Is it worth it?

Tried and tested algorithms are hard to come by, but is there gold at the end of the tunnel?

Getting an algorithm working, and trading profitably is a task in itself but is it all worth it and does it really make money… 

To many people, a trading algorithm may not seem like something that will make money. There’s that feeling of it being too good to be true. The thing is, they don’t all work. You will put in the work finding algorithms that work and having the strength to put money on it once you believe in it.

A trading algorithm is simply a piece of coded software that repeats the same coded processes over and over. It takes the right methodology to make it work, which we cover here.

Of course, it all depends on the capital you have available (LINK TO CALCULATOR) as to what the value of 1% is.

I think that many of us get caught up in capital value, but what about the income it brings…

After all, income let’s us budget for a period of time rather than draw down on a larger sum. Having an ongoing income is an incredible feeling, and creates a level of security that allows you to feel free to do the things you want, guilt free.

Finding the Entry

Getting into the market is an important part of trading. Considerations that will impact an entry include; the length of time (or pips) you expect to hold a trade and the distance of the stop loss from the entry price.

There are a series of concepts that will allow you to decide on an entry price that might improve your target and stop loss which include price history (often measured using candles), indicators measuring different data such as deviation, average price, changes in price, volumes and other price movement information.

These items can all be used to identify an entry point into the market. When you can see an opportunity where price will move in your favour over the expected period of time, that might be a potential entry.

The next part is being able to identify how you would logically code out the entry condition so that when it happens, your algorithm can trade in real time.

The first profitable algorithm idea for this article, is a wide set Bollinger Band which will identify when the market is out of its normal trading conditions and may present an opportunity.

Entering the trade

As you enter a trade, you also outline the risk in volume that you wish to take. In other words, how many units will you be buying. The overall risk is also determined by the size of your stop loss but the volume is the major determinant of risk. Many algorithms use a static risk where volume is either the same or manually adjusted from time to time. While adjustments would likely be infrequent, full automation where possible is ideal.

Two levers impact his heavily: Volume (Lots or units to trade) and Stop Loss size (how far is the stop loss from the entry price?)

The Second profitable algorithm idea for this article, is a fluctuation volume setting based on the balance or equity of the account. This will help to scale up in positive times and scale down in losing times. As the account balance increases, so to will your risk. In contrast, as the balance decreases during losing periods, so will your risk lessening the impact of losing times. This allows you to stay in the game longer and increase your successful periods of trade.

Exiting the trade

The exit of a trade is one of the most challenging parts of being a trader, algorithmic or otherwise. Whether you are in a huge profit, huge loss or somewhere in between, the emotional responses of fear and greed play a part. When the profit or loss if very little, do you hold on? When the profit is huge, will it keep going even further?

While algorithmic trading takes the edge off the trader, planning your algorithm will include making a decision of when to get out and when to hold throughout the trade.

Risk Reward is important, but just like the volume and stop loss relationship, the percentage win rate plays a major role in whether a risk reward ratio is going to pay off.

Two levers are at play: Win rate (trades that reach target before stopping out) and risk reward ratio (the multiple of the target size compared to the stop loss size).

While a good risk reward is often listed as 1:2 or higher, that only works if you have a system that gets winning trades at a decent rate.

By contrast, a win rate can be high but the risk reward ratio might be 1 : 0.5 meaning the target is close and easy to reach.

Managing the portfolio

Many algorithm creators consider one financial instrument at a time. But what about managing it as a portfolio. We know that controlling the market is not going to happen. We also know that our decisions won’t always be correct, hence the need for risk management. As one market falls, another shall rise and so a portfolio is the key to consistency provided it is a net positive algorithm overall.

Manage your algorithm like a fund manager rather than a speculator, a portfolio reduces risk, increases the number of opportunities and provides a level of certainty that a single market cannot.

All the work at the start

Making a trading algorithm is a huge deal about putting all the effort in at the start, and letting that work flourish into profit over time. Planning can be tedious and testing even more so. 

All the hard work will be at the beginning of creating and setting it up. Once its up and running, you don’t need to do much at all.

Planning the whole process out and combining techniques from this article will make success easier to come by.

There will be readers that feel these aren’t ‘ideas’ as they feel use of indicators are ideas. So here’s a few more profitable trading algorithm ideas to sweeten the deal.

Use seasonality and data testers to test the best months for buying and selling activity. Using that seasonality can help separate the known bad times out based on historical data.

Use prices on other assets to determine potential change in this asset. Many assets have a lag effect on other market movements which could be exploited if done correctly.

Integrate a statistical analysis framework to your algorithm to test for significance. The reading of an indicator might be saying sell, but the significance over past data may not be so kind. This could help to exclude insignificant trade setups.

Rather than using Bollinger Bands as a tool to find markets trading outside of their normal range, set deviation to 1 or less to create a normalisation of price so that trades are only placed in ‘normal’ conditions.

I am sure this has provided at least one idea to help you create a successful trading algorithm. If you’d rather just do what I do, head over to get your free membership (Limited Time Only) and access our algorithms now.


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