FOMC Minutes: A Day Like No Other? [0.11]

Posted on April 6, 2017, 7:09 p.m. by MKTSTK @ [source]

Yesterday seemed like any other day until FOMC minutes.

But at the end of the day, how different was yesterday from any other day?

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I awoke this morning to find the S&P 500 futures down about 11 points.

To answer these questions, let’s go back to a non-parametric machine learning model known as the Kernel Density Estimator (KDE).

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A Dead-Simple Hedge Ratio API [0.09]

Posted on March 31, 2017, 2:27 p.m. by MKTSTK @ [source]

As the title suggests, I created a dead simple hedge ratio API called Risk Hedger.

What is a Hedge Ratio?

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Searching for Answers… with Volatility [0.02]

Posted on March 22, 2017, 4:46 p.m. by MKTSTK @ [source]

Pretty random alternative datapoint for you to ponder. The last time they experienced growth like this was the run up to August 2015 when volatility spiked and equities fell out of bed.

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Simulating Correlated Random Walks for the S&P 500 [0.08]

Posted on March 16, 2017, 8:34 p.m. by MKTSTK @ [source]

Waaaaaay back in the day, I showed how to simulate correlated random walks using copulas….

If you wanted to do some large universe like the S&P 500 you had to do everything pairwise, and then you weren’t really capturing any higher dimension relationships within the market.

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A Regime Switching Model: Momentum vs Mean Reversion [0.19]

Posted on Aug. 12, 2016, 2:58 p.m. by MKTSTK @ [source]

Today I wanted to share a link to my first post on Quantopian, in which I describe a strategy that uses social data to switch between mean reversion and momentum based trading regimes.

This feat is accomplished using a new social factor I’ve created.

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Many assets exhibit bull or bear trends which persist for long periods of time. This problem is known as unbalanced classes in the machine learning field.

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A Curious Correlation between Social Media and Volatility [0.25]

Posted on April 7, 2016, 7:12 p.m. by MKTSTK @ [source]

On StockTwits, what we see is a conversation flow. This translates to not just trading volume, but a shift in asset classes as well: conversations flow from equities, into broader indexes, and different trading instruments altogether.

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How to Value Nadex Bull Spreads? [0.01]

Posted on March 30, 2016, 7:17 p.m. by MKTSTK @ [source]

Exotic options have always been a hobby of mine. In a way they are like double barrier options, in that there is an upper and lower strike price.

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The Conundrum of Strategy Sizing [0.38]

Posted on March 23, 2016, 2:43 p.m. by MKTSTK @ [source]

But sizing up isn’t always a straightforward proposition, as we must take liquidity into account. MKTSTK investigates this conundrum in this week’s podcast.

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Limits of Machine Learning Part 2 [0.17]

Posted on March 16, 2016, 7:51 p.m. by MKTSTK @ [source]

Last week’s podcast was pretty negative on the value of machine learning in trading, so this week I wanted to provide my own counterpoint and explore life within the limits I identified earlier. The last track is a music track I enjoy.

Enjoy music track


Some Limits of Machine Learning in Trading [0.03]

Posted on March 9, 2016, 5:36 p.m. by MKTSTK @ [source]

Aka my first podcast. It really is amazing.

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Classical Conditioning in the Markets [0.01]

Posted on Dec. 14, 2015, 11:47 a.m. by MKTSTK @ [source]

Long ago I had a junior trader, let’s call him M, who worked the overnight shift for me trading interest rates. There would be joyous stretches of time where everything would crank money and I would walk in to lots of profits and M smashing a tennis ball into the wall with one of his racquets (much to the dismay of the other overnight guys, but when you make money you can do what you want).

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Trading and Freedom [0.06]

Posted on Dec. 9, 2015, 5:17 p.m. by MKTSTK @ [source]

There are many professions that can ultimately lead to financial freedom in the future. Very few, however, allow for freedom along the journey to this goal as well.

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Signal to Noise: Humans as Bayesian Filters [0.10]

Posted on Nov. 8, 2015, 3:42 p.m. by MKTSTK @ [source]

Bayesian Filters are everywhere in Quant Trading.

The Signal-to-Noise Ratio (SNR) defines our relative preference for new versus old information.

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