This strategy outlines the development of a trading system for MetaTrader 5, focusing on leveraging a custom indicator based on the previous candle. The first phase, "Analyse market conditions," involves thoroughly researching historical price data to discern market trends and active trading hours, identifying key support and resistance levels, and considering factors such as economic news and price action patterns. For example, traders might use this information to avoid entering trades before significant economic announcements that could cause volatility.
In the second phase, "Design and code the indicator," the goal is to define and develop an MQL5 script that calculates the custom indicator accurately. Actions include writing and testing the script using historical and live data and optimizing it for performance. A practical example here would be backtesting the indicator across different time frames to ensure its reliability under various conditions.
The final phase, "Automate trading based on indicator signals," focuses on executing trades based on defined signals from the custom indicator. This includes developing rules for entering buy and sell positions, implementing risk management techniques like stop-loss, and testing the system in a demo environment before live deployment. For instance, trades could be automated to trigger buy actions when the ask price crosses above the indicator.
The strategies
⛳️ Strategy 1: Analyse market conditions
- Research historical price data to understand market trends
- Determine the most active trading hours for the financial instrument
- Identify support and resistance levels
- Check for economic news that could affect the market
- Examine the average volatility of the market
- Verify the liquidity of the financial instrument
- Analyse current price action patterns
- Consider using additional indicators for confirmation
- Review previous market reactions to similar conditions
- Ensure diversification in market analysis by consulting multiple sources
⛳️ Strategy 2: Design and code the indicator
- Define the formula for the custom indicator based on the given calculations
- Write MQL5 scripts to calculate the indicator using historical price data
- Test the accuracy of the indicator calculations on past data
- Ensure that the indicator updates correctly using live data
- Implement error handling to manage calculation exceptions
- Optimise the code for efficient computation
- Backtest the indicator over different time frames for reliability
- Evaluate the speed and performance of the indicator in real-time
- Document all code and functions for future reference
- Iterate improvements based on backtesting results
⛳️ Strategy 3: Automate trading based on indicator signals
- Develop rules for opening buy positions based on the ask price crossing the calculated indicator
- Code the strategy to open sell positions using the bid price cross below the indicator calculation
- Implement risk management measures like stop-loss and take-profit
- Test the automated trading system in a demo account environment
- Ensure the system logs all trade actions for review
- Analyse trade results and refine strategy parameters
- Address any latency issues in the order execution
- Monitor system performance continuously for unexpected behaviour
- Implement notifications for trade executions
- Prepare for deploying the system on a live account after successful testing
Bringing accountability to your strategy
It's one thing to have a plan, it's another to stick to it. We hope that the examples above will help you get started with your own strategy, but we also know that it's easy to get lost in the day-to-day effort.
That's why we built Tability: to help you track your progress, keep your team aligned, and make sure you're always moving in the right direction.
Give it a try and see how it can help you bring accountability to your strategy.
