Description
1 Introduction
Here you will assess trade flow as means of generating profit opportunities in 3 cryptotoken markets. We stress the word “opportunity” because at high data rates like these, and given the markets’ price-time priority, it is far easier to identify desirable trades in the data stream than it is to inject oneself profitably into the fray.
2 Data
We have preprocessed level 2 exchange messages from the Coinbase WebSocket API for you into a more digestible format.
2.1 Treatment
Load the data for all 3 pairs from the class website. For each one, split it into test and training sets, with your training set containing the first 20% of the data and the test set containing the remainder.
2.2 Format
The data has the following structure[1]
2.2.1 Trades
| 1618090137140737000 1618090137157544000 35690 | 1000000 | -1 | |
| 1618090137851379000 1618090137864544000 35700 | 29801980 | 2 | |
| 1618270615253262000 1618270615358639000 35760 | 2926932560 | -1 | |
| 1618270616012160000 1618270616105583000 35760
The Side is actually a sum of trade sides at the same price and time. 2.2.2 Book |
16673940 | -1 | |
| Ask1PriceMillionths 35700 35700 | 35770 | 35770 | |
| Bid1PriceMillionths 35690 35690 | 35760 | 35760 | |
| Ask1SizeBillionths 11872084060 11872084060 | 1255039420 | 1255039420 | |
| Bid1SizeBillionths 32957203990 32957203990 | 24752612680 | 24752612680 | |
| Ask2PriceMillionths 35710 35710 | 35780 | 35780 | |
| Bid2PriceMillionths 35680 35680 | 35750 | 35750 | |
| Ask2SizeBillionths 31032423370 30332423370 | 31011776970 | 31011776970 | |
| Bid2SizeBillionths 45284575470 45284575470 | 41785630850 | 41785630850 | |
received utc nanoseconds
3 Exercise
Write code to find τ-interval trade flow just prior[2] to each trade data point[3][4] i. Compute T-second forward returns. Regress them against each other in your training set, to find a coefficient β of regression.
For each data point in your test set you already have, so your return prediction is ˆ . Define a threshold j for ˆri and assume you might attempt to trade whenever j < |rˆi| .
4 Analysis
Assess the trading opportunities arising from using these return predictions in your test set. As part of this assessment, comment on the reliability of β, how you chose j, and what you might expect from using much longer training and test periods.
2
[1] Note that inaccuracies in clock settings, i.e. “clock skew”, can cause timestamps to appear later than the time at which they are recorded as having been received.
1
[2] We do not include the trade i data itself, because we are evaluating trade i in terms of the flow we would have been aware of just before it happened.
[3] NOTE: the trade data series does not necessarily have strictly increasing timestamps. Be sure not to include other trades at the same timestamp in your computation of Fi.
[4] It is not necessary to handle latency in your homework, but for your edification: a more careful implementation would account for lags. For a pessimistic approach we could choose L as, say, twice the 99th percentile of computational and communications lag. Then, it would use book data (not just trade data) to help compute return from time ti+L to ti+L+T and run regressions using that. The idea here is that it takes approximately time L to “do anything” about trade information.
