determine whether the sentiment is positive, negative, or neutral.
Market Sentiment Impact: Factor in social media sentiment to adjust trading strategies. For example, if sentiment for Bitcoin is overwhelmingly positive, increase buy signals; if negative, increase sell signals.
8. Backtesting & Strategy Optimization
Historical Data Backtesting: Allow the user to backtest trading strategies using historical data. The bot should simulate trades and provide performance reports, including profit/loss, win/loss ratio, Sharpe ratio, etc.
Strategy Optimization: Enable the bot to optimize trading strategies by running simulations and adjusting parameters (e.g., stop-loss levels, indicator thresholds) for maximum profitability.
9. Alerts & Notifications
Real-Time Alerts: The bot should send real-time notifications (via email, SMS, or Telegram) for important events such as significant price changes, executed trades, or when stop-loss/take-profit orders are triggered.
Customizable Alerts: Allow users to set custom price alerts or trade signals (e.g., notify when Bitcoin reaches a specific price).
10. User Interface (Optional)
Dashboard: Create a simple dashboard that allows the user to view current holdings, performance metrics, and live charts of cryptocurrency price movements and indicators.
Settings: Provide a settings panel where users can adjust their trading preferences, risk tolerance, and trading strategies.
11. Code Structure and Tools
Use Python as the primary language for the bot.
Libraries and tools to include:
Pandas, Numpy for data manipulation.
Scikit-learn, TensorFlow, Keras or PyTorch for machine learning.
Matplotlib, Plotly for data visualization and charts.
CCXT for exchange API integration.
SQL or NoSQL (MongoDB) for storing historical data and transaction records.
Ensure the code is modular, well-documented, and easily configurable by the user.
Final Notes:

Test the bot thoroughly on paper trading or simulation modes before going live.
Ensure proper error handling, especially for API requests and trade executions, to prevent unexpected behavior.
Implement logging for all trades and market activities to allow easy debugging and tracking.