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System Development with acrary
- Thread starter acrary
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Quote from acrary:
Next, take the trades and test them against random trades pulled from the same year (the edge test). Rank the trades versus random for each year of the backtest. If the trades score consistently above the 70th percentile then you can guess you've found a edge-based system. If not, then you have to assume you've found a temporal characteristic in the data that can be exploited for some period of time.
It'd be really interesting to see the performance stats on one or two of your better systems with and without the filters (ie entry only) to see how much of a difference they end up making.
-bulat
Ok, I've given up on the whole word processing "professional looking" project. I don't have the desire to become a expert on the Microsoft products. I also noticed on the work I've completed that I kept going through notes and pulling out more and more related material. I think I could probably create a 3 book series based on the material. I also realize most people won't go further than one or two systems and then either bank some coin or give up and realize it's too hard.
So, for part 2 of this journal I decided to work towards a goal.
The goal is to replicate the performance of Monroe Trout in the New Market Wizards book. In the beginning of the interview Jack Schwager described Trout's 5+ year performance numbers. A 67% annual return, 87% of all months profitable, and a max drawdown of just over 8%. This should be fun (at least for me).
I'll do this with 5 models or less and try to explain some stuff as I go along.
So, for part 2 of this journal I decided to work towards a goal.
The goal is to replicate the performance of Monroe Trout in the New Market Wizards book. In the beginning of the interview Jack Schwager described Trout's 5+ year performance numbers. A 67% annual return, 87% of all months profitable, and a max drawdown of just over 8%. This should be fun (at least for me).
I'll do this with 5 models or less and try to explain some stuff as I go along.
From the first part of this journal I posted some about weighting different systems using the modified sharpe ratio. Here's a run using the first model. I'm using only monthly performance numbers to come up with the weights. Obviously if only used one model the weights would be unimportant. The modified sharpe ratio posted is the monthly modified sharpe ratio.
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The weighting between the models is then passed into a money management program. To make the process easier to understand I'm using a fixed per-cent risk model for the selected portfolio. Here is a run using just the one model and projecting the performance out 12 months. For the first pass I'm just using 1% per-trade risk. I'm doing 100,000 passes in the Monte-Carlo run.
Looking at the numbers you can see we project a return of 36.8%, a average drawdown of 10.9%, and 69.5% of the months were expected to be profitable. None of the criteria were met using one model so obviously more than one will be needed.
Near the top of page one is the minimum funding to trade the model in the portfolio and here's how it's calculated.
How much do I need to fund a account?
M = Min. Required Capital
A = Average loss
P = Per-cent risk
DD = Drawdown to recover at 95% of Monte Carlo Sims in decimal ex. .2 = 20%
M = (A/P) * (1/(1-DD))
A = average loss of 1,000
P = 1% risk per-trade
DD = 19% drawdown at 95% level
M = (A/P) * (1/(1-DD))
M = (1000/.01) * (1/(1-.19))
M = 100,000 * (1/.81)
M = 100,000 * 1.236
M = 123,600
I use the 95% level as a cutoff because I'd stop trading a system at that point.
The projected per-cent months profitable is from the sheet I previously posted showing the effect of number of trades with profit factor to figure out the expected percentage of profitable months.
On page 2 are the Monte-Carlo runs. From this you can see there is about a 94% chance of this system being profitable at the end of 12 months.
Since the first model didn't get close to our goals I'll add a second model and you'll see how that works.
Looking at the numbers you can see we project a return of 36.8%, a average drawdown of 10.9%, and 69.5% of the months were expected to be profitable. None of the criteria were met using one model so obviously more than one will be needed.
Near the top of page one is the minimum funding to trade the model in the portfolio and here's how it's calculated.
How much do I need to fund a account?
M = Min. Required Capital
A = Average loss
P = Per-cent risk
DD = Drawdown to recover at 95% of Monte Carlo Sims in decimal ex. .2 = 20%
M = (A/P) * (1/(1-DD))
A = average loss of 1,000
P = 1% risk per-trade
DD = 19% drawdown at 95% level
M = (A/P) * (1/(1-DD))
M = (1000/.01) * (1/(1-.19))
M = 100,000 * (1/.81)
M = 100,000 * 1.236
M = 123,600
I use the 95% level as a cutoff because I'd stop trading a system at that point.
The projected per-cent months profitable is from the sheet I previously posted showing the effect of number of trades with profit factor to figure out the expected percentage of profitable months.
On page 2 are the Monte-Carlo runs. From this you can see there is about a 94% chance of this system being profitable at the end of 12 months.
Since the first model didn't get close to our goals I'll add a second model and you'll see how that works.
Attachments
Quote from acrary:
So, for part 2 of this journal I decided to work towards a goal.
The goal is to replicate the performance of Monroe Trout in the New Market Wizards book. In the beginning of the interview Jack Schwager described Trout's 5+ year performance numbers. A 67% annual return, 87% of all months profitable, and a max drawdown of just over 8%. This should be fun (at least for me).
I'll do this with 5 models or less and try to explain some stuff as I go along.
An optimistic goal, wish you luck. I assume the trading will be real money/hypothetical? If so, what size book are we talking about, $123,600? Trout was producing those returns on 9-figures.
Quote from riskarb:
An optimistic goal, wish you luck. I assume the trading will be real money/hypothetical? If so, what size book are we talking about, $123,600? Trout was producing those returns on 9-figures.
What I'm doing is describing the process which I think might be valuable to a developer. If you like, once we've achieved the goal (first model the results to achieve then achieve the results modeled), I'd be happy to post the month by month results on a going forward basis with any required changes.
We're back to the weighting program this time using two models.
You can see the modified sharpe ratio was improved by adding a second model (better than either of the single models by themselves). In the two model results there are a couple of things I'll go over. The first is the weighting. The position in the weighting is reflective of the position of the model under model #. In this case the best weighting to achieve the optimal modified sharpe ratio is 3 units of model 1 for every 1 unit of model 2. I call them units because they aren't contracts (they are volatility normalized results). Notice in the two model result there is a 12 month rolling correlation. To do this I use a minimum of 48 months of results. The rolling correlation starts after the first 12 months and then keeps figuring them for each month until the end of the data. The total of all the rolling months is then divided by the number of months tested. The number in the report is the average correlation. I also figure the standard deviation of the correlation for each two model pair and add/subtract it from the average to figure out how stable the correlation is. In this case the two models are negatively correlated with a pretty low standard deviation. It would take a more than 3 standard deviation month for them to be highly correlated. This would be a very good candidate for trading with the first model.
Under the money management contract multiplier notice everything is related to 1.0. I did this to improve the processing in the money management software. You'll see these numbers plugged in there.
You can see the modified sharpe ratio was improved by adding a second model (better than either of the single models by themselves). In the two model results there are a couple of things I'll go over. The first is the weighting. The position in the weighting is reflective of the position of the model under model #. In this case the best weighting to achieve the optimal modified sharpe ratio is 3 units of model 1 for every 1 unit of model 2. I call them units because they aren't contracts (they are volatility normalized results). Notice in the two model result there is a 12 month rolling correlation. To do this I use a minimum of 48 months of results. The rolling correlation starts after the first 12 months and then keeps figuring them for each month until the end of the data. The total of all the rolling months is then divided by the number of months tested. The number in the report is the average correlation. I also figure the standard deviation of the correlation for each two model pair and add/subtract it from the average to figure out how stable the correlation is. In this case the two models are negatively correlated with a pretty low standard deviation. It would take a more than 3 standard deviation month for them to be highly correlated. This would be a very good candidate for trading with the first model.
Under the money management contract multiplier notice everything is related to 1.0. I did this to improve the processing in the money management software. You'll see these numbers plugged in there.
Attachments
Quote from acrary:
What I'm doing is describing the process which I think might be valuable to a developer. If you like, once we've achieved the goal (first model the results to achieve then achieve the results modeled), I'd be happy to post the month by month results on a going forward basis with any required changes.
Yes, that would be interesting. Again, good luck with the process going forward.
Edit: thanks for the text file.
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