This is a summary of links featured on Quantocracy on Wednesday, 12/07/2022. To see our most recent links, visit the Quant Mashup. Read on readers!
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Building a Raspberry Pi Cluster for QSTrader Using SLURM – Part 5 [Quant Start]In the previous article we created a virtual environment and installed QSTrader on all our secondary nodes. We then carried out a test of the sixty forty strategy across all secondary nodes to make sure our installation had been successful. Now that we have successfully paralellised QSTrader we can start to carry out parameter sweeps for strategies. In this article we are going to carry out just
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Volume and Mean Reversion [Alvarez Quant Trading]Overall, I have had very little success integrating volume into any of my strategies. Either volume would have no predictive value or if it did, using it reduced the number of trades too much to be worthwhile. It has been a long while since I have looked into this and I had some new ideas. The Rules Test date range 1/1/2007 to 10/31/2022. I wanted to keep the rules simple. Buy Rules Stock is a
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Why I prefer probabilistic forecasts – hitting time probabilities [Sarem Seitz]Probabilistic forecasts are a more comprehensive way to predict future events compared to point forecasts. Probabilistic forecasts involve creating a model that predicts the entire probability distribution for a given future period, providing insight into all likely outcomes. This allows for the derivation of both point and interval forecasts. Point forecasts are easier to communicate to
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Doubling Down: Double Deep Q-networks for trading [Quant Dare]In previous posts, we have seen the basic RL algorithm, Deep Q learning (DQN). We have also seen it applied, using Neural Networks as the Agent, to an investment strategy. We finally even used it for a cryptocurrency investment strategy. This time, we will implement a slightly more advanced technique, Double Deep Q-networks (DDQN), and create a trading strategy using this algorithm. DQN revisited