# Tag Info

9

Using months of proprietary data that labels participants by their participant ID, it has been found that during periods of significant volatility, the composition of HFT participants in the book remains mostly constant as a fraction of the total BBO composition. What really changes, it was found, was that the fraction of low-frequency traders aggressing on ...

6

Financial modeling is often considered as a mixture of art and science. That is a way how you model your data depends on you. You can choose several approaches, for example: calculate max - min price for a given minute data - a very simple approach, calculate the standard deviation of minute-by-minute stock data, calculate GARCH family models for measuring ...

4

Well you have a few alternatives to lower your commissions. You can get your own broker number in which case you don't go through anyone, you go direct to the exchange so you just pay/get the active/passive rebate. If you are really HFT then this is often the route you take. For the case where you pay a commission to your broker, they are eating/taking ...

4

Quick summary: Your model should still be well specified, as long as: 1) You do the analysis on a heavily traded asset, e.g. IBM on NYSE, and 2) You use heteroskedasticity-consistent standard errors in your estimation framework, e.g. White's standard errors. I'm going to start the long answer by re-stating the question to make sure I've got it right. Let ...

2

As a beginner in AlgoTrading QuantConnect and Quantopian are great for practice and improving your skills but for a serious Algo Trader , they are basically useless. An Algo Trader requires flexibility to investigate trading ideas and add or remove libraries or parts of the system that do not work. You need to automatically and constantly reevaluate your ...

2

It is all a matter of frequency. For instance if you want to get annual realized volatility you multiply your last expression by $\sqrt{(N*251)}$ or the second to last expression by $\sqrt{(251)}$. In other words, your last expression is the 5-min realized volatility whereas the second to last expression is the daily realized volatility.

2

I use Yhang Zhang measure for intraday volatility for timeseries with a rolling 5 or 10 day window. I wrote a C++ and vba implementation which I'm happy to share if you wish. Takes olhc data and gives an 'estimate' of the volatility. For intraday trading (gamma hedging), I found it is a fairly good estimator of the days range. But I would caution on whether ...

2

The classic text for machine learning is 'The Elements of Statistical Learning' by Tibshirani et al. I believe the term "data mining" is often used synonymously with "machine learning".

2

A "flickering" order is one which is repeatedly submitted and cancelled (whether it's at the top of book or not). The answer from @chollida mentions that "the goal typically is to either slow down competitors quotes by flooding the gateway interface with noise" but I don't think that's necessarily true. Rather, I think many flickering quotes are caused by ...

2

I did not know this provider, but had a look. for daily data, the url seems to be http://www.netfonds.no/quotes/paperhistory.php?paper=GOOG.O&csv_format=txt for market depth: http://www.netfonds.no/quotes/posdump.php?date=20160303&paper=E-SABL.BTSE&csv_format=txt for intraday trades: http://www.netfonds.no/quotes/tradedump.php?paper=E-SABL.BTSE&...

2

Stock / ETF at 5-minute intervals can be downloaded from Yahoo Finance. See the code below: from urllib import urlretrieve import numpy as np, pandas as pd, sys import datetime as dt, requests import datetime, re, StringIO symbol = sys.argv[1] url='http://chartapi.finance.yahoo.com/instrument/1.0/%s/chartdata;type=quote;range=3d/csv' % symbol response = ...

2

For what concerns Forex data which is, however financial data after all, I often use http://www.histdata.com/. Their data is delivered in .CSV format. For timeframes, I quote the website: We can only deliver you time ordered Tick and M1 (1 minute) data. The data that we have available is organized by forex-pair/year/month. They also provide data for ...

1

I can think two techniques that may possible be of help. The first technique is the moving average adjusted returns originally proposed by Andersen et. al(2001). See Hansen et al. (2008) for details. In order to account for serial correlation an MA(1) is fitted to to the intraday returns data, the residual of which is then squared and aggregated over the ...

1

Q1.) Is there anything wrong in principle with this simple sampling strategy? I mean sampling is a valid strategy, it just may not be the best. WOuld a VWAP style price be better? Would just an average be better? Typically when no trade has happened you can model the price as the last, average of the bid/ask spread, etc. The price you want depends on ...

1

The code you posted is wrong since you do not model the time series behavior of the up/down process (ie if you have 10 up move and consequently 10 down move it is not the same as the opposite ie 10 down and after 10 up..). I would recommend you to use standards Arma Garch models apply on returns instead of modeling the process of up/down. These are (at ...

1

I second Tibshirani's book. There is an another edition you can download free on internet : http://www-bcf.usc.edu/~gareth/ISL/

1

There are many ways to calculate the volatility. timeframe does not metter. it can be monthly quarterly or daily data. You can call them as volatility metrics. Volatility Metrics Volatility is the degree of trading price over a specific time window. Historical volatility is the degree of price changes of past market prices.Volatility indicates the risk ...

1

By design, market makers do not exacerbate volatility because their trades are, as a whole, net passive.

1

HF data have a lot of auto correlation so common models to deal with this problems are ARFIMA, FIGARCH, Fractional Integrated GARCH. Engle recently propose the multiplicative components GARCH for high frequency data, which can include a mean model like and ARMA. In this post they explain how to implement it in R with the rugarch package, it takes some time ...

1

Exchanges provides the following six timestamps: Gateway In Timestamp-T1. Time at which the order was received by the Gateway from the members TCP connection. Gateway Out Timestamp-T2. This is the time when the order was dispatched by the Gateway to the Matching engine. Matcher In Timestamp-T3. This is the time the order was received by the Matching engine....

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