Tag: machine learning

Please read Part I and Part II for an introduction. Part III extends the treatment to other dollar indices. Part IV looked at an ensemble model from our learnings from Part III.

In this final part of our series on training a simple Support Vector Machine on currency indices to predict the NIFTY, we will incorporate a Simple Moving Average into our decision matrix. While our model in Part IV would go long/long-short based purely on the predictions of the SVM, the model we use here will go long only if both the prediction is positive and the NIFTY is above a 50-day SMA and go short only if both the prediction is negative and the NIFTY is below its 50-day SMA. Think of the SMA as a regime signifier.

Results

Cumulative returns:

BH: buy & hold
L0: Long-only using SMA 50 alone
LS0: Long-short using SMA 50 alone

L1: Long-only using DTWEXB SVM and SMA 50
LS1: Long-short using DTWEXB SVM SMA 50

L2: Long-only using DTWEXM SVM and SMA 50
LS2: Long-short using DTWEXM SVM SMA 50

L: Long-only using ensemble SVM and SMA 50
LS: Long-short using ensemble SVM SMA 50

The SVM that is only based on DTWEXM (LS2) give better returns than the one based on the ensemble model (LS.) However, the ensemble model had a slightly lower drawdown of the two. Also, when compared to the earlier version that did not use the SMA, LS2 under-performs by about 10%. However, the key difference is in the drawdowns. Using the SMA filter reduced drawdowns significantly, especially the one occurring in 2018.

Without SMA filter:

With SMA filter:

Conclusion

An SVM with a 4th degree polynomial over the DTWEXM currency index in conjunction with a 50-day SMA seems to be the winning combination.

Code and charts are on github.

Please read Part I and Part II for an introduction. Part III extends the treatment to other dollar indices.

In this part of our ongoing series on using SVMs on dollar indices to predict the NIFTY 50, we create an ensemble of two models. We combine one on DTWEXB, using an 8th degree polynomial kernel and another on DTWEXM, using a 4th degree polynomial kernel, to create long-only and long-short portfolios.

Results

Here are the cumulative returns of the standalone models and the ensemble:

The standalone model over DTWEXM (LS2) seems to outperform all other models, including buy and hold. However, the ensemble model (LS) has lower drawdowns and comes in at second place. Here are the list of drawdowns deeper than 5% over the test dataset:

In the next (and final) post, we will explore if we can add a simple technical signal into to mix to boost returns and reduce drawdowns.

Code and charts are on github.

Please read Part I and Part II for an introduction.

In Parts I and II, we saw how a polynomial kernel was probably a good way to tune an SVM. Also, there was no single degree parameter that was necessarily better than the others. In this post, we train three different polynomial kernel SVMs on two other dollar indices (DTWEXB and DTWEXO) and USDINR (DEXINUS) and tabulate their returns over the two different datasets and across different degree parameters.

To recap, The FRED publishes the following indices along with USDINR (DEXINUS):

1. DTWEXB: Trade Weighted U.S. Dollar Index: Broad
2. DTWEXM: Trade Weighted U.S. Dollar Index: Major Currencies
3. DTWEXO: Trade Weighted U.S. Dollar Index: Other Important Trading Partners

We modeled DTWEXM in Parts I and II. Here, we model the rest.

Results

2000-2018

2005-2018

Given the results above, we can ignore DTWEXO going forward. Surprisingly, DEXINUS (USDINR) does not predict the 2018 correction. In fact, the DEXINUS model using the 2005-2018 dataset replicates buy&hold. Hence, we will ignore DEXINUS as well. The SVM modeled on the 2005-2008 dataset using DTWEXB with degrees 5, 6 and 8 seem to have side-stepped the 2016 and 2018 corrections. Furthermore, degree 8 seems to have produced the best cumulative returns on the test set.

Next steps

Our observation from Part II was that a 2005-2018 dataset is probably a better set than 2000-2018. This is confirmed from the DTWEXB model above. In the next post, we will combine the DTWEXB(8) and DTWEXM(4) SVM models using the 2005-2018 datasets.

Code and charts are on github.

Please read Part I for an introduction.

Earlier, we saw that a 3rd degree polynomial kernel produced the best results on the test set. In this post, we explore if we can we get better results by tuning the degree parameter.

Outline

1. Use 1-, 2-, 5- and 10-week returns of DTWEXM to train an SVM using a polynomial kernel on subsequent 1-week returns of the NIFTY 50
2. Consider two datasets: one between the years 2000 and 2018 and the other between 2005 and 2018
3. Divide the dataset into training/validation/test sets in a 60/20/20 ratio
4. Use the validation test to tabulate out-performing degree parameters
5. Plot the cumulative return of a long-only, long-short and buy&hold NIFTY 50 strategy based on SVM predictions on the test set

Results

We find that there is no single degree parameter between the two datasets (#2 above) that consistently outperforms.

2000-2018 dataset

2005-2018 dataset

Here are the cumulative return charts for the best performing parameter:
2000-2018 dataset (8)

2005-2018 dataset (4)

While the first model (using the 2000-2018 dataset, 8th degree polynomial) failed to “predict” the 2018 correction in the NIFTY 50, the second one (2005-2018 dataset, 4th degree polynomial) seems to be able to side-step it. However, an SVM tuned with the 4th degree polynomial on the 2000-2018 dataset again failed to side-step the 2018 correction, indicating that we need to look more closely on how we choose our dataset – sometimes going too far back in time is counter-productive because the world changes.

Next Steps

In the next post, we will train a polynomial SVM with the other dollar indices (DTWEXB and DTWEXO) and USDINR (DEXINUS) and tabulate their predicted returns over different degrees.

Code and charts for this post are on github.