Pricing Options and Implied Volatility with Python
In 2012, my first options trade lost $9,000. 12 months later I was making $1,100 per week trading in my...
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In 2012, my first options trade lost $9,000. 12 months later I was making $1,100 per week trading in my...
Read moreDatasets from real-world scenarios are important for building and testing machine learning models. You may just want to have some...
Read moreStock returns can be heavily impacted by certain events. Sometimes these events are unexpected or a surprise (natural disasters, global...
Read moreIn this lecture, we review the theory behind Metropolis Monte Carlo modeling and apply these concepts to the simulations of...
Read moreGo to the code on GitHub
Read moreEnd-to-end project: get the data, train the model, place the order, get notified.
Read moreLearn how to perform algorithmic trading using Python in this complete course. Algorithmic trading means using computers to make investment...
Read moreMark 27.1 of the NAG Library contains a new routine, s30acf, for computing the implied volatility of a European option contract...
Read moreLendingClub is the world’s largest peer-to-peer lending platform. Until recently (through the end of 2018), LendingClub published a public dataset of...
Read moreIn finance, computation efficiency can be directly converted to trading profits sometimes. Quants are facing the challenges of trading off...
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