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Trading

under Data Science

Reddit has been at the epicenter of one of the biggest movements in the world of finance, and although it seemed like an unlikely source of such a movement — it’s hardly surprising in hindsight.

The trading-focused subreddits of Reddit are the backdrop for a huge amount of discussion about what is happening in the markets — so it is only logical to tap into this huge data source.

When building a data extraction tool like this, one of the first things we need to do is identify what the data we’re extracting is actually about — and for that we will be using named entity recognition (NER).

under Data Science

In this article the author uses Reddit sentiment data to inform trading strategies. He derives market sentiment in two ways using the wallstreetbets subreddit:

  1. Collecting comments from daily discussion submissions then running the VADER sentiment model to assess overall daily positive/negative sentiment.
  2. Collecting all submission titles per day then assessing daily bullish/bearish sentiment using keyword analysis.

under Python

The price of energy changes hourly, which opens up the possibility of temporal arbitrage: buying energy at a low price, storing it, and selling it later at a higher price. To successfully execute any temporal arbitrage strategy, some amount of confidence in future prices is required, to be able to expect to make a profit. In the case of energy arbitrage, the constraints of the energy storage system must also be considered. For example, batteries have limited capacity, limited rate of charging, and are not 100% efficient in that not all of the energy used to charge a battery will be available later for discharge.

under Quant Finance

The purpose of this article is to introduce the reader to some of the tools used to spot stock market trends.

We will utilize a data set consisting of five years of daily stock market data for Analog Devices. The time period we consider starts on January 1, 2013 and ends on December 31, 2017. We will start analyzing the data using line plots, then introduce candlestick charts. Patterns that can be seen in the candlestick chart will be introduced which can be used to spot changes in the market. We add another of level analysis by overlaying moving averages and discussing how these can help confirm trend changes. Finally, we construct a figure that concisely summarizes the stock price data for any company.