Using software to test strategies{16}

 

1. Using software to test strategies

What is back testing?

Back testing is the process of evaluating a trading strategy using historical market data to see how it would have performed it helps you identify strengths, weaknesses, and the optimal settings  of your strategy before risking real capital.

Popular back testing software and platforms

·         Meta trader 4/5( MT 4/MT5):

o   OFFERS BUILT – IN BACKTESTING capabilities for expert advisors (EAs) using historical data.

o   Easy to use with a user – friendly interface.

·         Trading view:

o   Allows you to script strategies in pine script and back test them using its extensive historical data.

o   Provides visual feedback with interactive charts.

·         Python –based platforms:

o   Libraries such as back trader, quant connects, and zip line let you code and back test strategies using python.

o   Offers flexibility and robust data handling.

·         Other platforms:

o   Commercial software like am broker or ninja trader that are popular among professional traders.

This image depicts various back testing platforms and software interfaces used for evaluating trading strategies.

2. How to back test a strategy

Step-by step process

1. Define your strategy parameters:

o   Clearly outline the rules of your trading strategy, including entry and exit signals, stop – loss, take –profit, and risk management rules.

2. Select historical data:

o   Use reliable historical market data that matches your trading instrument and time frame.

o   Ensure the data is cleaned free of errors. 

3. Program your strategy:

o   Code your strategy using the scripting language of your chosen platform (e.g. MQL4/MQL5 FOR MT4/MT5,pine script for trading view, or python for back trader).

o   Include all trading rules and risk management components.

4.run the back test:

o   Execute the back test over a sufficient period to capture different market conditions trending ,ranging, high volatility, etc.

o   Monitor key metrics like total return, drawdowns ,win rate, and risk – reward ratio.

5. Analyse the results:

o   Evaluate the performance metrics.

o   Identify any areas where the strategy may be underperforming and look for potential improvements.

3. Optimizing your strategy

Finding the best settings for profitability

1.       Parameter optimization:

o   Adjust variables:

Tweak various parameters of your strategy such as indicator periods, stop – loss distances , and take – profit levels.

o   Sensitivity analysis:

Determine how sensitive your strategy‘s performance is to changes in these parameters.

o   Use optimization tools:

Most platforms (like MT4/MT5 and trading view) offer built – in optimization features to test different parameter combinations automatically.

 

2.       Avoid over fitting:

o   Robustness check:

Ensure that your optimized parameters work across different market conditions and are not simply tailored to the historical data( curve – fitting).

o   Out- of – sample testing:

Split your data into in- sample (for optimization) and out –of sample (for validation) sets to test the strategy’s performance on unseen data.

3.       Risk-adjusted performance metrics:

o   Focus not only on the overall profit but also on risk –adjusted measures such as the Sharpe ratio, maximum drawdown, and sorting ratio.

o   Ensure that the optimized settings maintain a favourable risk – reward profile.

 This diagram outlines the process of testing various parameter combinations to find the optimal settings while monitoring risk – adjusted performance.

 

4.       Final thoughts

 

Back testing and optimizing are critical steps in the development of a robust trading strategy. They allow you to refine your approach, understand your strategy ‘ s behaviour under different market conditions , and improve overall profitability while managing risk effectively. Remember to:

 

·         Use reliable software and data.

·         Carefully define and code your strategy.

·         Perform thorough optimization without over fitting.

·         Validate your findings with out –of – sample tests.