7th day, huge profit

After almost recovering the losses from previous days, I had a lot of energy and I wanted to switch on the system as soon as possible. To be honest, this doesn’t matter. When I was designing the system, I decided to set up a time window to be able to control the algo and make sure that the positions were closed before the end of the day.

6E 06/20 breaking moment

I was expecting that the Fed approval of its new loan program and I knew that it will have a big impact in the markets that the algo trades.

Early in the morning, the USD was stronger than the Euro and the British Pound. The algo was triggered in both crosses almost at the same time while the futures were retracing.  Let see how these currency futures behaved during the day:

Euro FX

As I said before, the algo was triggered just after 11:30 London Time. I was a bit surprised but all of the conditions were met and the algo sent the market order. After that, I was monitoring the pile of red candles until 12:45. At that point, the market started recovering. Considering the news, I hoped that the market would break the highs of the day.

6E 06/20 trade chart

At the beginning of the US trading session, the news started coming up.  The FED approved the new loan to help to bolster local governments and small and mid-sized businesses. Obviously, all central banks and governments are trying to help their economies as much as they can. I believe that some of the measures will be highly effective while other ones will only help to increase the debts of the countries. At the same time, the initial jobless claims data was released. It was far worse than expected. The Euro rocketed from 1.0900 to 1.0960. After this moment, the market stayed ranging between 1.0940 and 1.0960 for four hours.

British Pound

6B 06/20 trade chart

The case of the British Pound was exactly the same but the daily range was lower than in the Euro. As you can see the retracement was bigger in the GBP/USD at least until 17:30 where it rebounded aggressively.

Trades and Account

It was a great day in terms of profit. The algo generated $3220 in the Euro and $688.75 in the British Pound.

Realized profit by the algo applied to EuroFx and British Pound Futures

It was the first day with a positive P/L and it was a boost to my confidence.

Account

Summary

When you have an algorithmic or even a simple trading strategy is difficult to deal with the news and the macroeconomic data releases. In the example shown, the market when in the direction of the algo but you should be aware that sometimes it will go against you. I would like to recommend to always use stop losses.

Simple trading system, does it work in the Nasdaq?

Introduction

There are so many trading styles and the traders can take their decisions from technical analysis, important levels, value investment, quantitative analysis, price movement, and order book study. Some of them combine more than one method at least to have another point of view or to have another idea generation source.

Nowadays, it's easy to find resources for trading. There are plenty of resources online, such as videos and courses. If you are more traditional, you can search for books and see what the people are saying about them.

Some of the most successful traders are known for being contrarians. What does this mean? Maybe they are aware of how powerful the trends could be, however, they are not investing for the long term. They are looking for a quick profit in a short period of time (depends about the how big is the position, who is executing and what are the targets, it can last from a few seconds to less than 3 months).  How do they act? Basically, if a stock or a future has been raising for a while and has a strong trend, they can consider that the product is overvalued and that it will revert to the moving average or at least it will revert enough to make a profit.

The idea

Now, we know what they do. I always thought about it. One of the problems is the timing when I should enter into a trade like that. There are so many statistical methods that you can apply to that. It can be based on the number of days (imagine that the stock has been raising for the last 60 days and you think that every “X” days, there is a retracement), it can be based on the price change (that you can consider it overvalued), it can be a combination of both. We can see that creativity is another part of the trading research. Probably, I will write a post about the whole process in the future but today we are going to review a simple idea.

The trading system is contrarian so it will consider yesterday % change. If yesterday the stock or the underlying product went up, the system will sell it today. And the other way round, if the stock fell yesterday, the system will buy it today.

Nasdaq

I’ve chosen the Nasdaq index as an example. It represents the technology stocks.

    Nasdaq continuous future, daily, source: TradingView

It hasn’t stopped rising since 2010. I wanted to show the period 2014-2018 that I will study in this article. Considering the strong bullish trend maybe I shouldn´t use a contrarian system. I will show you that one of the most important aspects is the risk management (always combine with a profitable system)

Backtest example

Before we start, I need to explain a couple of things. In my opinion, the market behaves differently when it goes up than when it goes down. The falls usually are very sharp. This is why I decided to choose a tighter stop for the sells. I’ve chosen the stops randomly, the buys have a stop of 4 ticks and the sells have a stop of 2 ticks. Let’s check the results:

                                        Backtesting, own elaboration using R

Good news! The mean is positive which is a good starting point. However, making $9.53 per trade is not enough without considering fees and slippage. The system makes $2190 on the best day. The worst lost is $20. The kurtosis is really high because all the values are concentrated around 0. To be honest, the system only makes money on the 3% of the trades. So it’s not tradable even if it makes 103.65% in four years. It would be great if the system had more entry requirements and the number of trades would be reduced. That way the statistics would improve a lot. The Sharpe ratio isn’t great. 

                                                           Max drawdown, own elaboration using R

Considering that the trading system loses in 97% of the trades, the max drawdown is very good. Obviously, each time that it loses, the amount is very small (around 0.2% of the portfolio)

Trading strategy performance, own elaboration 

We can see the characteristics of the system. A few profitable trades and a bunch of losing trades. The probability of taking the loss is very high with our tight stop losses. In other hand, every time that we are right, we make a lot of money.

Sum up

Sadly, there isn’t a good conclusion for this post. I think that I have a lot of work to improve this system.  In addition, this is not a professional way of running a backtest. We should have a period of time in which we test our idea, another period for optimization and different windows of time to test the optimized parameters. Also, we should always consider broker fees and slippage. The system shown is not tradable but it shows that a sounding risk management system is very important. Another idea that we should take from this post is that even a contrarian system can perform in a market that has a clear trend. I hope you like it.

Have a good trading!

Disclaimer

I wrote this article myself, and it expresses my own opinions that shouldn't be used as a trading advice. Trading carries considerable risk due to the high leverage involved

Momentum system

Sometimes we look for a clear trend in the different markets. This can be difficult these days because the volatility is back. I believe that there are many opportunities but you need to adjust the strategy’s timeframe. Obviously, when we see the chart at the final of the day,  it´s easy to see the levels where we should have bought or sold. I would be happy as far as you do this in order to learn the order flow and how the news affected the security. However, you shouldn’t take seriously the important levels of the day (if you are only checking the day instead of a longer timeframe) because anyone knows what is going to happen tomorrow. I wouldn´t like to discourage you if it works for you but in my opinion is like singing “if I were a rich man” without trying to make money.

Why is the data important? Introduction to the system

As I said indirectly in the first paragraph, I don’t know what the asset prices are going to do tomorrow. The most important for me, leaving my thoughts apart, is the historical data. Here is where statistics takes importance. Let’s get biased for a minute thinking that every time that the price is above an indicator (or another variable) we should buy and every time is below the chosen variable we should sell.

Let´s take the Moving Average 9 as this variable and check the probabilities.

                                               Probability table, example own elaboration

Only to sum up, we have a buy signal every time that the opening is above the MA9 and a sell-side every time the opening is below this indicator. This example is not the best one. I wouldn’t publish this screenshot in a book. Apart from this joke, we had 426 buy signals but only 203 days that the future closes above the opening. Investing in a strategy that has 47.7% success ratio doesn´t seem the smartest thing to do but this is where the money management comes! In the case of the sell signals, it's slightly better showing 52.4%.

Backtesting

Before I show the results I would like to confirm that I´ve used the 5-day moving average instead of the MA9.  The main reason is that the performance was better and the drawdown was lower. This test has been done with the FESB historical data (Eurostoxx Banks). The reason why I chose this product is because I thought that it was directional enough to apply the strategy.

                                                  Backtesting statistics, own elaboration using R programming

As you can see the backtesting results are not bad. Each trade generated 6.42 EUR on average. The biggest win was 740 euros which is great considering that we started with 10000 EUR. I used a tight stop loss, only 2 ticks. Even with that, we can see that the FESB is very directional and the stop was hit only on 53% of the trades. The return has been 56.60% in almost 3 years and a half.

      PnL curve, own elaboration using R programming

The most interesting thing is that after the max drawdown (1570 between the 98th day to the 270th day) it has been rising. Obviously, when your portfolio has grown and you are using the same money management and risk management, the drawdowns are smaller than at the beginning. 

                                                      Max drawdown, own elaboration using R programming

Each trade was simulated with 2 lots and the maximum daily loss was 20 EUR (2% of the initial capital)

Sum up

I like analyzing trading systems and I´ve been working on this strategy for a while. I´m surprised about its performance because I expected worse statistics. Surprisingly, the % of successful trades were almost the same as the previous example shown above. This post doesn´t show the real performance because the commisions are not included. I will write a post in the future explaining why the short-term moving averages fit better than the long term in this kind of system. I hope you like it.

Have a good trading!! 

Disclaimer

I wrote this article myself, and it expresses my own opinions that shouldn't be used as a trading advice. Trading carries considerable risk due to the high leverage involved

Trading system based on proprietary indicator, Part 3

This is the third part of the series of posts about the trading system based on my own indicator. We will see how the commissions affect the performance of the system. I will compare with the benchmark in the future.

Comparison table

Comparison table including the backtesting with the commissions included and deducted from the portfolio, own elaboration

As you can see there are big differences between the backtesting without commissions and the ones that include them. The daily average return differs in the amount I chose as a broker fee. In this case and considering the size and the price of the security, I decided that the commissions will be 40 Euros per trade (20 Euros per side, buy and sell) Obviously the maximum and minimum daily profit differs in the amount of the broker fee. (There is one problem that I haven't fixed in the 10 Yrs backtesting and 10 Yrs backtesting with Fees. The max profit differs due to an early error in the data) The skewness and kurtosis are exactly the same. The returns have been significantly affected by adding the commissions and taking out the value of the portfolio.In the case of the 5 Yrs Backtesting the return is almost half due to the commissions. Considering that the system trades the same size all the time, this issue was expected. The advantage of that is that as soon as the portfolio grows, and even if the loss is the same amount as the beginning, the loss represents a lower percentage of the portfolio. I chose this way as a risk management in which I risk more in the early years while the portfolio is growing. Probably I should link the trade size with the value of the portfolio but depending on the system or the period studied can generate worse performance and could be riskier. The Sharpe Ratio is affected as well because the returns are lower. Another important point is that including the fees the max drawdown is worse than the one shown before. Depending on how we invest our savings, we should run an extra spreadsheet with all the cost related to the investments. 

Graphical description of how the fees affected to the different backtesting

5 Years test

    Differences between the portfolio with and without commissions, own elaboration

Sometimes a chart represents an idea better than the words. Here we can see the impact of the commissions in the system. The difference in the last trade is almost 5000 Euros. The system returns 37.15% which is the equivalent to 7.43% per year. It´s a good return considering the risk taken. The system without including commissions returns more than 12% per year.

10 Years test

     Differences between the portfolio with and without commissions, own elaboration

The differences are bigger in the 10 years study. The difference between both systems is 28000 euros. At this point is better not to do these numbers, giving away this amount of money is crazy. The best aspect is that after fees it returns an incredible 425%.

Sum up

I hope you like it. You shouldn´t focus on the effect of the commissions or the performance. The most important idea is considering all the cost related to running the trading system or the investments. In this case, I simplify the idea considering that a broker executes the trade on your behalf. If you trade on your own, you should add the market data, the brokerage commissions, and the trading platform costs. There is another point that I haven´t commented, the taxes. Sadly the trading costs and the taxes (if you make money) will reduce your profits.

Have a good trading!!

Disclaimer

I wrote this article myself, and it expresses my own opinions that shouldn't be used as a trading advice. Trading carries considerable risk due to the high leverage involved

Trading system based on proprietary indicator, Part 2

Today I will show the trading system behaviour from October 2008. Let me introduce the macro situation before I review the backtesting results.

Brief description

The financial crisis started later in 2007. The stock market suffered a big correction in 2008. The volatility was far higher than nowadays. The central banks implemented the quantitative easing programs in order to stabilize the economies around the world. This is an example of the German Dax index and the Eurostoxx index.

     Source: TradingView, DAX vs Eurostoxx futures, daily, from 2008 to 2018

Results from October 2008

      10 Yrs Backtesting results, own elaboration with Excel and RStudio

The main difference is the volatility in the underlying. As you can imagine later in 2008 the volatility was really high and the stock market was in free falling until it bottomed in 2009.

As you can see the big bounces in 2008 are the reason for the big range shown in the backtest.

Comparison with the 5 Yrs Backtesting

                                          Comparison between the 5Yrs and 10Yrs backtesting, own elaboration

You can see a big improvement in the 10 Yr study vs the 5 Yr. The average profit was 151.28 EUR vs 95.86 EUR. The standard deviation and variance were higher due to the volatility from 2008 and 2013. The range is bigger as well because the stock was trading higher. Considering the strict risk management, I´m surprised about the winning trades percentage. I believe that a mean reversion strategy was the best one at this time, even more with the actions taken by the central banks. In addition, the return’s distribution changed and it shows higher extreme figures (in the positive side, which means a high probability of bigger profits) The system traded 733 times vs 130 times in the last 5 years, the profits are concentrated in the first 300 trades. The Sharpe Ratio is slightly worse.

                                          Max Drawdown, own elaboration using RStudio

I´m happy with this figure, losing 2820 EUR was the equivalent to 3.16% of the portfolio. This is a very conservative figure which I consider ideal. Sadly this is not applicable to another kind of strategies because the system opens and closes the positions on the same day.

         Portfolio growth, own elaboration using RStudio

There is not much to say about this chart. You can see the change in volatility from the first years to the recent years. The biggest profits are concentrated in the first 300 trades. The initial portfolio was 20000 euros. I haven´t included the commissions.

Sum up

We have seen how this system behaved during the last 10 years. You can think that is overfitted and this post doesn´t have value because I tested the system in the right period. This is not the purpose of this little article. I´m surprised with the performance but if you had bought the Dax in 2009, you would have multiplied your portfolio’s value by almost 4. In the next post, I will compare the trading system vs the DAX. I hope you like it. Thanks for reading.

Have a good trading!!

Disclaimer

I wrote this article myself, and it expresses my own opinions that shouldn't be used as a trading advice. Trading carries considerable risk due to the high leverage involved

Trading system based on a proprietary indicator Part 1

This is a new concept. I will do three parts to analyze the trading system in a better way and compare it with a benchmark. The idea is introducing the trading system, showing the backtesting (last 5 years), comparing with the 10 years backtesting, studying the system vs the benchmark and applying money management to see how the performance and risk parameters change.

Brief explanation of the trading system

The system is based on a proprietary indicator as you can see in the title. The idea behind this system is mean reversion. The levels are chosen from the study of the returns’ distribution. Does it sound familiar to you? In addition, a strict risk management system has been applied. The maximum loss allowed is 0.5% as we will see in the next points. The trading system trades 1000 shares in each trade (in the future I will apply money management) The initial portfolio

 

Results after the last 5 years (backtesting)

     Backtesting statistics from RStudio and Excel, own elaboration

As you can see the system makes 95.86 euros on average per trade. I would like to make clear that the system traded 133 trades and the commissions are not included. The maximum profit in a trade was 1650 euros while the biggest loss was limited to 100 euros.  The return was 12750 euros which is the equivalent of 63.75% in 5 years (around 12.75% per year). As you can imagine, considering the strict risk management, the losing trades percentage is higher than the winning trades percentage. But the average winner is higher than the average loss. The Sharpe Ratio is 4.32, which confirms the profitability of the system.

                                  Max Drawdown, own elaboration using RStudio

This is the measure that I like the most. The system only loses 925 euros in the worst trading period. If we check in percentage terms, it represents a 3.53% loss. According to the asymmetrical leverage rule with a gain of 3.57%, we offset the loss. This shows the importance of risk management.

      Trading system track record, own elaboration using RStudio

Here you can see how the portfolio has been performing. The most important is its consistency.

As a curiosity, performance comparison with different initial capital

With these numbers, we would be tempted to invest 5000 euros or less. The system doesn’t require a lot of capital. The main problem with a 5000 portfolio is that we will struggle because the costs are not included and we couldn´t trade 1000 shares each time.

Conclusion

This has been the first part of a series of posts about the same trading system. In my opinion, the performance and the risk metrics are good. In the next post, I will review the 10 years backtest. The purpose of that is to check that the system hasn´t been overfitted for the last 5 years and show how it performed in a longer period. I hope that you like it.

Have a good trading!!

Disclaimer

I wrote this article myself, and it expresses my own opinions that shouldn't be used as a trading advice. Trading carries considerable risk due to the high leverage involved

How can we make a strategy profitable modifying a couple of things?

Nowadays trading is in vogue, even more, if we consider the new cryptocurrency trend. Basically, everyone wants to jump in. The trader lifestyle is desired by all the people. Sadly, trading is harder than what the social media shows. The industry is changing a lot. Concepts such as machine learning, artificial intelligence are taking importance in leading investment banks and hedge funds as they are heavily investing in it.

Why do the biggest companies invest in machine learning, artificial intelligence, and algorithms?

It´s very difficult to replace an experienced trader because he knows how to adapt the strategies in different economic cycles and conditions. Some hedge fund managers are hiring a lot of developers and programmers to create algos that emulate the behaviour of their best traders. This seems really expensive, at least in the first years, but I believe that in the long run will save money for the hedge fund. How can you emulate the trader behaviour? In my humble opinion, I would divide the strategies applied by the trader in little pieces and I will study the trader´s track record in order to study the conditions (price, type of order, macro events on that day, news) of the trades. Once I understand the reasons I will try to replicate its piece of strategy and I will code it. Once it’s coded and tested, I will assign a subaccount to use this strategy and I will do the same process for each strategy. To sum up, I will have a trading account made-up of subaccounts that run a specific strategy. We can say that the main account is the portfolio and the subaccounts are different traders or fund managers.

This process can take a lot of time and some parts can be difficult to replicate.

What aspects should we modify to make a simple strategy profitable?

The strategy is based on the EURUSD futures but I'm not going to explain how it works. The main purpose of this post is to show you how to modify a simple strategy to improve the profitability and reduce the risk. It only trades once a day if the conditions are met. This backtest shows the last 5 years. The initial portfolio was 50000 USD.

Plain strategy

This is the strategy without any modification. 

     Statistics of the strategy, own elaboration using RStudio

The mean is positive and it shows that the system will make on average 16.21 USD per day. Sadly is not that easy, because there are winning days and losing days. The best day it banked a 3250USD profit. On the other hand, the worst day shows a loss of 2440 USD. The Sharpe Ratio is very low. The returns’ distribution was a normal distribution around 0. The main problem is that there were trades that lost a big percentage of the portfolio. This is why I decided to limit the loses in the second strategy. Let’s see the maximum Drawdown.

     Max drawdown and track record of the strategy, own elaboration using RStudio

Any serious investor can’t tolerate this drawdown considering the size of the portfolio. I wouldn’t be confident to use this system after reviewing the track record. Basically, it goes sideways.

Strategy 2, limiting loses

In this case, I decided to limit the loss to 600USD per day. Let’s see if the system has improved or not.

     Statistics of the strategy, own elaboration using RStudio

In general terms, this system is worse than the first one. The system makes 15,19 USD per trade on average, which is  1 dollar less than in the first strategy. The worst loss has been limited but the distribution contains more days on the negative side. The days with big swings generated the most part of the loses. The return is only 19.96% in the backtesting.

     Profit and Loss from trades distribution, own elaboration using RStudio

The losing days are concentrated around the maximum loss allowed.

     Max drawdown and track record of the strategy 2, own elaboration using RStudio

The distribution is not appealing to me. The best thing is that the max drawdown is smaller than in the first strategy. This system is clearly limited by days with big ranges.

Strategy 3, looking for different entries

Once I limited the losses of the first system and I checked that it wasn’t working as I would like it, I decided to change my entries. Will this be the solution?

     Statistics of the strategy, own elaboration using RStudio

Modifying the entries improved a lot the system. Now the system mades 49.88 USD per day. The standard deviation is lower. It would have returned 81.4% in 5 years, around 17% per year. What a change!! The winning days' percentage has increased and the Sharpe Ratio is very good. You should think that the commissions are not included.

    Max drawdown and Track record of the system, own elaboration

This is the best point. Look at the line! Now the system is consistent and the maximum drawdown has decreased a lot.

Conclusion

Even if you have read or heard about a successful trading system, you shouldn’t trade it without testing it before. We have seen that with minor tweaks the strategy can improve a lot. I hope that this post will help you to understand the process. The sky is the limit, in this field, the creativity doesn’t have limits. If you are a professional trader this can help you to test ideas and become more confident. Another important point is that you shouldn’t invest in these strategies even when the statistics are good. You should test them with a paper trading account and compare that the behaviour is similar to the previous backtesting. This is crucial because there is the risk of overfitting. I hope you like.

Have a good trading!

Disclaimer

I wrote this article myself, and it expresses my own opinions that shouldn't be used as a trading advice. Trading carries considerable risk due to the high leverage involved

Why should we save money and invest it? First lesson for a new investor

Introduction

Even if you have a small salary you can become a wealthy individual over the time. In order to do it, or at least to try, you need to be disciplined and save a certain percentage every month. There is no rule to save a certain percentage of your income every month. It depends on your personal situation and the goals that you set up before starting this journey.  

Example

Let’s make a hypothetical example. A young person has a salary of 20000$ per year after taxes. This person knows that he/she can live with this salary but he/she can’t afford to pay certain hobbies. Let me call this person David to make it easier. One day, David decides to write in a paper his desired lifestyle for the future. He doesn’t want to change the job because he likes it and the workplace is nearby. He knows that saving money is not enough, so he starts looking for an extra income. He lives in a medium size town so there are limited opportunities. After thinking about it, he decides that is going to save 10% of his salary and invest it at the final of the year. In this case, he will be saving 2000$ every year and this amount will be added to the portfolio at the final of the year.

Let’s supposed 4 type of portfolios he can invest in and the returns that he can get if he is committed with one of them.

         Different portfolios, own elaboration

These calculations are made by the assumption of that 2000$ are saved and added every year, the interest rate is fixed (2%, 5%, 7% or variable in the case of S&P500), it’s calculated for 40 years and he doesn’t withdraw any money.

As you can see if he decides to invest in a portfolio that returns 2% per year, after 40 years, he will have 123220.05$. Considering that in his lifetime has saved 80000$, means that this portfolio has made 43220.05$. This is a very conservative portfolio that probably is not the best to meet your goals. Let me compare the last figure (accumulated savings + return generated) of the rest of the portfolios:

                5% per year = 253679.53$

                7% per year = 427219.14$

                SP500 annual return = 1212688.82$

The last portfolio is the riskiest, but what a great return.

What are the steps to start your own journey?

1. Set up your future goals
2. Evaluate your current situation, make a spreadsheet with your income and expenses and figure out how much money you save and how much money you will put in the investment portfolio
3. Choose an investment that suits your risk aversion and risk-reward ratio, make sure that you understand the chosen investment and the risk involved.
4. Ask for advice about the best way to set up your ideal investment account, with a risk and money management system (online, in your bank, with a financial advisor, in a brokerage)
5. Be disciplined, keep saving an investment as your plan dictates

Why is the Risk management important?

One of the most important things to succeed as an investor is the risk management. I can’t talk about this topic because I haven’t described a strategy to follow. It depends on the type of investment you choose. But probably, the first thing I would teach to a new investor is the Asymmetrical Leverage. It refers to the required gain to recoup from a loss increases geometrically. 

      Asymmetrical Leverage, own elaboration

This chart is a clear representation of the definition above. It means that if you lose 10% of your portfolio you need to gain 11.1%, which is more than the original 10% loss. Let’s imagine a 1000$ portfolio that loses 10%, so the portfolio is valued at 900$. The difference with the original portfolio is 100$ which is the same amount we need to gain to recoup the initial portfolio. If you divide 100$ by the new value of the portfolio, 900$, the result is 11.1%

As you can see in the chart the amount to recoup grows geometrically as soon as we incur in bigger loses. 

I think this is the first thing that an investor or a trader should learn. If you check, all the successful investors and traders have a sounding risk management and I guess that they have this chart on the wall.

Conclusion

Saving part of our income and investing it over the time is one of the best things we can do. Every time I say investing, it's in a responsible way. There are a lot of practices to avoid such as invest according to the media or the comments on the internet. You should generate your own ideas or reasons. If you are not ready or you don't have time, there are a lot of kind of investments and professional services that can help you. In that case, and depending on your resources, you can invest in Exchange Traded Funds, talk with your bank, open an account in an asset management or invest in hedge funds. If you want to try the joy of trading or investing on your own, there are a lot of resources to learn the basics. I would recommend spending several months with a paper trading account. This post shows only a hypothetical example of how lucrative can be. If you don't, believe me, Tony Robbins has a book with real examples of people that committed to saving part of their income and they became successful financially speaking. Please bear in mind that the past returns are not indicative of the future ones. As I said one of the most important things is the risk management. All the best of luck in your journey!

Have a good trading!

Disclaimer

I wrote this article myself, and it expresses my own opinions that shouldn't be used as a trading advice. Trading carries considerable risk due to the high leverage involved

#asymmetricalLeverage #investing #journey #loses #profits #returns #rRskManagement #savings #TonyRobbins, #Trading #wealthy #creating value # compounding_interest

Quantitative Strategy FGBL Futures

Some time ago, I was checking some charts when I decided to create an algorithm. The product chosen was the FGBL, the Euro-Bund futures. Basically, I thought that was a relationship between the past and the future. It sounds familiar, doesn't it? I found the historical and I started working on it.

First I run the strategy without any stop or money management strategy but I was disappointed with the results. Sadly I don't have any screenshot of that.

Second, I decided to apply risk management and limit the amount I could lose.

This improved a lot the strategy but I wasn't happy at all. Using my background, reading, and learning, I started applying money management. It had a better risk-reward, but it was riskier. I backtested this system from the 04/01/2016 to the 30/12/2016.

These are a couple of tables that explain some ratios: 

Initial Portfolio 50000 euros Final Portfolio 77140 euros Annual Return 54,28% Positive days 457 Negative days 548 Positive trades (% of the total) 44,46% Negative trades (% of the total) 53,31% Mathematical Expectancy 9,1299 Positive days average 192,36 euros Negative days average -143,99euros Max Drawdown 28,55%

Portfolio value	Lots	Max risk per trade
40000 euros	3	1.5%
60000 euros	4	1.33%
80000 euros	5	1.25%

Please have in mind that I haven't included the cost of trading (execution costs, market data, trading platform) I believe that the execution cost would be around 12-13k, so the profit would be half of the figure shown above. It's riskier than a normal hedge fund because they normally have less than 20% drawdown. The asymmetric leverage is really important. I've never traded with this system and I can adjust the risk management and the money management to meet certain goals. I should have backtested at least 5 years and then do the out of sample to check that it behaves like the backtested sample. This is only an example of how to design a trading system. 

Disclaimer

I wrote this article myself, and it expresses my own opinions that shouldn't be used as a trading advice. Trading carries considerable risk due to the high leverages involved