# Berkeley Trading Competition Yosemite — Data

来源：https://www.kaggle.com/competitions/berkeleytradingcompetition2022/data

本地 Meta Kaggle 快照；不是本次实时页面。

# Dataset Description

In train.csv, we provided information about each of the trades from our strategy. The features included are

- pred edge: The predicted edge that our strategy generated. Predicted edge is in bips, where a bip is 1/100 of a percent. For example, if realized edge was 20 for a trade of $100 USD, our PNL would be $100*(20/10000) = $0.2
- stock: Stock that was being traded (A, B, C, or D) 
- location: Exchange the trade took place (NYSE, NASDAQ, or CSE) 
- price: Price per share of the stock
- size: Size of the transaction (in units of stock)
- dir: Whether our strategy bought or sold (B or S)
- counterparty: Who we traded against (Hedge Fund, Mutual Fund, or Retail) 
- sentiment: A score from -100 to 100 on how bearish/bullish retail investors are. You can think of this as sentiment data scraped from websites that retail traders frequent (Twitter/Reddit/etc)

We are trying to predict

- realized edge: the realized edge of our strategy. Realized edge is in bips. 
- PNL: price * size * (realized edge / 10000) 

While developing the strategy, Traders hired some unpaid interns to scrape data on the counterparty and sentiment. However, because we never paid them, the interns did a bad job on recording the data and we believe that 30-40% of the data in those columns are incorrect. There will be separate auctions held for higher quality data on the counterparty and sentiment.