Here you will find some automated trading strategies that are discussed in the strategy development and/or blog sections.
For each strategy, links are included to the pages that discuss its purpose and development.
Feel free to download them for your personal use. No subscription needed! (But why not subscribe so you can get future content updates?)
Freebies will be incrementally added as I create more content. Be sure to check back regularly!
The following MT4 algorithms are provided in .ex4 format. Click on the headings to download them.
Paste these .ex4 files into your MQL4 > Experts directory in your MT4 Data Folder. The Data Folder can be accessed via File > Open Data Folder.
This algorithm was incrementally developed using the MT4 workflow. Entries are determined using the Commodity Channel Index and a high-pass volatility filter. A catastrophic stop loss and time stop are used for exits.
Its performance from Jan 2015 – Dec 2019 is shown in the Tick Backtesting article.
StrategyQuant custom indicators are required for this algorithm.
This strategy was automatically generated using genetic evolution. Robustness tests include backtesting on 5 additional markets and walk-forward optimization.
The strategy logic is described here.
Its walk-forward performance using tick data, from Aug 2003 – Jun 2020, is shown in the Tick Backtesting article.
StrategyQuant custom indicators are required for this algorithm.
Performance comparisons were done on these two indicators, with the Bollinger Bands proving to be more profitable for trend trading.
For both strategies, long entries are triggered when price closes above the upper band. Longs are exited when price closes below the middle moving average. Vice versa for shorts.
StrategyQuant custom indicators are required for this algorithm.
This strategy was programmed using AlgoWizard, and subsequently optimized in MT4.
A buy stop order is placed whenever the faster moving average crosses above the slower moving average. Entries are restricted to the London and New York hours. ATR-based stop losses and profit targets are used for trade management.
StrategyQuant custom indicators are required for this algorithm.
We tested the Bollinger Bands’ two inbuilt trailing stops – the middle moving average and the standard deviation band. The standard deviation band was the clear winner.
This strategy uses the lower/upper standard deviation band as a trailing stop for long/short trades. A trailing stop activation level (in pips) is included.
This countertrend strategy is ideal for trading short-term reversals in noisy, range-bound markets.
Buy limit orders are placed when price closes below the lower Bollinger Band, and a reversal candlestick pattern has just occurred. Opposite for shorts.
The Quantitative Qualitative Estimation (QQE) and Triple Exponential Moving Average (TEMA) indicators were used to generate high-momentum trend following entries. The addition of the momentum-based QQE was found to significantly improve the reliability of the moving average entries.
StrategyQuant custom indicators are required for this strategy.
The Kaufman Efficiency Ratio was added to a Bollinger Band trend strategy to screen out impulsive price moves that often signal the beginning of a long trend.
StrategyQuant custom indicators are required for this strategy.
This algorithm counts the number of bars where the ADX indicator closed above a certain threshold. It can help determine the best markets and timeframes for your trend following strategy.