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Trading Bot Code
I want a software/Bot for Metatrader 5 that can give me 100% accurate trading signals on the financial market for majors, minors and metals
Build as both a rule-based system using technical indicators and a machine learning-based prediction model
Plan
1. Data Collection:
Use a trading API like metatrader5, Binance, Alpha Vantage, or Yahoo Finance to fetch live and historical market data for forex majors, minors, and metals.
2. Signal Generation:
Implement technical indicators (RSI, MACD, EMA, Fibonacci Retracement, Pivot Points, ADX (Average Directional Index), On-Balance Volume (OBV), Volume Oscillator, Average True Range (ATR), Bollinger Bands (also trend indicator), Stochastic Oscillator, Bollinger Bands, Parabolic SAR, Ichimoku Cloud, Moving Averages (MA), ) to generate buy/sell signals.
Integrate machine learning for predictive capabilities
Incorporate AI/ML models to predict market trends and refine strategies
3. Visualization
Display signals on charts using libraries like Matplotlib or Plotly
4. Execution & Alerts
Allow users to execute trades via API integration via telegram.
5. Risk Management:
Include stop-loss, take-profit levels, and risk-adjusted position sizing.
Add advanced strategies (combining MACD and RSI) to improve the accuracy
Integrate with trading platforms like MetaTrader 5 for executing trades
Add a backtesting module for testing both historical and live data
Add performance metric calculations like Sharpe Ratio and drawdown
Add live trade monitoring and error handling for MetaTrader5 API integration
add portfolio management for multiple assets
add advanced risk management such as trailing stops
Optimize asset weights for risk-adjusted returns
Integrate AI models for predictive analytics
Integrate deep learning models (e.g., LSTM/GRU) for time series forecasting
Dockerize the bot for easier cloud deployment
Integrate real-time monitoring with Telegram alerts for predictions and trades
Add more features and hyperparameter tuning for the LSTM/GRU model.
Generate daily reports with metrics like Sharpe Ratio and drawdown.
Add hyperparameter optimization for LSTM (e.g., GridSearchCV)
Add advanced cloud monitoring with logging and alerting tools
Integrate notifications via Telegram, or Email.
Send alerts for trade signals, errors, or unexpected behavior
include a Streamlit-based dashboard for monitoring trades and predictions
host the bot as a web service with REST APIs
add user authentication (e.g., OAuth or JWT) to secure the APIs
WebSocket-based live updates for the dashboard
add RBAC to manage access to sensitive endpoints like /trade
integrate live portfolio updates into the WebSocket stream
Send real-time alerts for margin calls, large drawdowns, or trade executions
add real-time notifications for margin calls or drawdowns
add detailed trade analytics in the dashboard (e.g., win rate, average PnL)
Plan for Trade Analytics (B):
1. Trade Metrics:
Calculate:
Win rate (% of profitable trades).
Average P&L (profit or loss per trade).
Maximum consecutive losses or wins.
Risk/Reward ratio.
2. Visualization:
Show these metrics and summary statistics in the dashboard.
Add charts for P&L distributions and cumulative profits.
Real-Time Notifications:
Alerts via Telegram and email for margin calls and high drawdowns.
Trade Analytics:
Metrics like win rate, average PnL, max consecutive wins/losses, and risk/reward ratio.
PnL distribution visualized as a bar chart.
add interactive trade management to the dashboard
add dedicated backtesting analytics section in the dashboard
Analyze backtesting results:
Metrics: Total returns, win rate, Sharpe ratio, and max drawdown.
Visualizations: Equity curve, P&L distribution, and trade statistics.
• Implementation:
Add a new section in the Streamlit dashboard.
Use analytics functions to calculate metrics and generate charts.
add trade filtering options to the dashboard
position sizing recommendations integrated with risk management
add trade size validation to flag risky trades in real-time
integrate position sizing calculations into live trade execution
add dynamic pip value calculations based on the trading pair
dedicated risk management section in the dashboard
add risk alerts to notify users when cumulative risk exceeds limits
integrate risk management into backtesting analytics
add portfolio-level risk analysis to evaluate cross-asset risks
interactive simulation tool for adjusting risk/reward parameters
include scenario analysis to simulate portfolio performance under different conditions
integrate portfolio optimization tools to suggest ideal asset weights
add scenario-based trade recommendations to align strategies with expected outcomes
integrate advanced optimization techniques like Black-Litterman for more flexible portfolio construction
add advanced risk/reward metrics to evaluate portfolio performance
trade execution integration to implement portfolio adjustments automatically
add multi-objective optimization to balance multiple risk/reward factors
automated performance tracking for real-time portfolio evaluation
add historical performance comparison against benchmarks like S&P 500
integrate real-time market data for live portfolio tracking
add multi-benchmark comparisons to evaluate portfolio performance against multiple indices
add heatmaps for visualizing portfolio risk and returns
add dynamic benchmark selection for user-defined comparisons
add customizable heatmaps for different time periods or asset groups
add overlap analysis to show common assets between benchmarks and portfolios
enable user-uploaded benchmark data for flexible comparisons
add asset-level contribution analysis to break down portfolio returns by asset
enable benchmark weight adjustments for user-defined benchmarks
add asset attribution analysis to understand what drives asset performance
add dynamic allocation recommendations based on performance metrics
(you have my permission to add and subtract anything relevant and irrelevant to enhance the bot’s performance and accuracy)