QS+1: Tracking My Friend's Trading Performance

All right, so my project is about tracking a friend of mine, a very close friend of mine who decided he doesn’t like the whole work thing. He never has, so he came to me one day and say I’m going to do foreign currency exchange trading. So this has become pretty popular of late where you can speculate on the fluctuation of different currencies, so for example, Euro/USD has a certain fluctuation. It goes up and you can predict on where it goes and you can buy against the market or predicting it upwards.

So he said I’m going to do that and this was the summer of 2010, so he had taken this expensive course with the experts who actually make a living of doing this, so he went to trade for a couple of month but he was making a lose by the end of 2010 or so and I was talking to him because I thought it was crazy and he was like I’m losing, but I’m going to keep at it, these things take time.

So I was like why don’t we track you and you can actually see your performance because if you don’t know because I would ask him how much are you losing and he’s like, he didn’t know. So I don’t think he wanted to know, so I said basically we should track and we’ll do it very simply because from my experience I know tracking a lot of things all a the same time is not great and it’s the quickest wat to stop tracking. I really wanted him to track this, so we set up a very simple Google doc sheet. Google is not super sophisticated but a lot of the sharing function and spreadsheet with different people set up, and we can share data and it’s beautiful.

So we tracked three simple measures. The first one’s just the date. The second was the amount he would bet for each trade; because for each trade you could say, well I’m going to bet like say $10 a pip. A pip is the smallest change of the market, up or down. So if you predict 10 pips correctly and you put $10 each pip you make $100 right, 10 times 10.

So basically he put the bet and what he had done and how many pips he had won that day. And that was it. So he would track that and we did this from February until September. So you can see February 1 we start there, and there is a lot of stuff going on in this graph and I won’t really go into all of it. But you can see if you just look from let’s say February until April there you can see steady performance, a lot of green, but tiny green. So he’s making a lot of wins, but when he loses, he loses very badly. And that was something I told him because I noticed when we start and the first thing I just graphed it and it was just really clear that when he lost he lost big you know.

Actually I did the analysis all the way up for all the data, and his trading performance is actually quite impressive; it’s 70%. That means out of 10 trades he predicts seven correctly and then you make money.

So that was really good, but it was like a syphon. All the money would immediately syphon though and it would immediately go out because of these huge loses. And they always came and very predictable. So the thing that you can do is for a foreign exchange market is you can set your lower limits, so it’s called the stop. So basically it’s if your trade goes bad it can kick you out right, and you can set this limit anywhere you like.

Now, you don’t want to set it too low because sometimes you dip down and then you go up and then you don’t want to get kicked out of the market when it goes up again. But I said to him, look you’re setting your stop way to level, you have to cut it off very earlier. He said well, what I just said. He said sometimes a trade will go negative but then it will go up again, so those trade will be cut off. I was like, well I don’t believe you but let’s measure it.

This was a very cleaver process; we called it the max against measure. So this is the number of pips it would go against for positive trades, only for positive trade because negative trades are negative. But we wanted to know which are the positive trades, how far it would dip before we go positive.

What I found that it would never really get past 20, and his stop was way down there. I mean it was 40, and 30 there. And at the beginning we don’t even get into the later stuff. So that basically defeated this argument.

I said based on this data you should at least 20 or even more conservative, because the average was right about 18.

So in the meantime we had a lot of debates about this and I really enjoyed just watching this foreign exchange. I just got to enjoy his data entry and where he had gone, and I would cheer him on and stuff like that.

He finally followed my advice, because in the beginning he wasn’t desperate enough, because he would just keep going as regular. But then he did it and the one day he followed my advice and it actually was the one that would have turned positive.

So that of course was not nice and I was like , well we just stick with it and the data shows, you know it’s not a single observation but I’m not sure if he was convinced. And but anyway I kept tracking on this for a while.

He already thought it was extremely useful just to track the data like this. So here are actually the net, the blue is the net incomes . so you can see the first two months he’s not losing much and then he’s starting to bet more money, so he’s not making money.

So he already thought it was very useful that we tracked the data. Now I did a lot of analysis because I just enjoyed, and I guess I’m in group number three. I just enjoyed you know, just playing with the data because you know he was trying to make this into a job, so I was like, okay let’s make you net profit, so I had this more elaborate table as well.

At the end of this he actually stopped trading. He finished his book and now he’s publishing his book and luckily he has now stopped all of this. But I did an analysis with all the data and I looked at each trade and then calculated what would have happened if he had set different kinds of stop. Remember the stops would cut off at the bottom right. Now at the right there is the amount of money he bet and that’s just a multiplier just to see if, I could take an average of all the trades. He fluctuated with all the different bets he took.

But as you can see, right at 15 and this was all the trades he did, he was making money, right. you can see if he had cut off and had the stop at 15 for all the trades, and if he had bet $10 each bet, he would have made $2500 for all the trades he did over that seven months. But you can se, 20 he still makes money and then you can see it quickly drops off, and this is what happened, he had an unlimited stop so he actually lost money.

So the lessons learned was a lot of stuff happened. We developed a lot of these unique measures collaboratively and I tracked him and was his policy advisor, and I was not doing anything on how he was doing the trading. Like he did the trading and he did it well actually, he just didn’t set the policy well. So then we mapped out all the transition data and then finally I calculated an optimal value based on the data that he had.

So that’s my project.