For a couple of months, I’ve been developing trading strategies using computer automation looking for better results. Many tests have been performed and I’m calling that journey Prosperity. I hope you enjoy this work of mine and help me develop it further if you are interested in so.

On the first backtest – with a thirteen-year range (from 2003 to 2016), Prosperity was a throwaway. There was no clear alpha.

#1 – No alpha! – Prosperity 1.0

Over 3 months after the initial release of Prosperity, setup modifications were made – through pure empirism, though – and we started to see some alpha.

#2 – Pushing to the limit – Prosperity 1.1

CAGR (compounded annual growth rate) climbed up to 24.82%. No code or logic of the program was changed. For the first time, we had decent results, a much better CAGR than the initial 11.46%.

Many empirism based attempts were made to improve CAGR. Improved versions of the program were established through that – reducing bugs and fake secondary trends.

I like to guide myself through the following equation to help me sort what can impact a trading strategy: A(t) = (1+L)∙k∙A(0)∙(1 + r + α – lc% – fc%)^t. Fractional Costs (fc), slippage, leveraging costs and commissions count as drag on the overall performance. We must be careful to address and account these costs.

The alpha, if existent, is also compounded. The return is viewed as market average. If the trading system doesn’t generate alpha or generate a negative alpha, it will also count as a performance dragger.

We can scale up a strategy K by multiplying all positives and negatives. We will soon be able to answer very simple questions:

– Does the strategy fail under pressure? If yes, the strategy cannot handle more capital;

– Will it keep its CAGR? CAGR is essential for the performance and the journey.

It is okay to scale, but it will not answer where you could actively invest more capital and leverage your profits.

Leveraging the strategy is not possible if we have no alpha. Prosperity 1.0 would not have given any profits, luckly it was not run on a real or margin account for futures trading. Version 1.0 would have a reduced output – not a leveraging system.

You cannot trade with a strategy with no alpha. Buying a fixed rate bond would be much better than underperforming the averages.

Testing under stress

Stress testing is essential to ger real value frm your trading strategy. I now tested using a little leverage since we have alpha. Scalling up the stake to $1M, we noticed a 28.17% CAGR, including all drags. That result represents an interesting level for any fund or trading corporation. These companies have their return close to market averages.

#3 – Prosperity 2.0 (now we’re flying, Houston)

The alpha is clearly seen in the continuous widening spread between blue and red lines.

Prosperity became a very different system after code modifications. Let’s say it now has a brand new engine and suspension, but its original horse-power and logic are the same. We now have a good performance and I want to explore its limits.

I’ll give you the real talk: we have to squeeze out more alpha (more performance) even if it’s more expensive to have it. If Prosperity leaves a good amount of profit after all expenses, we’re in business. Unlike a traditional business, being profitable is not the only rule – we have to outperform the averages.

Like my friend Diogo likes to say, there’s no free lunch in the market. If you want more, you have to do more – that’s always been the rule. In the trading world, it means: increase your risk to have higher returns or trade more efficiently.

At this point we can use leverage or scaling to get more performance. It’s a compounding game.

It’s a deterministic number of stocks that the algo selects to consider trading – it’s a much higher number than the minimum necessary. Why it does that? If you take the top 50 of a 300, 400 or 800 list, will it matter? In fact, yes. Even if the top 50 are still the top 50. Some might not be in the list if you allow other candidates to join the list.

How it works

The algorithm picks top market capitalizations (market caps) having top CAGRs over the last 90 days on the trend. There’s nothing new about it – but there’s an alpha.

# 4 – Price Movement

Black lines added to the chart act as linear regressions over price data. What Prosperity does reminds us of the Simple Momentum Rotation System the chart describes.

Methods of entry and exit positions have been changed in comparison. The algo goes wave after wave, floating on a price series. If it slants up, you can make a profit (long). The traveling black line specifies the holding time.

The exit is done by deselecting the stock if the upward slope goes below a minimum positive value. The stock is sold if it stops rising – this is the protection you have. There are trending rules that keep you in a position only if the 90-day trend is up. You’re not trading on market drawdowns – you already made a profit during those periods (Prosperity 2.0 – exposure line).

The black line can react soon to price change due to the 90-day evaluation period against all days of Simple Momentum Rotation System.

 

# 5 – Shipping Prosperity

Finally, we have an algorithm to ship. Approximate returns of 20% over one year trading US Equities with $ 100,000.00 initial capital for one single year.

Prosperity is a good option to hedge certain securities in a fund. High-risk and volatility options could be hedged using US Equities and Prosperity.

In a couple of months, Prosperity will receive a module to statistically adjust Beta and Sharpe percentages, according to the Alpha and Volatility.