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AI, Quantitative Analysis and Data Science Solutions for Finance and Manufacturing.

AI For Trading: What Could Possibly Go Wrong?

Note: the following post is purely to illustrate how to avoid pitfalls in trading. My motivation for this post was to give an example of mistakes that I have made myself all too often and I can fully appreciate how one would come to those conclusions. I’m sure that the authors are excellent in machine…
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March 23, 2018 0

Plotting Volatility Surface for Options

by Mary Lin, Tom Starke and Michelle Lin This blog post is a revised edition of Tom’s original blog post with a newer data set. More information, source code & inspiration can be found here. Code for this blog post is in our Github repository. Options are complex instruments with many moving parts. Specifically, options are contracts…
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January 4, 2018 0

Kdb+ vs. Python

What is kdb+? Kdb+ is a powerful column based time series database. It is commonly used in investment banks and hedge funds around the world for extremely fast time series analysis. Kdb+ uses vector language Q, which itself was built for speed and expressiveness. Q commands can be entered straight into the console, as compilation…
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October 12, 2017 0

Quant Basics 10: Performance Prediction With Machine Learning

In the previous post we plotted a response surface of our strategy parameters and their PnL in order to assess if our choice of parameters is rational and not just a local maxima, which rapidly drops off as we move away from it. In this section we investigate how we can use machine learning to…
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October 1, 2017 11

Quant Basics 9: Plotting A Response Surface

In the last section we looked at bootstrapping by random sampling one of our best strategy PnL curves in order to determine how stable and reliable the returns are. In this section we will look at the response surface of the returns, that is the PnL with respect to the underlying parameters. In our case…
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September 21, 2017 0

Quant Basics 8: Bootstrapping and Response Surface

In the last section we investigated how the strategies we’ve selected from our train-test cluster were distributed in the parameter plot. We saw that they form a dense cluster in that plot which indicates that the PnL’s we see are not a result of overfitting since we would expect them to be more randomly distributed.…
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September 20, 2017 0

Quantopian Advanced Workshop

Presenters: Dr Tom Starke & Michelle Lin The workshop taught how to use the pipeline API, which enables you to apply your trading ideas to massive universes of stocks and futures while reducing the performance penalty through lazy evaluation. Next we showed how factor models work. In the US alone there are currently more the…
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September 18, 2017 1

Monte Carlo Options Pricing in Two Lines of Python

This is an old video that I produced sitting on my bed in the morning in order to learn how to make basic Youtube videos. Nevertheless, I became quite popular. I apologise for looking a bit rough. Hope you enjoy it regardless. Here is the code: If that seems a bit complex, check out the…
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September 1, 2017 0

Quant Basics 7: Identify Best Parameters

In the previous post we used unsupervised machine learning to identify the best cluster of train-test results. The question is: is that cluster meaningful? If the parameters of the moving averages for the cluster are scattered evenly over the parameter space, this cluster may not be very meaningful. On the other hand, if the parameters…
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August 31, 2017 0

Quant Basics 6: Start With Machine Learning

In the previous section we ran a parameter sweep over the train and the test set of our strategy and looked at the average PnL. In this post we will start with system parameter permutation (SPP) in order to improve the performance of our system without falling prey to data mining bias. Remember that we…
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August 28, 2017 0