The best back testing and screening software I personally use.
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Hi, today we look at the stock screener and back testing software I personally use.
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I often get asked questions regarding my hybrid approach to the market, which combines technical and fundamental analysis.
For fundamentals I use Stockopedia and for technical analysis I use Market In Out, both with discounted links below.
Having covered Stockopedia in a previous video, today we take a quick look at the power of Market In Out.
The more mechanical and repeatable a system, the easier it is to stay consistent, but perhaps more importantly, the easier it is to back test a systems viability through objective data.
Market In Out has been my technical software choice for many years, providing complex screening and back testing options along with the many studies often discussed.
Here is a quick example of a strategy I’m currently researching, it’s a mean reversion strategy based on the findings of Larry Connors. The concept follows a broader trend but aims to enter a position on a pullback.
Connor suggests a two period RSI, and in theory he says an RSI reading of below 10 indicates an oversold price and therefore a buying opportunity. He also says the price must be above the 200-day moving average.
I put these simple criteria into the back test module, with some slight tweaks, selecting long positions on stocks included in the Russell 3000 index. I used stocks with a 2 period RSI below 8, and exited positions when priced crossed back above the 10-day exponential moving average.
In terms of position management on the initial back test, I did not include any stop loss.
I used a starting balance of $100,000 and risked a maximum 5% of capital, with a portfolio size of no more than 20 positions.
We add commission and spread costs here and then run the back test. I used the last 10 years to see how the strategy faired.
We can see the annual and monthly returns, with the equity curve above.
Notice how in 2020 the strategy performed very well due to many stocks becoming oversold during the pandemic, most of which recovered strongly soon after.
Overall, the strategy saw a compounded return of over 14 million dollars, equating to growth of over 14000% from a total of over 5000 trades.
The maximum drawdown was 41% but the average annual compounded return for the 10 years was a very respectable 62%.
The win rate was 67% and the average hold time was 10 days.
The purpose of this example however, is not to provide a complete strategy but rather show how back testing an idea can determine viability, whilst also providing a foundation for further research. This example is just that, a theoretical application of a strategy that is worth studying further.
Another example can be seen here, this time a simple rules-based strategy for Bitcoin.
The rules are simply; buy when the mack dee crosses above the signal line, ensuring price is above the 100-day exponential moving average, and sell when the mack dee crosses below the signal line.
We set the parameters here, using a $10,000 initial investment, holding just the one position, and adding the trade costs.
We run the back test from January 2015 to February 2021.
The results are generated, and we see a perfectly viable system to trade Bitcoin.
The drawdowns are almost eliminated in comparison to the equity profile of a buy and hold Bitcoin strategy, which saw a drawdown of 82% at one point.