This is a summary of links featured on Quantocracy on Tuesday, 03/22/2022. To see our most recent links, visit the Quant Mashup. Read on readers!
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Finding Alpha on the Internet (Part 3) [Derek Wong]We continue the series by replicating using as much data and code as provided in the source material. We create a three-state Gaussian Mixture Model and fit it so S&P500 data. I examine the output and give feedback about my coding replication and data sourcing along the way. Then I try to apply a proxy for adding economic data as a featureprevious posts Part 1 and Part 2. Disclaimer: Not
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Hacking 1-Minute Cryptocurrency Candlesticks: Capturing Binance Live Data [Quant at Risk]There is no question about how profitable the trading of cryptocurrencies can be. If you create an algorithmic strategy and stick to it, you can capture a +10% PnL wave sometimes even twice a day for a selected asset. Unfortunately, the opposite is true, too! The crypto-risks seem to follow the same patterns. But, lets be optimistic from the beginning. In this mini-series of articles, we will
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Intraday Stock Index Forecasting [Jonathan Kinlay]In a previous post I discussed modelling stock prices processes as Geometric brownian Motion processes: To recap briefly, we assume a process of the form: Where S0 is the initial stock price at time t = 0. The mean of such a process is: and standard deviation: In the post I showed how to estimate such a process with daily stock prices, using these to provide a forecast range of prices over a
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What Can We Learn from Insider Trading in the 18th Century? [Quantpedia]Directors, board members, and large shareholders are just some of those who might have non-public material information about their firm. Even though this information could be easily used to profit by trading their own stocks (stocks of the company they hold information about), this behavior is strictly prohibited. This is but one aspect of insider trading. There are two different possible