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QBUS2820 Assignment 2: Cryptocurrency Day Trading
创建日期：2024-05-08 17:10:24
标签： QBUS2820
Cryptocurrency Day Trading

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QBUS2820 Assignment 2: Cryptocurrency Day Trading

Overview
The assignment consists of two problems: One is forecasting and the other can be seen as classification.
This is a “real life” exercise, you will forecast actual future values, and we will measure the performance
in a test set that is not available yet (As opposed to the typical academic exercise of splitting a dataset
into train/test). Not even the teaching team has the “test set”.
You will provide forecasts for the daily returns for several cryptocurrency/USDT pairs. The forecasts are
used for day trading, that is, buying/selling tokens for a given crypto in the morning when the markets
open (at the opening price) and then selling/buying when the market close that same day.
A notebook will be uploaded to canvas in a later date to help with the programming and provide an
example of valid submission files/format.
The late submission penalty for the assignment is 5% of the assigned mark per day, and will prevent
winning any ‘extra points’ (see the Evaluation section)
Context (for flavor)
Imagine that you are a student at an (allegedly) prestigious university and are just learning about
forecasting. You have a friend/relative that is keen on cryptocurrencies and “investing”. In a casual
conversation with them, you mention that you are studying forecasting. This person is very proud of you
(really believes in you) and asks you to send them your predictions for the market, and they will invest
based on that. You mention that this is not a good idea, that you are just learning about forecasting,
furthermore market prediction is very hard (some people even say it is random). However, there is no
convincing them, they are adamant about using your forecasts.
To prevent catastrophic results, you devise a plan: on top of the forecasts, you will also provide an
“Overseer Artificial Intelligence”(AI) that will tell them whether to invest or not in an asset for any given
day, kind of a danger detection AI. The idea for this AI is to prevent an over-optimistic use of your
forecasts, because there is an uncertainty/risk around them. So the AI provides a clear-cut decision
about this uncertainty. This AI is of course a classifier.
The forecasts
You forecast the opening price and closing price for each cryptocurrency. Specifically, you forecast the
daily returns, as [ (closing_price) / (opening_price) - 1] for each day.
The forecasts are used for “day trading” but the trading strategy is out of your control, it is automatic,
depending on the value of the forecasts. The trading strategy is as follows:
• For each cryptocurrency and each day, if the forecast of the return is positive, the investor buys
a number of tokens (t) at the opening price. The amount (t) is determined by the price in dollars.
The investor always buys a fixed amount of US dollars each day for each currency. For example,
always buy s dollars' worth of tokens, getting t = (s/open_price) tokens. Assume that dollars and
tokens are infinitely divisible.
• At the end of the day, the investor sells the t tokens bought in the morning and the closing price,
getting (t x close_price) dollars back. So they end the day with more dollars that they started
with if the actual returns (not the forecasts!) are positive.
• If the forecast of the returns are negative, the investor always sells a fixed amount s dollars'
worth of tokens at the opening price, they “sell” t tokens. (t = s/open_price). At the end of the
day, they buy the t tokens back at the closing price.
• We assume that the investor already has enough dollars and tokens of each crypto to do the
trading at any given point.
• A simple interpretation: assuming that the forecasts are ‘accurate’ (they predict the sign of the
returns well), they make s x absolute_value(actual_returns) each day for each asset.
You will provide forecasts for the returns of each asset for the next 5 days.
You can use any of the forecast models seen in the unit (from the naïve to the ARIMA).
The classifier
The classifier makes binary predictions that control the investor, they can activate or deactivate the
investor. For each asset and each day, the classifier outputs one of the two classes ‘TRADE’ or ‘NOT
TRADE’. If the class predicted is ‘TRADE’ the investor goes on with their trading strategy that day for that
asset: they look at the forecast and they trade based on the sign of the returns. If the predicted class is
‘NOT TRADE’ the investor does nothing.
The classifier can use values of past returns and other variables included in the data (the past open and
close, high and low, volumes...) even from other assets. It can also include the forecasts for the given
day (for example, to detect if they go crazy). You can think of the classifier acting on top of the forecasts,
considering more variables and even more complex interaction.
You can use any classifier model that you like, you are not limited to logistic and Nearest Neighbours.
A key part is how you define the target variable.
The data
The information comes from the Binance Cryptocurrency Exchange, as reported by
https://www.CryptoDataDownload.com
Prices come from the crypto/USDT pairs, the daily datasets. For example BTC/USDT, for the daily Bitcoin
prices in US dollars.
You can calculate the daily returns as the variables by (open/close -1).
We will use the values from that webpage to measure the performance, to obtain the test set, for 5 days
after


The files for the historical prices can be found in the following urls (already in python). Be mindful and
do not download them more than necessary (once per day). Also be mindful of timezone changes.
crypto_urls = ["https://www.cryptodatadownload.com/cdd/Binance_BTCUSDT_d.csv",
"https://www.cryptodatadownload.com/cdd/Binance_ETHUSDT_d.csv",
"https://www.cryptodatadownload.com/cdd/Binance_LTCUSDT_d.csv",
"https://www.cryptodatadownload.com/cdd/Binance_BNBUSDT_d.csv",
"https://www.cryptodatadownload.com/cdd/Binance_XRPUSDT_d.csv",
"https://www.cryptodatadownload.com/cdd/Binance_EOSUSDT_d.csv",
"https://www.cryptodatadownload.com/cdd/Binance_TRXUSDT_d.csv",
"https://www.cryptodatadownload.com/cdd/Binance_NEOUSDT_d.csv",
"https://www.cryptodatadownload.com/cdd/Binance_ETCUSDT_d.csv",
"https://www.cryptodatadownload.com/cdd/Binance_XLMUSDT_d.csv",
"https://www.cryptodatadownload.com/cdd/Binance_DASHUSDT_d.csv",
"https://www.cryptodatadownload.com/cdd/Binance_XMRUSDT_d.csv"]
What you need to submit
• A csv file containing the forecasts for the returns for the next 5 days.
o The file has the columns: date, symbol, returns, action.
o ‘action’ means the prediction of the classifier, in ‘TRADE’ or ‘NOT TRADE’
o Symbol is the id of the pair, in the same format as it appears in the data from
cryptodatadownload.com (the symbol column in the crypto pairs files)
o The filename must be ‘ASG2_preds_QBUS2820.csv’
• A notebook that creates the forecasts and documents the decisions.
o Divide it into sections and document clearly what you are doing using markdown cells
before the code of each section. You can use one (or more) cells for the methodological
discussion, this should be separated from the cells that clarify technical (programming
parts).
o The notebook has to have an ‘Analytics’ section at the end where you will report
expected values for the average absolute error of the returns. You will also report
expected value of the amount of dollars that you end up with, assuming a value of
‘s=100’. Together with the 95% prediction intervals for both quantities (returns and final
dollars).
o The filename of the notebooks should be ‘ASG2_nbook_QBUS2820.ipynb’
o No pdf document is needed, make sure that the notebook is as clear as possible, the
Jupyter notebooks were created specifically for these kinds of data analysis tasks, you
can find many examples that interweave code and analysis.
Evaluation
Correctness of the application of the methodology:

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